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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. Neuromorphic neural interfaces: from neurophysiological inspiration to biohybrid coupling with nervous systems

    NASA Astrophysics Data System (ADS)

    Broccard, Frédéric D.; Joshi, Siddharth; Wang, Jun; Cauwenberghs, Gert

    2017-08-01

    Objective. Computation in nervous systems operates with different computational primitives, and on different hardware, than traditional digital computation and is thus subjected to different constraints from its digital counterpart regarding the use of physical resources such as time, space and energy. In an effort to better understand neural computation on a physical medium with similar spatiotemporal and energetic constraints, the field of neuromorphic engineering aims to design and implement electronic systems that emulate in very large-scale integration (VLSI) hardware the organization and functions of neural systems at multiple levels of biological organization, from individual neurons up to large circuits and networks. Mixed analog/digital neuromorphic VLSI systems are compact, consume little power and operate in real time independently of the size and complexity of the model. Approach. This article highlights the current efforts to interface neuromorphic systems with neural systems at multiple levels of biological organization, from the synaptic to the system level, and discusses the prospects for future biohybrid systems with neuromorphic circuits of greater complexity. Main results. Single silicon neurons have been interfaced successfully with invertebrate and vertebrate neural networks. This approach allowed the investigation of neural properties that are inaccessible with traditional techniques while providing a realistic biological context not achievable with traditional numerical modeling methods. At the network level, populations of neurons are envisioned to communicate bidirectionally with neuromorphic processors of hundreds or thousands of silicon neurons. Recent work on brain-machine interfaces suggests that this is feasible with current neuromorphic technology. Significance. Biohybrid interfaces between biological neurons and VLSI neuromorphic systems of varying complexity have started to emerge in the literature. Primarily intended as a

  7. Neuromorphic neural interfaces: from neurophysiological inspiration to biohybrid coupling with nervous systems.

    PubMed

    Broccard, Frédéric D; Joshi, Siddharth; Wang, Jun; Cauwenberghs, Gert

    2017-06-02

    Computation in nervous systems operates with different computational primitives, and on different hardware, than traditional digital computation and is thus subjected to different constraints from its digital counterpart regarding the use of physical resources such as time, space and energy. In an effort to better understand neural computation on a physical medium with similar spatiotemporal and energetic constraints, the field of neuromorphic engineering aims to design and implement electronic systems that emulate in very large-scale integration (VLSI) hardware the organization and functions of neural systems at multiple levels of biological organization, from individual neurons up to large circuits and networks. Mixed analog/digital neuromorphic VLSI systems are compact, consume little power and operate in real time independently of the size and complexity of the model. This article highlights the current efforts to interface neuromorphic systems with neural systems at multiple levels of biological organization, from the synaptic to the system level, and discusses the prospects for future biohybrid systems with neuromorphic circuits of greater complexity. Single silicon neurons have been interfaced successfully with invertebrate and vertebrate neural networks. This approach allowed the investigation of neural properties that are inaccessible with traditional techniques while providing a realistic biological context not achievable with traditional numerical modeling methods. At the network level, populations of neurons are envisioned to communicate bidirectionally with neuromorphic processors of hundreds or thousands of silicon neurons. Recent work on brain-machine interfaces suggests that this is feasible with current neuromorphic technology. Biohybrid interfaces between biological neurons and VLSI neuromorphic systems of varying complexity have started to emerge in the literature. Primarily intended as a computational tool for investigating fundamental questions

  8. 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.

  9. 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

  10. 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.

  11. 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

  12. 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

  13. 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.

  14. 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

  15. 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.

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

    PubMed

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

    2015-10-01

    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. 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. 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. 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.

  17. 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.

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

    PubMed

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

    2013-06-01

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

  19. 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.

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

    PubMed Central

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

    2013-01-01

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

  1. 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

  2. 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

  3. 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.

  4. 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

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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

  11. 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.

  12. 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.

  13. 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

  14. 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.

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

    PubMed

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

    2014-03-26

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

  16. 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

  17. 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…

  18. 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.

  19. 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.

  20. 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.

  1. 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

  2. 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

  3. 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

  4. 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

  5. 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).

  6. 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

  7. 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

  8. RatCar system for estimating locomotion states using neural signals with parameter monitoring: Vehicle-formed brain-machine interfaces for rat.

    PubMed

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

    2008-01-01

    An online brain-machine interface (BMI) in the form of a small vehicle, the 'RatCar,' has been developed. A rat had neural electrodes implanted in its primary motor cortex and basal ganglia regions to continuously record neural signals. Then, a linear state space model represents a correlation between the recorded neural signals and locomotion states (i.e., moving velocity and azimuthal variances) of the rat. The model parameters were set so as to minimize estimation errors, and the locomotion states were estimated from neural firing rates using a Kalman filter algorithm. The results showed a small oscillation to achieve smooth control of the vehicle in spite of fluctuating firing rates with noises applied to the model. Major variation of the model variables converged in a first 30 seconds of the experiments and lasted for the entire one hour session.

  9. 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.

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

    NASA Astrophysics Data System (ADS)

    Daneshvar, Eugene Dariush

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

  11. 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

  12. 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.

  13. 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.

  14. Next-generation probes, particles, and proteins for neural interfacing

    PubMed Central

    Rivnay, Jonathan; Wang, Huiliang; Fenno, Lief; Deisseroth, Karl; Malliaras, George G.

    2017-01-01

    Bidirectional interfacing with the nervous system enables neuroscience research, diagnosis, and therapy. This two-way communication allows us to monitor the state of the brain and its composite networks and cells as well as to influence them to treat disease or repair/restore sensory or motor function. To provide the most stable and effective interface, the tools of the trade must bridge the soft, ion-rich, and evolving nature of neural tissue with the largely rigid, static realm of microelectronics and medical instruments that allow for readout, analysis, and/or control. In this Review, we describe how the understanding of neural signaling and material-tissue interactions has fueled the expansion of the available tool set. New probe architectures and materials, nanoparticles, dyes, and designer genetically encoded proteins push the limits of recording and stimulation lifetime, localization, and specificity, blurring the boundary between living tissue and engineered tools. Understanding these approaches, their modality, and the role of cross-disciplinary development will support new neurotherapies and prostheses and provide neuroscientists and neurologists with unprecedented access to the brain. PMID:28630894

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

    PubMed

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

    2016-04-01

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

  16. 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

  17. 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.

  18. 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.

  19. 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.

  20. Interface Analysis of ID Systems.

    ERIC Educational Resources Information Center

    Gentry, Castelle G.; Trimby, Madeline J.

    This chapter considers methods of interface analysis, the stage in the instructional development process that involves the identification, interpretation, and prioritization of essential points of contact among systems and subsystem boundaries. The structure of interfaces, types of interfaces, interface characteristics, and a procedural model for…

  1. 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.

  2. 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

  3. Hafnium transistor process design for neural interfacing.

    PubMed

    Parent, David W; Basham, Eric J

    2009-01-01

    A design methodology is presented that uses 1-D process simulations of Metal Insulator Semiconductor (MIS) structures to design the threshold voltage of hafnium oxide based transistors used for neural recording. The methodology is comprised of 1-D analytical equations for threshold voltage specification, and doping profiles, and 1-D MIS Technical Computer Aided Design (TCAD) to design a process to implement a specific threshold voltage, which minimized simulation time. The process was then verified with a 2-D process/electrical TCAD simulation. Hafnium oxide films (HfO) were grown and characterized for dielectric constant and fixed oxide charge for various annealing temperatures, two important design variables in threshold voltage design.

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

    PubMed

    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

    2012-12-01

    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.

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

    PubMed Central

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

    2014-01-01

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

  6. Progress Towards Biocompatible Intracortical Microelectrodes for Neural Interfacing Applications

    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

  7. 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.

  8. Hafnium transistor design for neural interfacing.

    PubMed

    Parent, David W; Basham, Eric J

    2008-01-01

    A design methodology is presented that uses the EKV model and the g(m)/I(D) biasing technique to design hafnium oxide field effect transistors that are suitable for neural recording circuitry. The DC gain of a common source amplifier is correlated to the structural properties of a Field Effect Transistor (FET) and a Metal Insulator Semiconductor (MIS) capacitor. This approach allows a transistor designer to use a design flow that starts with simple and intuitive 1-D equations for gain that can be verified in 1-D MIS capacitor TCAD simulations, before final TCAD process verification of transistor properties. The DC gain of a common source amplifier is optimized by using fast 1-D simulations and using slower, complex 2-D simulations only for verification. The 1-D equations are used to show that the increased dielectric constant of hafnium oxide allows a higher DC gain for a given oxide thickness. An additional benefit is that the MIS capacitor can be employed to test additional performance parameters important to an open gate transistor such as dielectric stability and ionic penetration.

  9. 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

  10. 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

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

    PubMed

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

    2015-02-01

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

  12. 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.

  13. 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

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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

  19. 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.

  20. 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.

  1. 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.

  2. 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).

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

    PubMed Central

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

    2013-01-01

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

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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

  9. 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.

  10. 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

  11. 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

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

    PubMed

    Zhang, Xiaorong; Liu, Yuhong; Zhang, Fan; Ren, Jin; Sun, Yan Lindsay; Yang, Qing; Huang, He

    2011-09-06

    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.

  13. A model neural interface based on functional chemical stimulation.

    PubMed

    Mehenti, Neville Z; Fishman, Harvey A; Bent, Stacey F

    2007-08-01

    While functional electrical stimulation has been applied to treat a variety of neurological disorders, it cannot mimic function that is primarily achieved using neurochemical means. In this work, we present a neurotransmitter-based prosthetic interface in the form of a flexible microdevice that selectively releases chemical pulses through an aperture in a polymer membrane. The release profiles through the aperture are controlled by microfluidic switching in an underlying channel network. The profiles have been characterized using fluorescence microscopy as a function of pulse duration and frequency. Hippocampal neurons were cultured on the microdevices and cell stimulation via glutamate delivery was detected using calcium imaging. The release properties could be tuned to repeatedly elicit discrete action potentials in cells seeded proximate to the aperture, including single cell stimulation at 2 Hz. This model neural interface based on functional chemical stimulation may provide the biomimetic platform necessary to restore physiological pathways and function that electrical stimulation cannot fundamentally address.

  14. Organic electrode coatings for next-generation neural interfaces

    PubMed Central

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

    2014-01-01

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

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

    PubMed Central

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

    2013-01-01

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

  16. Titania nanotube arrays as interfaces for neural prostheses.

    PubMed

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

    2015-04-01

    Neural prostheses have become ever more acceptable treatments for many different types of neurological damage and disease. Here we investigate the use of two different morphologies of titania nanotube arrays as interfaces to advance the longevity and effectiveness of these prostheses. The nanotube arrays were characterized for their nanotopography, crystallinity, conductivity, wettability, surface mechanical properties and adsorption of key proteins: fibrinogen, albumin and laminin. The loosely packed nanotube arrays fabricated using a diethylene glycol based electrolyte, contained a higher presence of the anatase crystal phase and were subsequently more conductive. These arrays yielded surfaces with higher wettability and lower modulus than the densely packed nanotube arrays fabricated using water based electrolyte. Further the adhesion, proliferation and differentiation of the C17.2 neural stem cell line was investigated on the nanotube arrays. The proliferation ratio of the cells as well as the level of neuronal differentiation was seen to increase on the loosely packed arrays. The results indicate that loosely packed nanotube arrays similar to the ones produced here with a DEG based electrolyte, may provide a favorable template for growth and maintenance of C17.2 neural stem cell line. Copyright © 2015 Elsevier B.V. All rights reserved.

  17. 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.

  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. 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

  20. 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

  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. 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.

  3. 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.

  4. 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

  5. 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

  6. Interfacing Distributed Operating Systems.

    DTIC Science & Technology

    1987-07-01

    6SUPPLEMENTARV %C-A’ION ZOSATI CODES I8 SUBJECT TERMS ,C~r... --P -CPCP8 -d *NWy taY,~av bIO, fl-rN "ELD SUB GROUP Cronus cluster Link 11 Afloat...Correlation System (ACS) 9 ABST.AC- -6-Yn a,’r. nn’P ,,p ,dfIip,f bw bOUMb -~1fS, BBN has developed a distributed operating system, Cronus , which functions...in the context of a heterogeneous internetwork system architecture. Cronus is intended to introduce coherence and uniformity to a set of otherwise

  7. 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.

  8. 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

  9. 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.

  10. 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.

  11. 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

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

    PubMed

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

    2014-01-01

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

  13. 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

  14. 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)

  15. Memory Storage and Neural Systems.

    ERIC Educational Resources Information Center

    Alkon, Daniel L.

    1989-01-01

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

  16. 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

  17. 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.

  18. CMOS-based opto-electronic neural interface devices for optogenetics.

    PubMed

    Tokuda, Takashi; Noguchi, Satoki; Iwasaki, Satoru; Takehara, Hiroaki; Noda, Toshihiko; Sasagawa, Kiyotaka; Ohta, Jun

    2016-08-01

    CMOS-based opto-electronic neural interface devices are presented. The devices are designed with target application of in vitro and in vivo optogenetics. Two types of the opto-electronic neural interface devices are presented. One is single-chip type device for on-chip optogenetics, and the other is multi-chip type device with flexibility and wide-area coverage for in vivo optogenetics on brain. Design, packaging and functional evaluations are presented.

  19. 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

  20. 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.

  1. 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

  2. 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.

  3. Neural interfaces for somatosensory feedback: bringing life to a prosthesis

    PubMed Central

    Tyler, Dustin J.

    2017-01-01

    Purpose of review When an individual loses a limb, he/she loses touch with the world and with the people around him/her. Somatosensation is critical to the feeling of connection and control of one’s own body. Decades of attempts to replace lost somatosensation by sensory substitutions have been ineffective outside of the laboratory. This review discusses important recent results demonstrating chronic somatosensory restoration through direct peripheral nerve stimulation. Recent findings Stimulation of peripheral nerves results in somatosensory perception on the phantom limb. Sensations are localized to several independent and functionally relevant locations, such as the fingertips, thenar eminence, ulnar border and dorsal surface. Patterns in stimulation intensity change the perception experience by the user, opening new dimensions on neuromodulation. Summary Neural interfaces with sophisticated stimulation paradigms create a user’s perception of his/her hand to touch and manipulate objects. The pattern of intensity and frequency of stimulation is critical to the quality and intensity of perceived sensation. Restoring feeling has allowed the individuals to, ‘feel [my] hand for the first time since the accident,’ and ‘feel [my] wife touch my hand’. Individuals using a prosthetic hand with sensation can pull cherries and grapes from the stem, open water bottles and move objects without destroying these objects – all while audio and visually deprived. After regaining sensation, phantom pain is eliminated in individuals that had frequent, sometimes debilitating, pain following limb loss. With over 5 subject-years of experience, this work is leading the evolution of a new era in prostheses. Somatosensory prosthetics as a standard procedure to augment and restore somatosensation are now within our reach. PMID:26544029

  4. 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.

  5. 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

  6. 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.

  7. 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.

  8. 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.

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

    PubMed Central

    Fetz, Eberhard E

    2007-01-01

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

  10. 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.

  11. 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.

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

    PubMed Central

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

    2015-01-01

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

  13. 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.

  14. 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.

  15. 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.

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

    PubMed

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

    2016-08-01

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

  17. 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.

  18. Intelligent TDA Systems Using Neural Networks Phase 1 Final Report

    DTIC Science & Technology

    1992-05-01

    Untranslatable ConceDts ..................... 17 5.4.1.4 The Loaic Network Representation ............ 18 5.4.1.5 The Neural Network Representation...found that most slots are also translatable. 5.4.1.3 Untranslatable Concepts The portions of an expert system that we cannot translate include procedural...programming code as in user-defined functions and methods, and the user interface code. These untranslatable components are called expert system

  19. 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

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

    PubMed

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

    2012-01-01

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

  1. 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.

  2. Interfaces and Expert Systems for Online Retrieval.

    ERIC Educational Resources Information Center

    Kehoe, Cynthia A.

    1985-01-01

    This paper reviews the history of separate online system interfaces which led to efforts to develop expert systems for searching databases, particularly for end users, and introduces the research on such expert systems. Appended is a bibliography of sources on interfaces and expert systems for online retrieval. (Author/EJS)

  3. Spiking Neural Network Decoder for Brain-Machine Interfaces.

    PubMed

    Dethier, Julie; Gilja, Vikash; Nuyujukian, Paul; Elassaad, Shauki A; Shenoy, Krishna V; Boahen, Kwabena

    2011-01-01

    We used a spiking neural network (SNN) to decode neural data recorded from a 96-electrode array in premotor/motor cortex while a rhesus monkey performed a point-to-point reaching arm movement task. We mapped a Kalman-filter neural prosthetic decode algorithm developed to predict the arm's velocity on to the SNN using the Neural Engineering Framework and simulated it using Nengo, a freely available software package. A 20,000-neuron network matched the standard decoder's prediction to within 0.03% (normalized by maximum arm velocity). A 1,600-neuron version of this network was within 0.27%, and run in real-time on a 3GHz PC. These results demonstrate that a SNN can implement a statistical signal processing algorithm widely used as the decoder in high-performance neural prostheses (Kalman filter), and achieve similar results with just a few thousand neurons. Hardware SNN implementations-neuromorphic chips-may offer power savings, essential for realizing fully-implantable cortically controlled prostheses.

  4. 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.

  5. 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.

  6. 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

  7. 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

  8. 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.

  9. 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.

  10. 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

  11. 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.

  12. 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.

  13. 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…

  14. 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…

  15. 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.

  16. 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.

  17. The neural crest: a versatile organ system.

    PubMed

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

    2014-09-01

    The neural crest is the name given to the strip of cells at the junction between neural and epidermal ectoderm in neurula-stage vertebrate embryos, which is later brought to the dorsal neural tube as the neural folds elevate. The neural crest is a heterogeneous and multipotent progenitor cell population whose cells undergo EMT then extensively and accurately migrate throughout the embryo. Neural crest cells contribute to nearly every organ system in the body, with derivatives of neuronal, glial, neuroendocrine, pigment, and also mesodermal lineages. This breadth of developmental capacity has led to the neural crest being termed the fourth germ layer. The neural crest has occupied a prominent place in developmental biology, due to its exaggerated migratory morphogenesis and its remarkably wide developmental potential. As such, neural crest cells have become an attractive model for developmental biologists for studying these processes. Problems in neural crest development cause a number of human syndromes and birth defects known collectively as neurocristopathies; these include Treacher Collins syndrome, Hirschsprung disease, and 22q11.2 deletion syndromes. Tumors in the neural crest lineage are also of clinical importance, including the aggressive melanoma and neuroblastoma types. These clinical aspects have drawn attention to the selection or creation of neural crest progenitor cells, particularly of human origin, for studying pathologies of the neural crest at the cellular level, and also for possible cell therapeutics. The versatility of the neural crest lends itself to interlinked research, spanning basic developmental biology, birth defect research, oncology, and stem/progenitor cell biology and therapy. © 2014 Wiley Periodicals, Inc.

  18. Brain-controlled interfaces: movement restoration with neural prosthetics.

    PubMed

    Schwartz, Andrew B; Cui, X Tracy; Weber, Douglas J; Moran, Daniel W

    2006-10-05

    Brain-controlled interfaces are devices that capture brain transmissions involved in a subject's intention to act, with the potential to restore communication and movement to those who are immobilized. Current devices record electrical activity from the scalp, on the surface of the brain, and within the cerebral cortex. These signals are being translated to command signals driving prosthetic limbs and computer displays. Somatosensory feedback is being added to this control as generated behaviors become more complex. New technology to engineer the tissue-electrode interface, electrode design, and extraction algorithms to transform the recorded signal to movement will help translate exciting laboratory demonstrations to patient practice in the near future.

  19. 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

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. Graphics-System Color-Code Interface

    NASA Technical Reports Server (NTRS)

    Tulppo, J. S.

    1982-01-01

    Circuit originally developed for a flight simulator interfaces a computer graphics system with color monitor. Subsystem is intended for particular display computer (AGT-130, ADAGE Graphics Terminal) and specific color monitor (beam penetration tube--Penetron). Store-and-transmit channel is one of five in graphics/color-monitor interface. Adding 5-bit color code to existing graphics programs requires minimal programing effort.

  5. Echoes in correlated neural systems

    NASA Astrophysics Data System (ADS)

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

    2013-02-01

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

  6. Design and manufacturing challenges of optogenetic neural interfaces: a review

    NASA Astrophysics Data System (ADS)

    Goncalves, S. B.; Ribeiro, J. F.; Silva, A. F.; Costa, R. M.; Correia, J. H.

    2017-08-01

    Optogenetics is a relatively new technology to achieve cell-type specific neuromodulation with millisecond-scale temporal precision. Optogenetic tools are being developed to address neuroscience challenges, and to improve the knowledge about brain networks, with the ultimate aim of catalyzing new treatments for brain disorders and diseases. To reach this ambitious goal the implementation of mature and reliable engineered tools is required. The success of optogenetics relies on optical tools that can deliver light into the neural tissue. Objective/Approach: Here, the design and manufacturing approaches available to the scientific community are reviewed, and current challenges to accomplish appropriate scalable, multimodal and wireless optical devices are discussed. Significance: Overall, this review aims at presenting a helpful guidance to the engineering and design of optical microsystems for optogenetic applications.

  7. 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.

  8. 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.

  9. 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

  10. 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.

  11. Suitability of nitinol electrodes in neural prostheses such as endovascular neural interfaces.

    PubMed

    Wong, Yan T; Opie, Nicholas L; John, Sam E; Gerboni, Giulia; Rind, Gil S; Ronayne, Stephen M; Fox, Kate E; Oxley, Thomas J; Grayden, David B

    2016-08-01

    A major challenge facing neural prostheses is the development of electrodes that are well tolerated by the brain and body. A novel way to circumvent the need to perform an invasive craniotomy and penetration of the blood-brain barrier to implant electrodes, is to guide electrodes up into the cerebral veins and place electrodes on the vessel walls adjacent to neuronal populations. To aid in the development of these stent based devices, microelectrodes manufactured from Nitinol would allow electrodes to be implanted via a catheter and then once deployed, alter their shape to conform to the vessel walls. However, there is a paucity of data on whether Nitinol is a suitable material to record neural signals. Here we show that Nitinol is tolerated by the body and that it can effectively measure neural signals. Specifically, we electrochemically evaluate Nitinol electrodes in blood and record visually evoked potentials from sheep.

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

    PubMed

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

    2009-12-01

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

  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. 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

  15. A User Interface for Multiple Retrieval Systems.

    ERIC Educational Resources Information Center

    Teskey, Niall; And Others

    1987-01-01

    Reviews current systems designed to help end-users search online databases without the assistance of an intermediary and describes a prototype system which emulates the Deco (the text storage and retrieval system used by Unilever) interface on Dialog and Data-Star. Initial trials of the prototype system are reported. (15 references) (MES)

  16. 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

  17. 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.

  18. 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.}

  19. 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.

  20. 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.

  1. 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

  2. The role of interfaces in supervisory systems

    SciTech Connect

    Evans, J.W. ); Block, W.R. )

    1993-04-01

    A supervisory system uses remote terminal units (RTUs) to acquire data and to control equipment at power facilities. RTUs do not perform these functions directly but depend on interfaces at the facilities to perform supervisory control and data acquisition (SCADA) functions. These interfaces must maintain high levels of performance, reliability, and dependability in order to provide the confidence demanded for power system operation. This article describes some important considerations in the interface between a power system and an RTU. A functional block diagram of a power utility RTU is included. The SCADA-RTU interfaces to control station equipment through interposing relays and to measuring circuits through transducers, meters, and other devices. These interfaces have the benefits of being simple, easy to design, readily available, and inexpensive; and they have a proven reliability. Physically, this interface may be distributed throughout the power station or centralized within one or two cabinets. Available panel space and layout of station control centers often is an over-riding consideration.

  3. Neural control of magnetic suspension systems

    NASA Technical Reports Server (NTRS)

    Gray, W. Steven

    1993-01-01

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

  4. 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.

  5. 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.

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

    PubMed

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

    2007-11-01

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

  7. 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

  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. 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

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

    PubMed Central

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

    2014-01-01

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

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

    PubMed

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

    2014-12-01

    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. 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. 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. 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 computers.

  13. 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

  14. 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

  15. The quantum human central neural system.

    PubMed

    Alexiou, Athanasios; Rekkas, John

    2015-01-01

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

  16. 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.

  17. 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.

  18. Spiking neural P systems with multiple channels.

    PubMed

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

    2017-11-01

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

  19. A Hardware-Efficient Scalable Spike Sorting Neural Signal Processor Module for Implantable High-Channel-Count Brain Machine Interfaces.

    PubMed

    Yang, Yuning; Boling, Sam; Mason, Andrew J

    2017-08-01

    Next-generation brain machine interfaces demand a high-channel-count neural recording system to wirelessly monitor activities of thousands of neurons. A hardware efficient neural signal processor (NSP) is greatly desirable to ease the data bandwidth bottleneck for a fully implantable wireless neural recording system. This paper demonstrates a complete multichannel spike sorting NSP module that incorporates all of the necessary spike detector, feature extractor, and spike classifier blocks. To meet high-channel-count and implantability demands, each block was designed to be highly hardware efficient and scalable while sharing resources efficiently among multiple channels. To process multiple channels in parallel, scalability analysis was performed, and the utilization of each block was optimized according to its input data statistics and the power, area and/or speed of each block. Based on this analysis, a prototype 32-channel spike sorting NSP scalable module was designed and tested on an FPGA using synthesized datasets over a wide range of signal to noise ratios. The design was mapped to 130 nm CMOS to achieve 0.75 μW power and 0.023 mm(2) area consumptions per channel based on post synthesis simulation results, which permits scalability of digital processing to 690 channels on a 4×4 mm(2) electrode array.

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

    PubMed

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

    2013-06-01

    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. 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). 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. 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.

  1. 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. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  2. Kannada character recognition system using neural network

    NASA Astrophysics Data System (ADS)

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

    2013-03-01

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

  3. Evaluating neural networks and artificial intelligence systems

    NASA Astrophysics Data System (ADS)

    Alberts, David S.

    1994-02-01

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

  4. 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

  5. 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…

  6. 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…

  7. 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…

  8. 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…

  9. 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.

  10. Auto-deleting brain machine interface: Error detection using spiking neural activity in the motor cortex.

    PubMed

    Even-Chen, Nir; Stavisky, Sergey D; Kao, Jonathan C; Ryu, Stephen I; Shenoy, Krishna V

    2015-01-01

    Brain machine interfaces (BMIs) aim to assist people with paralysis by increasing their independence and ability to communicate, e.g., by using a cursor-based virtual keyboard. Current BMI clinical trials are hampered by modest performance that causes selection of wrong characters (errors) and thus reduces achieved typing rate. If it were possible to detect these errors without explicit knowledge of the task goal, this could be used to automatically "undo" wrong selections or even prevent upcoming wrong selections. We decoded imminent or recent errors during closed-loop BMI control from intracortical spiking neural activity. In our experiment, a non-human primate controlled a neurally-driven BMI cursor to acquire targets on a grid, which simulates a virtual keyboard. In offline analyses of this closed-loop BMI control data, we identified motor cortical neural signals indicative of task error occurrence. We were able to detect task outcomes (97% accuracy) and even predict upcoming task outcomes (86% accuracy) using neural activity alone. This novel strategy may help increase the performance and clinical viability of BMIs.

  11. 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.

  12. 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

  13. 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

  14. 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.

  15. 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

  16. Neural networks and dynamic complex systems

    SciTech Connect

    Fox, G.; Furmanski, Wojtek; Ho, Alex; Koller, J.; Simic, P.; Wong, Isaac

    1989-01-01

    We describe the use of neural networks for optimization and inference associated with a variety of complex systems. We show how a string formalism can be used for parallel computer decomposition, message routing and sequential optimizing compilers. We extend these ideas to a general treatment of spatial assessment and distributed artificial intelligence. 34 refs., 12 figs.

  17. Multidisciplinary Studies of Integrated Neural Network Systems

    DTIC Science & Technology

    1994-03-01

    high and low frequencies ........... 12 c. Cellular mechanisms of intensity processing in nucleus angularis ............... 12 D. Publications...Intelligent robotic control systems have been constructed with a hierarchical and modular organization, us- ing antagonistic actuation mechanisms and multi...terrestrial animal studied thus far [ 12]. Considerable progress has been made in determining the acoustic and neural bases of the head saccade (see

  18. 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.

  19. 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).

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

    PubMed

    Gatica-Rojas, Valeska; Méndez-Rebolledo, Guillermo

    2014-04-15

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

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

    PubMed Central

    Gatica-Rojas, Valeska; Méndez-Rebolledo, Guillermo

    2014-01-01

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

  2. 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.

  3. 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.

  4. A Modeling Pattern for Layered System Interfaces

    NASA Technical Reports Server (NTRS)

    Shames, Peter M.; Sarrel, Marc A.

    2015-01-01

    Communications between systems is often initially represented at a single, high level of abstraction, a link between components. During design evolution it is usually necessary to elaborate the interface model, defining it from several different, related viewpoints and levels of abstraction. This paper presents a pattern to model such multi-layered interface architectures simply and efficiently, in a way that supports expression of technical complexity, interfaces and behavior, and analysis of complexity. Each viewpoint and layer of abstraction has its own properties and behaviors. System elements are logically connected both horizontally along the communication path, and vertically across the different layers of protocols. The performance of upper layers depends on the performance of lower layers, yet the implementation of lower layers is intentionally opaque to upper layers. Upper layers are hidden from lower layers except as sources and sinks of data. The system elements may not be linked directly at each horizontal layer but only via a communication path, and end-to-end communications may depend on intermediate components that are hidden from them, but may need to be shown in certain views and analyzed for certain purposes. This architectural model pattern uses methods described in ISO 42010, Recommended Practice for Architectural Description of Software-intensive Systems and CCSDS 311.0-M-1, Reference Architecture for Space Data Systems (RASDS). A set of useful viewpoints and views are presented, along with the associated modeling representations, stakeholders and concerns. These viewpoints, views, and concerns then inform the modeling pattern. This pattern permits viewing the system from several different perspectives and at different layers of abstraction. An external viewpoint treats the systems of interest as black boxes and focuses on the applications view, another view exposes the details of the connections and other components between the black boxes

  5. 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.

  6. 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.

  7. 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.

  8. 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

  9. 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.

  10. 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

  11. 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.

  12. 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.

  13. 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.

  14. 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

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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

  20. A Chronically Implantable Bidirectional Neural Interface for Non-human Primates.

    PubMed

    Komatsu, Misako; Sugano, Eriko; Tomita, Hiroshi; Fujii, Naotaka

    2017-01-01

    Optogenetics has potential applications in the study of epilepsy and neuroprostheses, and for studies on neural circuit dynamics. However, to achieve translation to clinical usage, optogenetic interfaces that are capable of chronic stimulation and monitoring with minimal brain trauma are required. We aimed to develop a chronically implantable device for photostimulation of the brain of non-human primates. We used a micro-light-emitting diode (LED) array with a flexible polyimide film. The array was combined with a whole-cortex electrocorticographic (ECoG) electrode array for simultaneous photostimulation and recording. Channelrhodopsin-2 (ChR2) was virally transduced into the cerebral cortex of common marmosets, and then the device was epidurally implanted into their brains. We recorded the neural activity during photostimulation of the awake monkeys for 4 months. The neural responses gradually increased after the virus injection for ~8 weeks and remained constant for another 8 weeks. The micro-LED and ECoG arrays allowed semi-invasive simultaneous stimulation and recording during long-term implantation in the brains of non-human primates. The development of this device represents substantial progress in the field of optogenetic applications.

  1. 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

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

    PubMed

    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

    2012-12-01

    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.

  3. 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.

  4. A mixed-signal multichip neural recording interface with bandwidth reduction.

    PubMed

    Gosselin, B; Ayoub, A E; Roy, J-F; Sawan, M; Lepore, F; Chaudhuri, A; Guitton, D

    2009-06-01

    We present a multichip structure assembled with a medical-grade stainless-steel microelectrode array intended for neural recordings from multiple channels. The design features a mixed-signal integrated circuit (IC) that handles conditioning, digitization, and time-division multiplexing of neural signals, and a digital IC that provides control, bandwidth reduction, and data communications for telemetry toward a remote host. Bandwidth reduction is achieved through action potential detection and complete capture of waveforms by means of onchip data buffering. The adopted architecture uses high parallelism and low-power building blocks for safety and long-term implantability. Both ICs are fabricated in a CMOS 0.18-mum process and are subsequently mounted on the base of the microelectrode array. The chips are stacked according to a vertical integration approach for better compactness. The presented device integrates 16 channels, and is scalable to hundreds of recording channels. Its performance was validated on a testbench with synthetic neural signals. The proposed interface presents a power consumption of 138 muW per channel, a size of 2.30 mm(2), and achieves a bandwidth reduction factor of up to 48 with typical recordings.

  5. 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.

  6. 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.

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

    PubMed

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

    2014-01-01

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

  8. 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

  9. 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.

  10. 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

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

    NASA Astrophysics Data System (ADS)

    Wang, Yiwen; Principe, Jose C.

    2010-10-01

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

  13. Neural correlates of skill acquisition with a cortical brain-machine interface.

    PubMed

    Ganguly, Karunesh; Carmena, Jose M

    2010-11-01

    Research into the development of brain-machine interfaces (BMIs) has led to demonstrations of rodents, nonhuman primates, and humans controlling prosthetic devices in real time through modulation of neural signals. In particular, cortical BMI studies have shown that improvements in performance require learning and are associated with changes in neuronal tuning properties. These studies have further shown evidence of long-term improvements in performance with practice. The authors conducted experiments to understand long-term skill acquisition with BMIs and to characterize the neural correlates of improvements in task performance. They specifically assessed long-term acquisition of neuroprosthetic skill (i.e., accurate task performance readily recalled across days). In 2 monkeys performing a center-out task using a brain-controlled (BC) computer cursor, they closely monitored daily performance trends and the neural correlates under different conditions. Importantly, they assessed BC performance using a continuous-control multistep task. The authors first conducted experiments that mimicked experimental conditions commonly used. Specifically, a large set of neurons was incorporated with daily recalibration of the transform of neural activity to BC. Under such conditions, they found evidence of variable daily performance. In contrast, when a fixed transform was applied to stable recordings from an ensemble of neurons across days, there was consistent evidence of long-term skill acquisition. Such skill acquisition was associated with the crystallization of a cortical map for prosthetic control. Taken together, the results suggest that the primate motor cortex can achieve skilled control of a neuroprosthetic device through consolidation of a cortical representation.

  14. Dynamics of systems with two interfaces

    NASA Astrophysics Data System (ADS)

    Velasco, V. R.; Djafari-Rouhani, B.

    1982-08-01

    Systems with two interfaces (sandwich ABC, planar defect, film) are considered. A symmetrization scheme is used to obtain the symmetrical (S) and antisymmetrical (A) modes of the planar defect and the thin film for arbitrary thickness. When medium B is very thin (hk∥<<1), we obtain the eigenmodes of the planar defect in analytical form, clarifying some discrepancies previously existing in the study of such a system. The interaction energy of point defects is obtained in closed form. The particular case of two adatoms is considered.

  15. 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.

  16. Oscillatory activity in excitable neural systems

    NASA Astrophysics Data System (ADS)

    Hutt, Axel

    2010-01-01

    The brain is a complex system and exhibits various subsystems on different spatial and temporal scales. These subsystems are recurrent networks of neurons or populations that interact with each other. The single neurons are microscopic objects and evolve on a different time scale than macroscopic neural populations. To understand the dynamics of the brain, however, it is necessary to understand the dynamics of the brain network both on the microscopic and the macroscopic level and the interaction between the levels. The presented work introduces one to the major properties of single neurons and their interactions. The physical aspects of some standard mathematical models are discussed in some detail. The work shows that both single neurons and neural populations are excitable in the sense that small differences in an initial short stimulation may yield very different dynamical behaviour of the system. To illustrate the power of the neural population model discussed, the work applies the model to explain experimental activity in the delayed feedback system in weakly electric fish and the electroencephalogram (EEG).

  17. 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

  18. 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.

  19. 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

  20. 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.

  1. Olfactory instruction for fear: neural system analysis.

    PubMed

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

    2015-01-01

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

  2. 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.

  3. 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)

  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. Dynamical systems, attractors, and neural circuits.

    PubMed

    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.

  6. 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

  7. 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.

  8. 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.

  9. Support for User Interfaces for Distributed Systems

    NASA Technical Reports Server (NTRS)

    Eychaner, Glenn; Niessner, Albert

    2005-01-01

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

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

    PubMed

    Fels, S S; Hinton, G E

    1998-01-01

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

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

    PubMed

    Fels, S S; Hinton, G E

    1997-01-01

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

  12. 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.

  13. 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

  14. 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

  15. 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.

  16. 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.

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

    PubMed Central

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

    2013-01-01

    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. PMID:23574919

  18. 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

  19. 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.

  20. 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.

  1. 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.

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

    NASA Technical Reports Server (NTRS)

    1975-01-01

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

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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

  9. 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

  10. 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

  11. Optimizing the performance of neural interface devices with hybrid poly(3,4-ethylene dioxythiophene) (PEDOT)

    NASA Astrophysics Data System (ADS)

    Kuo, Chin-chen

    This thesis describes methods for improving the performance of poly(3,4-ethylenedioxythiophene) (PEDOT) as a direct neural interfacing material. The chronic foreign body response is always a challenge for implanted bionic devices. After long-term implantation (typically 2-4 weeks), insulating glial scars form around the devices, inhibiting signal transmission, which ultimately leads to device failure. The mechanical mismatch at the device-tissue interface is one of the issues that has been associated with chronic foreign body response. Another challenge for using PEDOT as a neural interface material is its mechanical failure after implantation. We observed cracking and delamination of PEDOT coatings on devices after extended implantations. In the first part of this thesis, we present a novel method for directly measuring the mechanical properties of a PEDOT thin film. Before investigating methods to improve the mechanical behavior of PEDOT, a comprehensive understanding of the mechanical properties of PEDOT thin film is required. A PEDOT thin film was machined into a dog-bone shape specimen with a dual beam FIB-SEM. With an OmniProbe, this PEDOT specimen could be attached onto a force sensor, while the other side was attached to OmniProbe. By moving the OmniProbe, the specimen could be deformed in tension, and a force sensor recorded the applied load on the sample simultaneously. Mechanical tensile tests were conducted in the FIB-SEM chamber along with in situ observation. With precise force measurement from the force sensor and the corresponding high resolution SEM images, we were able to convert the data to a stress-strain curve for further analysis. By analyzing these stress-strain curves, we were able to obtain information about PEDOT including the Young's modulus, strength of failure, strain to failure, and toughness (energy to failure). This information should be useful for future material selection and molecular design for specific applications. The second

  12. 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.

  13. 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.; hide

    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.

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

    NASA Astrophysics Data System (ADS)

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

    1999-05-01

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

  15. 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

  16. 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.

  17. 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.

  18. 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

  19. Examination of the Benefits of Standardized Interfaces on Space Systems

    DTIC Science & Technology

    2015-09-01

    is procured. As new space systems are developed, the usage of standardized interface can prove to be highly advantageous. The objective of the...majority of the cost can be attributed to nonrecurring engineering costs, since these systems are redesigned each time a space system is procured. As new ...a standardized interface as opposed to learning a new interface and all the problems associated with the new design, thus allowing management

  20. 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.

  1. Simulation of large systems with neural networks

    SciTech Connect

    Paez, T.L.

    1994-09-01

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

  2. 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.

  3. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2012-04-01

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

  5. 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.

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

    PubMed

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

    2012-04-01

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

  7. 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

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. Interface Reactions and Synthetic Reaction of Composite Systems

    PubMed Central

    Park, Joon Sik; Kim, Jeong Min

    2010-01-01

    Interface reactions in composite systems often determine their overall properties, since product phases usually formed at interfaces during composite fabrication processing make up a large portion of the composites. Since most composite materials represent a ternary or higher order materials system, many studies have focused on analyses of diffusion phenomena and kinetics in multicomponent systems. However, the understanding of the kinetic behavior increases the complexity, since the kinetics of each component during interdiffusion reactions need to be defined for interpreting composite behaviors. From this standpoint, it is important to clarify the interface reactions for producing compatible interfaces with desired product phases. A thermodynamic evaluation such as a chemical potential of involving components can provide an understanding of the diffusion reactions, which govern diffusion pathways and product phase formation. A strategic approach for designing compatible interfaces is discussed in terms of chemical potential diagrams and interface morphology, with some material examples.

  14. 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.

  15. 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

  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. 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.

  18. 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.

  19. Prediction of interface residues in protein-protein complexes by a consensus neural network method: test against NMR data.

    PubMed

    Chen, Huiling; Zhou, Huan-Xiang

    2005-10-01

    The number of structures of protein-protein complexes deposited to the Protein Data Bank is growing rapidly. These structures embed important information for predicting structures of new protein complexes. This motivated us to develop the PPISP method for predicting interface residues in protein-protein complexes. In PPISP, sequence profiles and solvent accessibility of spatially neighboring surface residues were used as input to a neural network. The network was trained on native interface residues collected from the Protein Data Bank. The prediction accuracy at the time was 70% with 47% coverage of native interface residues. Now we have extensively improved PPISP. The training set now consisted of 1156 nonhomologous protein chains. Test on a set of 100 nonhomologous protein chains showed that the prediction accuracy is now increased to 80% with 51% coverage. To solve the problem of over-prediction and under-prediction associated with individual neural network models, we developed a consensus method that combines predictions from multiple models with different levels of accuracy and coverage. Applied on a benchmark set of 68 proteins for protein-protein docking, the consensus approach outperformed the best individual models by 3-8 percentage points in accuracy. To demonstrate the predictive power of cons-PPISP, eight complex-forming proteins with interfaces characterized by NMR were tested. These proteins are nonhomologous to the training set and have a total of 144 interface residues identified by chemical shift perturbation. cons-PPISP predicted 174 interface residues with 69% accuracy and 47% coverage and promises to complement experimental techniques in characterizing protein-protein interfaces. . (c) 2005 Wiley-Liss, Inc

  20. 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.

  1. 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.

  2. 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).

  3. A neural network hybrid expert system

    SciTech Connect

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

    1991-01-01

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

  4. Neural fuzzy modeling of anaerobic biological wastewater treatment systems

    SciTech Connect

    Tay, J.H.; Zhang, X.

    1999-12-01

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

  5. 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.

  6. 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…

  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. 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.

  9. 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.

  10. 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.

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

    PubMed

    Huan, Nai-Jen; Palaniappan, Ramaswamy

    2004-09-01

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

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

    NASA Astrophysics Data System (ADS)

    Huan, Nai-Jen; Palaniappan, Ramaswamy

    2004-09-01

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

  13. 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).

  14. 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).

  15. 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.

  16. 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.

  17. Interfacing a Local System with OCLC: The Documentation Process.

    ERIC Educational Resources Information Center

    Purnell, Kathleen M.

    1979-01-01

    An analysis of the procedures used at Indiana University's Regional Campus Libraries Technical Services Center is presented to document a complex interface between the center's automated acquisitions/cataloging system and the OCLC system. (Author)

  18. 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.

  19. 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.

  20. Docosahexaenoic acid in neural signaling systems.

    PubMed

    Crawford, Michael A

    2006-01-01

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

  1. 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.

  2. 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.

  3. 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.

  4. Neural interface of mirror therapy in chronic stroke patients: a functional magnetic resonance imaging study.

    PubMed

    Bhasin, Ashu; Padma Srivastava, M V; Kumaran, Senthil S; Bhatia, Rohit; Mohanty, Sujata

    2012-01-01

    Recovery in stroke is mediated by neural plasticity. Neuro-restorative therapies improve recovery after stroke by promoting repair and function. Mirror neuron system (MNS) has been studied widely in humans in stroke and phantom sensations. Study subjects included 20 patients with chronic stroke and 10 healthy controls. Patients had clinical disease-severity scores, functional magnetic resonance imaging (fMRI) and diffuse tensor imaging (DTI) at baseline, 8 and at 24 weeks. Block design with alternate baseline and activation cycles was used with a total of 90 whole brain echo planar imaging (EPI) measurements (timed repetition (TR) = 4520 ms, timed echo (TE) = 44 ms, slices = 31, slice thickness = 4 mm, EPI factor 127, matrix = 128 × 128, FOV = 230 mm). Whole brain T1-weighted images were acquired using 3D sequence (MPRage) with 120 contiguous slices of 1.0 mm thickness. The mirror therapy was aimed via laptop system integrated with web camera, mirroring the movement of the unaffected hand. This therapy was administered for 5 days in a week for 60-90 min for 8 weeks. All the patients showed statistical significant improvement in Fugl Meyer and modified Barthel Index (P < 0.05) whereas the change in Medical Research Council (MRC) power grade was not significant post-therapy (8 weeks). There was an increase in the laterality index (LI) of ipsilesional BA 4 and BA 6 at 8 weeks exhibiting recruitment and focusing principles of neural plasticity. Mirror therapy simulated the "action-observation" hypothesis exhibiting recovery in patients with chronic stroke. Therapy induced cortical reorganization was also observed from our study.

  5. 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.

  6. 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.

  7. 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

  8. 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.

  9. 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.

  10. Charged systems in bulk and at interfaces

    NASA Astrophysics Data System (ADS)

    Moreira, André Guérin

    2001-05-01

    One of the rules-of-thumb of colloid and surface physics is that most surfaces are charged when in contact with a solvent, usually water. This is the case, for instance, in charge-stabilized colloidal suspensions, where the surface of the colloidal particles are charged (usually with a charge of hundreds to thousands of e, the elementary charge), monolayers of ionic surfactants sitting at an air-water interface (where the water-loving head groups become charged by releasing counterions), or bilayers containing charged phospholipids (as cell membranes). In this work, we look at some model-systems that, although being a simplified version of reality, are expected to capture some of the physical properties of real charged systems (colloids and electrolytes). We initially study the simple double layer, composed by a charged wall in the presence of its counterions. The charges at the wall are smeared out and the dielectric constant is the same everywhere. The Poisson-Boltzmann (PB) approach gives asymptotically exact counterion density profiles around charged objects in the weak-coupling limit of systems with low-valent counterions, surfaces with low charge density and high temperature (or small Bjerrum length). Using Monte Carlo simulations, we obtain the profiles around the charged wall and compare it with both Poisson-Boltzmann (in the low coupling limit) and the novel strong coupling (SC) theory in the opposite limit of high couplings. In the latter limit, the simulations show that the SC leads in fact to asymptotically correct density profiles. We also compare the Monte Carlo data with previously calculated corrections to the Poisson-Boltzmann theory. We also discuss in detail the methods used to perform the computer simulations. After studying the simple double layer in detail, we introduce a dielectric jump at the charged wall and investigate its effect on the counterion density distribution. As we will show, the Poisson-Boltzmann description of the double layer

  11. 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

  12. Biologically inspired neural network controller for an infrared tracking system

    NASA Astrophysics Data System (ADS)

    Frigo, Janette R.; Tilden, Mark W.

    1999-01-01

    Many biological system exhibit capable, adaptive behavior with a minimal nervous system such as those found in lower invertebrates. Scientists and engineers are studying biological system because these models may have real-world applications. the analog neural controller, herein, is loosely modeled after minimal biological nervous systems. The system consists of the controller and pair of sensor mounted on an actuator. It is implemented with an electrical oscillator network, two IR sensor and a dc motor, used as an actuator for the system. The system tracks an IR target source. The pointing accuracy of this neural network controller is estimated through experimental measurements and a numerical model of the system.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. Dopamine system: manager of neural pathways

    PubMed Central

    Hong, Simon

    2013-01-01

    There are a growing number of roles that midbrain dopamine (DA) neurons assume, such as, reward, aversion, alerting and vigor. Here I propose a theory that may be able to explain why the suggested functions of DA came about. It has been suggested that largely parallel cortico-basal ganglia-thalamo-cortico loops exist to control different aspects of behavior. I propose that (1) the midbrain DA system is organized in a similar manner, with different groups of DA neurons corresponding to these parallel neural pathways (NPs). The DA system can be viewed as the “manager” of these parallel NPs in that it recruits and activates only the task-relevant NPs when they are needed. It is likely that the functions of those NPs that have been consistently activated by the corresponding DA groups are facilitated. I also propose that (2) there are two levels of DA roles: the How and What roles. The How role is encoded in tonic and phasic DA neuron firing patterns and gives a directive to its target NP: how vigorously its function needs to be carried out. The tonic DA firing is to provide the needed level of DA in the target NPs to support their expected behavioral and mental functions; it is only when a sudden unexpected boost or suppression of activity is required by the relevant target NP that DA neurons in the corresponding NP act in a phasic manner. The What role is the implementational aspect of the role of DA in the target NP, such as binding to D1 receptors to boost working memory. This What aspect of DA explains why DA seems to assume different functions depending on the region of the brain in which it is involved. In terms of the role of the lateral habenula (LHb), the LHb is expected to suppress maladaptive behaviors and mental processes by controlling the DA system. The demand-based smart management by the DA system may have given animals an edge in evolution with adaptive behaviors and a better survival rate in resource-scarce situations. PMID:24367324

  18. Implantable Graphene-based Neural Electrode Interfaces for Electrophysiology and Neurochemistry in In Vivo Hyperacute Stroke Model.

    PubMed

    Liu, Ta-Chung; Chuang, Min-Chieh; Chu, Chao-Yi; Huang, Wei-Chen; Lai, Hsin-Yi; Wang, Chao-Ting; Chu, Wei-Lin; Chen, San-Yuan; Chen, You-Yin

    2016-01-13

    Implantable microelectrode arrays have attracted considerable interest due to their high temporal and spatial resolution recording of neuronal activity in tissues. We herein presented an implantable multichannel neural probe with multiple real-time monitoring of neural-chemical and neural-electrical signals by a nonenzymatic neural-chemical interface, which was designed by creating the newly developed reduced graphene oxide-gold oxide (rGO/Au2O3) nanocomposite electrode. The modified electrode on the neural probe was prepared by a facile one-step cyclic voltammetry (CV) electrochemical method with simultaneous occurrence of gold oxidation and GOs reduction to induce the intimate attachment by electrostatic interaction using chloride ions (Cl(-)). The rGO/Au2O3-modified electrode at a low deposition scan rate of 10 mVs(-1) displayed significantly improved electrocatalytic activity due to large active areas and well-dispersive attached rGO sheets. The in vitro amperometric response to H2O2 demonstrated a fast response of less than 5 s and a very low detection limit of 0.63 μM. In in vivo hyperacute stroke model, the concentration of H2O2 was measured as 100.48 ± 4.52 μM for rGO/Au2O3 electrode within 1 h photothrombotic stroke, which was much higher than that (71.92 μM ± 2.52 μM) for noncoated electrode via in vitro calibration. Simultaneously, the somatosensory-evoked potentials (SSEPs) test provided reliable and precise validation for detecting functional changes of neuronal activities. This newly developed implantable probe with localized rGO/Au2O3 nanocomposite electrode can serve as a rapid and reliable sensing platform for practical H2O2 detection in the brain or for other neural-chemical molecules in vivo.

  19. 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.

  20. 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.

  1. 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.

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

    PubMed

    Hazrati, Mehrnaz Kh; Erfanian, Abbas

    2010-09-01

    This paper presents a new online single-trial EEG-based brain-computer interface (BCI) for controlling hand holding and sequence of hand grasping and opening in an interactive virtual reality environment. The goal of this research is to develop an interaction technique that will allow the BCI to be effective in real-world scenarios for hand grasp control. One of the major challenges in the BCI research is the subject training. Currently, in most online BCI systems, the classifier was trained offline using the data obtained during the experiments without feedback, and used in the next sessions in which the subjects receive feedback. We investigated whether the subject could achieve satisfactory online performance without offline training while the subjects receive feedback from the beginning of the experiments during hand movement imagination. Another important issue in designing an online BCI system is the machine learning to classify the brain signal which is characterized by significant day-to-day and subject-to-subject variations and time-varying probability distributions. Due to these variabilities, we introduce the use of an adaptive probabilistic neural network (APNN) working in a time-varying environment for classification of EEG signals. The experimental evaluation on ten naïve subjects demonstrated that an average classification accuracy of 75.4% was obtained during the first experiment session (day) after about 3 min of online training without offline training, and 81.4% during the second session (day). The average rates during third and eighth sessions are 79.0% and 84.0%, respectively, using previously calculated classifier during the first sessions, without online training and without the need to calibrate. The results obtained from more than 5000 trials on ten subjects showed that the method could provide a robust performance over different experiment sessions and different subjects. 2010 IPEM. Published by Elsevier Ltd. All rights reserved.

  3. 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.

  4. 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.

  5. Neural networks for self-learning control systems

    NASA Technical Reports Server (NTRS)

    Nguyen, Derrick H.; Widrow, Bernard

    1990-01-01

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

  6. Neural networks for self-learning control systems

    NASA Technical Reports Server (NTRS)

    Nguyen, Derrick H.; Widrow, Bernard

    1990-01-01

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

  7. X window system based user interface in radiology.

    PubMed

    Heinilä, J; Yliaho, J; Ahonen, J; Viitanen, J; Kormano, M

    1994-05-01

    A teleradiology system was designed for image transfer between two hospitals. One of the main challenges of the work was the user interface, which was to be easy to operate and to learn, and was equipped with useful functions for image manipulation and diagnosing. The software tools used were the Unix operating system (HP-UX v.7.0), C programming language and the X Window System (or simply X). The graphical user interface (GUI) was based on OSF-Motif standard, and it was developed by using the HP-Interface Architect. Both OSF-Motif and HP-Interface Architect are based on X. The results of the development project were installed for clinical use in the Turku University Central Hospital. The work demonstrates, that the X Window System has useful and advantageous features for radiology department's computer network environment.

  8. 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.

  9. 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.

  10. 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.

  11. Polar interface phonons in ionic toroidal systems

    NASA Astrophysics Data System (ADS)

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

    2016-09-01

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

  12. 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.

  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. 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.

  15. 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.

  16. 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

  17. 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

  18. 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.

  19. 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.

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

    PubMed Central

    2011-01-01

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

  1. 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

  2. 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.

  3. Design of speaker recognition system based on artificial neural network

    NASA Astrophysics Data System (ADS)

    Chen, Yanhong; Wang, Li; Lin, Han; Li, Jinlong

    2012-10-01

    Speaker recognition is to recognize speaker's identity from its voice which contains physiological and behavioral characteristics unique to each individual. In this paper, the artificial neural network model, which has very good capacity of non-linear division in characteristic space, is used for pattern matching. The speaker's sample characteristic domain is built for his mixed voice characteristic signals based on Kmeanlbg algorithm. Then the dimension of the inputting eigenvector is reduced, and the redundant information is got rid of. On this basis, BP neural network is used to divide capacity area for characteristic space nonlinearly, and the BP neural network acts as a classifier for the speaker. Finally, a speaker recognition system based on the neural network is realized and the experiment results validate the recognition performance and robustness of the system.

  4. 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

  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. 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.

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

    PubMed

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

    2014-03-01

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

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

    PubMed

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

    2017-04-01

    Chronic brain stimulation has become a promising physical therapy with increased efficacy and efficiency in the treatment of neurodegenerative diseases. The application of deep brain electrical stimulation (DBS) combined with manganese-enhanced magnetic resonance imaging (MEMRI) provides an unbiased representation of the functional anatomy, which shows the communication between areas of the brain responding to the therapy. However, it is challenging for the current system to provide a real-time high-resolution image because the incorporated MnCl2 solution through microinjection usually results in image blurring or toxicity due to the uncontrollable diffusion of Mn(2+). In this study, we developed a new type of conductive nanogel-based neural interface composed of amphiphilic chitosan-modified poly(3,4 -ethylenedioxythiophene) (PMSDT) that can exhibit biomimic structural/mechanical properties and ionic/electrical conductivity comparable to that of Au. More importantly, the PMSDT enables metal-ligand bonding with Mn(2+) ions, so that the system can release Mn(2+) ions rather than MnCl2 solution directly and precisely controlled by electrical stimulation (ES) to achieve real-time high-resolution MEMRI. With the integration of PMSDT nanogel-based coating in polyimide-based microelectrode arrays, the post-implantation DBS enables frequency-dependent MR imaging in vivo, as well as small focal imaging in response to channel site-specific stimulation on the implant. The MR imaging of the implanted brain treated with 5-min electrical stimulation showed a thalamocortical neuronal pathway after 36 h, confirming the effective activation of a downstream neuronal circuit following DBS. By eliminating the susceptibility to artifact and toxicity, this system, in combination with a MR-compatible implant and a bio-compliant neural interface, provides a harmless and synchronic functional anatomy for DBS. The study demonstrates a model of MEMRI-functionalized DBS based on functional

  10. 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.

  11. 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.

  12. Representation of neural networks as Lotka-Volterra systems

    NASA Astrophysics Data System (ADS)

    Moreau, Yves; Louiès, Stéphane; Vandewalle, Joos; Brenig, Léon

    1999-03-01

    We study changes of coordinates that allow the representation of the ordinary differential equations describing continuous-time recurrent neural networks into differential equations describing predator-prey models—also called Lotka-Volterra systems. We transform the equations for the neural network first into quasi-monomial form, where we express the vector field of the dynamical system as a linear combination of products of powers of the variables. In practice, this transformation is possible only if the activation function is the hyperbolic tangent or the logistic sigmoïd. 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.

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

    USDA-ARS?s Scientific Manuscript database

    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 ...

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

    PubMed Central

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

    2008-01-01

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

  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. 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

  17. 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.

  18. A wireless system with stimulation and recording capabilities for interfacing peripheral nerves in rodents.

    PubMed

    Schonle, P; Michoud, F; Brun, N; Guex, A; Lacour, S P; Wang, Q; Huang, Q

    2016-08-01

    Considerable progress has been made in the last decade in implantable bioelectronic neurosystems. Yet most neural implants are used in acute and tethered experimental conditions. Here, we present a preliminary prototype of a multichannel system for simultaneous peripheral nerve stimulation and neural recording. The system comprises miniaturized electronics with a total volume of less then 1.4cm3 including a 3.7V battery which is expected to last for 94 days of standby operation or 18 hours of continuous recording and stimulation. Data read-out and device configuration are wireless. Visceral nerves in rodents are interfaced with compliant extraneural electrodes. The 100×350μm2 electrodes display a low impedance (1.8kn at 1kHz) with a PEDOT:PSS coating. We validated the prototype in acute experiments by applying electrical stimulation to the aortic depressor nerve (ADN), resulting in effective and reproducible decrease in blood pressure and heart rate. The combination of miniaturized electronics and flexible electrodes makes the presented system a versatile platform for future implantable devices interfacing small peripheral nerves and potentially enables new applications in the field of neuroscience.

  19. 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

  20. 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. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

  1. 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.

  2. 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.

  3. 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.

  4. Self-Organizing Neural Network Models for State Anticipatory Systems

    NASA Astrophysics Data System (ADS)

    Pöllä, Matti; Honkela, Timo

    2006-06-01

    A vital mechanism of high-level natural cognitive systems is the anticipatory capability of making decisions based on predicted events in the future. While in some cases the performance of computational cognitive systems can be improved by modeling anticipatory behavior, it has been shown that for many cognitive tasks anticipation is mandatory. In this paper, we review the use of self-organizing artificial neural networks in constructing the state-space model of an anticipatory system. The biologically inspired self-organizing map (SOM) and its topologically dynamic variants such as the growing neural gas (GNG) are discussed using illustrative examples of their performance.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

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

    PubMed

    Lightbody, G; Irwin, G W

    1997-01-01

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

  12. 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.

  13. 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.

  14. 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.

  15. 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…

  16. 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.

  17. 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.

  18. 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.

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

    PubMed

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

    2009-01-01

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

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

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

    PubMed Central

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

    2013-01-01

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

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

    SciTech Connect

    Patro, S.; Kolarik, W.J.

    1999-06-01

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

  9. 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.

  10. Natural Language Interfaces to Database Systems

    DTIC Science & Technology

    1988-10-01

    Techniques," Byte, June 1985, pp. 27)-2’). ,11 Marin David Condic, Modular Systems Research, MI, "Documentation: The Need for Review," Journal of Pascal ...Elliot Soloway, Yale University, "PROUST," BYTE, April 1985, pp. 179-190. 463 Alfred L. Schumer, "Set Extensions with Apple Pascal ," BYTE, May 1985...Larry Kerschberg (ed.), The Benjamin/Cummings Publishing Co., Inc. 1987, pp. 413-422. 669 Antonio L. Furtado, Marco A. Casanova , Luiz Tucherman, "A

  11. 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

  12. 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.

  13. 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.

  14. 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.

  15. Advanced human-system interface design review guidelines

    SciTech Connect

    O'Hara, J.M.

    1990-01-01

    Advanced, computer-based, human-system interface designs are emerging in nuclear power plant (NPP) control rooms. These developments may have significant implications for plant safety in that they will greatly affect the ways in which operators interact with systems. At present, however, the only guidance available to the US Nuclear Regulatory Commission (NRC) for the review of control room-operator interfaces, NUREG-0700, was written prior to these technological changes and is thus not designed to address them. The objective of the project reported in this paper is to develop an Advanced Control Room Design Review Guideline for use in performing human factors reviews of advanced operator interfaces. This guideline will be implemented, in part, as a portable, computer-based, interactive document for field use. The paper describes the overall guideline development methodology, the present status of the document, and the plans for further guideline testing and development. 21 refs., 3 figs.

  16. 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.

  17. 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

  18. 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.

  19. Interfaces in Random Field Ising Systems

    NASA Astrophysics Data System (ADS)

    Seppälä, Eira

    2001-03-01

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

  20. [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.

  1. 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.

  2. 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.

  3. 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

  4. 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.

  5. Web-based system for distant learning on neural networks and distributed systems

    NASA Astrophysics Data System (ADS)

    Papliatseyeu, Andrei; Verkhaturau, Aliaksei; Kharkevich, Vladimir; Lutkovski, Vladimir

    2005-02-01

    In this paper we present our system for distant learning support, and its current content -- neural networks and distributed systems courses. The system provides users with remote access to learning materials, features dynamic glossary of terms and supports inline multimedia.

  6. Establishing a novel modeling tool: a python-based interface for a neuromorphic hardware system.

    PubMed

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

    2009-01-01

    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.

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

    PubMed Central

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

    2008-01-01

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

  8. 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.

  9. 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.

  10. Neural dynamic optimization for control systems. I. Background.

    PubMed

    Seong, C Y; Widrow, B

    2001-01-01

    The paper presents neural dynamic optimization (NDO) as a method of optimal feedback control for nonlinear multi-input-multi-output (MIMO) systems. The main feature of NDO is that it enables neural networks to approximate the optimal feedback solution whose existence dynamic programming (DP) justifies, thereby reducing the complexities of computation and storage problems of the classical methods such as DP. This paper mainly describes the background and motivations for the development of NDO, while the two other subsequent papers of this topic present the theory of NDO and demonstrate the method with several applications including control of autonomous vehicles and of a robot arm, respectively.

  11. Neural dynamic optimization for control systems.II. Theory.

    PubMed

    Seong, C Y; Widrow, B

    2001-01-01

    The paper presents neural dynamic optimization (NDO) as a method of optimal feedback control for nonlinear multi-input-multi-output (MIMO) systems. The main feature of NDO is that it enables neural networks to approximate the optimal feedback solution whose existence dynamic programming (DP) justifies, thereby reducing the complexities of computation and storage problems of the classical methods such as DP. This paper mainly describes the theory of NDO, while the two other companion papers of this topic explain the background for the development of NDO and demonstrate the method with several applications including control of autonomous vehicles and of a robot arm, respectively.

  12. 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.

  13. 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.

  14. 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.

  15. Guide to the Stand-Damage Model interface management system

    Treesearch

    George Racin; J. J. Colbert

    1995-01-01

    This programmer's support document describes the Gypsy Moth Stand-Damage Model interface management system. Management of stand-damage data made it necessary to define structures to store data and provide the mechanisms to manipulate these data. The software provides a user-friendly means to manipulate files, graph and manage outputs, and edit input data. The...

  16. 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.

  17. 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.

  18. A rule-based neural controller for inverted pendulum system.

    PubMed

    Hao, J; Vandewalle, J; Tan, S

    1993-03-01

    This paper tries to demonstrate how a heuristic neural control approach can be used to solve a complex nonlinear control problem. The control task is to swing up a pendulum mounted on a cart from its stable position (vertically down) to the zero state (up right) and keep it there by applying a sequence of two opposing constant forces of equal magnitude to the mass center of the cart. In addition, the displacement of the cart itself is confined to within a preset limit during the swinging up action and it will eventually be brought to the origin of the track. This is truly a nontrivial nonlinear regulation problem and is considerably difficult compared to the pendulum balancing problem (and its variations) widely adopted as a benchmarking test system for neural controllers. Through the solution of this specific control problem, we try to illustrate a heuristic neural control approach with task decomposition, control rule extraction and neural net rule implementation as its basic elements. Specializing to the pendulum problem, the global control task is decomposed into subtasks namely pendulum positioning and cart positioning. Accordingly, three separate neural subcontrollers are designed to cater to the subtasks and their coordination, i.e., pendulum subcontroller (PSC), cart subcontroller (CSC) and the switching subcontroller (SSC). Each of the subcontrollers is designed based on the rules and guidelines obtained from the experiences of a human operator. The simulation result is included to show the actual performance of the controller.

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

    PubMed Central

    Bryant, Christopher L.; Gandhi, Neeraj J.

    2013-01-01

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

  20. Neural decoding of code modulated visual evoked potentials by spatio-temporal inverse filtering for brain computer interfaces.

    PubMed

    Sato, Jun-Ichi; Washizawa, Yoshikazu

    2016-08-01

    This study addresses neural decoding of a code modulated visual evoked potentials (c-VEPs). c-VEP was recently developed, and applied to brain computer interfaces (BCIs). c-VEP BCI exhibits faster communication speed than existing VEP-based BCIs. In c-VEP BCI, the canonical correlation analysis (CCA) that maximizes the correlation between an averaged signal and single trial signals is often used for the spatial filter. However, CCA does not utilize information of given PN sequence, and hence, the filtered signal may not have properties of PN sequence. In this paper, we propose a decoding method to restore the given PN sequence from the observed VEP. We compare linear and nonlinear spatio-temporal inverse filtering methods. For the linear method, the least mean square error and lasso are used to obtain the filter coefficients. For the non-linear method, the artificial neural network is used. The proposed methods exhibited better decoding performance, and higher classification accuracies than conventional CCA spatial filtered c-VEP BCI.

  1. 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.

  2. 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

  3. 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

  4. Dissipative rendering and neural network control system design

    NASA Technical Reports Server (NTRS)

    Gonzalez, Oscar R.

    1995-01-01

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

  5. 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. 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.

  7. 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.

  8. 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.

  9. Application of BP neural network in acoustic wave measurement system

    NASA Astrophysics Data System (ADS)

    Zhang, Meifeng

    2017-07-01

    Acoustic wave measurement technology is the supporting technology in acoustic wave field. It is important to study acoustic wave with high precision and reliability testing equipment and scientific testing methods. The mathematical model of this acoustic wave measurement system was analyzed on the building of the system. The BP neural network algorithm was used in order to attain the higher accuracy for the acoustic wave measurement system. Frequency domain calibration was carried out by which the amplitude/frequency character curve of this system could be obtained. Then the model of the system was established by BP neural network algorithm. Finally, the validity of the established model was tested. The conclusion was that the math model reflected the original acoustic wave measurement system’s character through the regression result in the frequency domain.

  10. Neural dynamic programming and its application to control systems

    NASA Astrophysics Data System (ADS)

    Seong, Chang-Yun

    There are few general practical feedback control methods for nonlinear MIMO (multi-input-multi-output) systems, although such methods exist for their linear counterparts. Neural Dynamic Programming (NDP) is proposed as a practical design method of optimal feedback controllers for nonlinear MIMO systems. NDP is an offspring of both neural networks and optimal control theory. In optimal control theory, the optimal solution to any nonlinear MIMO control problem may be obtained from the Hamilton-Jacobi-Bellman equation (HJB) or the Euler-Lagrange equations (EL). The two sets of equations provide the same solution in different forms: EL leads to a sequence of optimal control vectors, called Feedforward Optimal Control (FOC); HJB yields a nonlinear optimal feedback controller, called Dynamic Programming (DP). DP produces an optimal solution that can reject disturbances and uncertainties as a result of feedback. Unfortunately, computation and storage requirements associated with DP solutions can be problematic, especially for high-order nonlinear systems. This dissertation presents an approximate technique for solving the DP problem based on neural network techniques that provides many of the performance benefits (e.g., optimality and feedback) of DP and benefits from the numerical properties of neural networks. We formulate neural networks to approximate optimal feedback solutions whose existence DP justifies. We show the conditions under which NDP closely approximates the optimal solution. Finally, we introduce the learning operator characterizing the learning process of the neural network in searching the optimal solution. The analysis of the learning operator provides not only a fundamental understanding of the learning process in neural networks but also useful guidelines for selecting the number of weights of the neural network. As a result, NDP finds---with a reasonable amount of computation and storage---the optimal feedback solutions to nonlinear MIMO control

  11. 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

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

    PubMed

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

    2016-08-03

    The emerging field of bioelectronic medicine seeks methods for deciphering and modulating electrophysiological activity in the body to attain therapeutic effects at target organs. Current approaches to interfacing with peripheral nerves and muscles rely heavily on wires, creating problems for chronic use, while emerging wireless approaches lack the size scalability necessary to interrogate small-diameter nerves. Furthermore, conventional electrode-based technologies lack the capability to record from nerves with high spatial resolution or to record independently from many discrete sites within a nerve bundle. Here, we demonstrate neural dust, a wireless and scalable ultrasonic backscatter system for powering and communicating with implanted bioelectronics. We show that ultrasound is effective at delivering power to mm-scale devices in tissue; likewise, passive, battery-less communication using backscatter enables high-fidelity transmission of electromyogram (EMG) and electroneurogram (ENG) signals from anesthetized rats. These results highlight the potential for an ultrasound-based neural interface system for advancing future bioelectronics-based therapies. Copyright © 2016 Elsevier Inc. All rights reserved.

  13. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2017-08-01

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

  15. 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.

  16. 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.

  17. 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

  18. 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

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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

  8. 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

  9. 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.

  10. 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.

  11. Serotonin-immunoreactive neural system and contractile system in the hydroid Cladonema (Cnidaria, Hydrozoa).

    PubMed

    Mayorova, T D; Kosevich, I A

    2013-12-01

    Serotonin is a widespread neurotransmitter which is present in almost all animal phyla including lower metazoans such as Cnidaria. Serotonin detected in the polyps of several cnidarian species participates in the functioning of a neural system. It was suggested that serotonin coordinates polyp behavior. For example, serotonin may be involved in muscle contraction and/or cnidocyte discharge. However, the role of serotonin in cnidarians is not revealed completely yet. The aim of this study was to investigate the neural system of Cladonema radiatum polyps. We detected the net of serotonin-positive processes within the whole hydranth body using anti-serotonin antibodies. The hypostome and tentacles had denser neural net in comparison with the gastric region. Electron microscopy revealed muscle processes throughout the hydranth body. Neural processes with specific vesicles and neurotubules in their cytoplasm were also shown at an ultrastructural level. This work demonstrates the structure of serotonin-positive neural system and smooth muscle layer in C. radiatum hydranths.

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

    NASA Astrophysics Data System (ADS)

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

    2016-08-01

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

  13. 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

  14. 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

  15. 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.

  16. 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

  17. 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.

  18. 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

  19. Neural Mechanisms and Information Processing in Recognition Systems

    PubMed Central

    Ozaki, Mamiko; Hefetz, Abraham

    2014-01-01

    Nestmate recognition is a hallmark of social insects. It is based on the match/mismatch of an identity signal carried by members of the society with that of the perceiving individual. While the behavioral response, amicable or aggressive, is very clear, the neural systems underlying recognition are not fully understood. Here we contrast two alternative hypotheses for the neural mechanisms that are responsible for the perception and information processing in recognition. We focus on recognition via chemical signals, as the common modality in social insects. The first, classical, hypothesis states that upon perception of recognition cues by the sensory system the information is passed as is to the antennal lobes and to higher brain centers where the information is deciphered and compared to a neural template. Match or mismatch information is then transferred to some behavior-generating centers where the appropriate response is elicited. An alternative hypothesis, that of “pre-filter mechanism”, posits that the decision as to whether to pass on the information to the central nervous system takes place in the peripheral sensory system. We suggest that, through sensory adaptation, only alien signals are passed on to the brain, specifically to an “aggressive-behavior-switching center”, where the response is generated if the signal is above a certain threshold. PMID:26462936

  20. 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.