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Sample records for neural interface systems

  1. The science of neural interface systems.

    PubMed

    Hatsopoulos, Nicholas G; Donoghue, John P

    2009-01-01

    The ultimate goal of neural interface research is to create links between the nervous system and the outside world either by stimulating or by recording from neural tissue to treat or assist people with sensory, motor, or other disabilities of neural function. Although electrical stimulation systems have already reached widespread clinical application, neural interfaces that record neural signals to decipher movement intentions are only now beginning to develop into clinically viable systems to help paralyzed people. We begin by reviewing state-of-the-art research and early-stage clinical recording systems and focus on systems that record single-unit action potentials. We then address the potential for neural interface research to enhance basic scientific understanding of brain function by offering unique insights in neural coding and representation, plasticity, brain-behavior relations, and the neurobiology of disease. Finally, we discuss technical and scientific challenges faced by these systems before they are widely adopted by severely motor-disabled patients.

  2. 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

    2015-01-01

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

  3. Bidirectional Neural Interfaces

    PubMed Central

    Masters, Matthew R.; Thakor, Nitish V.

    2016-01-01

    A bidirectional neural interface is a device that transfers information into and out of the nervous system. This class of devices has potential to improve treatment and therapy in several patient populations. Progress in very-large-scale integration (VLSI) has advanced the design of complex integrated circuits. System-on-chip (SoC) devices are capable of recording neural electrical activity and altering natural activity with electrical stimulation. Often, these devices include wireless powering and telemetry functions. This review presents the state of the art of bidirectional circuits as applied to neuroprosthetic, neurorepair, and neurotherapeutic systems. PMID:26753776

  4. 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.

  5. 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

  6. 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.

  7. Efficient Decoding With Steady-State Kalman Filter in Neural Interface Systems

    PubMed Central

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

    2011-01-01

    The Kalman filter is commonly used in neural interface systems to decode neural activity and estimate the desired movement kinematics. We analyze a low-complexity Kalman filter implementation in which the filter gain is approximated by its steady-state form, computed offline before real-time decoding commences. We evaluate its performance using human motor cortical spike train data obtained from an intracortical recording array as part of an ongoing pilot clinical trial. We demonstrate that the standard Kalman filter gain converges to within 95% of the steady-state filter gain in 1.5 ± 0.5 s (mean ± s.d.). The difference in the intended movement velocity decoded by the two filters vanishes within 5 s, with a correlation coefficient of 0.99 between the two decoded velocities over the session length. We also find that the steady-state Kalman filter reduces the computational load (algorithm execution time) for decoding the firing rates of 25 ± 3 single units by a factor of 7.0 ± 0.9. We expect that the gain in computational efficiency will be much higher in systems with larger neural ensembles. The steady-state filter can thus provide substantial runtime efficiency at little cost in terms of estimation accuracy. This far more efficient neural decoding approach will facilitate the practical implementation of future large-dimensional, multisignal neural interface systems. PMID:21078582

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

    PubMed

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

    2011-02-01

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

  9. Enhancing Nervous System Recovery through Neurobiologics, Neural Interface Training, and Neurorehabilitation

    PubMed Central

    Krucoff, Max O.; Rahimpour, Shervin; Slutzky, Marc W.; Edgerton, V. Reggie; Turner, Dennis A.

    2016-01-01

    After an initial period of recovery, human neurological injury has long been thought to be static. In order to improve quality of life for those suffering from stroke, spinal cord injury, or traumatic brain injury, researchers have been working to restore the nervous system and reduce neurological deficits through a number of mechanisms. For example, neurobiologists have been identifying and manipulating components of the intra- and extracellular milieu to alter the regenerative potential of neurons, neuro-engineers have been producing brain-machine and neural interfaces that circumvent lesions to restore functionality, and neurorehabilitation experts have been developing new ways to revitalize the nervous system even in chronic disease. While each of these areas holds promise, their individual paths to clinical relevance remain difficult. Nonetheless, these methods are now able to synergistically enhance recovery of native motor function to levels which were previously believed to be impossible. Furthermore, such recovery can even persist after training, and for the first time there is evidence of functional axonal regrowth and rewiring in the central nervous system of animal models. To attain this type of regeneration, rehabilitation paradigms that pair cortically-based intent with activation of affected circuits and positive neurofeedback appear to be required—a phenomenon which raises new and far reaching questions about the underlying relationship between conscious action and neural repair. For this reason, we argue that multi-modal therapy will be necessary to facilitate a truly robust recovery, and that the success of investigational microscopic techniques may depend on their integration into macroscopic frameworks that include task-based neurorehabilitation. We further identify critical components of future neural repair strategies and explore the most updated knowledge, progress, and challenges in the fields of cellular neuronal repair, neural

  10. Enhancing Nervous System Recovery through Neurobiologics, Neural Interface Training, and Neurorehabilitation.

    PubMed

    Krucoff, Max O; Rahimpour, Shervin; Slutzky, Marc W; Edgerton, V Reggie; Turner, Dennis A

    2016-01-01

    After an initial period of recovery, human neurological injury has long been thought to be static. In order to improve quality of life for those suffering from stroke, spinal cord injury, or traumatic brain injury, researchers have been working to restore the nervous system and reduce neurological deficits through a number of mechanisms. For example, neurobiologists have been identifying and manipulating components of the intra- and extracellular milieu to alter the regenerative potential of neurons, neuro-engineers have been producing brain-machine and neural interfaces that circumvent lesions to restore functionality, and neurorehabilitation experts have been developing new ways to revitalize the nervous system even in chronic disease. While each of these areas holds promise, their individual paths to clinical relevance remain difficult. Nonetheless, these methods are now able to synergistically enhance recovery of native motor function to levels which were previously believed to be impossible. Furthermore, such recovery can even persist after training, and for the first time there is evidence of functional axonal regrowth and rewiring in the central nervous system of animal models. To attain this type of regeneration, rehabilitation paradigms that pair cortically-based intent with activation of affected circuits and positive neurofeedback appear to be required-a phenomenon which raises new and far reaching questions about the underlying relationship between conscious action and neural repair. For this reason, we argue that multi-modal therapy will be necessary to facilitate a truly robust recovery, and that the success of investigational microscopic techniques may depend on their integration into macroscopic frameworks that include task-based neurorehabilitation. We further identify critical components of future neural repair strategies and explore the most updated knowledge, progress, and challenges in the fields of cellular neuronal repair, neural interfacing

  11. 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.

  12. Conducting Polymers for Neural Prosthetic and Neural Interface Applications.

    PubMed

    Green, Rylie; Abidian, Mohammad Reza

    2015-12-09

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

  13. Conducting Polymers for Neural Prosthetic and Neural Interface Applications

    PubMed Central

    2015-01-01

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

  14. 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.

  15. A special purpose embedded system for neural machine interface for artificial legs.

    PubMed

    Zhang, Xiaorong; Huang, He; Yang, Qing

    2011-01-01

    This paper presents a design and implementation of a neural-machine interface (NMI) for artificial legs that can decode amputee's intended movement in real time. The newly designed NMI integrates an FPGA chip for fast processing and a microcontroller unit (MCU) with multiple on-chip analog-to-digital converters (ADCs) for real-time data sampling. The resulting embedded system is able to sample in real time 12 EMG signals and 6 mechanical signals and execute a special complex phase-dependent classifier for accurate recognition of the user's intended locomotion modes. The implementation and evaluation are based on Altera's Stratix III 3S150 FPGA device coupled with Freescale's MPC5566 MCU. The experimental results for classifying three locomotion modes (level-ground walking, stairs ascent, and stairs descent) based on data collected from an able-bodied human subject have shown acceptable performance for real-time controlling of artificial legs.

  16. 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

  17. 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

  18. Toward energy efficient neural interfaces.

    PubMed

    Peng, Chung-Ching; Xiao, Zhiming; Bashirullah, Rizwan

    2009-11-01

    This letter presents progress toward an energy efficient neural data acquisition transponder for brain-computer interfaces. The transponder utilizes a four-channel time-multiplexed analog front-end and an energy efficient short-range backscattering RF link to transmit digitized wireless data. In addition, a low-complexity autonomous and adaptive digital neural signal processor is proposed to minimize wireless bandwidth and overall power dissipation.

  19. Implantable Neural Interfaces for Sharks

    DTIC Science & Technology

    2007-05-01

    neural codes from peripheral nerve using electrode arrays; Use simple chemical stimuli & multiple locations Completed Amino acid – evoked...rosette · Odorant perfusion across the olfactory rosette (amino acids : histidine, glutamate, cysteine) Implantable Neural Interfaces for Sharks...methane sulphonate ) at 100 mg/L on spontaneous activity recorded in the olfactory lobe. Rate histograms in 5 sec bins as a function of time. The

  20. 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.

  1. 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.

  2. 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.

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

    PubMed

    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.

  4. 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.

  5. 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

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

    PubMed

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

    2016-12-16

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

  7. A Fully Integrated Wireless Compressed Sensing Neural Signal Acquisition System for Chronic Recording and Brain Machine Interface.

    PubMed

    Liu, Xilin; Zhang, Milin; Xiong, Tao; Richardson, Andrew G; Lucas, Timothy H; Chin, Peter S; Etienne-Cummings, Ralph; Tran, Trac D; Van der Spiegel, Jan

    2016-07-18

    Reliable, multi-channel neural recording is critical to the neuroscience research and clinical treatment. However, most hardware development of fully integrated, multi-channel wireless neural recorders to-date, is still in the proof-of-concept stage. To be ready for practical use, the trade-offs between performance, power consumption, device size, robustness, and compatibility need to be carefully taken into account. This paper presents an optimized wireless compressed sensing neural signal recording system. The system takes advantages of both custom integrated circuits and universal compatible wireless solutions. The proposed system includes an implantable wireless system-on-chip (SoC) and an external wireless relay. The SoC integrates 16-channel low-noise neural amplifiers, programmable filters and gain stages, a SAR ADC, a real-time compressed sensing module, and a near field wireless power and data transmission link. The external relay integrates a 32 bit low-power microcontroller with Bluetooth 4.0 wireless module, a programming interface, and an inductive charging unit. The SoC achieves high signal recording quality with minimized power consumption, while reducing the risk of infection from through-skin connectors. The external relay maximizes the compatibility and programmability. The proposed compressed sensing module is highly configurable, featuring a SNDR of 9.78 dB with a compression ratio of 8×. The SoC has been fabricated in a 180 nm standard CMOS technology, occupying 2.1 mm × 0.6 mm silicon area. A pre-implantable system has been assembled to demonstrate the proposed paradigm. The developed system has been successfully used for long-term wireless neural recording in freely behaving rhesus monkey.

  8. 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

  9. Application of competitive Hopfield neural network to brain-computer interface systems.

    PubMed

    Hsu, Wei-Yen

    2012-02-01

    We propose an unsupervised recognition system for single-trial classification of motor imagery (MI) electroencephalogram (EEG) data in this study. Competitive Hopfield neural network (CHNN) clustering is used for the discrimination of left and right MI EEG data posterior to selecting active segment and extracting fractal features in multi-scale. First, we use continuous wavelet transform (CWT) and Student's two-sample t-statistics to select the active segment in the time-frequency domain. The multiresolution fractal features are then extracted from wavelet data by means of modified fractal dimension. At last, CHNN clustering is adopted to recognize extracted features. Due to the characteristic of non-supervision, it is proper for CHNN to classify non-stationary EEG signals. The results indicate that CHNN achieves 81.9% in average classification accuracy in comparison with self-organizing map (SOM) and several popular supervised classifiers on six subjects from two data sets.

  10. 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

  11. 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…

  12. Neuro Talk. An interface for multifunctional neural engineering ASICs.

    PubMed

    Troyk, P R; Detlefsen, D A; DeMichele, G D; Kerns, D

    2006-01-01

    With the availability of modern application specific integrated circuit (ASIC) design tools, simulation packages, and low-cost commercial silicon foundry processes, it is becoming increasingly easy for any laboratory, or small company, to develop a custom ASIC. For stimulation, as well as recording, chips that perform specialized functions can be designed, fabricated, and tested within a time period of 2-3 months. In many cases, the desired functionality can only be obtained by using VLSI design methods. Despite this increase in ASIC functionality, as related to neural engineering applications, there exists no common interface protocol for communicating with, and controlling, neural engineering ASICs. This would be analogous to each company that manufactures PC-based systems to have no common method of communication, e.g. USB, GBIB, RS-232, etc. While it might seem elusive, we propose the specification and development of a universal interface protocol for neural engineering ASICs. We have named this interface, NeuroTalk.

  13. Influence of system integration and packaging on its inductive power link for an integrated wireless neural interface.

    PubMed

    Kim, Sohee; Harrison, Reid R; Solzbacher, Florian

    2009-12-01

    In an integrated wireless neural interface based on the Utah electrode array, the implanted electronics are supplied with power through inductive coupling between two coils. This inductive link is affected by conductive and dielectric materials and media surrounding the implant coil. In this study, the influences of the integration of an implant coil on a silicon-based IC and electrode array, thin-film Parylene-C encapsulation, and physiological medium surrounding the coil were investigated systematically and quantitatively by empirical measurements. A few embodiments of implant coils with different geometrical parameters were made with a diameter of approximately 5.5 mm by winding fine wire with a diameter of approximately 50 mum. The parasitic influences affecting the inductive link were empirically investigated by measuring the electrical properties of coils in different configurations and in different media. The distance of power transmission between the transmit and receive coils was measured when the receive coil was in air and immersed in phosphate buffered saline solution to simulate an implanted physiological environment. The results from this study quantitatively address the influences of factors such as device integration, encapsulation, and implantation on its inductive power link, and suggest how to maximize the efficiency in power transmission for such neural interface devices powered inductively.

  14. 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.

  15. 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.

  16. 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

  17. 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

  18. Stretchable Polymeric Multielectrode Array for Conformal Neural Interfacing

    PubMed Central

    Guo, Liang; Ma, Mingming; Zhang, Ning; Langer, Robert

    2014-01-01

    Highly stretchable neural interface of concurrent robust electrical and mechanical properties is developed with a conducting polymer film as the sole conductor for both electrodes and leads. This neural interface offers benefits of conducting polymer electrodes in a demanding stretchable format, including low electrode impedance and high charge injection capacity, due to large electroactive surface area of the electrode. PMID:24150828

  19. 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

  20. 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).

  1. 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

  2. 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

  3. 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.

  4. Novel in vitro and in vivo neural interfaces: normal and accelerated failure assessment.

    PubMed

    Otto, Kevin; Williams, Justin

    2012-01-01

    Neural implantation of devices and the subsequent tissue response are complex and cascading physical and biological phenomena. Creation of reliable neural interfaces remains a significant challenge. Penetrating central nervous system interfaces persist as the most challenging to realize but continue to be the most attractive because of the information bandwidth advantages they provide. This rich information source is essential for achieving next-generation prosthetic control. Specific challenges of penetrating central nervous system interfaces arise because of the reactive tissue response to the initial injury due to device insertion as well as the continued response due to device indwelling. These responses consist of biochemical signaling events, microglial activation, and astrogliotic cell reorganization that result in biophysical changes of the tissue near the implanted device and finally, electrophysiological neural cell/signal loss (Figure 1). The ultimate realization of reliable penetrating neural interfaces will require careful science and engineering approaches incorporating knowledge of relevant and critical biological, physical, and chemical factors, especially their interrelationship. In this article, we describe a comprehensive strategy to assess the reliability of penetrating central neural interfaces based on the biology and pathology of the injury and indwelling tissue responses. Our strategy involves a parallel, self-informing approach by simultaneous development of new in vitro and in vivo assessment techniques as well as using these state-of-the-art techniques to conduct accelerated lifetime assessments of neural interface degradation.

  5. Systems interface biology

    PubMed Central

    Doyle, Francis J; Stelling, Jörg

    2006-01-01

    The field of systems biology has attracted the attention of biologists, engineers, mathematicians, physicists, chemists and others in an endeavour to create systems-level understanding of complex biological networks. In particular, systems engineering methods are finding unique opportunities in characterizing the rich behaviour exhibited by biological systems. In the same manner, these new classes of biological problems are motivating novel developments in theoretical systems approaches. Hence, the interface between systems and biology is of mutual benefit to both disciplines. PMID:16971329

  6. Systems interface biology.

    PubMed

    Doyle, Francis J; Stelling, Jörg

    2006-10-22

    The field of systems biology has attracted the attention of biologists, engineers, mathematicians, physicists, chemists and others in an endeavour to create systems-level understanding of complex biological networks. In particular, systems engineering methods are finding unique opportunities in characterizing the rich behaviour exhibited by biological systems. In the same manner, these new classes of biological problems are motivating novel developments in theoretical systems approaches. Hence, the interface between systems and biology is of mutual benefit to both disciplines.

  7. 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

  8. Microfluidic neurotransmiter-based neural interfaces for retinal prosthesis.

    PubMed

    Iezzi, Raymond; Finlayson, Paul; Xu, Yong; Katragadda, Rakesh

    2009-01-01

    Natural inter-neuronal communication is mediated primarily via neurotransmitter-gated ion channels. While most of the methods for neural interfacing have been based upon electrical stimulation, neurotransmitter-based approaches for the spatially and temporally controlled delivery of neurotransmitters are relatively new. Methods of neurotransmitter stimulation retinal prosthesis may provide new ways to control neural excitation. Experimental results for retinal ganglion cell stimulation demonstrate the feasibility of a neurotransmitter-based retinal prosthesis.

  9. 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.

  10. The Pursuit of Chronically Reliable Neural Interfaces: A Materials Perspective

    PubMed Central

    Guo, Liang

    2016-01-01

    Brain–computer interfaces represent one of the most astonishing technologies in our era. However, the grand challenge of chronic instability and limited throughput of the electrode–tissue interface has significantly hindered the further development and ultimate deployment of such exciting technologies. A multidisciplinary research workforce has been called upon to respond to this engineering need. In this paper, I briefly review this multidisciplinary pursuit of chronically reliable neural interfaces from a materials perspective by analyzing the problem, abstracting the engineering principles, and summarizing the corresponding engineering strategies. I further draw my future perspectives by extending the proposed engineering principles. PMID:28082862

  11. The Pursuit of Chronically Reliable Neural Interfaces: A Materials Perspective.

    PubMed

    Guo, Liang

    2016-01-01

    Brain-computer interfaces represent one of the most astonishing technologies in our era. However, the grand challenge of chronic instability and limited throughput of the electrode-tissue interface has significantly hindered the further development and ultimate deployment of such exciting technologies. A multidisciplinary research workforce has been called upon to respond to this engineering need. In this paper, I briefly review this multidisciplinary pursuit of chronically reliable neural interfaces from a materials perspective by analyzing the problem, abstracting the engineering principles, and summarizing the corresponding engineering strategies. I further draw my future perspectives by extending the proposed engineering principles.

  12. 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.

  13. 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

  14. 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.

  15. 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

  16. 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

  17. 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

  18. Review: Human Intracortical recording and neural decoding for brain-computer interfaces.

    PubMed

    Brandman, David M; Cash, Sydney S; Hochberg, Leigh R

    2017-03-02

    Brain Computer Interfaces (BCIs) use neural information recorded from the brain for voluntary control of external devices. The development of BCI systems has largely focused on improving functional independence for individuals with severe motor impairments, including providing tools for communication and mobility. In this review, we describe recent advances in intracortical BCI technology and provide potential directions for further research.

  19. 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

  20. Incorporating an optical waveguide into a neural interface

    SciTech Connect

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

    2016-11-08

    An optical waveguide integrated into a multielectrode array (MEA) neural interface includes a device body, at least one electrode in the device body, at least one electrically conducting lead coupled to the at least one electrode, at least one optical channel in the device body, and waveguide material in the at least one optical channel. The fabrication of a neural interface device includes the steps of providing a device body, providing at least one electrode in the device body, providing at least one electrically conducting lead coupled to the at least one electrode, providing at least one optical channel in the device body, and providing a waveguide material in the at least one optical channel.

  1. 3D-nanostructured boron-doped diamond for microelectrode array neural interfacing.

    PubMed

    Piret, Gaëlle; Hébert, Clément; Mazellier, Jean-Paul; Rousseau, Lionel; Scorsone, Emmanuel; Cottance, Myline; Lissorgues, Gaelle; Heuschkel, Marc O; Picaud, Serge; Bergonzo, Philippe; Yvert, Blaise

    2015-06-01

    The electrode material is a key element in the design of long-term neural implants and neuroprostheses. To date, the ideal electrode material offering high longevity, biocompatibility, low-noise recording and high stimulation capabilities remains to be found. We show that 3D-nanostructured boron doped diamond (BDD), an innovative material consisting in a chemically stable material with a high aspect ratio structure obtained by encapsulation of a carbon nanotube template within two BDD nanolayers, allows neural cell attachment, survival and neurite extension. Further, we developed arrays of 20-μm-diameter 3D-nanostructured BDD microelectrodes for neural interfacing. These microelectrodes exhibited low impedances and low intrinsic recording noise levels. In particular, they allowed the detection of low amplitude (10-20 μV) local-field potentials, single units and multiunit bursts neural activity in both acute whole embryonic hindbrain-spinal cord preparations and long-term hippocampal cell cultures. Also, cyclic voltammetry measurements showed a wide potential window of about 3 V and a charge storage capacity of 10 mC.cm(-2), showing high potentiality of this material for neural stimulation. These results demonstrate the attractiveness of 3D-nanostructured BDD as a novel material for neural interfacing, with potential applications for the design of biocompatible neural implants for the exploration and rehabilitation of the nervous system.

  2. 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.

  3. 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.

  4. Establishing a fiber-optic-based optical neural interface.

    PubMed

    Adamantidis, Antoine R; Zhang, Feng; de Lecea, Luis; Deisseroth, Karl

    2014-08-01

    Selective expression of opsins in genetically defined neurons makes it possible to control a subset of neurons without affecting nearby cells and processes in the intact brain, but light must still be delivered to the target brain structure. Light scattering limits the delivery of light from the surface of the brain. For this reason, we have developed a fiber-optic-based optical neural interface (ONI), which allows optical access to any brain structure in freely moving mammals. The ONI system is constructed by modifying the small animal cannula system from PlasticsOne. The system for bilateral stimulation consists of a bilateral cannula guide that has been stereotactically implanted over the target brain region, a screw cap for securing the optical fiber to the animal's head, a fiber guard modified from the internal cannula adapter, and a bare fiber whose length is customized based on the depth of the target region. For unilateral stimulation, a single-fiber system can be constructed using unilateral cannula parts from PlasticsOne. We describe here the preparation of the bilateral ONI system and its use in optical stimulation of the mouse or rat brain. Delivery of opsin-expressing virus and implantation of the ONI may be conducted in the same surgical session; alternatively, with a transgenic animal no opsin virus is delivered during the surgery. Similar procedures are useful for deep or superficial injections (even for neocortical targets, although in some cases surface light-emitting diodes or cortex-apposed fibers can be used for the most superficial cortical targets).

  5. A fully implantable 96-channel neural data acquisition system.

    PubMed

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

    2009-04-01

    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.

  6. A fully implantable 96-channel neural data acquisition system

    NASA Astrophysics Data System (ADS)

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

    2009-04-01

    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.

  7. 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

  8. Studies in RF power communication, SAR, and temperature elevation in wireless implantable neural interfaces.

    PubMed

    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.

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

    PubMed

    Shenoy, Krishna V; Carmena, Jose M

    2014-11-19

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

  10. Multichannel neural recording with a 128 Mbps UWB wireless transmitter for implantable brain-machine interfaces.

    PubMed

    Ando, H; Takizawa, K; Yoshida, T; Matsushita, K; Hirata, M; Suzuki, T

    2015-01-01

    To realize a low-invasive and high accuracy BMI (Brain-machine interface) system, we have already developed a fully-implantable wireless BMI system which consists of ECoG neural electrode arrays, neural recording ASICs, a Wi-Fi based wireless data transmitter and a wireless power receiver with a rechargeable battery. For accurate estimation of movement intentions, it is important for a BMI system to have a large number of recording channels. In this paper, we report a new multi-channel BMI system which is able to record up to 4096-ch ECoG data by multiple connections of 64-ch ASICs and time division multiplexing of recorded data. This system has an ultra-wide-band (UWB) wireless unit for transmitting the recorded neural signals to outside the body. By preliminary experiments with a human body equivalent liquid phantom, we confirmed 4096-ch UWB wireless data transmission at 128 Mbps mode below 20 mm distance.

  11. Voltage biasing, cyclic voltammetry, & electrical impedance spectroscopy for neural interfaces.

    PubMed

    Wilks, Seth J; Richner, Tom J; Brodnick, Sarah K; Kipke, Daryl R; Williams, Justin C; Otto, Kevin J

    2012-02-24

    Electrical impedance spectroscopy (EIS) and cyclic voltammetry (CV) measure properties of the electrode-tissue interface without additional invasive procedures, and can be used to monitor electrode performance over the long term. EIS measures electrical impedance at multiple frequencies, and increases in impedance indicate increased glial scar formation around the device, while cyclic voltammetry measures the charge carrying capacity of the electrode, and indicates how charge is transferred at different voltage levels. As implanted electrodes age, EIS and CV data change, and electrode sites that previously recorded spiking neurons often exhibit significantly lower efficacy for neural recording. The application of a brief voltage pulse to implanted electrode arrays, known as rejuvenation, can bring back spiking activity on otherwise silent electrode sites for a period of time. Rejuvenation alters EIS and CV, and can be monitored by these complementary methods. Typically, EIS is measured daily as an indication of the tissue response at the electrode site. If spikes are absent in a channel that previously had spikes, then CV is used to determine the charge carrying capacity of the electrode site, and rejuvenation can be applied to improve the interface efficacy. CV and EIS are then repeated to check the changes at the electrode-tissue interface, and neural recordings are collected. The overall goal of rejuvenation is to extend the functional lifetime of implanted arrays.

  12. Making brain–machine interfaces robust to future neural variability

    PubMed Central

    Sussillo, David; Stavisky, Sergey D.; Kao, Jonathan C.; Ryu, Stephen I.; Shenoy, Krishna V.

    2016-01-01

    A major hurdle to clinical translation of brain–machine interfaces (BMIs) is that current decoders, which are trained from a small quantity of recent data, become ineffective when neural recording conditions subsequently change. We tested whether a decoder could be made more robust to future neural variability by training it to handle a variety of recording conditions sampled from months of previously collected data as well as synthetic training data perturbations. We developed a new multiplicative recurrent neural network BMI decoder that successfully learned a large variety of neural-to-kinematic mappings and became more robust with larger training data sets. Here we demonstrate that when tested with a non-human primate preclinical BMI model, this decoder is robust under conditions that disabled a state-of-the-art Kalman filter-based decoder. These results validate a new BMI strategy in which accumulated data history are effectively harnessed, and may facilitate reliable BMI use by reducing decoder retraining downtime. PMID:27958268

  13. Development of bioactive conducting polymers for neural interfaces.

    PubMed

    Poole-Warren, Laura; Lovell, Nigel; Baek, Sungchul; Green, Rylie

    2010-01-01

    Bioelectrodes for neural recording and neurostimulation are an integral component of a number of neuroprosthetic devices, including the commercially available cochlear implant, and developmental devices, such as the bionic eye and brain-machine interfaces. Current electrode designs limit the application of such devices owing to suboptimal material properties that lead to minimal interaction with the target neural tissue and the formation of fibrotic capsules. In designing an ideal bioelectrode, a number of design criteria must be considered with respect to physical, mechanical, electrical and biological properties. Conducting polymers have the potential to address the synergistic interaction of these properties and show promise as superior coatings for next-generation electrodes in implant devices.

  14. 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

  15. 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

  16. 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.

  17. 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

  18. 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

  19. 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

  20. A reconfigurable neural signal processor (NSP) for brain machine interfaces.

    PubMed

    Darmanjian, Shalom; Cieslewski, Grzegorz; Morrison, Scott; Dang, Benjamin; Gugel, Karl; Principe, Jose

    2006-01-01

    In this paper, we present a design for a wearable computational DSP system that alleviates the issues of a previous design and provides a much smaller and lower power solution for the overall BMI goals. The system first acquires the neural data through a high speed data bus in order to train and evaluate prediction models. Then it wirelessly transmits the predicted results to a simulated robot arm. This system has been built and successfully tested with real and simulated data.

  1. Artificial Neural Network Analysis System

    DTIC Science & Technology

    2007-11-02

    Target detection, multi-target tracking and threat identification of ICBM and its warheads by sensor fusion and data fusion of sensors in a fuzzy neural network system based on the compound eye of a fly.

  2. 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.

  3. Integration of High-Charge-Injection-Capacity Electrodes onto Polymer Softening Neural Interfaces.

    PubMed

    Arreaga-Salas, David E; Avendaño-Bolívar, Adrian; Simon, Dustin; Reit, Radu; Garcia-Sandoval, Aldo; Rennaker, Robert L; Voit, Walter

    2015-12-09

    Softening neural interfaces are implanted stiff to enable precise insertion, and they soften in physiological conditions to minimize modulus mismatch with tissue. In this work, a high-charge-injection-capacity iridium electrode fabrication process is detailed. For the first time, this process enables integration of iridium electrodes onto softening substrates using photolithography to define all features in the device. Importantly, no electroplated layers are utilized, leading to a highly scalable method for consistent device fabrication. The iridium electrode is metallically bonded to the gold conductor layer, which is covalently bonded to the softening substrate via sulfur-based click chemistry. The resulting shape-memory polymer neural interfaces can deliver more than 2 billion symmetric biphasic pulses (100 μs/phase), with a charge of 200 μC/cm(2) and geometric surface area (GSA) of 300 μm(2). A transfer-by-polymerization method is used in combination with standard semiconductor processing techniques to fabricate functional neural probes onto a thiol-ene-based, thin film substrate. Electrical stability is tested under simulated physiological conditions in an accelerated electrical aging paradigm with periodic measurement of electrochemical impedance spectra (EIS) and charge storage capacity (CSC) at various intervals. Electrochemical characterization and both optical and scanning electron microscopy suggest significant breakdown of the 600 nm-thick parylene-C insulation, although no delamination of the conductors or of the final electrode interface was observed. Minor cracking at the edges of the thin film iridium electrodes was occasionally observed. The resulting devices will provide electrical recording and stimulation of the nervous system to better understand neural wiring and timing, to target treatments for debilitating diseases, and to give neuroscientists spatially selective and specific tools to interact with the body. This approach has uses for

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

    PubMed Central

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

    2017-01-01

    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

  5. Conducting polymers with immobilised fibrillar collagen for enhanced neural interfacing.

    PubMed

    Liu, Xiao; Yue, Zhilian; Higgins, Michael J; Wallace, Gordon G

    2011-10-01

    Conducting polymers with pendant functionality are advantageous in various bionic and organic bioelectronic applications, as they allow facile incorporation of bio-regulative cues to provide bio-mimicry and conductive environments for cell growth, differentiation and function. In this work, polypyrrole substrates doped with chondroitin sulfate (CS), an extracellular matrix molecule bearing carboxylic acid moieties, were electrochemically synthesized and conjugated with type I collagen. During the coupling process, the conjugated collagen formed a 3-dimensional fibrillar matrix in situ at the conducting polymer interface, as evidenced by atomic force microscopy (AFM) and fluorescence microscopy under aqueous physiological conditions. Cyclic voltammetry (CV) and impedance measurement confirmed no significant reduction in the electroactivity of the fibrillar collagen-modified conducting polymer substrates. Rat pheochromocytoma (nerve) cells showed increased differentiation and neurite outgrowth on the fibrillar collagen, which was further enhanced through electrical stimulation of the underlying conducting polymer substrate. Our study demonstrates that the direct coupling of ECM components such as collagen, followed by their further self-assembly into 3-dimensional matrices, has the potential to improve the neural-electrode interface of implant electrodes by encouraging nerve cell attachment and differentiation.

  6. Modality-Specific Axonal Regeneration: Toward Selective Regenerative Neural Interfaces

    PubMed Central

    Lotfi, Parisa; Garde, Kshitija; Chouhan, Amit K.; Bengali, Ebrahim; Romero-Ortega, Mario I.

    2011-01-01

    Regenerative peripheral nerve interfaces have been proposed as viable alternatives for the natural control of robotic prosthetic devices. However, sensory and motor axons at the neural interface are of mixed sub-modality types, which difficult the specific recording from motor axons and the eliciting of precise sensory modalities through selective stimulation. Here we evaluated the possibility of using type specific neurotrophins to preferentially entice the regeneration of defined axonal populations from transected peripheral nerves into separate compartments. Segregation of mixed sensory fibers from dorsal root ganglion neurons was evaluated in vitro by compartmentalized diffusion delivery of nerve growth factor (NGF) and neurotrophin-3 (NT-3), to preferentially entice the growth of TrkA+ nociceptive and TrkC+ proprioceptive subsets of sensory neurons, respectively. The average axon length in the NGF channel increased 2.5-fold compared to that in saline or NT-3, whereas the number of branches increased threefold in the NT-3 channels. These results were confirmed using a 3D “Y”-shaped in vitro assay showing that the arm containing NGF was able to entice a fivefold increase in axonal length of unbranched fibers. To address if such segregation can be enticed in vivo, a “Y”-shaped tubing was used to allow regeneration of the transected adult rat sciatic nerve into separate compartments filled with either NFG or NT-3. A significant increase in the number of CGRP+ pain fibers were attracted toward the sural nerve, while N-52+ large-diameter axons were observed in the tibial and NT-3 compartments. This study demonstrates the guided enrichment of sensory axons in specific regenerative chambers, and supports the notion that neurotrophic factors can be used to segregate sensory and perhaps motor axons in separate peripheral interfaces. PMID:22016734

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

    PubMed Central

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

    2014-01-01

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

  8. 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.

  9. Long term in-vitro functional stability and recording longevity of fully integrated wireless neural interfaces based on Utah Slant Electrode Array (USEA)

    PubMed Central

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

    2011-01-01

    We evaluate the encapsulation and packaging reliability of fully integrated wireless neural interface based on 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 integrated neural interface (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 constitutes a major milestone in long term stability, and allows to study and evaluate the encapsulation reliability, functional stability, and its potential usefulness for wireless neural interface for future chronic implants. PMID:21775785

  10. 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)

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

    PubMed

    Hamedi, M; Salleh, Sh-Hussain; Tan, T S; Ismail, K; Ali, J; Dee-Uam, C; Pavaganun, C; Yupapin, P P

    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.

  12. 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

  13. 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

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

    PubMed

    Polanco, Michael; Bawab, Sebastian; Yoon, Hargsoon

    2016-06-16

    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.

  15. 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

  16. Neural system prediction and identification challenge.

    PubMed

    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.

  17. A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm.

    PubMed

    Dethier, Julie; Nuyujukian, Paul; Eliasmith, Chris; Stewart, Terry; Elassaad, Shauki A; Shenoy, Krishna V; Boahen, Kwabena

    2011-01-01

    Motor prostheses aim to restore function to disabled patients. Despite compelling proof of concept systems, barriers to clinical translation remain. One challenge is to develop a low-power, fully-implantable system that dissipates only minimal power so as not to damage tissue. To this end, we implemented a Kalman-filter based decoder via a spiking neural network (SNN) and tested it in brain-machine interface (BMI) experiments with a rhesus monkey. The Kalman filter was trained to predict the arm's velocity and mapped on to the SNN using the Neural Engineering Framework (NEF). A 2,000-neuron embedded Matlab SNN implementation runs in real-time and its closed-loop performance is quite comparable to that of the standard Kalman filter. The success of this closed-loop decoder holds promise for hardware SNN implementations of statistical signal processing algorithms on neuromorphic chips, which may offer power savings necessary to overcome a major obstacle to the successful clinical translation of neural motor prostheses.

  18. Flexible and Highly Biocompatible Nanofiber-Based Electrodes for Neural Surface Interfacing.

    PubMed

    Heo, Dong Nyoung; Kim, Han-Jun; Lee, Yi Jae; Heo, Min; Lee, Sang Jin; Lee, Donghyun; Do, Sun Hee; Lee, Soo Hyun; Kwon, Il Keun

    2017-03-28

    Polyimide (PI)-based electrodes have been widely used as flexible biosensors in implantable device applications for recording biological signals. However, the long-term quality of neural signals obtained from PI-based nerve electrodes tends to decrease due to nerve damage by neural tissue compression, mechanical mismatch, and insufficient fluid exchange between the neural tissue and electrodes. Here, we resolve these problems with a developed PI nanofiber (NF)-based nerve electrode for stable neural signal recording, which can be fabricated via electrospinning and inkjet printing. We demonstrate an NF-based nerve electrode that can be simply fabricated and easily applied due to its high permeability, flexibility, and biocompatibility. Furthermore, the electrode can record stable neural signals for extended periods of time, resulting in decreased mechanical mismatch, neural compression, and contact area. NF-based electrodes with highly flexible and body-fluid-permeable properties could enable future neural interfacing applications.

  19. Brain-Computer Interface for Control of Wheelchair Using Fuzzy Neural Networks.

    PubMed

    Abiyev, Rahib H; Akkaya, Nurullah; Aytac, Ersin; Günsel, Irfan; Çağman, Ahmet

    2016-01-01

    The design of brain-computer interface for the wheelchair for physically disabled people is presented. The design of the proposed system is based on receiving, processing, and classification of the electroencephalographic (EEG) signals and then performing the control of the wheelchair. The number of experimental measurements of brain activity has been done using human control commands of the wheelchair. Based on the mental activity of the user and the control commands of the wheelchair, the design of classification system based on fuzzy neural networks (FNN) is considered. The design of FNN based algorithm is used for brain-actuated control. The training data is used to design the system and then test data is applied to measure the performance of the control system. The control of the wheelchair is performed under real conditions using direction and speed control commands of the wheelchair. The approach used in the paper allows reducing the probability of misclassification and improving the control accuracy of the wheelchair.

  20. 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.

  1. Radar System Classification Using Neural Networks

    DTIC Science & Technology

    1991-12-01

    This study investigated methods of improving the accuracy of neural networks in the classification of large numbers of classes. A literature search...revealed that neural networks have been successful in the radar classification problem, and that many complex problems have been solved using systems...of multiple neural networks . The experiments conducted were based on 32 classes of radar system data. The neural networks were modelled using a program

  2. 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.

  3. Vertically aligned carbon nanofiber architecture as a multifunctional 3-D neural electrical interface.

    PubMed

    Nguyen-Vu, T D Barbara; Chen, Hua; Cassell, Alan M; Andrews, Russell J; Meyyappan, M; Li, Jun

    2007-06-01

    Developing biomaterial constructs that closely mimic the natural tissue microenvironment with its complex chemical and physical cues is essential for improving the function and reliability of implantable devices, especially those that require direct neural-electrical interfaces. Here we demonstrate that free-standing vertically aligned carbon nanofiber (VACNF) arrays can be used as a multifunctional 3-D brush-like nanoengineered matrix that interpenetrates the neuronal network of PC12 cells. We found that PC12 neuron cells cultured on VACNF substrates can form extended neural network upon proper chemical and biochemical modifications. The soft 3-D VACNF architecture provides a new platform to fine-tune the topographical, mechanical, chemical, and electrical cues at subcellular nanoscale. This new biomaterial platform can be used for both fundamental studies of material-cell interactions and the development of chronically stable implantable neural devices. Micropatterned multiplex VACNF arrays can be selectively controlled by electrical and electrochemical methods to provide localized stimulation with extraordinary spatiotemporal resolution. Further development of this technology may potentially result in a highly multiplex closed-loop system with multifunctions for neuromodulation and neuroprostheses.

  4. Direct inductive stimulation for energy-efficient wireless neural interfaces.

    PubMed

    Ha, Sohmyung; Khraiche, Massoud L; Silva, Gabriel A; Cauwenberghs, Gert

    2012-01-01

    Advanced neural stimulator designs consume power and produce unwanted thermal effects that risk damage to surrounding tissue. In this work, we present a simplified architecture for wireless neural stimulators that relies on a few circuit components including an inductor, capacitor and a diode to elicit an action potential in neurons. The feasibility of the design is supported with analytical models of the inductive link, electrode, electrolyte, membrane and channels of neurons. Finally, a flexible implantable prototype of the design is fabricated and tested in vitro on neural tissue.

  5. 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

    2015-12-16

    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.

  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. Intra-Vascular Neural Interface with Nano-Wire Electrode

    NASA Astrophysics Data System (ADS)

    Watanabe, Hirobumi; Takahashi, Hirokazu; Nakao, Masayuki; Walton, Kerry; Llinás, Rodolfo R.

    A less-invasive recording technique capable of simultaneously monitoring the activity of significant number (103 ∼ 104) of neurons is a vital step in developing an effective brain-machine interface. Although there are many excellent techniques for recording activities of a single neuron or a group of neurons, there is no methodology for accessing large number of cells in a behaving experimental animal or human individual. Brain vascular parenchyma offers the promising candidate to solve this problem. We have proposed the use of myriad of nano-wire-electrodes that are introduced into the Central Nervous System through the vascular system to address any brain area. In this study we design a microcatheter for ex vivo experiments. Using a Wollaston platinum wire we design a submicron-scale electrode, and develop the fabrication method. We then evaluate the mechanical property of the electrode to flow into the intricacies of the capillary bed in ex vivo Xenopus laevis. Furthermore, we demonstrate the feasibility of intravascular recording in the spinal cord of Xenopus laevis.

  8. Neural Substrates for Semantic Memory of Familiar Songs: Is There an Interface between Lyrics and Melodies?

    PubMed Central

    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

  9. 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.

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

    PubMed Central

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

    2015-01-01

    Abstract. Our understanding of neural information processing could potentially be advanced by combining flexible three-dimensional (3-D) neuroimaging and stimulation. Recent developments in optogenetics suggest that neurophotonic approaches are in principle highly suited for noncontact stimulation of network activity patterns. In particular, two-photon holographic optical neural stimulation (2P-HONS) has emerged as a leading approach for multisite 3-D excitation, and combining it with temporal focusing (TF) further enables axially confined yet spatially extended light patterns. Here, we study key steps toward bidirectional cell-targeted 3-D interfacing by introducing and testing a hybrid new 2P-TF-HONS stimulation path for accurate parallel optogenetic excitation into a recently developed hybrid multiphoton 3-D imaging system. The system is shown to allow targeted all-optical probing of in vitro cortical networks expressing channelrhodopsin-2 using a regeneratively amplified femtosecond laser source tuned to 905 nm. These developments further advance a prospective new tool for studying and achieving distributed control over 3-D neuronal circuits both in vitro and in vivo. PMID:26217673

  11. A Neural Network Object Recognition System

    DTIC Science & Technology

    1990-07-01

    useful for exploring different neural network configurations. There are three main computation phases of a model based object recognition system...segmentation, feature extraction, and object classification. This report focuses on the object classification stage. For segmentation, a neural network based...are available with the current system. Neural network based feature extraction may be added at a later date. The classification stage consists of a

  12. 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.

  13. 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.

  14. 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.

  15. 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

  16. Neural mechanisms of brain-computer interface control.

    PubMed

    Halder, S; Agorastos, D; Veit, R; Hammer, E M; Lee, S; Varkuti, B; Bogdan, M; Rosenstiel, W; Birbaumer, N; Kübler, A

    2011-04-15

    Brain-computer interfaces (BCIs) enable people with paralysis to communicate with their environment. Motor imagery can be used to generate distinct patterns of cortical activation in the electroencephalogram (EEG) and thus control a BCI. To elucidate the cortical correlates of BCI control, users of a sensory motor rhythm (SMR)-BCI were classified according to their BCI control performance. In a second session these participants performed a motor imagery, motor observation and motor execution task in a functional magnetic resonance imaging (fMRI) scanner. Group difference analysis between high and low aptitude BCI users revealed significantly higher activation of the supplementary motor areas (SMA) for the motor imagery and the motor observation tasks in high aptitude users. Low aptitude users showed no activation when observing movement. The number of activated voxels during motor observation was significantly correlated with accuracy in the EEG-BCI task (r=0.53). Furthermore, the number of activated voxels in the right middle frontal gyrus, an area responsible for processing of movement observation, correlated (r=0.72) with BCI-performance. This strong correlation highlights the importance of these areas for task monitoring and working memory as task goals have to be activated throughout the BCI session. The ability to regulate behavior and the brain through learning mechanisms involving imagery such as required to control a BCI constitutes the consequence of ideo-motor co-activation of motor brain systems during observation of movements. The results demonstrate that acquisition of a sensorimotor program reflected in SMR-BCI-control is tightly related to the recall of such sensorimotor programs during observation of movements and unrelated to the actual execution of these movement sequences.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. Multidisciplinary Studies of Integrated Neural Network Systems

    DTIC Science & Technology

    1994-03-01

    They accomplish this by partitioning the system into functional sub-units in a quasi-hierarchical structure of neural network modules. We studied...three specific examples of this system integration strategy and modeled their operation for the purpose of creating new neural network architectures and

  2. 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…

  3. Connecting Neurons to a Mobile Robot: An In Vitro Bidirectional Neural Interface

    PubMed Central

    Novellino, A.; D'Angelo, P.; Cozzi, L.; Chiappalone, M.; Sanguineti, V.; Martinoia, S.

    2007-01-01

    One of the key properties of intelligent behaviors is the capability to learn and adapt to changing environmental conditions. These features are the result of the continuous and intense interaction of the brain with the external world, mediated by the body. For this reason “embodiment” represents an innovative and very suitable experimental paradigm when studying the neural processes underlying learning new behaviors and adapting to unpredicted situations. To this purpose, we developed a novel bidirectional neural interface. We interconnected in vitro neurons, extracted from rat embryos and plated on a microelectrode array (MEA), to external devices, thus allowing real-time closed-loop interaction. The novelty of this experimental approach entails the necessity to explore different computational schemes and experimental hypotheses. In this paper, we present an open, scalable architecture, which allows fast prototyping of different modules and where coding and decoding schemes and different experimental configurations can be tested. This hybrid system can be used for studying the computational properties and information coding in biological neuronal networks with far-reaching implications for the future development of advanced neuroprostheses. PMID:18350128

  4. Enabling Low-Power, Multi-Modal Neural Interfaces Through a Common, Low-Bandwidth Feature Space.

    PubMed

    Irwin, Zachary T; Thompson, David E; Schroeder, Karen E; Tat, Derek M; Hassani, Ali; Bullard, Autumn J; Woo, Shoshana L; Urbanchek, Melanie G; Sachs, Adam J; Cederna, Paul S; Stacey, William C; Patil, Parag G; Chestek, Cynthia A

    2016-05-01

    Brain-Machine Interfaces (BMIs) have shown great potential for generating prosthetic control signals. Translating BMIs into the clinic requires fully implantable, wireless systems; however, current solutions have high power requirements which limit their usability. Lowering this power consumption typically limits the system to a single neural modality, or signal type, and thus to a relatively small clinical market. Here, we address both of these issues by investigating the use of signal power in a single narrow frequency band as a decoding feature for extracting information from electrocorticographic (ECoG), electromyographic (EMG), and intracortical neural data. We have designed and tested the Multi-modal Implantable Neural Interface (MINI), a wireless recording system which extracts and transmits signal power in a single, configurable frequency band. In prerecorded datasets, we used the MINI to explore low frequency signal features and any resulting tradeoff between power savings and decoding performance losses. When processing intracortical data, the MINI achieved a power consumption 89.7% less than a more typical system designed to extract action potential waveforms. When processing ECoG and EMG data, the MINI achieved similar power reductions of 62.7% and 78.8%. At the same time, using the single signal feature extracted by the MINI, we were able to decode all three modalities with less than a 9% drop in accuracy relative to using high-bandwidth, modality-specific signal features. We believe this system architecture can be used to produce a viable, cost-effective, clinical BMI.

  5. Conducting polymers for neural interfaces: challenges in developing an effective long-term implant.

    PubMed

    Green, Rylie A; Lovell, Nigel H; Wallace, Gordon G; Poole-Warren, Laura A

    2008-01-01

    Metal electrode materials used in active implantable devices are often associated with poor long-term stimulation and recording performance. Modification of these materials with conducting polymer coatings has been suggested as an approach for improving the neural tissue-electrode interface and increasing the effective lifetime of these implants. Neural interfaces ideally have intimate contact between the excitable tissue and the electrode to maintain signal quality and activation of neural cells. The outcomes of current research into conducting polymers as coatings has potential to enhance this tissue-material contact by increasing the electrode surface area and roughness as well as allowing delivery of bioactive signals to neural cells. However, challenges facing conducting polymers include poor electroactive stability and mechanical properties as well as control of the mobility, concentration and presentation of bioactive molecules. The impact of biological inclusions on polymer properties and their ongoing performance in neural prosthetics requires a greater understanding with future research aimed at controlling and optimising film characteristics for long-term performance. Optimising the electrode interface will require a trade-off between desired electrical, mechanical, chemical and biological properties.

  6. Vacuum-actuated percutaneous insertion/implantation tool for flexible neural probes and interfaces

    DOEpatents

    Sheth, Heeral; Bennett, William J.; Pannu, Satinderpall S.; Tooker, Angela C.

    2017-03-07

    A flexible device insertion tool including an elongated stiffener with one or more suction ports, and a vacuum connector for interfacing the stiffener to a vacuum source, for attaching the flexible device such as a flexible neural probe to the stiffener during insertion by a suction force exerted through the suction ports to, and to release the flexible device by removing the suction force.

  7. 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.

  8. 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.

  9. 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.

  10. Brain-Computer Interface for Control of Wheelchair Using Fuzzy Neural Networks

    PubMed Central

    Akkaya, Nurullah; Aytac, Ersin; Günsel, Irfan; Çağman, Ahmet

    2016-01-01

    The design of brain-computer interface for the wheelchair for physically disabled people is presented. The design of the proposed system is based on receiving, processing, and classification of the electroencephalographic (EEG) signals and then performing the control of the wheelchair. The number of experimental measurements of brain activity has been done using human control commands of the wheelchair. Based on the mental activity of the user and the control commands of the wheelchair, the design of classification system based on fuzzy neural networks (FNN) is considered. The design of FNN based algorithm is used for brain-actuated control. The training data is used to design the system and then test data is applied to measure the performance of the control system. The control of the wheelchair is performed under real conditions using direction and speed control commands of the wheelchair. The approach used in the paper allows reducing the probability of misclassification and improving the control accuracy of the wheelchair. PMID:27777953

  11. 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.

  12. 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.

  13. 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

  14. Cell attachment functionality of bioactive conducting polymers for neural interfaces.

    PubMed

    Green, Rylie A; Lovell, Nigel H; Poole-Warren, Laura A

    2009-08-01

    Bioactive coatings for neural electrodes that are tailored for cell interactions have the potential to produce superior implants with improved charge transfer capabilities. In this study synthetically produced anionically modified laminin peptides DEDEDYFQRYLI and DCDPGYIGSR were used to dope poly(3,4-ethylenedioxythiophene) (PEDOT) electrodeposited on platinum (Pt) electrodes. Performance of peptide doped films was compared to conventional polymer PEDOT/paratoluene sulfonate (pTS) films using SEM, XPS, cyclic voltammetry, impedance spectroscopy, mechanical hardness and adherence. Bioactivity of incorporated peptides and their affect on cell growth was assessed using a PC12 neurite outgrowth assay. It was demonstrated that large peptide dopants produced softer PEDOT films with a minimal decrease in electrochemical stability, compared to the conventional dopant, pTS. Cell studies revealed that the YFQRYLI ligand retained neurite outgrowth bioactivity when DEDEDYFQRYLI was used as a dopant, but the effect was strongly dependant on initial cell attachment. Alternate peptide dopant, DCDPGYIGSR was found to impart superior cell attachment properties when compared to DEDEDYFQRYLI, but attachment on both peptide doped polymers could be enhanced by coating with whole native laminin.

  15. Techniques for Interfacing Multiplex Systems.

    DTIC Science & Technology

    1981-02-01

    Study 6 1.2. 3 Selection of Techniques 7 1.3 RECOMMENDATIONS AND SUMMARY 7 1.3.1 ARINC and H009 (F-15) Interfaces 7 1. 3. 2 Basic Signal...Override Transmitter Shutdown 72 3.4.8 Selected Transmitter Shutdown 72 viii TABLE OF CONTENTS (cont t d) Page I 3.4.9 Override Selected Transmitter...Common Mode Rejection 90 4.0 SELECTION OF TECHNIQUES 91 4.1 BACKGROUND AND RATIONALE 91 4.1.1 Ease of Retrofit 91 4. 1. 2 Future Implementation 92 4

  16. 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.

  17. Neural interface methods and apparatus to provide artificial sensory capabilities to a subject

    DOEpatents

    Buerger, Stephen P.; Olsson, III, Roy H.; Wojciechowski, Kenneth E.; Novick, David K.; Kholwadwala, Deepesh K.

    2017-01-24

    Embodiments of neural interfaces according to the present invention comprise sensor modules for sensing environmental attributes beyond the natural sensory capability of a subject, and communicating the attributes wirelessly to an external (ex-vivo) portable module attached to the subject. The ex-vivo module encodes and communicates the attributes via a transcutaneous inductively coupled link to an internal (in-vivo) module implanted within the subject. The in-vivo module converts the attribute information into electrical neural stimuli that are delivered to a peripheral nerve bundle within the subject, via an implanted electrode. Methods and apparatus according to the invention incorporate implantable batteries to power the in-vivo module allowing for transcutaneous bidirectional communication of low voltage (e.g. on the order of 5 volts) encoded signals as stimuli commands and neural responses, in a robust, low-error rate, communication channel with minimal effects to the subjects' skin.

  18. 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

  19. Interfacing with Neural Activity via Femtosecond Laser Stimulation of Drug-Encapsulating Liposomal Nanostructures

    PubMed Central

    Mackay, Sean M.; Wui Tan, Eng

    2016-01-01

    External control over rapid and precise release of chemicals in the brain potentially provides a powerful interface with neural activity. Optical manipulation techniques, such as optogenetics and caged compounds, enable remote control of neural activity and behavior with fine spatiotemporal resolution. However, these methods are limited to chemicals that are naturally present in the brain or chemically suitable for caging. Here, we demonstrate the ability to interface with neural functioning via a wide range of neurochemicals released by stimulating loaded liposomal nanostructures with femtosecond lasers. Using a commercial two-photon microscope, we released inhibitory or excitatory neurochemicals to evoke subthreshold and suprathreshold changes in membrane potential in a live mouse brain slice. The responses were repeatable and could be controlled by adjusting laser stimulation characteristics. We also demonstrate the release of a wider range of chemicals—which previously were impossible to release by optogenetics or uncaging—including synthetic analogs of naturally occurring neurochemicals. In particular, we demonstrate the release of a synthetic receptor-specific agonist that exerts physiological effects on long-term synaptic plasticity. Further, we show that the loaded liposomal nanostructures remain functional for weeks in a live mouse. In conclusion, we demonstrate new techniques capable of interfacing with live neurons, and extendable to in vivo applications. PMID:27896311

  20. 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.}

  1. 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.

  2. 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.

  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.

  4. Methods for Estimating Neural Firing Rates, and Their Application to Brain-Machine Interfaces

    PubMed Central

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

    2009-01-01

    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

  5. Chronic multisite brain recordings from a totally implantable bidirectional neural interface: experience in 5 patients with Parkinson's disease.

    PubMed

    Swann, Nicole C; de Hemptinne, Coralie; Miocinovic, Svjetlana; Qasim, Salman; Ostrem, Jill L; Galifianakis, Nicholas B; Luciano, Marta San; Wang, Sarah S; Ziman, Nathan; Taylor, Robin; Starr, Philip A

    2017-04-14

    OBJECTIVE Dysfunction of distributed neural networks underlies many brain disorders. The development of neuromodulation therapies depends on a better understanding of these networks. Invasive human brain recordings have a favorable temporal and spatial resolution for the analysis of network phenomena but have generally been limited to acute intraoperative recording or short-term recording through temporarily externalized leads. Here, the authors describe their initial experience with an investigational, first-generation, totally implantable, bidirectional neural interface that allows both continuous therapeutic stimulation and recording of field potentials at multiple sites in a neural network. METHODS Under a physician-sponsored US Food and Drug Administration investigational device exemption, 5 patients with Parkinson's disease were implanted with the Activa PC+S system (Medtronic Inc.). The device was attached to a quadripolar lead placed in the subdural space over motor cortex, for electrocorticography potential recordings, and to a quadripolar lead in the subthalamic nucleus (STN), for both therapeutic stimulation and recording of local field potentials. Recordings from the brain of each patient were performed at multiple time points over a 1-year period. RESULTS There were no serious surgical complications or interruptions in deep brain stimulation therapy. Signals in both the cortex and the STN were relatively stable over time, despite a gradual increase in electrode impedance. Canonical movement-related changes in specific frequency bands in the motor cortex were identified in most but not all recordings. CONCLUSIONS The acquisition of chronic multisite field potentials in humans is feasible. The device performance characteristics described here may inform the design of the next generation of totally implantable neural interfaces. This research tool provides a platform for translating discoveries in brain network dynamics to improved neurostimulation

  6. 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

  7. Development and Evaluation of Micro-Electrocorticography Arrays for Neural Interfacing Applications

    NASA Astrophysics Data System (ADS)

    Schendel, Amelia Ann

    Neural interfaces have great promise for both electrophysiological research and therapeutic applications. Whether for the study of neural circuitry or for neural prosthetic or other therapeutic applications, micro-electrocorticography (micro-ECoG) arrays have proven extremely useful as neural interfacing devices. These devices strike a balance between invasiveness and signal resolution, an important step towards eventual human application. The objective of this research was to make design improvements to micro-ECoG devices to enhance both biocompatibility and device functionality. To best evaluate the effectiveness of these improvements, a cranial window imaging method for in vivo monitoring of the longitudinal tissue response post device implant was developed. Employment of this method provided valuable insight into the way tissue grows around micro-ECoG arrays after epidural implantation, spurring a study of the effects of substrate geometry on the meningeal tissue response. The results of the substrate footprint comparison suggest that a more open substrate geometry provides an easy path for the tissue to grow around to the top side of the device, whereas a solid device substrate encourages the tissue to thicken beneath the device, between the electrode sites and the brain. The formation of thick scar tissue between the recording electrode sites and the neural tissue is disadvantageous for long-term recorded signal quality, and thus future micro-ECoG device designs should incorporate open-architecture substrates for enhanced longitudinal in vivo function. In addition to investigating improvements for long-term device reliability, it was also desired to enhance the functionality of micro-ECoG devices for neural electrophysiology research applications. To achieve this goal, a completely transparent graphene-based device was fabricated for use with the cranial window imaging method and optogenetic techniques. The use of graphene as the conductive material provided

  8. 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.

  9. 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

  10. Poly(3,4-ethylenedioxythiophene) as a Micro-Neural Interface Material for Electrostimulation.

    PubMed

    Wilks, Seth J; Richardson-Burns, Sarah M; Hendricks, Jeffrey L; Martin, David C; Otto, Kevin J

    2009-01-01

    Chronic microstimulation-based devices are being investigated to treat conditions such as blindness, deafness, pain, paralysis, and epilepsy. Small-area electrodes are desired to achieve high selectivity. However, a major trade-off with electrode miniaturization is an increase in impedance and charge density requirements. Thus, the development of novel materials with lower interfacial impedance and enhanced charge storage capacity is essential for the development of micro-neural interface-based neuroprostheses. In this report, we study the use of conducting polymer poly(3,4-ethylenedioxythiophene) (PEDOT) as a neural interface material for microstimulation of small-area iridium electrodes on silicon-substrate arrays. Characterized by electrochemical impedance spectroscopy, electrodeposition of PEDOT results in lower interfacial impedance at physiologically relevant frequencies, with the 1 kHz impedance magnitude being 23.3 +/- 0.7 kOmega, compared to 113.6 +/- 3.5 kOmega for iridium oxide (IrOx) on 177 mum(2) sites. Further, PEDOT exhibits enhanced charge storage capacity at 75.6 +/- 5.4 mC/cm(2) compared to 28.8 +/- 0.3 mC/cm(2) for IrOx, characterized by cyclic voltammetry (50 mV/s). These improvements at the electrode interface were corroborated by observation of the voltage excursions that result from constant current pulsing. The PEDOT coatings provide both a lower amplitude voltage and a more ohmic representation of the applied current compared to IrOx. During repetitive pulsing, PEDOT-coated electrodes show stable performance and little change in electrical properties, even at relatively high current densities which cause IrOx instability. These findings support the potential of PEDOT coatings as a micro-neural interface material for electrostimulation.

  11. 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

    2015-06-03

    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.

  12. 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.

  13. 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

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

    PubMed

    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, approximately 200 microm in diameter. To capitalize on the unique advantages of this system, we specifically targeted ChR2 to excitatory cells in vivo with the CaMKIIalpha promoter. Under these conditions, the intensity of light exiting the fiber ( approximately 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

  15. 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

  16. Integrated low noise low power interface for neural bio-potentials recording and conditioning

    NASA Astrophysics Data System (ADS)

    Bottino, Emanuele; Martinoia, Sergio; Valle, Maurizio

    2005-06-01

    The recent progress in both neurobiology and microelectronics suggests the creation of new, powerful tools to investigate the basic mechanisms of brain functionality. In particular, a lot of efforts are spent by scientific community to define new frameworks devoted to the analysis of in-vitro cultured neurons. One possible approach is recording their spiking activity to monitor the coordinated cellular behaviour and get insights about neural plasticity. Due to the nature of neurons action-potentials, when considering the design of an integrated microelectronic-based recording system, a number of problems arise. First, one would desire to have a high number of recording sites (i.e. several hundreds): this poses constraints on silicon area and power consumption. In this regard, our aim is to integrate-through on-chip post-processing techniques-hundreds of bio-compatible microsensors together with CMOS standard-process low-power (i.e. some tenths of uW per channel) conditioning electronics. Each recording channel is provided with sampling electronics to insure synchronous recording so that, for example, cross-correlation between signals coming from different sites can be performed. Extra-cellular potentials are in the range of [50-150] uV, so a comparison in terms of noise-efficiency was carried out among different architectures and very low-noise pre-amplification electronics (i.e. less than 5 uVrms) was designed. As spikes measurements are made with respect to the voltage of a reference electrode, we opted for an AC-coupled differential-input preamplifier provided with band-pass filtering capability. To achieve this, we implemented large time-constant (up to seconds) integrated components in the preamp feedback path. Thus, we got rid also of random slow-drifting DC-offsets and common mode signals. The paper will present our achievements in the design and implementation of a fully integrated bio-abio interface to record neural spiking activity. In particular

  17. 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.

  18. 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.

  19. 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…

  20. 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…

  1. Asynchronous BCI and local neural classifiers: an overview of the Adaptive Brain Interface project.

    PubMed

    Millán, José del R; Mouriño, Josep

    2003-06-01

    In this communication, we give an overview of our work on an asynchronous brain-computer interface (where the subject makes self-paced decisions on when to switch from one mental task to the next) that responds every 0.5 s. A local neural classifier tries to recognize three different mental tasks; it may also respond "unknown" for uncertain samples as the classifier has incorporated statistical rejection criteria. We report our experience with 15 subjects. We also briefly describe two brain-actuated applications we have developed: a virtual keyboard and a mobile robot (emulating a motorized wheelchair).

  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.

  3. 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.

  4. 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

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. Quantum neural network-based EEG filtering for a brain-computer interface.

    PubMed

    Gandhi, Vaibhav; Prasad, Girijesh; Coyle, Damien; Behera, Laxmidhar; McGinnity, Thomas Martin

    2014-02-01

    A novel neural information processing architecture inspired by quantum mechanics and incorporating the well-known Schrodinger wave equation is proposed in this paper. The proposed architecture referred to as recurrent quantum neural network (RQNN) can characterize a nonstationary stochastic signal as time-varying wave packets. A robust unsupervised learning algorithm enables the RQNN to effectively capture the statistical behavior of the input signal and facilitates the estimation of signal embedded in noise with unknown characteristics. The results from a number of benchmark tests show that simple signals such as dc, staircase dc, and sinusoidal signals embedded within high noise can be accurately filtered and particle swarm optimization can be employed to select model parameters. The RQNN filtering procedure is applied in a two-class motor imagery-based brain-computer interface where the objective was to filter electroencephalogram (EEG) signals before feature extraction and classification to increase signal separability. A two-step inner-outer fivefold cross-validation approach is utilized to select the algorithm parameters subject-specifically for nine subjects. It is shown that the subject-specific RQNN EEG filtering significantly improves brain-computer interface performance compared to using only the raw EEG or Savitzky-Golay filtered EEG across multiple sessions.

  10. 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.

  11. 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

  12. Multimodal Neural Network for Rapid Serial Visual Presentation Brain Computer Interface

    PubMed Central

    Manor, Ran; Mishali, Liran; Geva, Amir B.

    2016-01-01

    Brain computer interfaces allow users to preform various tasks using only the electrical activity of the brain. BCI applications often present the user a set of stimuli and record the corresponding electrical response. The BCI algorithm will then have to decode the acquired brain response and perform the desired task. In rapid serial visual presentation (RSVP) tasks, the subject is presented with a continuous stream of images containing rare target images among standard images, while the algorithm has to detect brain activity associated with target images. In this work, we suggest a multimodal neural network for RSVP tasks. The network operates on the brain response and on the initiating stimulus simultaneously, providing more information for the BCI application. We present two variants of the multimodal network, a supervised model, for the case when the targets are known in advanced, and a semi-supervised model for when the targets are unknown. We test the neural networks with a RSVP experiment on satellite imagery carried out with two subjects. The multimodal networks achieve a significant performance improvement in classification metrics. We visualize what the networks has learned and discuss the advantages of using neural network models for BCI applications. PMID:28066220

  13. Multimodal Neural Network for Rapid Serial Visual Presentation Brain Computer Interface.

    PubMed

    Manor, Ran; Mishali, Liran; Geva, Amir B

    2016-01-01

    Brain computer interfaces allow users to preform various tasks using only the electrical activity of the brain. BCI applications often present the user a set of stimuli and record the corresponding electrical response. The BCI algorithm will then have to decode the acquired brain response and perform the desired task. In rapid serial visual presentation (RSVP) tasks, the subject is presented with a continuous stream of images containing rare target images among standard images, while the algorithm has to detect brain activity associated with target images. In this work, we suggest a multimodal neural network for RSVP tasks. The network operates on the brain response and on the initiating stimulus simultaneously, providing more information for the BCI application. We present two variants of the multimodal network, a supervised model, for the case when the targets are known in advanced, and a semi-supervised model for when the targets are unknown. We test the neural networks with a RSVP experiment on satellite imagery carried out with two subjects. The multimodal networks achieve a significant performance improvement in classification metrics. We visualize what the networks has learned and discuss the advantages of using neural network models for BCI applications.

  14. 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

  15. 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.

  16. 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.

  17. 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

  18. 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

  19. Sensory system for implementing a human-computer interface based on electrooculography.

    PubMed

    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.

  20. The labeled systems of multiple neural networks.

    PubMed

    Nemissi, M; Seridi, H; Akdag, H

    2008-08-01

    This paper proposes an implementation scheme of K-class classification problem using systems of multiple neural networks. Usually, a multi-class problem is decomposed into simple sub-problems solved independently using similar single neural networks. For the reason that these sub-problems are not equivalent in their complexity, we propose a system that includes reinforced networks destined to solve complicated parts of the entire problem. Our approach is inspired from principles of the multi-classifiers systems and the labeled classification, which aims to improve performances of the networks trained by the Back-Propagation algorithm. We propose two implementation schemes based on both OAO (one-against-all) and OAA (one-against-one). The proposed models are evaluated using iris and human thigh databases.

  1. A neural network based speech recognition system

    NASA Astrophysics Data System (ADS)

    Carroll, Edward J.; Coleman, Norman P., Jr.; Reddy, G. N.

    1990-02-01

    An overview is presented of the development of a neural network based speech recognition system. The two primary tasks involved were the development of a time invariant speech encoder and a pattern recognizer or detector. The speech encoder uses amplitude normalization and a Fast Fourier Transform to eliminate amplitude and frequency shifts of acoustic clues. The detector consists of a back-propagation network which accepts data from the encoder and identifies individual words. This use of neural networks offers two advantages over conventional algorithmic detectors: the detection time is no more than a few network time constants, and its recognition speed is independent of the number of the words in the vocabulary. The completed system has functioned as expected with high tolerance to input variation and with error rates comparable to a commercial system when used in a noisy environment.

  2. This Neural Implant is designed to be implanted in the Human Central and Nervous System

    SciTech Connect

    2013-10-29

    A new class of neural implants being developed at the Livermore Lab are the first clinical quality devices capable of two-way conversations with the human nervous systems. Unlike existing interfaces that only sense or only stimulate, these devices are capable of stimulating and sensing using both electric and chemical signals.

  3. This Neural Implant is designed to be implanted in the Human Central and Nervous System

    ScienceCinema

    None

    2016-10-19

    A new class of neural implants being developed at the Livermore Lab are the first clinical quality devices capable of two-way conversations with the human nervous systems. Unlike existing interfaces that only sense or only stimulate, these devices are capable of stimulating and sensing using both electric and chemical signals.

  4. A configurable realtime DWT-based neural data compression and communication VLSI system for wireless implants.

    PubMed

    Yang, Yuning; Kamboh, Awais M; Mason, Andrew J

    2014-04-30

    This paper presents the design of a complete multi-channel neural recording compression and communication system for wireless implants that addresses the challenging simultaneous requirements for low power, high bandwidth and error-free communication. The compression engine implements discrete wavelet transform (DWT) and run length encoding schemes and offers a practical data compression solution that faithfully preserves neural information. The communication engine encodes data and commands separately into custom-designed packet structures utilizing a protocol capable of error handling. VLSI hardware implementation of these functions, within the design constraints of a 32-channel neural compression implant, is presented. Designed in 0.13μm CMOS, the core of the neural compression and communication chip occupies only 1.21mm(2) and consumes 800μW of power (25μW per channel at 26KS/s) demonstrating an effective solution for intra-cortical neural interfaces.

  5. 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

  6. 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.

  7. 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.

  8. Multiparametric Imaging of Organ System Interfaces.

    PubMed

    Vandoorne, Katrien; Nahrendorf, Matthias

    2017-04-01

    Cardiovascular diseases are a consequence of genetic and environmental risk factors that together generate arterial wall and cardiac pathologies. Blood vessels connect multiple systems throughout the entire body and allow organs to interact via circulating messengers. These same interactions facilitate nervous and metabolic system's influence on cardiovascular health. Multiparametric imaging offers the opportunity to study these interfacing systems' distinct processes, to quantify their interactions, and to explore how these contribute to cardiovascular disease. Noninvasive multiparametric imaging techniques are emerging tools that can further our understanding of this complex and dynamic interplay. Positron emission tomography/magnetic resonance imaging and multichannel optical imaging are particularly promising because they can simultaneously sample multiple biomarkers. Preclinical multiparametric diagnostics could help discover clinically relevant biomarker combinations pivotal for understanding cardiovascular disease. Interfacing systems important to cardiovascular disease include the immune, nervous, and hematopoietic systems. These systems connect with classical cardiovascular organs, such as the heart and vasculature, and with the brain. The dynamic interplay between these systems and organs enables processes, such as hemostasis, inflammation, angiogenesis, matrix remodeling, metabolism, and fibrosis. As the opportunities provided by imaging expand, mapping interconnected systems will help us decipher the complexity of cardiovascular disease and monitor novel therapeutic strategies.

  9. 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

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

    PubMed Central

    Muthuswamy, Jit; Anand, Sindhu; Sridharan, Arati

    2011-01-01

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

  11. 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

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

  12. 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.

  13. Low-Cost Wireless Neural Recording System and Software

    PubMed Central

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

    2014-01-01

    We describe a flexible wireless neural recording system, which is comprised of a 15-channel analog FM transmitter, digital receiver and custom user interface software for data acquisition. The analog front-end is constructed from commercial off the shelf (COTS) components and weighs 6.3g (including batteries) and is capable of transmitting over 24 hours up to a range over 3m with a 25μVrms in-vivo noise floor. The Software Defined Radio (SDR) and the acquisition software provide a data acquisition platform with real time data display and can be customized based on the specifications of various experiments. The described system was characterized with in-vitro and in-vivo experiments and the results are presented. PMID:19965244

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

    PubMed

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

    2009-01-01

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

  15. Advances in Neural Information Processing Systems

    NASA Astrophysics Data System (ADS)

    Jordan, Michael I.; Lecun, Yann; Solla, Sara A.

    2001-11-01

    The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. The conference is interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, vision, speech and signal processing, reinforcement learning and control, implementations, and diverse applications. Only about 30 percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. This CD-ROM contains the entire proceedings of the twelve Neural Information Processing Systems conferences from 1988 to 1999. The files are available in the DjVu image format developed by Yann LeCun and his group at AT&T Labs. The CD-ROM includes free browsers for all major platforms. Michael I. Jordan is Professor of Computer Science and of Statistics at the University of California, Berkeley. Yann LeCun is Head of the Image Processing Research Department at AT&T Labs-Research. Sara A. Solla is Professor of Physics and of Physiology at Northwestern University.

  16. 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.

  17. 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)

  18. Neurale Netwerken en Radarsystemen (Neural Networks and Radar Systems)

    DTIC Science & Technology

    1989-08-01

    general issues in cognitive science", Parallel distributed processing, Vol 1: Foundations, Rumelhart et al. 1986 pp 110-146 THO rapport Pagina 151 36 D.E...34Neural networks (part 2)",Expert Focus, IEEE Expert, Spring 1988. 61 J.A. Anderson, " Cognitive and Psychological Computations with Neural Models", IEEE...Pagina 154 69 David H. Ackley, Geoffrey E. Hinton and Terrence J. Sejnowski, "A Learning Algorithm for Boltzmann machines", cognitive science 9, 147-169

  19. Nonlinear system identification and control based on modular neural networks.

    PubMed

    Puscasu, Gheorghe; Codres, Bogdan

    2011-08-01

    A new approach for nonlinear system identification and control based on modular neural networks (MNN) is proposed in this paper. The computational complexity of neural identification can be greatly reduced if the whole system is decomposed into several subsystems. This is obtained using a partitioning algorithm. Each local nonlinear model is associated with a nonlinear controller. These are also implemented by neural networks. The switching between the neural controllers is done by a dynamical switcher, also implemented by neural networks, that tracks the different operating points. The proposed multiple modelling and control strategy has been successfully tested on simulated laboratory scale liquid-level system.

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

    DOE PAGES

    Chapman, Christopher A. R.; Chen, Hao; Stamou, Marianna; ...

    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

  1. 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.

  2. 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

  3. Insect-machine interface: a carbon nanotube-enhanced flexible neural probe.

    PubMed

    Tsang, W M; Stone, Alice L; Otten, David; Aldworth, Zane N; Daniel, Tom L; Hildebrand, John G; Levine, Richard B; Voldman, Joel

    2012-03-15

    We developed microfabricated flexible neural probes (FNPs) to provide a bi-directional electrical link to the moth Manduca sexta. These FNPs can deliver electrical stimuli to, and capture neural activity from, the insect's central nervous system. They are comprised of two layers of polyimide with gold sandwiched in between in a split-ring geometry that incorporates the bi-cylindrical anatomical structure of the insect's ventral nerve cord. The FNPs provide consistent left and right abdominal stimulation both across animals and within an individual animal. The features of the stimulation (direction, threshold charge) are aligned with anatomical features of the moth. We also have used these FNPs to record neuronal activity in the ventral nerve cord of the moth. Finally, by integrating carbon nanotube (CNT)-Au nanocomposites into the FNPs we have reduced the interfacial impedance between the probe and the neural tissue, thus reducing the magnitude of stimulation voltage. This in turn allows use of the FNPs with a wireless stimulator, enabling stimulation and flight biasing of freely flying moths. Together, these FNPs present a potent new platform for manipulating and measuring the neural circuitry of insects, and for other nerves in humans and other animals with similar dimensions as the ventral nerve cord of the moth.

  4. Computer simulations of learning in neural systems.

    PubMed

    Salu, Y

    1983-04-01

    Recent experiments have shown that, in some cases, strengths of synaptic ties are being modified in learning. However, it is not known what the rules that control those modifications are, especially what determines which synapses will be modified and which will remain unchanged during a learning episode. Two postulated rules that may solve that problem are introduced. To check their effectiveness, the rules are tested in many computer models that simulate learning in neural systems. The simulations demonstrate that, theoretically, the two postulated rules are effective in organizing the synaptic changes. If they are found to also exist in biological systems, these postulated rules may be an important element in the learning process.

  5. Dynamic Brain-Machine Interface: a novel paradigm for bidirectional interaction between brains and dynamical systems.

    PubMed

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

    2011-01-01

    Brain-Machine Interfaces (BMIs) are systems which mediate communication between brains and artificial devices. Their long term goal is to restore motor functions, and this ultimately demands the development of a new generation of bidirectional brain-machine interfaces establishing a two-way brain-world communication channel, by both decoding motor commands from neural activity and providing feedback to the brain by electrical stimulation. Taking inspiration from how the spinal cord of vertebrates mediates communication between the brain and the limbs, here we present a model of a bidirectional brain-machine interface that interacts with a dynamical system by generating a control policy in the form of a force field. In our model, bidirectional communication takes place via two elements: (a) a motor interface decoding activities recorded from a motor cortical area, and (b) a sensory interface encoding the state of the controlled device into electrical stimuli delivered to a somatosensory area. We propose a specific mathematical model of the sensory and motor interfaces guiding a point mass moving in a viscous medium, and we demonstrate its performance by testing it on realistically simulated neural responses.

  6. 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.

  7. 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

  8. 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

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

    PubMed

    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.

  10. 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.

  11. Enhancement of Interface Characteristics of Neural Probe Based on Graphene, ZnO Nanowires, and Conducting Polymer PEDOT.

    PubMed

    Ryu, Mingyu; Yang, Jae Hoon; Ahn, Yumi; Sim, Minkyung; Lee, Kyung Hwa; Kim, Kyungsoo; Lee, Taeju; Yoo, Seung-Jun; Kim, So Yeun; Moon, Cheil; Je, Minkyu; Choi, Ji-Woong; Lee, Youngu; Jang, Jae Eun

    2017-03-29

    In the growing field of brain-machine interface (BMI), the interface between electrodes and neural tissues plays an important role in the recording and stimulation of neural signals. To minimize tissue damage while retaining high sensitivity, a flexible and a smaller electrode with low impedance is required. However, it is a major challenge to reduce electrode size while retaining the conductive characteristics of the electrode. In addition, the mechanical mismatch between stiff electrodes and soft tissues creates damaging reactive tissue responses. Here, we demonstrate a neural probe structure based on graphene, ZnO nanowires, and conducting polymer that provides flexibility and low impedance performance. A hybrid Au and graphene structure was utilized to achieve both flexibility and good conductivity. Using ZnO nanowires to increase the effective surface area drastically decreased the impedance value and enhanced the signal-to-noise ratio (SNR). A poly[3,4-ethylenedioxythiophene] (PEDOT) coating on the neural probe improved the electrical characteristics of the electrode while providing better biocompatibility. In vivo neural signal recordings showed that our neural probe can detect clearer signals.

  12. 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.

  13. Implementation of Fuzzy Inference Systems Using Neural Network Techniques

    DTIC Science & Technology

    1992-03-01

    rules required to implement the system, which are usually supplied by ’experts’. One alternative is to use a neural network -type architecture to implement...the fuzzy inference system, and neural network -type training techniques to ’learn’ the control parameters needed by the fuzzy inference system. By...using a generalized version of a neural network , the rules of the fuzzy inference system can be learned without the assistance of experts.

  14. 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.

  15. 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.

  16. Intermediate photovoltaic system/utility interface experience

    NASA Astrophysics Data System (ADS)

    Biringer, K. L.; McDowell, J. F.; Rogers, C. B.; Haskins, D. E.

    A description is given of 11 intermediate photovoltaic application projects, including the Arizona Public Service Company project, the E-Systems 27 kW photovoltaic concentrator application experiment, a 110 kW photovoltaic application experiment in Orlando, Florida, the Lea County photovoltaic flat plate photovoltaic experiment in southeastern New Mexico, the Mt. Laguna photovoltaic flat plate installation in California, the San Bernardino 35 kW photovoltaic flat plate project in California, and the Solar Power flat plate photovoltaic experiment in Massachusetts. It is pointed out that the most significant point to be made relative to the interface of photovoltaic systems with the utility grid is that it can be done successfully.

  17. Straddle Carrier Interface and Dispatching System

    SciTech Connect

    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 and 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.

  18. A Neural Network Based Speech Recognition System

    DTIC Science & Technology

    1990-02-01

    encoder and identifies individual words. This use of neural networks offers two advantages over conventional algorithmic detectors: the detection...environment. Keywords: Artificial intelligence; Neural networks : Back propagation; Speech recognition.

  19. 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

  20. 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

  1. 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

  2. Is Neural Activity Detected by ERP-Based Brain-Computer Interfaces Task Specific?

    PubMed Central

    Wenzel, Markus A.; Almeida, Inês; Blankertz, Benjamin

    2016-01-01

    Objective Brain-computer interfaces (BCIs) that are based on event-related potentials (ERPs) can estimate to which stimulus a user pays particular attention. In typical BCIs, the user silently counts the selected stimulus (which is repeatedly presented among other stimuli) in order to focus the attention. The stimulus of interest is then inferred from the electroencephalogram (EEG). Detecting attention allocation implicitly could be also beneficial for human-computer interaction (HCI), because it would allow software to adapt to the user’s interest. However, a counting task would be inappropriate for the envisaged implicit application in HCI. Therefore, the question was addressed if the detectable neural activity is specific for silent counting, or if it can be evoked also by other tasks that direct the attention to certain stimuli. Approach Thirteen people performed a silent counting, an arithmetic and a memory task. The tasks required the subjects to pay particular attention to target stimuli of a random color. The stimulus presentation was the same in all three tasks, which allowed a direct comparison of the experimental conditions. Results Classifiers that were trained to detect the targets in one task, according to patterns present in the EEG signal, could detect targets in all other tasks (irrespective of some task-related differences in the EEG). Significance The neural activity detected by the classifiers is not strictly task specific but can be generalized over tasks and is presumably a result of the attention allocation or of the augmented workload. The results may hold promise for the transfer of classification algorithms from BCI research to implicit relevance detection in HCI. PMID:27792781

  3. Brain-computer interface classifier for wheelchair commands using neural network with fuzzy particle swarm optimization.

    PubMed

    Chai, Rifai; Ling, Sai Ho; Hunter, Gregory P; Tran, Yvonne; Nguyen, Hung T

    2014-09-01

    This paper presents the classification of a three-class mental task-based brain-computer interface (BCI) that uses the Hilbert-Huang transform for the features extractor and fuzzy particle swarm optimization with cross-mutated-based artificial neural network (FPSOCM-ANN) for the classifier. The experiments were conducted on five able-bodied subjects and five patients with tetraplegia using electroencephalography signals from six channels, and different time-windows of data were examined to find the highest accuracy. For practical purposes, the best two channel combinations were chosen and presented. The three relevant mental tasks used for the BCI were letter composing, arithmetic, and Rubik's cube rolling forward, and these are associated with three wheelchair commands: left, right, and forward, respectively. An additional eyes closed task was collected for testing and used for on-off commands. The results show a dominant alpha wave during eyes closure with average classification accuracy above 90%. The accuracies for patients with tetraplegia were lower compared to the able-bodied subjects; however, this was improved by increasing the duration of the time-windows. The FPSOCM-ANN provides improved accuracies compared to genetic algorithm-based artificial neural network (GA-ANN) for three mental tasks-based BCI classifications with the best classification accuracy achieved for a 7-s time-window: 84.4% (FPSOCM-ANN) compared to 77.4% (GA-ANN). More comparisons on feature extractors and classifiers were included. For two-channel classification, the best two channels were O1 and C4, followed by second best at P3 and O2, and third best at C3 and O2. Mental arithmetic was the most correctly classified task, followed by mental Rubik's cube rolling forward and mental letter composing.

  4. Neuroanatomical affiliation visualization-interface system.

    PubMed

    Palombi, Olivier; Shin, Jae-Won; Watson, Charles; Paxinos, George

    2006-01-01

    A number of knowledge management systems have been developed to allow users to have access to large quantity of neuroanatomical data. The advent of three-dimensional (3D) visualization techniques allows users to interact with complex 3D object. In order to better understand the structural and functional organization of the brain, we present Neuroanatomical Affiliations Visualization-Interface System (NAVIS) as the original software to see brain structures and neuroanatomical affiliations in 3D. This version of NAVIS has made use of the fifth edition of "The Rat Brain in Stereotaxic coordinates" (Paxinos and Watson, 2005). The NAVIS development environment was based on the scripting language name Python, using visualization toolkit (VTK) as 3D-library and wxPython for the graphic user interface. The following manuscript is focused on the nucleus of the solitary tract (Sol) and the set of affiliated structures in the brain to illustrate the functionality of NAVIS. The nucleus of the Sol is the primary relay center of visceral and taste information, and consists of 14 distinct subnuclei that differ in cytoarchitecture, chemoarchitecture, connections, and function. In the present study, neuroanatomical projection data of the rat Sol were collected from selected literature in PubMed since 1975. Forty-nine identified projection data of Sol were inserted in NAVIS. The standard XML format used as an input for affiliation data allows NAVIS to update data online and/or allows users to manually change or update affiliation data. NAVIS can be extended to nuclei other than Sol.

  5. The Surface Immobilization of the Neural Adhesion Molecule L1 on Neural Probes and its Effect on Neuronal Density and Gliosis at the Probe/Tissue Interface

    PubMed Central

    Azemi, Erdrin; Lagenaur, Carl; Cui, Xinyan Tracy

    2012-01-01

    Brain tissue inflammatory responses, including neuronal loss and gliosis at the neural electrode/tissue interface, limit the recording stability and longevity of neural probes. The neural adhesion molecule L1 specifically promotes neurite outgrowth and neuronal survival. In this study, we covalently immobilized L1 on the surface of silicon based neural probes and compared the tissue response between L1 modified and non-modified probes implanted in the rat cortex after 1, 4, and 8 weeks. The effect of L1 on neuronal health and survival, and glial cell reactions were evaluated with immunohistochemistry and quantitative image analysis. Similar to previous findings, persistent glial activation and significant decreases of neuronal and axonal densities were found at the vicinity of the non-modified probes. In contrast, the immediate area (100 μm) around the L1 modified probe showed no loss of neuronal bodies and a significantly increased axonal density relative to background. In this same region, immunohistochemistry analyses show a significantly lower activation of microglia and reaction of astrocytes around the L1 modified probes when compared to the control probes. These improvements in tissue reaction induced by the L1 coating are likely to lead to improved functionality of the implanted neural electrodes during chronic recordings. PMID:20933270

  6. 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.

  7. Characterization of Alumina Interfaces in TBC Systems

    SciTech Connect

    Pint, Bruce A; More, Karren Leslie

    2009-01-01

    Interfacial segregants in thermally grown {alpha}-Al{sub 2}O{sub 3} scales formed during high temperature exposure of thermal barrier coating systems reflect the oxygen-active dopants present in the bond coating and substrate, such as Y and Hf. These dopants diffuse outward and segregate to the substrate-alumina interface and the alumina grain boundaries. Related studies suggest that these segregants affect the growth and mechanical properties of the alumina-scale; however, the characterization of segregation in alumina formed on coated superalloy systems has been limited. Segregation examples evaluated using analytical transmission electron microscopy are given from traditional Pt-modified aluminide coatings and newer Pt diffusion coatings. Model systems are used to illustrate that grain boundary segregants on the columnar alumina boundaries are not because of the reverse diffusion of cations from the Y{sub 2}O{sub 3}-stabilized ZrO{sub 2} top coating, and that interstitial elements in the substrate likely affect the outward flux of cation dopants. The dynamic nature of this segregation and oxygen-potential gradient-driven diffusion is discussed in light of observations of substrate dopant and interstitial contents affecting coating performance.

  8. 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.

  9. Microfabrication, characterization and in vivo MRI compatibility of diamond microelectrodes array for neural interfacing.

    PubMed

    Hébert, Clément; Warnking, Jan; Depaulis, Antoine; Garçon, Laurie Amandine; Mermoux, Michel; Eon, David; Mailley, Pascal; Omnès, Franck

    2015-01-01

    Neural interfacing still requires highly stable and biocompatible materials, in particular for in vivo applications. Indeed, most of the currently used materials are degraded and/or encapsulated by the proximal tissue leading to a loss of efficiency. Here, we considered boron doped diamond microelectrodes to address this issue and we evaluated the performances of a diamond microelectrode array. We described the microfabrication process of the device and discuss its functionalities. We characterized its electrochemical performances by cyclic voltammetry and impedance spectroscopy in saline buffer and observed the typical diamond electrode electrochemical properties, wide potential window and low background current, allowing efficient electrochemical detection. The charge storage capacitance and the modulus of the electrochemical impedance were found to remain in the same range as platinum electrodes used for standard commercial devices. Finally we observed a reduced Magnetic Resonance Imaging artifact when the device was implanted on a rat cortex, suggesting that boron doped-diamond is a very promising electrode material allowing functional imaging.

  10. General Purpose Computer (GPC) to GPC systems interface description

    NASA Technical Reports Server (NTRS)

    Breyer, B. C.

    1976-01-01

    The General Purpose Computer (GPC) 'subsystem' of the Orbiter Data Processing System was described. Two interface areas are discussed. One is the area of GPC intraconnections and intracommunications involving the hardware/software interface between the Central Processing Unit (CPU) and the Input/Output Processor (IOP). The other is the area of GPC interconnections and intercommunications and involves the hardware/software interface between the five Orbiter GPC's. Based on the detailed GPC interface given, it is felt that the basic CPU to IOP interface and the GPC to GPC interface have the potential for trouble free operation. However, due to the complexity of the interface and the criticality of GPC synchronization to overall avionics performance, the GPC to GPC interface should be carefully evaluated when attempting to resolve test anomalies that may involve GPC timing and synchronization errors.

  11. 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.

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

    DTIC Science & Technology

    1993-01-01

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

  13. Manufacturing, assembling and packaging of miniaturized implants for neural prostheses and brain-machine interfaces

    NASA Astrophysics Data System (ADS)

    Stieglitz, Thomas

    2009-05-01

    Implantable medical devices to interface with muscles, peripheral nerves, and the brain have been developed for many applications over the last decades. They have been applied in fundamental neuroscientific studies as well as in diagnosis, therapy and rehabilitation in clinical practice. Success stories of these implants have been written with help of precision mechanics manufacturing techniques. Latest cutting edge research approaches to restore vision in blind persons and to develop an interface with the human brain as motor control interface, however, need more complex systems and larger scales of integration and higher degrees of miniaturization. Microsystems engineering offers adequate tools, methods, and materials but so far, no MEMS based active medical device has been transferred into clinical practice. Silicone rubber, polyimide, parylene as flexible materials and silicon and alumina (aluminum dioxide ceramics) as substrates and insulation or packaging materials, respectively, and precious metals as electrodes have to be combined to systems that do not harm the biological target structure and have to work reliably in a wet environment with ions and proteins. Here, different design, manufacturing and packaging paradigms will be presented and strengths and drawbacks will be discussed in close relation to the envisioned biological and medical applications.

  14. Covalent bonding of YIGSR and RGD to PEDOT/PSS/MWCNT-COOH composite material to improve the neural interface.

    PubMed

    Wang, Kun; Tang, Rong-Yu; Zhao, Xiao-Bo; Li, Jun-Jie; Lang, Yi-Ran; Jiang, Xiao-Xia; Sun, Hong-Ji; Lin, Qiu-Xia; Wang, Chang-Yong

    2015-11-28

    The development of coating materials for neural interfaces has been a pursued to improve the electrical, mechanical and biological performances. For these goals, a bioactive coating was developed in this work featuring a poly(3,4-ethylenedioxythiophene) (PEDOT)/carbon nanotube (CNT) composite and covalently bonded YIGSR and RGD. Its biological effect and electrical characteristics were assessed in vivo on microwire arrays (MWA). The coated electrodes exhibited a significantly higher charge storage capacity (CSC) and lower electrochemical impedance at 1 kHz which are desired to improve the stimulating and recording performances, respectively. Acute neural recording experiments revealed that coated MWA possess a higher signal/noise ratio capturing spikes undetected by uncoated electrodes. Moreover, coated MWA possessed more active sites and single units, and the noise floor of coated electrodes was lower than that of uncoated electrodes. There is little information in the literature concerning the chronic performance of bioactively modified neural interfaces in vivo. Therefore in this work, chronic in vivo tests were conducted and the PEDOT/PSS/MWCNT-polypeptide coated arrays exhibited excellent performances with the highest mean maximal amplitude from day 4 to day 12 during which the acute response severely compromised the performance of the electrodes. In brief, we developed a simple method of covalently bonding YIGSR and RGD to a PEDOT/PSS/MWCNT-COOH composite improving both the biocompatibility and electrical performance of the neural interface. Our findings suggest that YIGSR and RGD modified PEDOT/PSS/MWCNT is a promising bioactivated composite coating for neural recording and stimulating.

  15. Covalent bonding of YIGSR and RGD to PEDOT/PSS/MWCNT-COOH composite material to improve the neural interface

    NASA Astrophysics Data System (ADS)

    Wang, Kun; Tang, Rong-Yu; Zhao, Xiao-Bo; Li, Jun-Jie; Lang, Yi-Ran; Jiang, Xiao-Xia; Sun, Hong-Ji; Lin, Qiu-Xia; Wang, Chang-Yong

    2015-11-01

    The development of coating materials for neural interfaces has been a pursued to improve the electrical, mechanical and biological performances. For these goals, a bioactive coating was developed in this work featuring a poly(3,4-ethylenedioxythiophene) (PEDOT)/carbon nanotube (CNT) composite and covalently bonded YIGSR and RGD. Its biological effect and electrical characteristics were assessed in vivo on microwire arrays (MWA). The coated electrodes exhibited a significantly higher charge storage capacity (CSC) and lower electrochemical impedance at 1 kHz which are desired to improve the stimulating and recording performances, respectively. Acute neural recording experiments revealed that coated MWA possess a higher signal/noise ratio capturing spikes undetected by uncoated electrodes. Moreover, coated MWA possessed more active sites and single units, and the noise floor of coated electrodes was lower than that of uncoated electrodes. There is little information in the literature concerning the chronic performance of bioactively modified neural interfaces in vivo. Therefore in this work, chronic in vivo tests were conducted and the PEDOT/PSS/MWCNT-polypeptide coated arrays exhibited excellent performances with the highest mean maximal amplitude from day 4 to day 12 during which the acute response severely compromised the performance of the electrodes. In brief, we developed a simple method of covalently bonding YIGSR and RGD to a PEDOT/PSS/MWCNT-COOH composite improving both the biocompatibility and electrical performance of the neural interface. Our findings suggest that YIGSR and RGD modified PEDOT/PSS/MWCNT is a promising bioactivated composite coating for neural recording and stimulating.

  16. Poly(vinyl alcohol)/poly(acrylic acid) hydrogel coatings for improving electrode-neural tissue interface.

    PubMed

    Lu, Yi; Wang, Dingfang; Li, Tao; Zhao, Xueqing; Cao, Yuliang; Yang, Hanxi; Duan, Yanwen Y

    2009-09-01

    A major problem which hinders the applications of neural prostheses is the inconsistent performance caused by tissue responses during long-term implantation. The study investigated a new approach for improving the electrode-neural tissue interface. Hydrogel poly(vinyl alcohol)/poly(acrylic acid) interpenetrating polymer networks (PVA/PAA IPNs) were synthesized and tailored as coatings for poly(dimethylsiloxane) (PDMS) based neural electrodes with the aid of plasma pretreatment. Changes in the electrochemical impedance and maximum charge injection (Q(inj)) limits of the coated iridium oxide microelectrodes were negligible. Protein adsorption on PDMS was reduced by approximately 85% after coating. In the presence of nerve growth factor (NGF), neurite extension of rat pheochromocytoma (PC12) cells was clearly greater on PVA/PAA IPN films than on PDMS substrates. Furthermore, the tissue responses of PDMS implants coated with PVA/PAA IPN films were studied by 6-week implantation in the cortex of rats, which found that the glial fibrillary acidic protein (GFAP) immunoreactivity in animals (n=8) receiving coated implants was significantly lower (p<0.05) compared to that of uncoated implants (n=7) along the entire distance of 150 microm from the outer skirt to the implant interface. The coated film remained on the surface of the explanted implants, confirmed by scanning electron microscopy (SEM). All of these suggest the hydrogel coating is feasible and favorable to neural electrode applications.

  17. Modeling the Electrode-Neuron Interface of Cochlear Implants: Effects of Neural Survival, Electrode Placement, and the Partial Tripolar Configuration

    PubMed Central

    Goldwyn, Joshua H.; Bierer, Steven M.; Bierer, Julie A.

    2010-01-01

    The partial tripolar electrode configuration is a relatively novel stimulation strategies that can generate more spatially focused electric fields than the commonly used monopolar configuration. Focused stimulation strategies should improve spectral resolution in cochlear implant users, but may also be more sensitive to local irregularities in the electrode-neuron interface. In this study, we develop a practical computer model of cochlear implant stimulation that can simulate neural activation in a simplified cochlear geometry and we relate the resulting patterns of neural activity to basic psychophysical measures. We examine how two types of local irregularities in the electrode-neuron interface, variations in spiral ganglion nerve density and electrode position within the scala tympani, affect the simulated neural activation patterns and how these patterns change with electrode configuration. The model shows that higher partial tripolar fractions activate more spatially restricted populations of neurons at all current levels and require higher current levels to excite a given number of neurons. We find that threshold levels are more sensitive at high partial tripolar fractions to both types of irregularities, but these effects are not independent. In particular, at close electrode-neuron distances, activation is typically more spatially localized which leads to a greater influence of neural dead regions. PMID:20580801

  18. An Artificial Neural Network Control System for Spacecraft Attitude Stabilization

    DTIC Science & Technology

    1990-06-01

    training is based on the concept of enforced performance. A neural network will learn to meet a specific performance goal if the performance standard...is the only solution to a problem. Performance index training is devised to teach the neural network the time-optimal control law for the system. Real...time adaptation of a neural network in closed loop control of the Crew/Equipment Retriever was demonstrated in computer simulations.

  19. Exchange bias of the interface spin system at the Fe/MgO interface.

    PubMed

    Fan, Y; Smith, K J; Lüpke, G; Hanbicki, A T; Goswami, R; Li, C H; Zhao, H B; Jonker, B T

    2013-06-01

    The ferromagnet/oxide interface is key to developing emerging multiferroic and spintronic technologies with new functionality. Here we probe the Fe/MgO interface magnetization, and identify a new exchange bias phenomenon manifested only in the interface spin system, and not in the bulk. The interface magnetization exhibits a pronounced exchange bias, and the hysteresis loop is shifted entirely to one side of the zero field axis. However, the bulk magnetization does not, in marked contrast to typical systems where exchange bias is manifested in the net magnetization. This reveals the existence of an antiferromagnetic exchange pinning layer at the interface, identified here as FeO patches that exist even for a nominally 'clean' interface. These results demonstrate that atomic moments at the interface are non-collinear with the bulk magnetization, and therefore may affect the net anisotropy or serve as spin scattering sites. We control the exchange bias magnitude by varying the interface oxygen concentration and Fe-O bonding.

  20. 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.

  1. 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.

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

    PubMed

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

    2015-05-01

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

  3. System Identification for Nonlinear Control Using Neural Networks

    NASA Technical Reports Server (NTRS)

    Stengel, Robert F.; Linse, Dennis J.

    1990-01-01

    An approach to incorporating artificial neural networks in nonlinear, adaptive control systems is described. The controller contains three principal elements: a nonlinear inverse dynamic control law whose coefficients depend on a comprehensive model of the plant, a neural network that models system dynamics, and a state estimator whose outputs drive the control law and train the neural network. Attention is focused on the system identification task, which combines an extended Kalman filter with generalized spline function approximation. Continual learning is possible during normal operation, without taking the system off line for specialized training. Nonlinear inverse dynamic control requires smooth derivatives as well as function estimates, imposing stringent goals on the approximating technique.

  4. 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.

  5. A Graphical Operator Interface for a Telerobotic Inspection System

    NASA Technical Reports Server (NTRS)

    Kim, W. S.; Tso, K. S.; Hayati, S.

    1993-01-01

    Operator interface has recently emerged as an important element for efficient and safe operatorinteractions with the telerobotic system. Recent advances in graphical user interface (GUI) andgraphics/video merging technologies enable development of more efficient, flexible operatorinterfaces. This paper describes an advanced graphical operator interface newly developed for aremote surface inspection system at Jet Propulsion Laboratory. The interface has been designed sothat remote surface inspection can be performed by a single operator with an integrated robot controland image inspection capability. It supports three inspection strategies of teleoperated human visual inspection, human visual inspection with automated scanning, and machine-vision-based automated inspection.

  6. 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).

  7. 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…

  8. 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.

  9. 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.

  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.

  11. NASA Docking System (NDS) Interface Definitions Document (IDD)

    NASA Technical Reports Server (NTRS)

    Tabakman, Alexander; England, Warren

    2013-01-01

    The contents of this document define the integrated performance and interface design for NASA Docking System (NDS) Block 1 and the International Docking Adapter. The intent of this IDD is to provide the interface design for using, installing, and interfacing to the NDS Block 1 that will enable successful docking to the IDA. This document is under the control of the ISS Development Projects Office (OG).

  12. Fusion techniques of fuzzy systems and neural networks, and fuzzy systems and genetic algorithms

    NASA Astrophysics Data System (ADS)

    Takagi, Hideyuki

    1993-12-01

    This paper overviews four combinations of fuzzy logic, neural networks and genetic algorithms: (1) neural networks to auto-design fuzzy systems, (2) employing fuzzy rule structure to construct structured neural networks, (3) genetic algorithms to auto-design fuzzy systems, and (4) a fuzzy knowledge-based system to control genetic parameter dynamically.

  13. 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).

  14. Voice recognition interface for a radiology information system

    NASA Astrophysics Data System (ADS)

    Hinson, William H.; Boehme, Johannes M.; Choplin, Robert H.; Santago, Peter, II

    1990-08-01

    We have implemented a voice recognition interface using a Dragon Systems VoiceScribe-1000 Speech Recognition system installed on an AT&T 6310 personal computer. The Dragon Systems DragonKey software allows the user to emulate keyboard functions using the speech recognition system and replaces the presently used bar code system. The software supports user voice training, grammar design and compilation, as well as speech recognition. We have successfully integrated this voice interface in the clinical report generation system for most standard mammography studies. We have found that the voice system provides a simple, user-friendly interface which is more widely accepted in a medical environment because of its similarities to tradition dictation. Although the system requires some initial time for voice training, it avoids potential delays in transcription and proofreading. This paper describes the design and implementation of this voice recognition interface in our department.

  15. KIM-1 interface adapter to 3-wire teletype systems

    NASA Technical Reports Server (NTRS)

    Burhans, R. W.

    1976-01-01

    The KIM-1 circuit designed for use with a full duplex isolated 4 terminal system is described. Operation of the circuit with a 3 wire system in conjunction with a single +5v supply interface is discussed.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. Interface Evaluation for Open System Architectures

    DTIC Science & Technology

    2014-03-01

    Weight Value Hierarchy ...................................................................................70 Global Weights...69 Figure 19: Interface Evaluation Framework Value Hierarchy Including Global Weights 73 Figure 20...strategy (e.g., the extent of market acceptance and availability of products that comply with a selected standard)” (OSJTF, 2004, p. 16) 4

  1. Solidification along the interface between demixed liquids in monotectic systems.

    PubMed

    Hüter, C; Boussinot, G; Brener, E A; Temkin, D E

    2011-05-01

    The steady-state solidification along the liquid-liquid interface in the monotectic system is discussed. A boundary-integral formulation describing the diffusion in the two liquid phases is given and the corresponding equations for the three interfaces (two solid-liquid interfaces and one liquid-liquid interface) are solved. Scaling relations are extracted from the results and supported by analytic arguments in the limit of small deviation from the monotectic temperature. We present also a complementary phase-field simulation, which proves the stability of the process.

  2. 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.

  3. Nonlinear behavior in small neural systems

    NASA Astrophysics Data System (ADS)

    Wheeler, Diek Winters

    This work addresses the nonlinear behavior of one or two model neurons under the influence of different stimuli, whether they be forms of chaos control or varieties of added noise. This is a step towards the ultimate objective of exploring the notion that a neural system might utilize a mechanism such as a memory-searching chaotic attractor to locate and retrieve stable-memory limit cycles. The biological realism of the Hopfield neuron models is discussed, and the concept of an ``effective'' neuron is introduced. The dynamical effects of adding inertial/inductance terms to an effective-neuron system are presented along with arguments for the biological relevance of such terms. A two neuron system with one or two inertial terms added is shown to exhibit chaos. The chaos is confirmed by Lyapunov exponents, power spectra, and phase-space plots. The effects of multiplicative and additive noise on the dynamics of a two effective-neuron system are investigated. One of the neurons possesses an added inertial term so the system is able to generate chaotic dynamics. The multiplicative noise is added to the connection parameter J 11, and the additive noise is added to the equation for U 2 like an external driving force. Using J11 as a bifurcation parameter, the system is examined as it passes from limit cycle dynamics to chaotic dynamics. Both types of noise are found to lower the bifurcation point with respect to its deterministic value, and both cause the dynamics to expand in phase space. For equivalent levels of noise, additive noise is found to have a stronger effect on the dynamics than multiplicative noise. The bifurcation points are explored by means of ensembles of the largest Lyapunov exponents derived from the stochastic dynamics. A brief overview is presented of the current state of control theory in chaotic systems. One control method, Hübler's [74] technique of using aperiodic forces to drive nonlinear oscillators to resonance, is analyzed. The technique is

  4. Microfluidic on-chip fluorescence-activated interface control system.

    PubMed

    Haiwang, Li; Nguyen, N T; Wong, T N; Ng, S L

    2010-11-22

    A microfluidic dynamic fluorescence-activated interface control system was developed for lab-on-a-chip applications. The system consists of a straight rectangular microchannel, a fluorescence excitation source, a detection sensor, a signal conversion circuit, and a high-voltage feedback system. Aqueous NaCl as conducting fluid and aqueous glycerol as nonconducting fluid were introduced to flow side by side into the straight rectangular microchannel. Fluorescent dye was added to the aqueous NaCl to work as a signal representing the interface position. Automatic control of the liquid interface was achieved by controlling the electroosmotic effect that exists only in the conducting fluid using a high-voltage feedback system. A LABVIEW program was developed to control the output of high-voltage power supply according the actual interface position, and then the interface position is modified as the output of high-voltage power supply. At last, the interface can be moved to the desired position automatically using this feedback system. The results show that the system presented in this paper can control an arbitrary interface location in real time. The effects of viscosity ratio, flow rates, and polarity of electric field were discussed. This technique can be extended to switch the sample flow and droplets automatically.

  5. 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.

  6. 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.

  7. Optical production systems using neural networks and symbolic substitution

    NASA Technical Reports Server (NTRS)

    Botha, Elizabeth; Casasent, David; Barnard, Etienne

    1988-01-01

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

  8. 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.

  9. Interface circuit synthesis of system-on-chip

    NASA Astrophysics Data System (ADS)

    Kao, Chi-Chou

    2012-07-01

    Most of the intellectual properties (IPs) of system-on-chip (SoC) are provided by different vendors, and thus they may have various characteristics, making the interface circuit synthesis of SoC a time-consuming and error-prone process. The main contribution of this article is to present an interface synthesis algorithm for power minimisation in interface circuit design of SoC. Moreover, we also study the power and area trade-off in interface circuit synthesis of such systems. By starting from the power-minimal solution, we perform a sequence of power relaxation operations and area-minimising procedures to produce a set of solutions for a given SoC interface circuit design with power and area trade-off considerations. The experimental results demonstrate the effectiveness of our algorithms.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. Natural Language Interfaces to Database Systems

    DTIC Science & Technology

    1988-10-01

    Towards a High Level User Interface,’ 4th International Conference E-R Approved (IEEE), Sept 1985, pp. 36-43. 173 Mimi Kao, Nick Cercone, WoShun Luk, Lab...147. 268 Leo Mark, Nick Roussopoulos, Dept. of Computer Science, University of Maryland, "Integration of Data, Schema and Meta-schema in the Context...Michael G. Dyer, Peter N. Johnson, CJ. Yang, Steve Harley , Dept. of Computer Science, Yale University, "BORIS - An Experiment in In-Depth Understanding of

  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.

  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. 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.

  18. 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.

  19. Institute for Brain and Neural Systems

    DTIC Science & Technology

    2009-10-06

    data. In Proceedings of the International Conference on Artificial Neural Networks (ICANN), pages 308–313. Agarwal, S., Awan, A., and Roth , D. (2004...University of Edinburgh, Scotland . Guyon, I., Weston, J., Barnhill, S., and Vapnik, V. (2002). Gene selection for cancer classification using support

  20. Design and Hardware Implementation of Neural Systems.

    DTIC Science & Technology

    1991-12-15

    orientation specificity in a neuron network for visual image decomposition. AIP Conference on Neuronal Networks for Computing, Snowbird, Utah, April, 1989...P. and Blackman, D. Orientation tuning and orientation specificity in a neuron network for visual image decomposition. AIP Conference on Neuronal ... Networks for Computing, Snowbird, Utah, April, 1989. Mueller, P., Neural computation of pattern primitives, (Abstract) AIP Conference, Snowbird, Utah

  1. 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.

  2. 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

  3. Performance evaluation of a holographic optical neural network system

    NASA Astrophysics Data System (ADS)

    Lu, Thomas T.; Kostrzewski, Andrew A.; Chou, Hung; Wu, Shudong; Lin, Freddie S.

    1993-02-01

    One of the most outstanding properties of artificial neural networks is their capability for massive interconnection and parallel processing. Recently, specialized electronic neural network processors and VLSI neural chips have been introduced to the commercial market. The number of parallel channels they can handle is limited because of the limited parallel interconnections number with one-dimensional (1-D) electronic wires. High resolution pattern recognition problems may require a large number of neurons for parallel processing of the image. The holographic optical neural network (HONN) based on high resolution volume holographic materials is capable of providing 3-D massive parallel interconnection of tens of thousand of neurons. A HONN with 3600 neurons, contained in a portable briefcase, has been developed. Rotation-shift-scale invariant pattern recognition operations have been demonstrated with this system. System parameters, such as signal-to-noise ratio, dynamic range, and processing speed, will be discussed.

  4. 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.

  5. 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.

  6. The Criticality Hypothesis in Neural Systems

    NASA Astrophysics Data System (ADS)

    Karimipanah, Yahya

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

  7. Observer's Interface for Solar System Target Specification

    NASA Astrophysics Data System (ADS)

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

    2016-10-01

    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.

  8. Nonlinear system identification based on internal recurrent neural networks.

    PubMed

    Puscasu, Gheorghe; Codres, Bogdan; Stancu, Alexandru; Murariu, Gabriel

    2009-04-01

    A novel approach for nonlinear complex system identification based on internal recurrent neural networks (IRNN) is proposed in this paper. The computational complexity of neural identification can be greatly reduced if the whole system is decomposed into several subsystems. This approach employs internal state estimation when no measurements coming from the sensors are available for the system states. A modified backpropagation algorithm is introduced in order to train the IRNN for nonlinear system identification. The performance of the proposed design approach is proven on a car simulator case study.

  9. Representation of neural networks as Lotka-Volterra systems

    SciTech Connect

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

    1999-03-22

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

  10. 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.

  11. Neural correlates of user-initiated motor success and failure - A brain-computer interface perspective.

    PubMed

    Yazmir, Boris; Reiner, Miriam

    2016-11-02

    Any motor action is, by nature, potentially accompanied by human errors. In order to facilitate development of error-tailored Brain-Computer Interface (BCI) correction systems, we focused on internal, human-initiated errors, and investigated EEG correlates of user outcome successes and errors during a continuous 3D virtual tennis game against a computer player. We used a multisensory, 3D, highly immersive environment. Missing and repelling the tennis ball were considered, as 'error' (miss) and 'success' (repel). Unlike most previous studies, where the environment "encouraged" the participant to perform a mistake, here errors happened naturally, resulting from motor-perceptual-cognitive processes of incorrect estimation of the ball kinematics, and can be regarded as user internal, self-initiated errors. Results show distinct and well-defined Event-Related Potentials (ERPs), embedded in the ongoing EEG, that differ across conditions by waveforms, scalp signal distribution maps, source estimation results (sLORETA) and time-frequency patterns, establishing a series of typical features that allow valid discrimination between user internal outcome success and error. The significant delay in latency between positive peaks of error- and success-related ERPs, suggests a cross-talk between top-down and bottom-up processing, represented by an outcome recognition process, in the context of the game world. Success-related ERPs had a central scalp distribution, while error-related ERPs were centro-parietal. The unique characteristics and sharp differences between EEG correlates of error/success provide the crucial components for an improved BCI system. The features of the EEG waveform can be used to detect user action outcome, to be fed into the BCI correction system.

  12. 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 ...

  13. 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.

  14. System Engineering Concept Demonstration, Interface Standards Studies. Volume 4

    DTIC Science & Technology

    1992-12-01

    standard operations, such as copy, delete , move, and store, and policies of the Common Programming Interface (CPI). As a central focus of the AD/Cycle...l to the tool. A number of system tools, such as mail, dir, copy, delete , conform to the SLCSE interface. A tool writer has the option of developing...elements can be accessed and deleted . Structure modifications are performed through structure editing. These operations are independent of the type of

  15. 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.

  16. 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

    2016-07-05

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

  17. 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

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

    PubMed

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

    2012-07-01

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

  19. Research on image autofocus system based on neural network

    NASA Astrophysics Data System (ADS)

    Chen, Guojin; Liu, Bin; Gong, Ye; Xu, Guohua; Shi, Huli

    2003-09-01

    AF (auto-focus) becomes a very important function in many kinds of photoelectricity imaging systems. The popular AF is realized by means of measuring distance. In this paper, a new AF means is presented, which is realized by a neural network. Because the NN (neural network) has the capacity of non-linear processing, it can be used to estimate image's definition and adjust PID controller's parameters. When such a NN is embedded in an AF system, the system will have the features of real time, high veracity and self-adaptation.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. The role of space data systems interface standards

    NASA Technical Reports Server (NTRS)

    Stephens, R.; Kummer, H.; Wanke, H.

    1985-01-01

    This paper will discuss data systems interface standards including the goals and benefits in their application to space flight programs. Recent trends toward increased international cooperation in space research and applications missions have resulted in a greater emphasis on compatible interface standards among the participating agencies. The discussion will highlight the Consultative Committee for Space Data Systems (CCSDS), a consortium of 19 space agencies formed in 1982 in order to provide a technical forum for developing guidelines for compatible data systems standards among cooperating space agencies worldwide. CCSDS effort is seen as contributing significantly to the objectives of the IAF's Symposium on Space Exploration.

  5. A fitness analysis system with an intelligent interface.

    PubMed

    Parisi, A V; Allen, G D

    1992-11-01

    This paper describes the development of a system with an intelligent interface for analysis of physiological correlates of athletes' physical performance capacities. The system improves the interface between the physiologist and the coach and provides scientific information in a systematic and coherent fashion. The recommendations provided are based on the results of a series of physiological tests. The implementation of the system is described with emphasis placed on recognition of the internal structure of the knowledge, independence from a particular shell, design for future expansion and maintenance and the integration with existing information resources.

  6. 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.

  7. 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.

  8. 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…

  9. 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.

  10. 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.

  11. 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.

  12. Delayed standard neural network models for control systems.

    PubMed

    Liu, Meiqin

    2007-09-01

    In order to conveniently analyze the stability of recurrent neural networks (RNNs) and successfully synthesize the controllers for nonlinear systems, similar to the nominal model in linear robust control theory, the novel neural network model, named delayed standard neural network model (DSNNM) is presented, which is the interconnection of a linear dynamic system and a bounded static delayed (or nondelayed) nonlinear operator. By combining a number of different Lyapunov functionals with S-procedure, some useful criteria of global asymptotic stability and global exponential stability for the continuous-time DSNNMs (CDSNNMs) and discrete-time DSNNMs (DDSNNMs) are derived, whose conditions are formulated as linear matrix inequalities (LMIs). Based on the stability analysis, some state-feedback control laws for the DSNNM with input and output are designed to stabilize the closed-loop systems. Most RNNs and neurocontrol nonlinear systems with (or without) time delays can be transformed into the DSNNMs to be stability-analyzed or stabilization-synthesized in a unified way. In this paper, the DSNNMs are applied to analyzing the stability of the continuous-time and discrete-time RNNs with or without time delays, and synthesizing the state-feedback controllers for the chaotic neural-network-system and discrete-time nonlinear system. It turns out that the DSNNM makes the stability conditions of the RNNs easily verified, and provides a new idea for the synthesis of the controllers for the nonlinear systems.

  13. 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.

  14. 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

  15. 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.

  16. 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.

  17. Image identification system based on an optical broadcast neural network and a pulse coupled neural network preprocessor stage.

    PubMed

    Lamela, Horacio; Ruiz-Llata, Marta

    2008-04-01

    We describe the concept of a vision system based on an optoelectronic hardware neural processor. The proposed system is composed of a pulse coupled neural network (PCNN) preprocessor stage that converts an input image into a temporal pulsed pattern. These pulses are inputs to the optical broadcast neural network (OBNN) processor, which classifies the input pattern between a set of reference patterns based on a pattern matching strategy. The PCNN is to provide immunity to the scale, rotation, and translation of objects in the image. The OBNN provides high parallelism and a high speed hardware neural processor.

  18. Proposing an Abstracted Interface and Protocol for Computer Systems.

    SciTech Connect

    Resnick, David Richard; Ignatowski, Mike

    2014-07-01

    While it made sense for historical reasons to develop different interfaces and protocols for memory channels, CPU to CPU interactions, and I/O devices, ongoing developments in the computer industry are leading to more converged requirements and physical implementations for these interconnects. As it becomes increasingly common for advanced components to contain a variety of computational devices as well as memory, the distinction between processors, memory, accelerators, and I/O devices become s increasingly blurred. As a result, the interface requirements among such components are converging. There is also a wide range of new disruptive technologies that will impact the computer market in the coming years , including 3D integration and emerging NVRAM memory. Optimal exploitation of these technologies cannot be done with the existing memory, storage, and I/O interface standards. The computer industry has historically made major advances when industry players have been able to add innovation behind a standard interface. The standard interface provides a large market for their products and enables relatively quick and widespread adoption. To enable a new wave of innovation in the form of advanced memory products and accelerators, we need a new standard interface explicitly designed to provide both the performance and flexibility to support new system integration solutions.

  19. Proposing an Abstracted Interface and Protocol for Computer Systems.

    SciTech Connect

    Resnick, David Richard; Ignatowski, Mike

    2014-07-01

    While it made sense for historical reasons to develop different interfaces and protocols for memory channels, CPU to CPU interactions, and I/O devices, ongoing developments in the computer industry are leading to more converged requirements and physical implementations for these interconnects. As it becomes increasingly common for advanced components to contain a variety of computational devices as well as memory, the distinction between processors, memory, accelerators, and I/O devices become s increasingly blur red. As a result, the interface requirements among such components are converging. There is also a wide range of new disruptive technologies that will impact the computer market in the coming years , including 3D integration and emerging NVRAM memory. Optimal exploitation of these technologies cannot be done with the existing memory , storage, and I/O interface standards. The computer industry has historically made major advances when industry players have been able to add innovation behind a standard interface. The standard interface provides a large market for their products and enable s relatively quick and widespread adoption. To enable a new wave of innovation in the form of advanced memory products and accelerators, we need a new standard interface explicitly design ed to provide both the performance and flexibility to support new system integration solutions.

  20. 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.

  1. 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.

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

    NASA Astrophysics Data System (ADS)

    Lee, Andrew J.; Casasent, David P.

    1990-09-01

    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 sateffites 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 (sateffite) 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 effiptical path of a part in image space the 3-D pose of the satellite is determined. Digital simulation results using this algorithm are presented for various sateffite poses and lighting conditions. 1

  3. EEG-Based Classification of Motor Imagery Tasks Using Fractal Dimension and Neural Network for Brain-Computer Interface

    NASA Astrophysics Data System (ADS)

    Phothisonothai, Montri; Nakagawa, Masahiro

    In this study, we propose a method of classifying a spontaneous electroencephalogram (EEG) approach to a brain-computer interface. Ten subjects, aged 21-32 years, volunteered to imagine left-and right- hand movements. An independent component analysis based on a fixed-point algorithm is used to eliminate the activities found in the EEG signals. We use a fractal dimension value to reveal the embedded potential responses in the human brain. The different fractal dimension values between the relaxing and imaging periods are computed. Featured data is classified by a three-layer feed-forward neural network based on a simple backpropagation algorithm. Two conventional methods, namely, the use of the autoregressive (AR) model and the band power estimation (BPE) as features, and the linear discriminant analysis (LDA) as a classifier, are selected for comparison in this study. Experimental results show that the proposed method is more effective than the conventional methods.

  4. 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

  5. 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.

  6. 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.

  7. Macroscopic coherent structures in a stochastic neural network: from interface dynamics to coarse-grained bifurcation analysis.

    PubMed

    Avitable, Daniele; Wedgwood, Kyle C A

    2017-02-01

    We study coarse pattern formation in a cellular automaton modelling a spatially-extended stochastic neural network. The model, originally proposed by Gong and Robinson (Phys Rev E 85(5):055,101(R), 2012), is known to support stationary and travelling bumps of localised activity. We pose the model on a ring and study the existence and stability of these patterns in various limits using a combination of analytical and numerical techniques. In a purely deterministic version of the model, posed on a continuum, we construct bumps and travelling waves analytically using standard interface methods from neural field theory. In a stochastic version with Heaviside firing rate, we construct approximate analytical probability mass functions associated with bumps and travelling waves. In the full stochastic model posed on a discrete lattice, where a coarse analytic description is unavailable, we compute patterns and their linear stability using equation-free methods. The lifting procedure used in the coarse time-stepper is informed by the analysis in the deterministic and stochastic limits. In all settings, we identify the synaptic profile as a mesoscopic variable, and the width of the corresponding activity set as a macroscopic variable. Stationary and travelling bumps have similar meso- and macroscopic profiles, but different microscopic structure, hence we propose lifting operators which use microscopic motifs to disambiguate them. We provide numerical evidence that waves are supported by a combination of high synaptic gain and long refractory times, while meandering bumps are elicited by short refractory times.

  8. 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.

  9. [Electronic medical record--interface specifications with medical informatics systems].

    PubMed

    Mocanu, Carmen; Mocanu, Mihai

    2007-01-01

    The paper presents the initial efforts of description and implementation for a new scheme of electronic patients recording, based on distributed database for chronic ophthalmologic diseases. Structural specifications derived from principal system's goals are the implementation of an efficient and flexible way of patients' data administration, using actual Web technologies, permitting future extensions, without reducing in performances and without exponential cost increasing. A very important aspect, that must be take into consideration is their interfacing with other medical programs and systems, as the systems for recording clinical data, monitoring systems (Patient Administrations Systems - PAS) for demographical data, systems for monitoring of treatment (Hippocrates program), web systems, including wireless.

  10. Analysis of piezoelectric energy harvesting system with tunable SECE interface

    NASA Astrophysics Data System (ADS)

    Lefeuvre, E.; Badel, A.; Brenes, A.; Seok, S.; Woytasik, M.; Yoo, C.-S.

    2017-03-01

    Numerous interface circuits have been proposed over the past years to improve the performances of piezoelectric energy harvesting devices. The so-called synchronous electric charge extraction interface (SECE) brought the advantage of harvesting power independently of the load voltage. In counterpart, its performances exhibited sensitivity to the electromechanical coupling. It was shown, in particular, that harvested power was significantly decreased at high coupling. To overcome this drawback, the so-called tunable SECE interface has recently been proposed. Instead of the total charge extraction performed by the original SECE, the tuning method consists in extracting only a portion of the electric charge. This paper presents the analytical modeling of an energy harvesting system composed of a linear piezoelectric resonator associated to the tunable SECE interface. Contrary to previous model limited to describe the system behavior at resonance, this model enables to extend the analysis off-resonance. The presented theoretical analysis and experimental results clearly show the possibility to increase both the power and the frequency bandwidth by adequate control of the tunable SECE interface.

  11. 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

  12. 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

  13. 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.

  14. Neural systems approaches to the neurogenetics of autism spectrum disorders.

    PubMed

    Piggot, J; Shirinyan, D; Shemmassian, S; Vazirian, S; Alarcón, M

    2009-11-24

    Autism is generally accepted as the most genetic of all the developmental neuropsychiatric syndromes. However, despite more than several decades of genetic study, the etiology of autism remains unknown, largely due to the genetic and phenotypic diversity, or heterogeneity, of this disorder, and the lack of biologically based classification systems. At the same time, in the neuroimaging literature, the body of research identifying candidate neural systems underlying aspects of autistic impairment has grown considerably, fueled by the advent of technologies such as functional magnetic resonance imaging (fMRI). Yet the findings from these neuroimaging studies have not been incorporated to inform the collection of samples for genetic studies of autism, which are predominantly based on a diagnosis of the disorder. This article presents a review of the genetics of autism and describes the genetic approaches that have been applied, including the phenotypic strategies that have been used to address heterogeneity and optimize the power of these genetic studies. With the increasing recognition that there may be different "autisms" (Geschwind and Levitt, 2007) with unique neural mechanisms, it is argued that neural systems research, using technologies such as fMRI, currently allows for the identification of more biologically informative phenotypes for genetic studies of autism and is positioned to identify informative neuroimaging markers for "neurogenetic" studies of the disorder. To illustrate this, we describe several candidate neural systems for the social communication impairment seen in autism, and the characteristic behavioral and physiological manifestations associated with these that could be incorporated into phenotypic assessments.

  15. A dynamic interface for capsaicinoid systems biology.

    PubMed

    Mazourek, Michael; Pujar, Anuradha; Borovsky, Yelena; Paran, Ilan; Mueller, Lukas; Jahn, Molly M

    2009-08-01

    Capsaicinoids are the pungent alkaloids that give hot peppers (Capsicum spp.) their spiciness. While capsaicinoids are relatively simple molecules, much is unknown about their biosynthesis, which spans diverse metabolisms of essential amino acids, phenylpropanoids, benzenoids, and fatty acids. Pepper is not a model organism, but it has access to the resources developed in model plants through comparative approaches. To aid research in this system, we have implemented a comprehensive model of capsaicinoid biosynthesis and made it publicly available within the SolCyc database at the SOL Genomics Network (http://www.sgn.cornell.edu). As a preliminary test of this model, and to build its value as a resource, targeted transcripts were cloned as candidates for nearly all of the structural genes for capsaicinoid biosynthesis. In support of the role of these transcripts in capsaicinoid biosynthesis beyond correct spatial and temporal expression, their predicted subcellular localizations were compared against the biosynthetic model and experimentally determined compartmentalization in Arabidopsis (Arabidopsis thaliana). To enable their use in a positional candidate gene approach in the Solanaceae, these genes were genetically mapped in pepper. These data were integrated into the SOL Genomics Network, a clade-oriented database that incorporates community annotation of genes, enzymes, phenotypes, mutants, and genomic loci. Here, we describe the creation and integration of these resources as a holistic and dynamic model of the characteristic specialized metabolism of pepper.

  16. 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

  17. 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.

  18. 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.

  19. An approach to developing user interfaces for space systems

    NASA Astrophysics Data System (ADS)

    Shackelford, Keith; McKinney, Karen

    1993-08-01

    Inherent weakness in the traditional waterfall model of software development has led to the definition of the spiral model. The spiral model software development lifecycle model, however, has not been applied to NASA projects. This paper describes its use in developing real time user interface software for an Environmental Control and Life Support System (ECLSS) Process Control Prototype at NASA's Marshall Space Flight Center.

  20. 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.

  1. 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.

  2. 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.

  3. Modal reduction of brake squeal systems using complex interface modes

    NASA Astrophysics Data System (ADS)

    Besset, S.; Sinou, J.-J.

    2017-02-01

    This paper deals with a new efficient reduction method for predicting the stability analysis of a damped nonlinear brake system subjected to friction-induced vibration. The originality of the present work is to propose a generalized double modal synthesis method that combines a classical modal reduction and a condensation at the frictional interface by computing a reduced complex mode basis. Comparisons with the existing double modal synthesis reduction method are performed. It is demonstrated that the new suggested reduction technique is more efficient. It allows to provide satisfactory results with a small number of interfaces modes.

  4. 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.

  5. 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.

  6. Neural transduction in Xenopus laevis lateral line system.

    PubMed

    Strelioff, D; Honrubia, V

    1978-03-01

    1. The process of neural excitation in hair cell systems was studied in an in vitro preparation of the Xenopus laevis (African clawed toad) lateral line organ. A specially designed stimulus chamber was used to apply accurately controlled pressure, water movement, or electrical stimuli, and to record the neural responses of the two afferent fibers innervating each organ or stitch. The objective of the study was to determine the characteristics of the neural responses to these stimuli, and thus gain insight into the transduction process. 2. A sustained deflection of the hair cell cilia due to a constant flow of water past the capula resulted in a maintained change in the mean firing rate (MFR) of the afferent fibers. The data also demonstrated that the neural response was proportional to the velocity of the water flow and indicated that both deflection and movement of the cilia were the effective physiological stimuli for this hair cell system. 3. The preparations responded to sinusoidal water movements (past the capula) over the entire frequency range of the stimulus chamber, 0.1-130 Hz, and were most sensitive between 10 and 40 Hz. The variation of the MFR and the percent modulation indicated that the average dynamic range of each organ was 23.5 dB. 4. The thresholds, if any, for sustained pressure changes and for sinusoidal pressure variations in the absence of water movements were very high. Due to the limitations of the stimulus chamber it was not possible to generate pressure stimuli of sufficient magnitude to elicit a neural response without also generating suprathreshold water-movement stimuli. Sustained pressures had no detectable effect on the neural response to water-movement stimuli. 5. The preparations were very sensitive to electrical potentials applied across the toad skin on which the hair cells were located. Potentials which made the ciliated surfaces of the hair cells positive with respect to their bases increased the MFR of the fibers, whereas

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

    PubMed

    Green, Andrea M; Angelaki, Dora E

    2010-01-01

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

  8. Information and entropy in neural networks and interacting systems

    NASA Astrophysics Data System (ADS)

    Shafee, Fariel

    In this dissertation we present a study of certain characteristics of interacting systems that are related to information. The first is periodicity, correlation and other information-related properties of neural networks of integrate-and-fire type. We also form quasiclassical and quantum generalizations of such networks and identify the similarities and differences with the classical prototype. We indicate why entropy may be an important concept for a neural network and why a generalization of the definition of entropy may be required. Like neural networks, large ensembles of similar units that interact also need a generalization of classical information-theoretic concepts. We extend the concept of Shannon entropy in a novel way, which may be relevant when we have such interacting systems, and show how it differs from Shannon entropy and other generalizations, such as Tsallis entropy. We indicate how classical stochasticity may arise in interactions with an entangled environment in a quantum system in terms of Shannon's and generalized entropies and identify the differences. Such differences are also indicated in the use of certain prior probability distributions to fit data as per Bayesian rules. We also suggest possible quantum versions of pattern recognition, which is the principal goal of information processing in most neural networks.

  9. Cognitive training for impaired neural systems in neuropsychiatric illness.

    PubMed

    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 some

  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. Neural Network Control of a Magnetically Suspended Rotor System

    NASA Technical Reports Server (NTRS)

    Choi, Benjamin B.

    1998-01-01

    Magnetic bearings offer significant advantages because they do not come into contact with other parts during 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. At the NASA Lewis Research Center, a neural network was selected as a nonlinear controller because it generates a neural model without any detailed information regarding the internal working of the magnetic bearing system. It can be used even for systems that are too complex for an accurate system model to be derived. A feed-forward architecture with a back-propagation learning algorithm was selected because of its proven performance, accuracy, and relatively easy implementation.

  12. 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.

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

    PubMed

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

    2016-11-14

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

  14. 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.

  15. 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.

  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. Spacecraft power system controller based on neural network

    NASA Astrophysics Data System (ADS)

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

    2011-09-01

    Neural control is a branch of the general field of intelligent control, which is based on the concept of artificial intelligence. This work presents the spacecraft orbit determination, dimensioning of the renewable power system, and mathematical modeling of spacecraft power system which are required for simulating the system. The complete system is simulated using MATLAB-SIMULINK. The NN controller outperform PID in the extreme range of non-linearity. Well trained neural controller can operate at different conditions of load current at different orbital periods without any tuning such in case of PID controller. So an artificial neural network (ANN) based model has been developed for the optimum operation of spacecraft power system. An ANN is trained using a back propagation with Levenberg-Marquardt algorithm. The best validation performance is obtained for mean square error is equal to 9.9962×10 -11 at epoch 637. The regression between the network output and the corresponding target is equal to 100% which means a high accuracy. NNC architecture gives satisfactory results with small number of neurons, hence better in terms of memory and time are required for NNC implementation. The results indicate that the proposed control unit using ANN can be successfully used for controlling the spacecraft power system in low earth orbit (LEO). Therefore, this technique is going to be a very useful tool for the interested designers in space field.

  18. 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.

  19. 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

  20. 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.

  1. Conservative motor systems, behavioral modulation and neural plasticity.

    PubMed

    Pellis, Sergio M

    2010-12-06

    Neural plasticity is a term that encompasses a vast array of changes in the nervous system in response to a wide range of environmental disturbances. The conservative manner in which nervous systems produce behavior is explored in the act of scratching the head. Whether the scratching is done with the hind leg (flamingos and axis deer) or the hand (spider monkey), it is shown that, when scratching their heads, animals follow a simple rule to avoid making multiple movements simultaneously with different parts of their bodies. Closer inspection of such a computational cost-saving scheme reveals that neural plasticity may best enhance motor performance when it occurs at higher levels of brain organization. The example of how complex social behavior, play fighting, is organized in rats shows that cortical systems can modify the contextual use of species-typical, or well-learned, behavior patterns, rather than producing new behavior patterns.

  2. 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

  3. 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

  4. Emergent complexity in simple neural systems

    PubMed Central

    Oster, George

    2009-01-01

    The ornate and diverse patterns of seashells testify to the complexity of living systems. Provocative computational explorations have shown that similarly complex patterns may arise from the collective interaction of a small number of rules. This suggests that, although a system may appear complex, it may still be understood in terms of simple principles. It is still debatable whether shell patterns emerge from some undiscovered simple principles, or are the consequence of an irreducibly complex interaction of many effects. Recent work by Boettiger, Ermentrout and Oster on the biological mechanisms of shell patterning has provided compelling evidence that, at least for this system, simplicity produces diversity and complexity. PMID:20195452

  5. Superparamagnetic segmentation by excitable neural systems.

    PubMed

    Neirotti, Juan P; Kurcbart, Samuel M; Caticha, Nestor

    2003-09-01

    Magnetic modeling for clustering or segmentation purposes can either associate the image data to external quenched fields or to the interactions among a set of auxiliary variables. The latter gives rise to superparamagnetic segmentation and is usually done with Potts systems. We have used the superparamagnetic clustering technique to segment images, with the aid of different associated systems. Results using Potts model are comparable to those obtained using excitable FitzHugh-Nagumo and Morris-Lecar model neurons. Interactions between the associated system components are a function of the difference of luminosity on a gray scale of neighbor pixels and the difference of membrane potential.

  6. Neural Network and Fuzzy Logic Technology for Naval Flight Control Systems

    DTIC Science & Technology

    1991-08-06

    it is still uncertain what neural network and fuzzy logic functions are both technologically feasible and suitable for flight control system...this program is focused on the development of a neural network FCS design tool, a neural network flight control law emulator, a fuzzy logic automatic...carrier landing system and a neural network flight control configuration management system. For each project, some initial results are given. Also

  7. 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

  8. NNIC—neural network image compressor for satellite positioning system

    NASA Astrophysics Data System (ADS)

    Danchenko, Pavel; Lifshits, Feodor; Orion, Itzhak; Koren, Sion; Solomon, Alan D.; Mark, Shlomo

    2007-04-01

    We have developed an algorithm, based on novel techniques of data compression and neural networks for the optimal positioning of a satellite. The algorithm is described in detail, and examples of its application are given. The heart of this algorithm is the program NNIC—neural network image compressor. This program was developed for compression color and grayscale images with artificial neural networks (ANNs). NNIC applies three different methods for compression. Two of them are based on neural networks architectures—multilayer perceptron and kohonen network. The third is based on a widely used method of discrete cosine transform, the basis for the JPEG standard. The program also serves as a tool for determining numerical and visual quality parameters of compression and comparison between different methods. A number of advantages and disadvantages of the compression using ANNs were discovered in the course of the present research, some of them presented in this report. The thrust of the report is the discussion of ANNs implementation problems for modern platforms, such as a satellite positioning system that include intensive image flowing and processing.

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

    PubMed Central

    Mi, Yuanyuan; Lin, Xiaohan; Wu, Si

    2016-01-01

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

  10. Low-reflection-coefficient liquid interfaces for system characterization.

    PubMed

    Hall, T J; Madsen, E L; Dong, F; Medina, I R; Frank, G R

    2001-07-01

    The use of liquid brominated hydrocarbons to form a planar reflecting interface with water is described. Gravity-based planar reflecting surfaces with known reflection coefficients can be used in system characterization for quantitative ultrasonics, and a set of surfaces with a range of reflection coefficients allows calibration of the output power and receiver gain of ultrasonic imaging systems. The substances reported here are immiscible in water and form interfaces with water, resulting in a broad range of acoustic reflection coefficients. Reflection coefficients were measured at temperatures from 18-24 degrees C for "pure" substances and for mixtures of two brominated hydrocarbons. Results show that reflection coefficients are weakly dependent on temperature and that, at a specific temperature, a significant range of arbitrarily small reflection coefficients is available, in the case of the mixtures, by the appropriate choice of weight-percents of the two brominated hydrocarbons.

  11. Spiking Neural P Systems with Neuron Division and Dissolution

    PubMed Central

    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

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

    PubMed

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

    2016-07-06

    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.

  13. Graphic user interface-based nuclear medicine reporting system.

    PubMed

    Sanger, J J

    1993-03-01

    A graphically based, computerized report generation program has been developed and deployed at a dozen nuclear medicine facilities. The system is based on the Macintosh graphical user interface (GUI) and has been designed to be easy to learn and use. The system allows the nuclear medicine practitioner to generate reports for any nuclear medicine or nuclear cardiology procedure without transcriptionist support, dramatically decreasing report turnaround time. The system includes a relational database engine that allows cost-effective storage and rapid retrieval of final reports and also supports facsimile transmission of reports directly to referring clinicians' offices.

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

    DTIC Science & Technology

    2010-09-24

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

  15. Some new results on system identification with dynamic neural networks.

    PubMed

    Yu, W; Li, X

    2001-01-01

    Nonlinear system online identification via dynamic neural networks is studied in this paper. The main contribution of the paper is that the passivity approach is applied to access several new stable properties of neuro identification. The conditions for passivity, stability, asymptotic stability, and input-to-state stability are established in certain senses. We conclude that the gradient descent algorithm for weight adjustment is stable in an L(infinity) sense and robust to any bounded uncertainties.

  16. Challenges for user-interface designers of telemedicine systems.

    PubMed

    Salvemini, A V

    1999-01-01

    Problems associated with telemedicine systems include high telecommunications costs, lack of physician interest, and failure to build evaluation into the design process from the onset of the telemedicine project. An overview of the human-factors engineering approach to systems design and how it can be applied to the development of telemedicine systems is described. Design of an interface is based on an analysis of user capabilities, tasks, and work environment. Task analyses are performed to understand and document the interaction between a user's work activities and a system. Two characteristics of a human factors approach that are important for telemedicine are: (1) defining and measuring user performance, and (2) involving users in the design and testing of a system. Usability goals are operationally defined and tracked to quantify performance. Having users participate in the design, testing, and critique of a system also increases the likelihood that the system will be accepted and used after it is released.

  17. 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.

  18. 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.

  19. Neural systems supporting the control of affective and cognitive conflicts.

    PubMed

    Ochsner, Kevin N; Hughes, Brent; Robertson, Elaine R; Cooper, Jeffrey C; Gabrieli, John D E

    2009-09-01

    Although many studies have examined the neural bases of controlling cognitive responses, the neural systems for controlling conflicts between competing affective responses remain unclear. To address the neural correlates of affective conflict and their relationship to cognitive conflict, the present study collected whole-brain fMRI data during two versions of the Eriksen flanker task. For these tasks, participants indicated either the valence (affective task) or the semantic category (cognitive task) of a central target word while ignoring flanking words that mapped onto either the same (congruent) or a different (incongruent) response as the target. Overall, contrasts of incongruent > congruent trials showed that bilateral dorsal ACC, posterior medial frontal cortex, and dorsolateral pFC were active during both kinds of conflict, whereas rostral medial pFC and left ventrolateral pFC were differentially active during affective or cognitive conflict, respectively. Individual difference analyses showed that separate regions of rostral cingulate/ventromedial pFC and left ventrolateral pFC were positively correlated with the magnitude of response time interference. Taken together, the findings that controlling affective and cognitive conflicts depends upon both common and distinct systems have important implications for understanding the organization of control systems in general and their potential dysfunction in clinical disorders.

  20. 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

    2014-10-08

    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.

  1. 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.

  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. Sympathetic neural modulation of the immune system

    SciTech Connect

    Madden, K.S.

    1989-01-01

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

  4. Lumbo-sacral neural crest contributes to the avian enteric nervous system independently of vagal neural crest.

    PubMed

    Hearn, C; Newgreen, D

    2000-07-01

    Most of the avian enteric nervous system is derived from the vagal neural crest, but a minority of the neural cells in the hindgut, and to an even lesser extent in the midgut, are of lumbo-sacral crest origin. Since the lumbo-sacral contribution was not detected or deemed negligible in the absence of vagal cells, it had been hypothesised that lumbo-sacral neural crest cells require vagal crest cells to contribute to the enteric nervous system. In contrast, zonal aganglionosis, a rare congenital human bowel disease led to the opposite suggestion, that lumbo-sacral cells could compensate for the absence of vagal cells to construct a complete enteric nervous system. To test these notions, we combined E4 chick midgut and hindgut, isolated prior to arrival of neural precursors, with E1. 7 chick vagal and/or E2.7 quail lumbo-sacral neural tube as crest donors, and grafted these to the chorio-allantoic membrane of E9 chick hosts. Double and triple immuno-labelling for quail cells (QCPNA), neural crest cells (HNK-1), neurons and neurites (neurofilament) and glial cells (GFAP) indicated that vagal crest cells produced neurons and glia in large ganglia throughout the entire intestinal tissues. Lumbo-sacral crest contributed small numbers of neurons and glial cells in the presence or absence of vagal cells, chiefly in colorectum, but not in nearby small intestinal tissue. Thus for production of enteric neural cells the avian lumbo-sacral neural crest neither requires the vagal neural crest, nor significantly compensates for its lack. However, enteric neurogenesis of lumbo-sacral cells requires the hindgut microenvironment, whereas that of vagal cells is not restricted to a particular intestinal region.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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

  12. 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.

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

    PubMed

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

    2014-04-01

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

  14. Development of wrist rehabilitation robot and interface system.

    PubMed

    Yamamoto, Ikuo; Matsui, Miki; Inagawa, Naohiro; Hachisuka, Kenji; Wada, Futoshi; Hachisuka, Akiko; Saeki, Satoru

    2015-01-01

    The authors have developed a practical wrist rehabilitation robot for hemiplegic patients. It consists of a mechanical rotation unit, sensor, grip, and computer system. A myoelectric sensor is used to monitor the extensor carpi radialis longus/brevis muscle and flexor carpi radialis muscle activity during training. The training robot can provoke training through myoelectric sensors, a biological signal detector and processor in advance, so that patients can undergo effective training of extention and flexion in an excited condition. In addition, both-wrist system has been developed for mirror effect training, which is the most effective function of the system, so that autonomous training using both wrists is possible. Furthermore, a user-friendly screen interface with easily recognizable touch panels has been developed to give effective training for patients. The developed robot is small size and easy to carry. The developed aspiring interface system is effective to motivate the training of patients. The effectiveness of the robot system has been verified in hospital trails.

  15. 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.

  16. 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

  17. 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

  18. Synchronization of an uncertain chaotic system via recurrent neural networks

    NASA Astrophysics Data System (ADS)

    Tan, Wen; Wang, Yao-Nan

    2005-01-01

    Incorporating distributed recurrent networks with high-order connections between neurons, the identification and synchronization problem of an unknown chaotic system in the presence of unmodelled dynamics is investigated. Based on the Lyapunov stability theory, the weights learning algorithm for the recurrent high-order neural network model is presented. Also, analytical results concerning the stability properties of the scheme are obtained. Then adaptive control law for eliminating synchronization error of uncertain chaotic plant is developed via Lyapunov methodology. The proposed scheme is applied to model and synchronize an unknown Rossler system.

  19. 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.

  20. Clinical applications of penetrating neural interfaces and Utah Electrode Array technologies

    NASA Astrophysics Data System (ADS)

    Normann, Richard A.; Fernandez, Eduardo

    2016-12-01

    This paper briefly describes some of the recent progress in the development of penetrating microelectrode arrays and highlights the use of two of these devices, Utah electrode arrays and Utah slanted electrode arrays, in two therapeutic interventions: recording volitional skeletal motor commands from the central nervous system, and recording motor commands and evoking somatosensory percepts in the peripheral nervous system (PNS). The paper also briefly explores other potential sites for microelectrode array interventions that could be profitably pursued and that could have important consequences in enhancing the quality of life of patients that has been compromised by disorders of the central and PNSs.

  1. Performing four basic arithmetic operations with spiking neural P systems.

    PubMed

    Zeng, Xiangxiang; Song, Tao; Zhang, Xingyi; Pan, Linqiang

    2012-12-01

    Recently, Gutiérrez-Naranjo and Leporati considered performing basic arithmetic operations on a new class of bio-inspired computing devices-spiking neural P systems (for short, SN P systems). However, the binary encoding mechanism used in their research looks like the encoding approach in electronic circuits, instead of the style of spiking neurons (in usual SN P systems, information is encoded as the time interval between spikes). In this work, four SN P systems are constructed as adder, subtracter, multiplier, and divider, respectively. In these systems, a number is inputted to the system as the interval of time elapsed between two spikes received by input neuron, the result of a computation is the time between the moments when the output neuron spikes.

  2. Chandra Automatic Processing Task Interface: An Adaptable System Architecture

    NASA Astrophysics Data System (ADS)

    Grier, J. D., Jr.; Plummer, D.

    2007-10-01

    The Chandra Automatic Processing Task Interface (CAPTAIN) is an operations interface to Chandra Automatic Processing (AP) that provides detail management and execution of the AP pipelines. In particular, this kind of management is used in Special Automatic Processing (SAP) where there is a need to select specific pipelines that require non-standard handling for reprocessing of a given data set. Standard AP currently contains approximately 200 pipelines with complex interactions between them. As AP has evolved over the life of the mission, so has the number and attributes of these pipelines. As a result, CAPTAIN provides a system architecture capable of managing and adapting to this evolving system. This adaptability has allowed CAPTAIN to also be used to initiate Chandra Source Catalog Automatic Processing (Level~3 AP) and positions it for use with future automatic processing systems. This paper describes the approach to the development of the CAPTAIN system architecture and the maintainable, extensible and reusable software architecture by which it is implemented.

  3. Distributed photovoltaic systems - Addressing the utility interface issues

    NASA Astrophysics Data System (ADS)

    Firstman, S. I.; Vachtsevanos, G. J.

    This paper reviews work conducted in the United States on the impact of dispersed photovoltaic sources upon utility operations. The photovoltaic (PV) arrays are roof-mounted on residential houses and connected, via appropriate power conditioning equipment, to the utility grid. The presence of such small (4-6 Kw) dispersed generators on the distribution network raises questions of a technical, economic and institutional nature. After a brief identification of utility interface issues, the paper addresses such technical concerns as protection of equipment and personnel safety, power quality and utility operational stability. A combination of experimental and analytical approaches has been adopted to arrive at solutions to these problems. Problem areas, under various PV system penetration scenarios, are identified and conceptual designs of protection and control equipment and operating policies are developed so that system reliability is maintained while minimizing capital costs. It is hoped that the resolution of balance-of-system and grid interface questions will ascertain the economic viability of photovoltaic systems and assist in their widespread utilization in the future.

  4. 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.

  5. 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.

  6. 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.

  7. Advanced operator/system interface concepts for the Space Station

    NASA Technical Reports Server (NTRS)

    Case, C. M.; Lin, P. S. Y.

    1986-01-01

    Concepts and data developed as part of the Preliminary Space Station Automation and Robotics Plan are reviewed as well as candidate selection criteria, technology assessments, and preliminary candidate recommendations. A need for development of advanced operator/systems interface (OSI) concepts to support future Space Station automation and robotics applications is identified. Four candidate applications, illustrating the potential benefits of an advanced OSI, are described. These include: (1) a conversational OSI system, (2) a laboratory experiment manipulator system, (3) a module safety advisor, and (4) an integrated maintenance/training system. These specific automation and robotics applications are expected to occur relatively early in the growth of the Space Station and to provide significant commercial and station benefits throughout the life of the station.

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

    PubMed

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

    2014-02-01

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

  9. 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.

  10. 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.

  11. Hardware implementation of a neural vision system based on a neural network using integrated and fire neurons

    NASA Astrophysics Data System (ADS)

    González, M.; Lamela, H.; Jiménez, M.; Gimeno, J.; Ruiz-Llata, M.

    2007-04-01

    In this paper we present the scheme for a control circuit used in an image processing system which is to be implemented in a neural network which has a high level of connectivity and reconfiguration of neurons for integration and trigger based on the Address-Event Representation. This scheme will be employed as a pre-processing stage for a vision system which employs as its core processing an Optical Broadcast Neural Network (OBNN). [Optical Engineering letters 42 (9), 2488(2003)]. The proposed vision system allows the possibility to introduce patterns from any acquisition system of images, for posterior processing.

  12. 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

  13. 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.

  14. 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.

  15. Description of the PRISM system architecture and user interface

    NASA Astrophysics Data System (ADS)

    Constanza, P.; Larsson, C.; Thiemann, H.; Wedi, N.

    2003-04-01

    The PRISM system architecture enables the user to perform numerical experiments, allowing to couple interchangeable model components, e.g. atmosphere, ocean, biosphere, chemistry, via standardised interfaces. The coupler is based on standard interfaces implemented in the different model components. The exchange of data between the components will occur either in a direct way between components or through the coupler. The general architecture provides the infrastructure to configure, submit, monitor and subsequently post process, archive and diagnose the results of these coupled model experiments. There is an emphasis on choosing an architectural design that allows these activities to be done remotely, e.g. without the user physically being in the same place where the numerical computations take place. The PRISM general architecture gives the choice to the user either to work locally or to work through a central PRISM site where the user will be registered. Locally or via the Internet, the user will be able to use the same graphical user interface. The choice will depend of the local availability of the required resources. In addition a supervisor monitor program (SMS) gives full control over model simulations during run-time. The technology that realises the proposed architecture is known as "Web services". This includes the use of web servers, application servers, resource directories, discovery mechanisms and messages services. Currently there is no client software that can be used with browsers that does not build on Java technology. Java supports all the mechanisms needed for implementing web services using available standards. From a system maintenance point of view using one technology, Java, is the preferred way as this simplifies the task of adhering to multiple standards. The issue of standardisation of interfaces is important for complex and configurable systems such as PRISM. For example the extensible Markup Language (XML) allows for standardisation of

  16. Methods and systems for monitoring a solid-liquid interface

    DOEpatents

    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.

  17. Neural circuit recording from an intact cockroach nervous system.

    PubMed

    Titlow, Josh S; Majeed, Zana R; Hartman, H Bernard; Burns, Ellen; Cooper, Robin L

    2013-11-04

    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.

  18. Operator interface design considerations for a PACS information management system

    NASA Astrophysics Data System (ADS)

    Steinke, James E.; Nabijee, Kamal H.; Freeman, Rick H.; Prior, Fred W.

    1990-08-01

    As prototype PACS grow into fully digital departmental and hospital-wide systems, effective information storage and retrieval mechanisms become increasingly important. Thus far, designers of PACS workstations have concentrated on image communication and display functionality. The new challenge is to provide appropriate operator interface environments to facilitate information retrieval. The "Marburg Model" 1 provides a detailed analysis of the functions, control flows and data structures used in Radiology. It identifies a set of "actors" who perform information manipulation functions. Drawing on this model and its associated methodology it is possible to identify four modes of use of information systems in Radiology: Clinical Routine, Research, Consultation, and Administration. Each mode has its own specific access requirements and views of information. An operator interface strategy appropriate for each mode will be proposed. Clinical Routine mode is the principal concern of PACS primary diagnosis workstations. In a full PACS implementation, such workstations must provide a simple and consistent navigational aid for the on-line image database, a local work list of cases to be reviewed, and easy access to information from other hospital information systems. A hierarchical method of information access is preferred because it provides the ability to start at high-level entities and iteratively narrow the scope of information from which to select subsequent operations. An implementation using hierarchical, nested software windows which fulfills such requirements shall be examined.

  19. TGeoCad: an Interface between ROOT and CAD Systems

    NASA Astrophysics Data System (ADS)

    Luzzi, C.; Carminati, F.

    2014-06-01

    In the simulation of High Energy Physics experiment a very high precision in the description of the detector geometry is essential to achieve the required performances. The physicists in charge of Monte Carlo Simulation of the detector need to collaborate efficiently with the engineers working at the mechanical design of the detector. Often, this collaboration is made hard by the usage of different and incompatible software. ROOT is an object-oriented C++ framework used by physicists for storing, analyzing and simulating data produced by the high-energy physics experiments while CAD (Computer-Aided Design) software is used for mechanical design in the engineering field. The necessity to improve the level of communication between physicists and engineers led to the implementation of an interface between the ROOT geometrical modeler used by the virtual Monte Carlo simulation software and the CAD systems. In this paper we describe the design and implementation of the TGeoCad Interface that has been developed to enable the use of ROOT geometrical models in several CAD systems. To achieve this goal, the ROOT geometry description is converted into STEP file format (ISO 10303), which can be imported and used by many CAD systems.

  20. Optical-digital-neural network system for aided target recognition

    NASA Astrophysics Data System (ADS)

    Farr, Keith B.; Hartman, Richard L.

    1995-07-01

    Many military systems have a critical need for aided target recognition, or cuing. This includes several systems with wide field-of-view search missions such as the UAV, EFOG-M, and Comanche. This report discusses one new approach: a multiple region of interest processor based on diffraction pattern sampling and digital neural network processing. In this concept an optical system segments the image into multiple, rectangular regions of interest and in parallel converts each ROI, be it visible, IR, or radar, to a spatial frequency power spectrum and samples that spectrum for 64 features. A neural network learns to correlate those features with target classes or identifications. A digital system uses the network weights to recognize unknown targets. The research discussed in this report using a single ROI processor showed a very high level of performance. Out of 1024 trials with models of five targets of F- 14, F-18, HIND, SCUD, and M1 tanks, there were 1023 correct classifications and 1 incorrect classification. Out of 1514 trials with those images plus 490 real clutter scenes, there were 1514 correct decisions between target or no-target. Of the 1024 target detections, there were 1023 correct classifications. Out of 60 trials with low resolution IR images of real scenes, there were 60 correct decisions between target and no-target. Of the 40 target detections, there were 40 correct classifications.

  1. 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

    2014-06-05

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

  2. Fuzzy neural network technique for system state forecasting.

    PubMed

    Li, Dezhi; Wang, Wilson; Ismail, Fathy

    2013-10-01

    In many system state forecasting applications, the prediction is performed based on multiple datasets, each corresponding to a distinct system condition. The traditional methods dealing with multiple datasets (e.g., vector autoregressive moving average models and neural networks) have some shortcomings, such as limited modeling capability and opaque reasoning operations. To tackle these problems, a novel fuzzy neural network (FNN) is proposed in this paper to effectively extract information from multiple datasets, so as to improve forecasting accuracy. The proposed predictor consists of both autoregressive (AR) nodes modeling and nonlinear nodes modeling; AR models/nodes are used to capture the linear correlation of the datasets, and the nonlinear correlation of the datasets are modeled with nonlinear neuron nodes. A novel particle swarm technique [i.e., Laplace particle swarm (LPS) method] is proposed to facilitate parameters estimation of the predictor and improve modeling accuracy. The effectiveness of the developed FNN predictor and the associated LPS method is verified by a series of tests related to Mackey-Glass data forecast, exchange rate data prediction, and gear system prognosis. Test results show that the developed FNN predictor and the LPS method can capture the dynamics of multiple datasets effectively and track system characteristics accurately.

  3. 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

  4. Solid-liquid interface free energy in binary systems: theory and atomistic calculations for the (110) Cu-Ag interface.

    PubMed

    Frolov, T; Mishin, Y

    2009-08-07

    We analyze thermodynamics of solid-liquid interfaces in binary systems when the solid is in a nonhydrostatic state of stress. The difficulty lies in the fact that chemical potential of at least one of the chemical components in a nonhydrostatic solid is an undefined quantity. We show, nevertheless, that the interface free energy gamma can be defined as excess of an appropriate thermodynamic potential that depends on the chemical potentials in the liquid phase. We derive different forms of the adsorption equation for solid-liquid interfaces, with differential coefficients representing excesses of extensive properties. This leads, in particular, to the formulation of interface stress tau(ij) as an appropriate excess over nonhydrostatic bulk stresses. The interface stress is not unique unless the solid is in a hydrostatic state of stress. We also derive Gibbs-Helmholtz type equations that can be applied for thermodynamic integration of gamma. All thermodynamic relations derived here are presented in forms suitable for atomistic simulations. In particular, the excess quantities can be computed without constructing interface profiles. As an application, we perform semigrand canonical Monte Carlo simulations of the (110) solid-liquid interface in the Cu-Ag system. We show that gamma computed by thermodynamic integration along a coexistence path decreases with increasing composition difference between the phases. At the same time, tau(ij) remains negative (i.e., the interface is in a state of compression), drastically increases in magnitude, and becomes highly anisotropic. Some of the interface excess properties are computed by different methods and demonstrate accurate agreement with each other, confirming the correctness of our analysis.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. Neural network-based systems for handprint OCR applications.

    PubMed

    Ganis, M D; Wilson, C L; Blue, J L

    1998-01-01

    Over the last five years or so, neural network (NN)-based approaches have been steadily gaining performance and popularity for a wide range of optical character recognition (OCR) problems, from isolated digit recognition to handprint recognition. We present an NN classification scheme based on an enhanced multilayer perceptron (MLP) and describe an end-to-end system for form-based handprint OCR applications designed by the National Institute of Standards and Technology (NIST) Visual Image Processing Group. The enhancements to the MLP are based on (i) neuron activations functions that reduce the occurrences of singular Jacobians; (ii) successive regularization to constrain the volume of the weight space; and (iii) Boltzmann pruning to constrain the dimension of the weight space. Performance characterization studies of NN systems evaluated at the first OCR systems conference and the NIST form-based handprint recognition system are also summarized.

  10. 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.

  11. Neural mechanisms coordinating the female reproductive system in the locust.

    PubMed

    Lange, Angela B

    2009-01-01

    The production of viable offspring is a complex task, involving courtship, mating, maturation of eggs, ovulation, fertilization of eggs, and oviposition. With particular regard to the female, the reproductive system must produce eggs at the appropriate time and deposit them after fertilization in an appropriate place. Thus, the various structures of the reproductive system must be tightly coordinated and integrated. This review focuses on the female reproductive system and the neural mechanisms that lead to its integrated control. Central pattern generators, that are linked, control oviposition digging behavior, and contractions of the lower lateral and upper common oviducts that lead to retention of eggs. Sensory neurons also provide information about the presence of an egg in the genital chamber via a feedback loop to coordinate the spermatheca and thereby, fertilization. Neuropeptides and amines can modulate central and peripheral control mechanisms. These neural mechanisms are integrated such as to produce coordinated behavior, leading to the accomplishment of the ultimate task, that of producing viable offspring.

  12. 75 FR 33898 - Agency Information Collection (VA Loan Electronic Reporting Interface (VALERI) System) Activity...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2010-06-15

    ... From the Federal Register Online via the Government Publishing Office DEPARTMENT OF VETERANS AFFAIRS Agency Information Collection (VA Loan Electronic Reporting Interface (VALERI) System) Activity.... 2900-0021.'' SUPPLEMENTARY INFORMATION: Title: VA Loan Electronic Reporting Interface (VALERI)...

  13. 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.

  14. 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

  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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. Neural primacy of the salience processing system in schizophrenia.

    PubMed

    Palaniyappan, Lena; Simmonite, Molly; White, Thomas P; Liddle, Elizabeth B; Liddle, Peter F

    2013-08-21

    For effective information processing, two large-scale distributed neural networks appear to be critical: a multimodal executive system anchored on the dorsolateral prefrontal cortex (DLPFC) and a salience system anchored on the anterior insula. Aberrant interaction among distributed networks is a feature of psychiatric disorders such as schizophrenia. We used whole-brain Granger causal modeling using resting fMRI and observed a significant failure of both the feedforward and reciprocal influence between the insula and the DLPFC in schizophrenia. Further, a significant failure of directed influence from bilateral visual cortices to the insula was also seen in patients. These findings provide compelling evidence for a breakdown of the salience-execution loop in the clinical expression of psychosis. In addition, this offers a parsimonious explanation for the often-observed "frontal inefficiency," the failure to recruit prefrontal system when salient or novel information becomes available in patients with schizophrenia.

  1. 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.

  2. 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

  3. Man-machine interface system for neuromuscular training and evaluation based on EMG and MMG signals.

    PubMed

    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.

  4. 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.

  5. Neural Network Noise Anomaly Recognition System and Method

    DTIC Science & Technology

    2000-10-04

    determine when an input waveform deviates from learned noise characteristics. A plurality of neural networks is preferably provided, which each receives a...plurality of samples of intervals or windows of the input waveform. Each of the neural networks produces an output based on whether an anomaly is...detected with respect to the noise, which the neural network is trained to detect. The plurality of outputs of the neural networks is preferably applied to

  6. 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.

  7. An Anatomical Interface between Memory and Oculomotor Systems.

    PubMed

    Shen, Kelly; Bezgin, Gleb; Selvam, Rajajee; McIntosh, Anthony R; Ryan, Jennifer D

    2016-11-01

    Visual behavior is guided by memories from prior experience and knowledge of the visual scene. The hippocampal system (HC), in particular, has been implicated in the guidance of saccades: Amnesic patients, following damage to the HC, exhibit selective deficits in their gaze patterns. However, the neural circuitry by which mnemonic representations influence the oculomotor system remains unknown. We used a data-driven, network-based approach on directed anatomical connectivity from the macaque brain to reveal an extensive set of polysnaptic pathways spanning the extrastriate, posterior parietal and prefrontal cortices that potentially mediate the exchange of information between the memory and visuo-oculomotor systems. We additionally show how the potential for directed information flow from the hippocampus to oculomotor control areas is exceptionally high. In particular, the dorsolateral pFC and FEF-regions known to be responsible for the cognitive control of saccades-are topologically well positioned to receive information from the hippocampus. Together with neuropsychological evidence of altered gaze patterns following damage to the hippocampus, our findings suggest that a reconsideration of hippocampal involvement in oculomotor guidance is needed.

  8. Evaluation of a Compact Hybrid Brain-Computer Interface System

    PubMed Central

    Müller, Klaus-Robert; Schmitz, Christoph H.

    2017-01-01

    We realized a compact hybrid brain-computer interface (BCI) system by integrating a portable near-infrared spectroscopy (NIRS) device with an economical electroencephalography (EEG) system. The NIRS array was located on the subjects' forehead, covering the prefrontal area. The EEG electrodes were distributed over the frontal, motor/temporal, and parietal areas. The experimental paradigm involved a Stroop word-picture matching test in combination with mental arithmetic (MA) and baseline (BL) tasks, in which the subjects were asked to perform either MA or BL in response to congruent or incongruent conditions, respectively. We compared the classification accuracies of each of the modalities (NIRS or EEG) with that of the hybrid system. We showed that the hybrid system outperforms the unimodal EEG and NIRS systems by 6.2% and 2.5%, respectively. Since the proposed hybrid system is based on portable platforms, it is not confined to a laboratory environment and has the potential to be used in real-life situations, such as in neurorehabilitation. PMID:28373984

  9. 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

  10. 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.

  11. 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.

  12. 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

  13. Asymmetric neural development in the C. elegans olfactory system

    PubMed Central

    Hsieh, Yi-Wen; Alqadah, Amel; Chuang, Chiou-Fen

    2014-01-01

    Asymmetries in the nervous system have been observed throughout the animal kingdom. Deviations of brain asymmetries are associated with a variety of neurodevelopmental disorders; however, there has been limited progress in determining how normal asymmetry is established in vertebrates. In the C. elegans chemosensory system, two pairs of morphologically symmetrical neurons exhibit molecular and functional asymmetries. This review focuses on the development of antisymmetry of the pair of AWC olfactory neurons, from transcriptional regulation of general cell identity, establishment of asymmetry through neural network formation and calcium signaling, to the maintenance of asymmetry throughout the life of the animal. Many of the factors that are involved in AWC development have homologs in vertebrates, which may potentially function in the development of vertebrate brain asymmetry. PMID:24478264

  14. 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.

  15. Neural Networks and other Techniques for Fault Identification and Isolation of Aircraft Systems

    DTIC Science & Technology

    2003-06-01

    in this paper is based on the use of neural networks (NNs) as on-line learning non-linear approximators. The performances of two different neural...Fault identification, isolation. and accommodation have become critical issues in the overall performance of advanced aircraft systems. Neural ... Networks have shown to be a very attractive alternative to classic adaptation methods for identification and control of non-linear dynamic systems. The

  16. 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.

  17. 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.

  18. Biomedical device interfacing to clinical information systems: a primer.

    PubMed

    Moorman, Bridget

    2008-01-01

    I am pleased that we get to take advantage of Bridget Moorman's background, experience, and perspective in this installment of IT World. One of the most nerve-racking tasks we run into these days is getting disparate medical devices to talk to each other over a network. This is especially so if the device you're trying to communicate with doesn't support network connectivity. Bridget shares her experience here not only with a great high-level view of network interfacing, but also with references to dig into all the grim details. She shows us a lot of facets to consider when assembling such a network. You've got to convert to hit the ramp then translate and aggregate before gaining access to the clinical information system cloud. If that doesn't make sense, read on! -Jeff Kabachinski, IT World columnist.

  19. 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.

  20. 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.

  1. 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.

  2. Methods and systems for monitoring a solid-liquid interface

    DOEpatents

    Stoddard, Nathan G [Gettysburg, PA; Clark, Roger F [Frederick, MD

    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, . . . ).

  3. Microfluidic hubs, systems, and methods for interface fluidic modules

    SciTech Connect

    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.

  4. 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.

  5. Living ordered neural networks as model systems for signal processing

    NASA Astrophysics Data System (ADS)

    Villard, C.; Amblard, P. O.; Becq, G.; Gory-Fauré, S.; Brocard, J.; Roth, S.

    2007-06-01

    Neural circuit architecture is a fundamental characteristic of the brain, and how architecture is bound to biological functions is still an open question. Some neuronal geometries seen in the retina or the cochlea are intriguing: information is processed in parallel by several entities like in "pooling" networks which have recently drawn the attention of signal processing scientists. These systems indeed exhibit the noise-enhanced processing effect, which is also actively discussed in the neuroscience community at the neuron scale. The aim of our project is to use in-vitro ordered neuron networks as living paradigms to test ideas coming from the computational science. The different technological bolts that have to be solved are enumerated and the first results are presented. A neuron is a polarised cell, with an excitatory axon and a receiving dendritic tree. We present how soma confinement and axon differentiation can be induced by surface functionalization techniques. The recording of large neuron networks, ordered or not, is also detailed and biological signals shown. The main difficulty to access neural noise in the case of weakly connected networks grown on micro electrode arrays is explained. This open the door to a new detection technology suitable for sub-cellular analysis and stimulation, whose development will constitute the next step of this project.

  6. Fuzzy stochastic neural network model for structural system identification

    NASA Astrophysics Data System (ADS)

    Jiang, Xiaomo; Mahadevan, Sankaran; Yuan, Yong

    2017-01-01

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

  7. 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

  8. 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.

  9. 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.

  10. The Molecular Interface Between the SUMO and Ubiquitin Systems.

    PubMed

    Staudinger, Jeff L

    2017-01-01

    The SUMO conjugation system regulates key cellular processes including cell growth, division, mitochondrial dynamics, and the maintenance of genome stability in eukaryotic cells. The ubiquitin conjugation system regulates the stability of a myriad of vital cellular proteins in a signal-dependent manner by targeting them for destruction via the proteasome-mediated degradation pathway. Recent research efforts have unveiled an evolutionarily conserved and fundamental molecular interface between the SUMO and ubiquitin systems. A coordinated and integrated interaction between these two pathways plays a key role in adapting the SUMO-related stress response to alterations in sub-cellular protein localization, specific protein recruitment strategies, and the regulation of stress-inducible protein stability. This chapter will describe the interconnected and interdependent role of the SUMO and ubiquitin systems in mediating DNA damage repair and the genesis and the resolution of inflammatory-related diseases such as cancer. New insights regarding the interdependence of these two important post-translational modifications with nuclear receptor superfamily members will also be highlighted.

  11. 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

  12. Evaluation of different speech and touch interfaces to in-vehicle music retrieval systems.

    PubMed

    Garay-Vega, L; Pradhan, A K; Weinberg, G; Schmidt-Nielsen, B; Harsham, B; Shen, Y; Divekar, G; Romoser, M; Knodler, M; Fisher, D L

    2010-05-01

    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.

  13. 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

  14. Design of efficient and simple interface testing equipment for opto-electric tracking system

    NASA Astrophysics Data System (ADS)

    Liu, Qiong; Deng, Chao; Tian, Jing; Mao, Yao

    2016-10-01

    Interface testing for opto-electric tracking system is one important work to assure system running performance, aiming to verify the design result of every electronic interface matching the communication protocols or not, by different levels. Opto-electric tracking system nowadays is more complicated, composed of many functional units. Usually, interface testing is executed between units manufactured completely, highly depending on unit design and manufacture progress as well as relative people. As a result, it always takes days or weeks, inefficiently. To solve the problem, this paper promotes an efficient and simple interface testing equipment for opto-electric tracking system, consisting of optional interface circuit card, processor and test program. The hardware cards provide matched hardware interface(s), easily offered from hardware engineer. Automatic code generation technique is imported, providing adaption to new communication protocols. Automatic acquiring items, automatic constructing code architecture and automatic encoding are used to form a new program quickly with adaption. After simple steps, a standard customized new interface testing equipment with matching test program and interface(s) is ready for a waiting-test system in minutes. The efficient and simple interface testing equipment for opto-electric tracking system has worked for many opto-electric tracking system to test entire or part interfaces, reducing test time from days to hours, greatly improving test efficiency, with high software quality and stability, without manual coding. Used as a common tool, the efficient and simple interface testing equipment for opto-electric tracking system promoted by this paper has changed traditional interface testing method and created much higher efficiency.

  15. The evaluation and extension of TAE in the development of a user interface management system

    NASA Technical Reports Server (NTRS)

    Burkhart, Brenda; Sugar, Ross

    1986-01-01

    The development of a user interface management system (UIMS) for an information gathering and display system is discussed. The system interface requirements are outlined along with the UIMS functional characteristics. Those systems requirements which are supported by the current Transportable Applications Executive (TAE) are listed and necessary modifications to the TAE are described.

  16. 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.

  17. 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

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

    PubMed

    Brown, Ramsay A; Swanson, Larry W

    2013-09-01

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

  20. 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.

  1. 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.

  2. Used fuel management system architecture and interface analyses

    SciTech Connect

    Nutt, Mark; Howard, Robert; Busch, Ingrid; Carter, Joe; Delley, Alexcia; Hardin, Ernest; Kalinina, Elena; Cotton, Thomas

    2013-07-01

    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)

  3. Use of Multiple Parallel Interface Strategies To Create a Seamless Accessible Interface for Next-Generation Information Systems.

    ERIC Educational Resources Information Center

    Vanderheiden, Gregg C.

    Information systems in public places such as community centers and libraries require some means to provide access to individuals with physical, visual, and, if sound is involved, hearing impairments. This paper proposes a seamless adaptable human interface protocol that would allow users to incrementally modify the command and presentation aspects…

  4. Optical infrared flame detection system with neural networks

    NASA Astrophysics Data System (ADS)

    Huseynov, Javid J.; Baliga, Shankar B.

    2007-09-01

    A model for an infrared (IR) flame detection system using artificial neural networks (ANN) is presented. The joint time-frequency analysis (JTFA) in the form of a Short-Time Fourier Transform (STFT) is used for extracting relevant input features for a set of ANNs. Each ANN is trained using the backpropagation conjugate-gradient (CG) method to distinguish all hydrocarbon flames from a particular type of environmental nuisance and background noise. Signal saturation caused by the increased intensity of IR sources at closer distances is resolved by an adjustable gain control. A classification scheme with trained ANN connection weights was implemented on a digital signal processor for use in an industrial hydrocarbon flame detector.

  5. 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

  6. 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.

  7. Software Interface Assessment of the Centralized Aviation Flight Records System (CAFRS) 4.0

    DTIC Science & Technology

    2015-05-01

    Software Interface Assessment of the Centralized Aviation Flight Records System (CAFRS) 4.0 by Gina A Pomranky-Hartnett, David B Durbin, and...21005-5425 ARL-TR-7289 May 2015 Software Interface Assessment of the Centralized Aviation Flight Records System (CAFRS) 4.0 Gina A...Interface Assessment of the Centralized Aviation Flight Records System (CAFRS) 4.0 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT

  8. Hands-Free Manipulation Using Simple Bio-Potential Interface System

    NASA Astrophysics Data System (ADS)

    Takahashi, Kazuhiko; Nakauke, Takashi; Hashimoto, Masafumi

    This paper proposes a nonverbal interface system using bio-potential signals, such as EOG and EMG, measured by a brain-computer interface and investigates its possibility of application to control of a hands-free manipulation system. A simple gesture recognition algorithm is presented to estimate the user's thinking from the EOG and EMG signals. To evaluate the feasibility and the characteristics of the interface system for hands-free manipulation, moving control experiments in 3D virtual space are carried out and the effectiveness of the proposed interface system is confirmed.

  9. Interacting with a security system: The Argus user interface

    SciTech Connect

    Behrin, E.; Davis, G.E.

    1993-12-31

    In the mid-1980s the Lawrence Livermore National Laboratory (LLNL) developed the Argus Security System. Key requirements were to eliminate the telephone as a verification device for opening and closing alarm stations and to allow need-to-know access through local enrollment at alarm stations. Resulting from these requirements was an LLNL-designed user interface called the Remote Access Panel (RAP). The Argus RAP interacts with Argus field processors to allow secure station mode changes and local station enrollment, provides user direction and response, and assists station maintenance personnel. It consists of a tamper-detecting housing containing a badge reader, a keypad with sight screen, special-purpose push buttons and a liquid-crystal display. This paper discusses Argus system concepts, RAP design, functional characteristics and its physical configurations. The paper also describes the RAP`s use in access-control booths, it`s integration with biometrics and its operation for multi-person-rule stations and compartmented facilities.

  10. Pilot interface with fly by wire control systems

    NASA Technical Reports Server (NTRS)

    Melvin, W. W.

    1986-01-01

    Aircraft designers are rapidly moving toward full fly by wire control systems for transport aircraft. Aside from pilot interface considerations such as location of the control input device and its basic design such as side stick, there appears to be a desire to change the fundamental way in which a pilot applies manual control. A typical design would have the lowest order of manual control be a control wheel steering mode in which the pilot is controlling an autopilot. This deprives the pilot of the tactile sense of angle of attack which is inherent in present aircraft by virtue of certification requirements for static longitudinal stability whereby a pilot must either force the aircraft away from its trim angle of attack or trim to a new angle of attack. Whether or not an aircraft actually has positive stability, it can be made to feel to a pilot as though it does by artificial feel. Artificial feel systems which interpret pilot input as pitch rate or G rate with automatic trim have proven useful in certain military combat maneuvers, but their transposition to other more normal types of manual control may not be justified.

  11. 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.

  12. Adaptive estimation of hand movement trajectory in an EEG based brain-computer interface system

    NASA Astrophysics Data System (ADS)

    Robinson, Neethu; Guan, Cuntai; Vinod, A. P.

    2015-12-01

    Objective. The various parameters that define a hand movement such as its trajectory, speed, etc, are encoded in distinct brain activities. Decoding this information from neurophysiological recordings is a less explored area of brain-computer interface (BCI) research. Applying non-invasive recordings such as electroencephalography (EEG) for decoding makes the problem more challenging, as the encoding is assumed to be deep within the brain and not easily accessible by scalp recordings. Approach. EEG based BCI systems can be developed to identify the neural features underlying movement parameters that can be further utilized to provide a detailed and well defined control command set to a BCI output device. A real-time continuous control is better suited for practical BCI systems, and can be achieved by continuous adaptive reconstruction of movement trajectory than discrete brain activity classifications. In this work, we adaptively reconstruct/estimate the parameters of two-dimensional hand movement trajectory, namely movement speed and position, from multi-channel EEG recordings. The data for analysis is collected by performing an experiment that involved center-out right-hand movement tasks in four different directions at two different speeds in random order. We estimate movement trajectory using a Kalman filter that models the relation between brain activity and recorded parameters based on a set of defined predictors. We propose a method to define these predictor variables that includes spatial, spectral and temporally localized neural information and to select optimally informative variables. Main results. The proposed method yielded correlation of (0.60 ± 0.07) between recorded and estimated data. Further, incorporating the proposed predictor subset selection, the correlation achieved is (0.57 ± 0.07, p {\\lt }0.004) with significant gain in stability of the system, as well as dramatic reduction in number of predictors (76%) for the savings of computational

  13. 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.

  14. 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

  15. Functional fusion of living systems with synthetic electrode interfaces

    PubMed Central

    Staufer, Oskar; Weber, Sebastian; Bengtson, C Peter; Bading, Hilmar; Spatz, Joachim P

    2016-01-01

    Summary The functional fusion of “living” biomaterial (such as cells) with synthetic systems has developed into a principal ambition for various scientific disciplines. In particular, emerging fields such as bionics and nanomedicine integrate advanced nanomaterials with biomolecules, cells and organisms in order to develop novel strategies for applications, including energy production or real-time diagnostics utilizing biomolecular machineries “perfected” during billion years of evolution. To date, hardware–wetware interfaces that sample or modulate bioelectric potentials, such as neuroprostheses or implantable energy harvesters, are mostly based on microelectrodes brought into the closest possible contact with the targeted cells. Recently, the possibility of using electrochemical gradients of the inner ear for technical applications was demonstrated using implanted electrodes, where 1.12 nW of electrical power was harvested from the guinea pig endocochlear potential for up to 5 h (Mercier, P.; Lysaght, A.; Bandyopadhyay, S.; Chandrakasan, A.; Stankovic, K. Nat. Biotech. 2012, 30, 1240–1243). More recent approaches employ nanowires (NWs) able to penetrate the cellular membrane and to record extra- and intracellular electrical signals, in some cases with subcellular resolution (Spira, M.; Hai, A. Nat. Nano. 2013, 8, 83–94). Such techniques include nanoelectric scaffolds containing free-standing silicon NWs (Robinson, J. T.; Jorgolli, M.; Shalek, A. K.; Yoon, M. H.; Gertner, R. S.; Park, H. Nat Nanotechnol. 2012, 10, 180–184) or NW field-effect transistors (Qing, Q.; Jiang, Z.; Xu, L.; Gao, R.; Mai, L.; Lieber, C. Nat. Nano. 2013, 9, 142–147), vertically aligned gallium phosphide NWs (Hällström, W.; Mårtensson, T.; Prinz, C.; Gustavsson, P.; Montelius, L.; Samuelson, L.; Kanje, M. Nano Lett. 2007, 7, 2960–2965) or individually contacted, electrically active carbon nanofibers. The latter of these approaches is capable of recording electrical

  16. Functional fusion of living systems with synthetic electrode interfaces.

    PubMed

    Staufer, Oskar; Weber, Sebastian; Bengtson, C Peter; Bading, Hilmar; Spatz, Joachim P; Rustom, Amin

    2016-01-01

    The functional fusion of "living" biomaterial (such as cells) with synthetic systems has developed into a principal ambition for various scientific disciplines. In particular, emerging fields such as bionics and nanomedicine integrate advanced nanomaterials with biomolecules, cells and organisms in order to develop novel strategies for applications, including energy production or real-time diagnostics utilizing biomolecular machineries "perfected" during billion years of evolution. To date, hardware-wetware interfaces that sample or modulate bioelectric potentials, such as neuroprostheses or implantable energy harvesters, are mostly based on microelectrodes brought into the closest possible contact with the targeted cells. Recently, the possibility of using electrochemical gradients of the inner ear for technical applications was demonstrated using implanted electrodes, where 1.12 nW of electrical power was harvested from the guinea pig endocochlear potential for up to 5 h (Mercier, P.; Lysaght, A.; Bandyopadhyay, S.; Chandrakasan, A.; Stankovic, K. Nat. Biotech. 2012, 30, 1240-1243). More recent approaches employ nanowires (NWs) able to penetrate the cellular membrane and to record extra- and intracellular electrical signals, in some cases with subcellular resolution (Spira, M.; Hai, A. Nat. Nano. 2013, 8, 83-94). Such techniques include nanoelectric scaffolds containing free-standing silicon NWs (Robinson, J. T.; Jorgolli, M.; Shalek, A. K.; Yoon, M. H.; Gertner, R. S.; Park, H. Nat Nanotechnol. 2012, 10, 180-184) or NW field-effect transistors (Qing, Q.; Jiang, Z.; Xu, L.; Gao, R.; Mai, L.; Lieber, C. Nat. Nano. 2013, 9, 142-147), vertically aligned gallium phosphide NWs (Hällström, W.; Mårtensson, T.; Prinz, C.; Gustavsson, P.; Montelius, L.; Samuelson, L.; Kanje, M. Nano Lett. 2007, 7, 2960-2965) or individually contacted, electrically active carbon nanofibers. The latter of these approaches is capable of recording electrical responses from oxidative events

  17. Algebraic and adaptive learning in neural control systems

    NASA Astrophysics Data System (ADS)

    Ferrari, Silvia

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

  18. User interface guidelines for the Integrated Booking System prototype (IBS-P)

    SciTech Connect

    Truett, T.; Yow, T. ); Wheeler, V.; Stamm, S.; Valentine, D. . Transportation Center)

    1991-05-01

    The User Interface Guidelines for the Integrated Booking System -- Prototype (IBS-P) describes the design requirements for the human- computer interface. The user interface design conforms to standards reported in the open literature as well as to standards provided through Department of Defense guidelines. The IBS-P interface was evaluated by personnel at Headquarters Military Traffic Management Command (MTMC) and at each of the MTMC Area Commands. As a result of comments received during demonstrations of the prototype at these sites, modifications to the design were made, as appropriate. The user interfaces was well accepted by the end users.

  19. 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

  20. Problems of neural memory

    NASA Astrophysics Data System (ADS)

    Mikaelian, Andrei L.

    2005-01-01

    The paper considers the neural memory of the human brain from the viewpoint of visual information processing. A model that explains the principle of data recording and storing, memory relaxation, associative remembering and other memory functions is offered. The model of associative memory is based on the methods of holography, "wave biochemistry" and autowaves. Brief consideration is given to the associative properties of holographic neural structures and the memory architecture using running chemical reactions. The paper also outlines the problem of developing artificial memory elements for restoring the brain functions and possible interface devices for coupling neurons to electronic systems.