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Sample records for neural recording system

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

    Science.gov (United States)

    Yin, Ming; Ghovanloo, Maysam

    2013-01-01

    We have used a well-known technique in wireless communication, pulse width modulation (PWM) of time division multiplexed (TDM) signals, within the architecture of a novel wireless integrated neural recording (WINeR) system. We have evaluated the performance of the PWM-based architecture and indicated its accuracy and potential sources of error through detailed theoretical analysis, simulations, and measurements on a setup consisting of a 15-channel WINeR prototype as the transmitter and two types of receivers; an Agilent 89600 vector signal analyzer and a custom wideband receiver, with 36 and 75 MHz of maximum bandwidth, respectively. Furthermore, we present simulation results from a realistic MATLAB-Simulink model of the entire WINeR system to observe the system behavior in response to changes in various parameters. We have concluded that the 15-ch WINeR prototype, which is fabricated in a 0.5-μm standard CMOS process and consumes 4.5 mW from ±1.5 V supplies, can acquire and wirelessly transmit up to 320 k-samples/s to a 75-MHz receiver with 8.4 bits of resolution, which is equivalent to a wireless data rate of ~ 2.26 Mb/s. PMID:19497823

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

    Science.gov (United States)

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

    2017-04-01

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

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

    Science.gov (United States)

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

    2015-08-01

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

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

    Science.gov (United States)

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

    2017-12-21

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

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

    Directory of Open Access Journals (Sweden)

    Ahnsei Shon

    2017-12-01

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

  6. Toward a distributed free-floating wireless implantable neural recording system.

    Science.gov (United States)

    Pyungwoo Yeon; Xingyuan Tong; Byunghun Lee; Mirbozorgi, Abdollah; Ash, Bruce; Eckhardt, Helmut; Ghovanloo, Maysam

    2016-08-01

    To understand the complex correlations between neural networks across different regions in the brain and their functions at high spatiotemporal resolution, a tool is needed for obtaining long-term single unit activity (SUA) across the entire brain area. The concept and preliminary design of a distributed free-floating wireless implantable neural recording (FF-WINeR) system are presented, which can enabling SUA acquisition by dispersedly implanting tens to hundreds of untethered 1 mm3 neural recording probes, floating with the brain and operating wirelessly across the cortical surface. For powering FF-WINeR probes, a 3-coil link with an intermediate high-Q resonator provides a minimum S21 of -22.22 dB (in the body medium) and -21.23 dB (in air) at 2.8 cm coil separation, which translates to 0.76%/759 μW and 0.6%/604 μW of power transfer efficiency (PTE) / power delivered to a 9 kΩ load (PDL), in body and air, respectively. A mock-up FF-WINeR is implemented to explore microassembly method of the 1×1 mm2 micromachined silicon die with a bonding wire-wound coil and a tungsten micro-wire electrode. Circuit design methods to fit the active circuitry in only 0.96 mm2 of die area in a 130 nm standard CMOS process, and satisfy the strict power and performance requirements (in simulations) are discussed.

  7. A comparison of neural tube defects identified by two independent routine recording systems for congenital malformations in Northern Ireland.

    Science.gov (United States)

    Nevin, N C; McDonald, J R; Walby, A L

    1978-12-01

    The efficiency of two systems for recording congenital malformations has been compared; one system, the Registrar General's Congenital Malformation Notification, is based on registering all malformed infants, and the other, the Child Health System, records all births. In Northern Ireland for three years [1974--1976], using multiple sources of ascertainment, a total of 686 infants with neural tube defects was identified among 79 783 live and stillbirths. The incidence for all neural tube defects in 8 60 per 1 000 births. The Registrar General's Congenital Malformation Notification System identified 83.6% whereas the Child Health System identified only 63.3% of all neural tube defects. Both systems together identified 86.2% of all neural tube defects. The two systems are suitable for monitoring of malformations and the addition of information from the Genetic Counselling Clinics would enhance the data for epidemiological studies.

  8. An Inductively-Powered Wireless Neural Recording System with a Charge Sampling Analog Front-End.

    Science.gov (United States)

    Lee, Seung Bae; Lee, Byunghun; Kiani, Mehdi; Mahmoudi, Babak; Gross, Robert; Ghovanloo, Maysam

    2016-01-15

    An inductively-powered wireless integrated neural recording system (WINeR-7) is presented for wireless and battery less neural recording from freely-behaving animal subjects inside a wirelessly-powered standard homecage. The WINeR-7 system employs a novel wide-swing dual slope charge sampling (DSCS) analog front-end (AFE) architecture, which performs amplification, filtering, sampling, and analog-to-time conversion (ATC) with minimal interference and small amount of power. The output of the DSCS-AFE produces a pseudo-digital pulse width modulated (PWM) signal. A circular shift register (CSR) time division multiplexes (TDM) the PWM pulses to create a TDM-PWM signal, which is fed into an on-chip 915 MHz transmitter (Tx). The AFE and Tx are supplied at 1.8 V and 4.2 V, respectively, by a power management block, which includes a high efficiency active rectifier and automatic resonance tuning (ART), operating at 13.56 MHz. The 8-ch system-on-a-chip (SoC) was fabricated in a 0.35-μm CMOS process, occupying 5.0 × 2.5 mm 2 and consumed 51.4 mW. For each channel, the sampling rate is 21.48 kHz and the power consumption is 19.3 μW. In vivo experiments were conducted on freely behaving rats in an energized homecage by continuously delivering 51.4 mW to the WINeR-7 system in a closed-loop fashion and recording local field potentials (LFP).

  9. Minimally-Invasive Neural Interface for Distributed Wireless Electrocorticogram Recording Systems

    Directory of Open Access Journals (Sweden)

    Sun-Il Chang

    2018-01-01

    Full Text Available This paper presents a minimally-invasive neural interface for distributed wireless electrocorticogram (ECoG recording systems. The proposed interface equips all necessary components for ECoG recording, such as the high performance front-end integrated circuits, a fabricated flexible microelectrode array, and wireless communication inside a miniaturized custom-made platform. The multiple units of the interface systems can be deployed to cover a broad range of the target brain region and transmit signals via a built-in intra-skin communication (ISCOM module. The core integrated circuit (IC consists of 16-channel, low-power push-pull double-gated preamplifiers, in-channel successive approximation register analog-to-digital converters (SAR ADC with a single-clocked bootstrapping switch and a time-delayed control unit, an ISCOM module for wireless data transfer through the skin instead of a power-hungry RF wireless transmitter, and a monolithic voltage/current reference generator to support the aforementioned analog and mixed-signal circuit blocks. The IC was fabricated using 250 nm CMOS processes in an area of 3.2 × 0.9 mm2 and achieved the low-power operation of 2.5 µW per channel. Input-referred noise was measured as 5.62 µVrms for 10 Hz to 10 kHz and ENOB of 7.21 at 31.25 kS/s. The implemented system successfully recorded multi-channel neural activities in vivo from a primate and demonstrated modular expandability using the ISCOM with power consumption of 160 µW.

  10. Minimally-Invasive Neural Interface for Distributed Wireless Electrocorticogram Recording Systems.

    Science.gov (United States)

    Chang, Sun-Il; Park, Sung-Yun; Yoon, Euisik

    2018-01-17

    This paper presents a minimally-invasive neural interface for distributed wireless electrocorticogram (ECoG) recording systems. The proposed interface equips all necessary components for ECoG recording, such as the high performance front-end integrated circuits, a fabricated flexible microelectrode array, and wireless communication inside a miniaturized custom-made platform. The multiple units of the interface systems can be deployed to cover a broad range of the target brain region and transmit signals via a built-in intra-skin communication (ISCOM) module. The core integrated circuit (IC) consists of 16-channel, low-power push-pull double-gated preamplifiers, in-channel successive approximation register analog-to-digital converters (SAR ADC) with a single-clocked bootstrapping switch and a time-delayed control unit, an ISCOM module for wireless data transfer through the skin instead of a power-hungry RF wireless transmitter, and a monolithic voltage/current reference generator to support the aforementioned analog and mixed-signal circuit blocks. The IC was fabricated using 250 nm CMOS processes in an area of 3.2 × 0.9 mm² and achieved the low-power operation of 2.5 µW per channel. Input-referred noise was measured as 5.62 µV rms for 10 Hz to 10 kHz and ENOB of 7.21 at 31.25 kS/s. The implemented system successfully recorded multi-channel neural activities in vivo from a primate and demonstrated modular expandability using the ISCOM with power consumption of 160 µW.

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

    Science.gov (United States)

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

    2011-08-01

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

  12. Online Recorded Data-Based Composite Neural Control of Strict-Feedback Systems With Application to Hypersonic Flight Dynamics.

    Science.gov (United States)

    Xu, Bin; Yang, Daipeng; Shi, Zhongke; Pan, Yongping; Chen, Badong; Sun, Fuchun

    2017-09-25

    This paper investigates the online recorded data-based composite neural control of uncertain strict-feedback systems using the backstepping framework. In each step of the virtual control design, neural network (NN) is employed for uncertainty approximation. In previous works, most designs are directly toward system stability ignoring the fact how the NN is working as an approximator. In this paper, to enhance the learning ability, a novel prediction error signal is constructed to provide additional correction information for NN weight update using online recorded data. In this way, the neural approximation precision is highly improved, and the convergence speed can be faster. Furthermore, the sliding mode differentiator is employed to approximate the derivative of the virtual control signal, and thus, the complex analysis of the backstepping design can be avoided. The closed-loop stability is rigorously established, and the boundedness of the tracking error can be guaranteed. Through simulation of hypersonic flight dynamics, the proposed approach exhibits better tracking performance.

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

    Science.gov (United States)

    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.

  14. A wireless recording system that utilizes Bluetooth technology to transmit neural activity in freely moving animals

    Science.gov (United States)

    Hampson, Robert E.; Collins, Vernell; Deadwyler, Sam A.

    2009-01-01

    A new wireless transceiver is described for recording individual neuron firing from behaving rats utilizing Bluetooth transmission technology and a processor onboard for discrimination of neuronal waveforms and associated time stamps. This universal brain activity transmitter (UBAT) is attached to rodents via a backpack and amplifier headstage and can transmit 16 channels of captured neuronal firing data via a Bluetooth transceiver chip over very large and unconstrained distances. The onboard microprocessor of the UBAT allows flexible online control over waveform isolation criteria via transceiver instruction and the two-way communication capacity allows for closed-loop applications between neural events and behavioral or physiological processes which can be modified by transceiver instructions. A detailed description of the multiplexer processing of channel data as well as examples of neuronal recordings in different behavioral testing contexts is provided to demonstrate the capacity for robust transmission within almost any laboratory environment. A major advantage of the UBAT is the long transmission range and lack of object-based line of sight interference afforded by Bluetooth technology, allowing flexible recording capabilities within multiple experimental paradigms without interruption. Continuous recordings over very large distance separations from the monitor station are demonstrated providing experimenters with recording advantages not previously available with other telemetry devices. PMID:19524612

  15. A wireless recording system that utilizes Bluetooth technology to transmit neural activity in freely moving animals.

    Science.gov (United States)

    Hampson, Robert E; Collins, Vernell; Deadwyler, Sam A

    2009-09-15

    A new wireless transceiver is described for recording individual neuron firing from behaving rats utilizing Bluetooth transmission technology and a processor onboard for discrimination of neuronal waveforms and associated time stamps. This universal brain activity transmitter (UBAT) is attached to rodents via a backpack and amplifier headstage and can transmit 16 channels of captured neuronal firing data via a Bluetooth transceiver chip over very large and unconstrained distances. The onboard microprocessor of the UBAT allows flexible online control over waveform isolation criteria via transceiver instruction and the two-way communication capacity allows for closed-loop applications between neural events and behavioral or physiological processes which can be modified by transceiver instructions. A detailed description of the multiplexer processing of channel data as well as examples of neuronal recordings in different behavioral testing contexts is provided to demonstrate the capacity for robust transmission within almost any laboratory environment. A major advantage of the UBAT is the long transmission range and lack of object-based line of sight interference afforded by Bluetooth technology, allowing flexible recording capabilities within multiple experimental paradigms without interruption. Continuous recordings over very large distance separations from the monitor station are demonstrated providing experimenters with recording advantages not previously available with other telemetry devices.

  16. Evolvable synthetic neural system

    Science.gov (United States)

    Curtis, Steven A. (Inventor)

    2009-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Takashi eTateno

    2014-10-01

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

  18. Recent Advances in Neural Recording Microsystems

    Directory of Open Access Journals (Sweden)

    Benoit Gosselin

    2011-04-01

    Full Text Available The accelerating pace of research in neuroscience has created a considerable demand for neural interfacing microsystems capable of monitoring the activity of large groups of neurons. These emerging tools have revealed a tremendous potential for the advancement of knowledge in brain research and for the development of useful clinical applications. They can extract the relevant control signals directly from the brain enabling individuals with severe disabilities to communicate their intentions to other devices, like computers or various prostheses. Such microsystems are self-contained devices composed of a neural probe attached with an integrated circuit for extracting neural signals from multiple channels, and transferring the data outside the body. The greatest challenge facing development of such emerging devices into viable clinical systems involves addressing their small form factor and low-power consumption constraints, while providing superior resolution. In this paper, we survey the recent progress in the design and the implementation of multi-channel neural recording Microsystems, with particular emphasis on the design of recording and telemetry electronics. An overview of the numerous neural signal modalities is given and the existing microsystem topologies are covered. We present energy-efficient sensory circuits to retrieve weak signals from neural probes and we compare them. We cover data management and smart power scheduling approaches, and we review advances in low-power telemetry. Finally, we conclude by summarizing the remaining challenges and by highlighting the emerging trends in the field.

  19. Improving Neural Recording Technology at the Nanoscale

    Science.gov (United States)

    Ferguson, John Eric

    Neural recording electrodes are widely used to study normal brain function (e.g., learning, memory, and sensation) and abnormal brain function (e.g., epilepsy, addiction, and depression) and to interface with the nervous system for neuroprosthetics. With a deep understanding of the electrode interface at the nanoscale and the use of novel nanofabrication processes, neural recording electrodes can be designed that surpass previous limits and enable new applications. In this thesis, I will discuss three projects. In the first project, we created an ultralow-impedance electrode coating by controlling the nanoscale texture of electrode surfaces. In the second project, we developed a novel nanowire electrode for long-term intracellular recordings. In the third project, we created a means of wirelessly communicating with ultra-miniature, implantable neural recording devices. The techniques developed for these projects offer significant improvements in the quality of neural recordings. They can also open the door to new types of experiments and medical devices, which can lead to a better understanding of the brain and can enable novel and improved tools for clinical applications.

  20. A dual slope charge sampling analog front-end for a wireless neural recording system.

    Science.gov (United States)

    Lee, Seung Bae; Lee, Byunghun; Gosselin, Benoit; Ghovanloo, Maysam

    2014-01-01

    This paper presents a novel dual slope charge sampling (DSCS) analog front-end (AFE) architecture, which amplifies neural signals by taking advantage of the charge sampling concept for analog signal conditioning, such as amplification and filtering. The presented DSCS-AFE achieves amplification, filtering, and sampling in a simultaneous fashion, while consuming very small amount of power. The output of the DSCS-AFE produces a pulse width modulated (PWM) signal that is proportional to the input voltage amplitude. A circular shift register (CSR) utilizes time division multiplexing (TDM) of the PWM pulses to create a pseudo-digital TDM-PWM signal that can feed a wireless transmitter. The 8-channel system-on-a-chip was fabricated in a 0.35-μm CMOS process, occupying 2.4 × 2.1 mm(2) and consuming 255 μW from a 1.8V supply. Measured input-referred noise for the entire system, including the FPGA in order to recover PWM signal is 6.50 μV(rms) in the 288 Hz~10 kHz range. For each channel, sampling rate is 31.25 kHz, and power consumption is 31.8 μW.

  1. Conducting polymer coated neural recording electrodes

    Science.gov (United States)

    Harris, Alexander R.; Morgan, Simeon J.; Chen, Jun; Kapsa, Robert M. I.; Wallace, Gordon G.; Paolini, Antonio G.

    2013-02-01

    Objective. Neural recording electrodes suffer from poor signal to noise ratio, charge density, biostability and biocompatibility. This paper investigates the ability of conducting polymer coated electrodes to record acute neural response in a systematic manner, allowing in depth comparison of electrochemical and electrophysiological response. Approach. Polypyrrole (Ppy) and poly-3,4-ethylenedioxythiophene (PEDOT) doped with sulphate (SO4) or para-toluene sulfonate (pTS) were used to coat iridium neural recording electrodes. Detailed electrochemical and electrophysiological investigations were undertaken to compare the effect of these materials on acute in vivo recording. Main results. A range of charge density and impedance responses were seen with each respectively doped conducting polymer. All coatings produced greater charge density than uncoated electrodes, while PEDOT-pTS, PEDOT-SO4 and Ppy-SO4 possessed lower impedance values at 1 kHz than uncoated electrodes. Charge density increased with PEDOT-pTS thickness and impedance at 1 kHz was reduced with deposition times up to 45 s. Stable electrochemical response after acute implantation inferred biostability of PEDOT-pTS coated electrodes while other electrode materials had variable impedance and/or charge density after implantation indicative of a protein fouling layer forming on the electrode surface. Recording of neural response to white noise bursts after implantation of conducting polymer-coated electrodes into a rat model inferior colliculus showed a general decrease in background noise and increase in signal to noise ratio and spike count with reduced impedance at 1 kHz, regardless of the specific electrode coating, compared to uncoated electrodes. A 45 s PEDOT-pTS deposition time yielded the highest signal to noise ratio and spike count. Significance. A method for comparing recording electrode materials has been demonstrated with doped conducting polymers. PEDOT-pTS showed remarkable low fouling during

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

    International Nuclear Information System (INIS)

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

    2007-01-01

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

  3. Neural Systems Laboratory

    Data.gov (United States)

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

  4. Evolvable Neural Software System

    Science.gov (United States)

    Curtis, Steven A.

    2009-01-01

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

  5. A multi-channel low-power system-on-chip for single-unit recording and narrowband wireless transmission of neural signal.

    Science.gov (United States)

    Bonfanti, A; Ceravolo, M; Zambra, G; Gusmeroli, R; Spinelli, A S; Lacaita, A L; Angotzi, G N; Baranauskas, G; Fadiga, L

    2010-01-01

    This paper reports a multi-channel neural recording system-on-chip (SoC) with digital data compression and wireless telemetry. The circuit consists of a 16 amplifiers, an analog time division multiplexer, an 8-bit SAR AD converter, a digital signal processor (DSP) and a wireless narrowband 400-MHz binary FSK transmitter. Even though only 16 amplifiers are present in our current die version, the whole system is designed to work with 64 channels demonstrating the feasibility of a digital processing and narrowband wireless transmission of 64 neural recording channels. A digital data compression, based on the detection of action potentials and storage of correspondent waveforms, allows the use of a 1.25-Mbit/s binary FSK wireless transmission. This moderate bit-rate and a low frequency deviation, Manchester-coded modulation are crucial for exploiting a narrowband wireless link and an efficient embeddable antenna. The chip is realized in a 0.35- εm CMOS process with a power consumption of 105 εW per channel (269 εW per channel with an extended transmission range of 4 m) and an area of 3.1 × 2.7 mm(2). The transmitted signal is captured by a digital TV tuner and demodulated by a wideband phase-locked loop (PLL), and then sent to a PC via an FPGA module. The system has been tested for electrical specifications and its functionality verified in in-vivo neural recording experiments.

  6. Neural Interfaces for Intracortical Recording: Requirements, Fabrication Methods, and Characteristics.

    Science.gov (United States)

    Szostak, Katarzyna M; Grand, Laszlo; Constandinou, Timothy G

    2017-01-01

    Implantable neural interfaces for central nervous system research have been designed with wire, polymer, or micromachining technologies over the past 70 years. Research on biocompatible materials, ideal probe shapes, and insertion methods has resulted in building more and more capable neural interfaces. Although the trend is promising, the long-term reliability of such devices has not yet met the required criteria for chronic human application. The performance of neural interfaces in chronic settings often degrades due to foreign body response to the implant that is initiated by the surgical procedure, and related to the probe structure, and material properties used in fabricating the neural interface. In this review, we identify the key requirements for neural interfaces for intracortical recording, describe the three different types of probes-microwire, micromachined, and polymer-based probes; their materials, fabrication methods, and discuss their characteristics and related challenges.

  7. Neural Interfaces for Intracortical Recording: Requirements, Fabrication Methods, and Characteristics

    Directory of Open Access Journals (Sweden)

    Katarzyna M. Szostak

    2017-12-01

    Full Text Available Implantable neural interfaces for central nervous system research have been designed with wire, polymer, or micromachining technologies over the past 70 years. Research on biocompatible materials, ideal probe shapes, and insertion methods has resulted in building more and more capable neural interfaces. Although the trend is promising, the long-term reliability of such devices has not yet met the required criteria for chronic human application. The performance of neural interfaces in chronic settings often degrades due to foreign body response to the implant that is initiated by the surgical procedure, and related to the probe structure, and material properties used in fabricating the neural interface. In this review, we identify the key requirements for neural interfaces for intracortical recording, describe the three different types of probes—microwire, micromachined, and polymer-based probes; their materials, fabrication methods, and discuss their characteristics and related challenges.

  8. Stimulation and recording electrodes for neural prostheses

    CERN Document Server

    Pour Aryan, Naser; Rothermel, Albrecht

    2015-01-01

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

  9. The visual development of hand-centered receptive fields in a neural network model of the primate visual system trained with experimentally recorded human gaze changes.

    Science.gov (United States)

    Galeazzi, Juan M; Navajas, Joaquín; Mender, Bedeho M W; Quian Quiroga, Rodrigo; Minini, Loredana; Stringer, Simon M

    2016-01-01

    Neurons have been found in the primate brain that respond to objects in specific locations in hand-centered coordinates. A key theoretical challenge is to explain how such hand-centered neuronal responses may develop through visual experience. In this paper we show how hand-centered visual receptive fields can develop using an artificial neural network model, VisNet, of the primate visual system when driven by gaze changes recorded from human test subjects as they completed a jigsaw. A camera mounted on the head captured images of the hand and jigsaw, while eye movements were recorded using an eye-tracking device. This combination of data allowed us to reconstruct the retinal images seen as humans undertook the jigsaw task. These retinal images were then fed into the neural network model during self-organization of its synaptic connectivity using a biologically plausible trace learning rule. A trace learning mechanism encourages neurons in the model to learn to respond to input images that tend to occur in close temporal proximity. In the data recorded from human subjects, we found that the participant's gaze often shifted through a sequence of locations around a fixed spatial configuration of the hand and one of the jigsaw pieces. In this case, trace learning should bind these retinal images together onto the same subset of output neurons. The simulation results consequently confirmed that some cells learned to respond selectively to the hand and a jigsaw piece in a fixed spatial configuration across different retinal views.

  10. High-Density Stretchable Electrode Grids for Chronic Neural Recording.

    Science.gov (United States)

    Tybrandt, Klas; Khodagholy, Dion; Dielacher, Bernd; Stauffer, Flurin; Renz, Aline F; Buzsáki, György; Vörös, János

    2018-04-01

    Electrical interfacing with neural tissue is key to advancing diagnosis and therapies for neurological disorders, as well as providing detailed information about neural signals. A challenge for creating long-term stable interfaces between electronics and neural tissue is the huge mechanical mismatch between the systems. So far, materials and fabrication processes have restricted the development of soft electrode grids able to combine high performance, long-term stability, and high electrode density, aspects all essential for neural interfacing. Here, this challenge is addressed by developing a soft, high-density, stretchable electrode grid based on an inert, high-performance composite material comprising gold-coated titanium dioxide nanowires embedded in a silicone matrix. The developed grid can resolve high spatiotemporal neural signals from the surface of the cortex in freely moving rats with stable neural recording quality and preserved electrode signal coherence during 3 months of implantation. Due to its flexible and stretchable nature, it is possible to minimize the size of the craniotomy required for placement, further reducing the level of invasiveness. The material and device technology presented herein have potential for a wide range of emerging biomedical applications. © 2018 The Authors. Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  11. Towards a magnetoresistive platform for neural signal recording

    Science.gov (United States)

    Sharma, P. P.; Gervasoni, G.; Albisetti, E.; D'Ercoli, F.; Monticelli, M.; Moretti, D.; Forte, N.; Rocchi, A.; Ferrari, G.; Baldelli, P.; Sampietro, M.; Benfenati, F.; Bertacco, R.; Petti, D.

    2017-05-01

    A promising strategy to get deeper insight on brain functionalities relies on the investigation of neural activities at the cellular and sub-cellular level. In this framework, methods for recording neuron electrical activity have gained interest over the years. Main technological challenges are associated to finding highly sensitive detection schemes, providing considerable spatial and temporal resolution. Moreover, the possibility to perform non-invasive assays would constitute a noteworthy benefit. In this work, we present a magnetoresistive platform for the detection of the action potential propagation in neural cells. Such platform allows, in perspective, the in vitro recording of neural signals arising from single neurons, neural networks and brain slices.

  12. A digitally assisted, signal folding neural recording amplifier.

    Science.gov (United States)

    Chen, Yi; Basu, Arindam; Liu, Lei; Zou, Xiaodan; Rajkumar, Ramamoorthy; Dawe, Gavin Stewart; Je, Minkyu

    2014-08-01

    A novel signal folding and reconstruction scheme for neural recording applications that exploits the 1/f(n) characteristics of neural signals is described in this paper. The amplified output is 'folded' into a predefined range of voltages by using comparison and reset circuits along with the core amplifier. After this output signal is digitized and transmitted, a reconstruction algorithm can be applied in the digital domain to recover the amplified signal from the folded waveform. This scheme enables the use of an analog-to-digital convertor with less number of bits for the same effective dynamic range. It also reduces the transmission data rate of the recording chip. Both of these features allow power and area savings at the system level. Other advantages of the proposed topology are increased reliability due to the removal of pseudo-resistors, lower harmonic distortion and low-voltage operation. An analysis of the reconstruction error introduced by this scheme is presented along with a behavioral model to provide a quick estimate of the post reconstruction dynamic range. Measurement results from two different core amplifier designs in 65 nm and 180 nm CMOS processes are presented to prove the generality of the proposed scheme in the neural recording applications. Operating from a 1 V power supply, the amplifier in 180 nm CMOS has a gain of 54.2 dB, bandwidth of 5.7 kHz, input referred noise of 3.8 μVrms and power dissipation of 2.52 μW leading to a NEF of 3.1 in spike band. It exhibits a dynamic range of 66 dB and maximum SNDR of 43 dB in LFP band. It also reduces system level power (by reducing the number of bits in the ADC by 2) as well as data rate to 80% of a conventional design. In vivo measurements validate the ability of this amplifier to simultaneously record spike and LFP signals.

  13. neural control system

    International Nuclear Information System (INIS)

    Elshazly, A.A.E.

    2002-01-01

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

  14. EEG in the classroom: Synchronised neural recordings during video presentation

    DEFF Research Database (Denmark)

    Poulsen, Andreas Trier; Kamronn, Simon Due; Dmochowski, Jacek

    2017-01-01

    We performed simultaneous recordings of electroencephalography (EEG) from multiple students in a classroom, and measured the inter-subject correlation (ISC) of activity evoked by a common video stimulus. The neural reliability, as quantified by ISC, has been linked to engagement and attentional......-evoked neural responses, known to be modulated by attention, can be tracked for groups of students with synchronized EEG acquisition. This is a step towards real-time inference of engagement in the classroom....

  15. Disturbance recording system

    International Nuclear Information System (INIS)

    Chandra, A.K.; Deshpande, S.V.; Mayya, A.; Vaidya, U.W.; Premraj, M.K.; Patil, N.B.

    1994-01-01

    A computerized system for disturbance monitoring, recording and display has been developed for use in nuclear power plants and is versatile enough to be used where ever a large number of parameters need to be recorded, e.g. conventional power plants, chemical industry etc. The Disturbance Recording System (DRS) has been designed to continuously monitor a process plant and record crucial parameters. The DRS provides a centralized facility to monitor and continuously record 64 process parameters scanned every 1 sec for 5 days. The system also provides facility for storage of 64 parameters scanned every 200 msec during 2 minutes prior to and 3 minutes after a disturbance. In addition the system can initiate, on demand, the recording of 8 parameters at a fast rate of every 5 msec for a period of 5 sec. and thus act as a visicorder. All this data is recorded in non-volatile memory and can be displayed, printed/plotted and used for subsequent analysis. Since data can be stored densely on floppy disks, the volume of space required for archival storage is also low. As a disturbance recorder, the DRS allows the operator to view the state of the plant prior to occurrence of the disturbance and helps in identifying the root cause. (author). 10 refs., 7 figs

  16. Digital recording system

    International Nuclear Information System (INIS)

    Chandra, A.K.; Deshpande, S.V.; Iyer, A.; Vaidya, U.W.

    1987-01-01

    A large number of critical process parameters in nuclear power plants have hitherto been monitored using electromechanical chart recorders. The reducing costs of electronics systems have led to a trend towards modernizing power plant control rooms by computerizing all the panel instrumentation. As a first step, it has been decided to develop a digital recording system to record the values of 48 process parameters. The system as developed and described in this report is more than a replacement for recorders; it offers substantial advantages in terms of lower overall system cost, excellent time resolution, accurate data and absolute synchronization for correlated signals. The system provides high speed recording of 48 process parameters, maintains historical records and permits retrieval and display of archival information on a colour monitor, a plotter and a printer. It is implemented using a front end data acquisition unit connected on a serial link to a PC-XT computer with 20 MB Winchester. The system offers an extremely user friendly man machine interaction, based on a hierarchical paged menu driven scheme. Softwre development for this system has been carried out using the C language. (author). 9 figs

  17. Artificial Neural Network Analysis System

    Science.gov (United States)

    2001-02-27

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

  18. Neural systems for control

    National Research Council Canada - National Science Library

    Omidvar, Omid; Elliott, David L

    1997-01-01

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

  19. Quality assurance records and records' system

    International Nuclear Information System (INIS)

    Link, M.; Martinek, J.

    1980-01-01

    For nuclear power plants extensive proof of quality is required which has to be documented reliably by quality records. With respect to the paper volume it is the most comprehensive 'curriculum vitae' of the technique. Traditional methods of information and recording are unsatisfactory for meeting regulatory requirements for maintaining the QA-aspects of status reporting, completeness, traceability and retrieval. Therefore KWU has established a record (documentation) subsystem within the overall component qualification system. Examples of the general documentation requirements, the procedure and handling in accordance with this subsystem for mechanical equipment are to be described examplarily. Topics are: - National and international requirements - Definition of QA records - Modular and product orientated KWU-record subsystem - Criteria for developing records - Record control, distribution, collection, storage - New documentation techniques (microfilm, data processing) - Education and training of personnel. (orig./RW)

  20. A review on power reducing methods of neural recording amplifiers

    Directory of Open Access Journals (Sweden)

    samira mehdipour

    2016-10-01

    Full Text Available Implantable multi-channel neural recording Microsystems comprise a large number of neural amplifiers, that can affect the overall power consumption and chip area of the analog part of the system.power, noise, size and dc offset are the main challenge faced by designers. Ideally the output of the opamp should be at zero volts when the inputs are grounded.In reality the input terminals are at slightly different dc potentials.The input offset voltage is defined as the voltage that must be applied between the two input terminals of the opamp to obtain zero volts at the output. Amplifier must have capability to reject this dc offset. First method that uses a capacitor feedback network with ac coupling of input devices to reject the offset is very popular in designs.very small low-cutoff frequency.The second method employs a closed-loop resistive feedback and electrode capacitance to form a highpass filter.Moreover,The third method adopts the symmetric floating resistor the feedback path of low noise amplifier to achieve low-frequency cutoff and rejects DC offset voltage. .In some application we can use folded cascade topology.The telescopic topology is a good candidate in terms of providing large gain and phase margin while dissipating small power. the cortical VLSI neuron model reducing power consumption of circuits.Power distribution is the best way to reduce power, noise and silicon area. The total power consumption of the amplifier array is reduced by applying the partial OTA sharing technique. The silicon area is reduced as a benefit of sharing the bulky capacitor.

  1. [Automated anesthesia record systems].

    Science.gov (United States)

    Heinrichs, W; Mönk, S; Eberle, B

    1997-07-01

    The introduction of electronic anaesthesia documentation systems was attempted as early as in 1979, although their efficient application has become reality only in the past few years. The advantages of the electronic protocol are apparent: Continuous high quality documentation, comparability of data due to the availability of a data bank, reduction in the workload of the anaesthetist and availability of additional data. Disadvantages of the electronic protocol have also been discussed in the literature. By going through the process of entering data on the course of the anaesthetic procedure on the protocol sheet, the information is mentally absorbed and evaluated by the anaesthetist. This information may, however, be lost when the data are recorded fully automatically-without active involvement on the part of the anaesthetist. Recent publications state that by using intelligent alarms and/or integrated displays manual record keeping is no longer necessary for anaesthesia vigilance. The technical design of automated anaesthesia records depends on an integration of network technology into the hospital. It will be appropriate to connect the systems to the internet, but safety requirements have to be followed strictly. Concerning the database, client server architecture as well as language standards like SQL should be used. Object oriented databases will be available in the near future. Another future goal of automated anaesthesia record systems will be using knowledge based technologies within these systems. Drug interactions, disease related anaesthetic techniques and other information sources can be integrated. At this time, almost none of the commercially available systems has matured to a point where their purchase can be recommended without reservation. There is still a lack of standards for the subsequent exchange of data and a solution to a number of ergonomic problems still remains to be found. Nevertheless, electronic anaesthesia protocols will be required in

  2. Artificial earthquake record generation using cascade neural network

    Directory of Open Access Journals (Sweden)

    Bani-Hani Khaldoon A.

    2017-01-01

    Full Text Available This paper presents the results of using artificial neural networks (ANN in an inverse mapping problem for earthquake accelerograms generation. This study comprises of two parts: 1-D site response analysis; performed for Dubai Emirate at UAE, where eight earthquakes records are selected and spectral matching are performed to match Dubai response spectrum using SeismoMatch software. Site classification of Dubai soil is being considered for two classes C and D based on shear wave velocity of soil profiles. Amplifications factors are estimated to quantify Dubai soil effect. Dubai’s design response spectra are developed for site classes C & D according to International Buildings Code (IBC -2012. In the second part, ANN is employed to solve inverse mapping problem to generate time history earthquake record. Thirty earthquakes records and their design response spectrum with 5% damping are used to train two cascade forward backward neural networks (ANN1, ANN2. ANN1 is trained to map the design response spectrum to time history and ANN2 is trained to map time history records to the design response spectrum. Generalized time history earthquake records are generated using ANN1 for Dubai’s site classes C and D, and ANN2 is used to evaluate the performance of ANN1.

  3. Intelligent neural network diagnostic system

    International Nuclear Information System (INIS)

    Mohamed, A.H.

    2010-01-01

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

  4. Quality assurance records system

    International Nuclear Information System (INIS)

    1979-01-01

    This Safety Guide was prepared as part of the Agency's programme, referred to as the NUSS programme, for establishing Codes of Practice and Safety Guides relating to nuclear power plants. It supplements the IAEA Code of Practice on Quality Assurance for Safety in Nuclear Power Plants (IAEA Safety Series No.50-C-QA), which requires that for each nuclear power plant a system for the generation, identification, collection, indexing, filing, storing, maintenance and disposition of quality assurance records shall be established and executed in accordance with written procedures and instructions. The purpose of this Safety Guide is to provide assistance in the establishment and operation of such a system. An orderly established and maintained records system is considered to be part of the means of providing a basis for an appropriate level of confidence that the activities which affect the quality of a nuclear power plant have been performed in accordance with the specific requirements and that the required quality has been achieved and is maintained

  5. Neural System Prediction and Identification Challenge

    Directory of Open Access Journals (Sweden)

    Ioannis eVlachos

    2013-12-01

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

  6. Neural system prediction and identification challenge.

    Science.gov (United States)

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

    2013-01-01

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

  7. Wireless Neural Recording With Single Low-Power Integrated Circuit

    Science.gov (United States)

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

    2010-01-01

    We present benchtop and in vivo experimental results from an integrated circuit designed for wireless implantable neural recording applications. The chip, which was fabricated in a commercially available 0.6-μm 2P3M BiCMOS process, contains 100 amplifiers, a 10-bit analog-to-digital converter (ADC), 100 threshold-based spike detectors, and a 902–928 MHz frequency-shift-keying (FSK) transmitter. Neural signals from a selected amplifier are sampled by the ADC at 15.7 kSps and telemetered over the FSK wireless data link. Power, clock, and command signals are sent to the chip wirelessly over a 2.765-MHz inductive (coil-to-coil) link. The chip is capable of operating with only two off-chip components: a power/command receiving coil and a 100-nF capacitor. PMID:19497825

  8. Wireless neural recording with single low-power integrated circuit.

    Science.gov (United States)

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

    2009-08-01

    We present benchtop and in vivo experimental results from an integrated circuit designed for wireless implantable neural recording applications. The chip, which was fabricated in a commercially available 0.6- mum 2P3M BiCMOS process, contains 100 amplifiers, a 10-bit analog-to-digital converter (ADC), 100 threshold-based spike detectors, and a 902-928 MHz frequency-shift-keying (FSK) transmitter. Neural signals from a selected amplifier are sampled by the ADC at 15.7 kSps and telemetered over the FSK wireless data link. Power, clock, and command signals are sent to the chip wirelessly over a 2.765-MHz inductive (coil-to-coil) link. The chip is capable of operating with only two off-chip components: a power/command receiving coil and a 100-nF capacitor.

  9. Circuit Models and Experimental Noise Measurements of Micropipette Amplifiers for Extracellular Neural Recordings from Live Animals

    Directory of Open Access Journals (Sweden)

    Chang Hao Chen

    2014-01-01

    Full Text Available Glass micropipettes are widely used to record neural activity from single neurons or clusters of neurons extracellularly in live animals. However, to date, there has been no comprehensive study of noise in extracellular recordings with glass micropipettes. The purpose of this work was to assess various noise sources that affect extracellular recordings and to create model systems in which novel micropipette neural amplifier designs can be tested. An equivalent circuit of the glass micropipette and the noise model of this circuit, which accurately describe the various noise sources involved in extracellular recordings, have been developed. Measurement schemes using dead brain tissue as well as extracellular recordings from neurons in the inferior colliculus, an auditory brain nucleus of an anesthetized gerbil, were used to characterize noise performance and amplification efficacy of the proposed micropipette neural amplifier. According to our model, the major noise sources which influence the signal to noise ratio are the intrinsic noise of the neural amplifier and the thermal noise from distributed pipette resistance. These two types of noise were calculated and measured and were shown to be the dominating sources of background noise for in vivo experiments.

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

    Science.gov (United States)

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

    2015-01-01

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

  11. Time Multiplexed Active Neural Probe with 1356 Parallel Recording Sites

    Directory of Open Access Journals (Sweden)

    Bogdan C. Raducanu

    2017-10-01

    Full Text Available We present a high electrode density and high channel count CMOS (complementary metal-oxide-semiconductor active neural probe containing 1344 neuron sized recording pixels (20 µm × 20 µm and 12 reference pixels (20 µm × 80 µm, densely packed on a 50 µm thick, 100 µm wide, and 8 mm long shank. The active electrodes or pixels consist of dedicated in-situ circuits for signal source amplification, which are directly located under each electrode. The probe supports the simultaneous recording of all 1356 electrodes with sufficient signal to noise ratio for typical neuroscience applications. For enhanced performance, further noise reduction can be achieved while using half of the electrodes (678. Both of these numbers considerably surpass the state-of-the art active neural probes in both electrode count and number of recording channels. The measured input referred noise in the action potential band is 12.4 µVrms, while using 678 electrodes, with just 3 µW power dissipation per pixel and 45 µW per read-out channel (including data transmission.

  12. Fractal Interfaces for Stimulating and Recording Neural Implants

    Science.gov (United States)

    Watterson, William James

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

  13. Microfluidic Actuation of Carbon Nanotube Fibers for Neural Recordings

    Science.gov (United States)

    Vercosa, Daniel G.

    Implantable devices to record and stimulate neural circuits have led to breakthroughs in neuroscience; however, technologies capable of electrical recording at the cellular level typically rely on rigid metals that poorly match the mechanical properties of soft brain tissue. As a result these electrodes often cause extensive acute and chronic injury, leading to short electrode lifetime. Recently, flexible electrodes such as Carbon Nanotube fibers (CNTf) have emerged as an attractive alternative to conventional electrodes and studies have shown that these flexible electrodes reduce neuro-inflammation and increase the quality and longevity of neural recordings. Insertion of these new compliant electrodes, however, remains challenge. The stiffening agents necessary to make the electrodes rigid enough to be inserted increases device footprint, which exacerbates brain damage during implantation. To overcome this challenge we have developed a novel technology to precisely implant and actuate high-performance, flexible carbon nanotube fiber (CNTf) microelectrodes without using a stiffening agents or shuttles. Instead, our technology uses drag forces within a microfluidic device to drive electrodes into tissue while minimizing the amount of fluid that is ejected into the tissue. In vitro experiments in brain phantoms, show that microfluidic actuated CNTf can be implanted at least 4.5 mm depth with 30 microm precision, while keeping the total volume of fluid ejected below 0.1 microL. As proof of concept, we inserted CNTfs in the small cnidarian Hydra littoralis and observed compound action potentials corresponding to contractions and in agreement with the literature. Additionally, brain slices extracted from transgenic mice were used to show that our device can be used to record spontaneous and light evoked activity from the cortex and deep brain regions such as the thalamic reticular nucleus (TRN). Overall our microfluidic actuation technology provides a platform for

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

    Directory of Open Access Journals (Sweden)

    Bahr Andreas

    2016-09-01

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

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

    Science.gov (United States)

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

    2018-02-01

    Objective. Foreign body response to indwelling cortical microelectrodes limits the reliability of neural stimulation and recording, particularly for extended chronic applications in behaving animals. The extent to which this response compromises the chronic stability of neural devices depends on many factors including the materials used in the electrode construction, the size, and geometry of the indwelling structure. Here, we report on the development of microelectrode arrays (MEAs) based on amorphous silicon carbide (a-SiC). Approach. This technology utilizes a-SiC for its chronic stability and employs semiconductor manufacturing processes to create MEAs with small shank dimensions. The a-SiC films were deposited by plasma enhanced chemical vapor deposition and patterned by thin-film photolithographic techniques. To improve stimulation and recording capabilities with small contact areas, we investigated low impedance coatings on the electrode sites. The assembled devices were characterized in phosphate buffered saline for their electrochemical properties. Main results. MEAs utilizing a-SiC as both the primary structural element and encapsulation were fabricated successfully. These a-SiC MEAs had 16 penetrating shanks. Each shank has a cross-sectional area less than 60 µm2 and electrode sites with a geometric surface area varying from 20 to 200 µm2. Electrode coatings of TiN and SIROF reduced 1 kHz electrode impedance to less than 100 kΩ from ~2.8 MΩ for 100 µm2 Au electrode sites and increased the charge injection capacities to values greater than 3 mC cm‑2. Finally, we demonstrated functionality by recording neural activity from basal ganglia nucleus of Zebra Finches and motor cortex of rat. Significance. The a-SiC MEAs provide a significant advancement in the development of microelectrodes that over the years has relied on silicon platforms for device manufacture. These flexible a-SiC MEAs have the potential for decreased tissue damage and reduced

  16. The LILARTI neural network system

    Energy Technology Data Exchange (ETDEWEB)

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

    1992-10-01

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

  17. Two multichannel integrated circuits for neural recording and signal processing.

    Science.gov (United States)

    Obeid, Iyad; Morizio, James C; Moxon, Karen A; Nicolelis, Miguel A L; Wolf, Patrick D

    2003-02-01

    We have developed, manufactured, and tested two analog CMOS integrated circuit "neurochips" for recording from arrays of densely packed neural electrodes. Device A is a 16-channel buffer consisting of parallel noninverting amplifiers with a gain of 2 V/V. Device B is a 16-channel two-stage analog signal processor with differential amplification and high-pass filtering. It features selectable gains of 250 and 500 V/V as well as reference channel selection. The resulting amplifiers on Device A had a mean gain of 1.99 V/V with an equivalent input noise of 10 microV(rms). Those on Device B had mean gains of 53.4 and 47.4 dB with a high-pass filter pole at 211 Hz and an equivalent input noise of 4.4 microV(rms). Both devices were tested in vivo with electrode arrays implanted in the somatosensory cortex.

  18. Simultaneous neural and movement recording in large-scale immersive virtual environments.

    Science.gov (United States)

    Snider, Joseph; Plank, Markus; Lee, Dongpyo; Poizner, Howard

    2013-10-01

    Virtual reality (VR) allows precise control and manipulation of rich, dynamic stimuli that, when coupled with on-line motion capture and neural monitoring, can provide a powerful means both of understanding brain behavioral relations in the high dimensional world and of assessing and treating a variety of neural disorders. Here we present a system that combines state-of-the-art, fully immersive, 3D, multi-modal VR with temporally aligned electroencephalographic (EEG) recordings. The VR system is dynamic and interactive across visual, auditory, and haptic interactions, providing sight, sound, touch, and force. Crucially, it does so with simultaneous EEG recordings while subjects actively move about a 20 × 20 ft² space. The overall end-to-end latency between real movement and its simulated movement in the VR is approximately 40 ms. Spatial precision of the various devices is on the order of millimeters. The temporal alignment with the neural recordings is accurate to within approximately 1 ms. This powerful combination of systems opens up a new window into brain-behavioral relations and a new means of assessment and rehabilitation of individuals with motor and other disorders.

  19. Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network.

    Science.gov (United States)

    Yang, Zhongliang; Huang, Yongfeng; Jiang, Yiran; Sun, Yuxi; Zhang, Yu-Jin; Luo, Pengcheng

    2018-04-20

    Automatically extracting useful information from electronic medical records along with conducting disease diagnoses is a promising task for both clinical decision support(CDS) and neural language processing(NLP). Most of the existing systems are based on artificially constructed knowledge bases, and then auxiliary diagnosis is done by rule matching. In this study, we present a clinical intelligent decision approach based on Convolutional Neural Networks(CNN), which can automatically extract high-level semantic information of electronic medical records and then perform automatic diagnosis without artificial construction of rules or knowledge bases. We use collected 18,590 copies of the real-world clinical electronic medical records to train and test the proposed model. Experimental results show that the proposed model can achieve 98.67% accuracy and 96.02% recall, which strongly supports that using convolutional neural network to automatically learn high-level semantic features of electronic medical records and then conduct assist diagnosis is feasible and effective.

  20. Automating occupational protection records systems

    International Nuclear Information System (INIS)

    Lyon, M.; Martin, J.B.

    1991-10-01

    Occupational protection records have traditionally been generated by field and laboratory personnel, assembled into files in the safety office, and eventually stored in a warehouse or other facility. Until recently, these records have been primarily paper copies, often handwritten. Sometimes, the paper is microfilmed for storage. However, electronic records are beginning to replace these traditional methods. The purpose of this paper is to provide guidance for making the transition to automated record keeping and retrieval using modern computer equipment. This paper describes the types of records most readily converted to electronic record keeping and a methodology for implementing an automated record system. The process of conversion is based on a requirements analysis to assess program needs and a high level of user involvement during the development. The importance of indexing the hard copy records for easy retrieval is also discussed. The concept of linkage between related records and its importance relative to reporting, research, and litigation will be addressed. 2 figs

  1. Learning in Artificial Neural Systems

    Science.gov (United States)

    Matheus, Christopher J.; Hohensee, William E.

    1987-01-01

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

  2. Neural Control of the Immune System

    Science.gov (United States)

    Sundman, Eva; Olofsson, Peder S.

    2014-01-01

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

  3. Corrosion of tungsten microelectrodes used in neural recording applications.

    Science.gov (United States)

    Patrick, Erin; Orazem, Mark E; Sanchez, Justin C; Nishida, Toshikazu

    2011-06-15

    In neuroprosthetic applications, long-term electrode viability is necessary for robust recording of the activity of neural populations used for generating communication and control signals. The corrosion of tungsten microwire electrodes used for intracortical recording applications was analyzed in a controlled bench-top study and compared to the corrosion of tungsten microwires used in an in vivo study. Two electrolytes were investigated for the bench-top electrochemical analysis: 0.9% phosphate buffered saline (PBS) and 0.9% PBS containing 30 mM of hydrogen peroxide. The oxidation and reduction reactions responsible for corrosion were found by measurement of the open circuit potential and analysis of Pourbaix diagrams. Dissolution of tungsten to form the tungstic ion was found to be the corrosion mechanism. The corrosion rate was estimated from the polarization resistance, which was extrapolated from the electrochemical impedance spectroscopy data. The results show that tungsten microwires in an electrolyte of PBS have a corrosion rate of 300-700 μm/yr. The corrosion rate for tungsten microwires in an electrolyte containing PBS and 30 mM H₂O₂ is accelerated to 10,000-20,000 μm/yr. The corrosion rate was found to be controlled by the concentration of the reacting species in the cathodic reaction (e.g. O₂ and H₂O₂). The in vivo corrosion rate, averaged over the duration of implantation, was estimated to be 100 μm/yr. The reduced in vivo corrosion rate as compared to the bench-top rate is attributed to decreased rate of oxygen diffusion caused by the presence of a biological film and a reduced concentration of available oxygen in the brain. Copyright © 2011 Elsevier B.V. All rights reserved.

  4. A Low Noise Amplifier for Neural Spike Recording Interfaces

    Directory of Open Access Journals (Sweden)

    Jesus Ruiz-Amaya

    2015-09-01

    Full Text Available 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.

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

    Science.gov (United States)

    Kent, Alexander R.; Grill, Warren M.

    2014-08-01

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

  6. Neural neworks in a management information systems

    Directory of Open Access Journals (Sweden)

    Jana Weinlichová

    2009-01-01

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

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

    Science.gov (United States)

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

    2013-04-01

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

  8. Echoes in correlated neural systems

    International Nuclear Information System (INIS)

    Helias, M; Tetzlaff, T; Diesmann, M

    2013-01-01

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

  9. Neural systems for tactual memories.

    Science.gov (United States)

    Bonda, E; Petrides, M; Evans, A

    1996-04-01

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

  10. Medical Archive Recording System (MARS)

    OpenAIRE

    Mohammad Reza Tajvidi

    2007-01-01

    In this talk, one of the most efficient, and reliable integrated tools for CD/DVD production workflow, called Medical Archive Recording System (MARS) by ETIAM Company, France, which is a leader in multimedia connectivity for healthcare in Europe, is going to be introduced. "nThis tool is used to record all patient studies, route the studies to printers and PACS automatically, print key images and associated reports and log all study production for automated post processing/archiving. Its...

  11. Simultaneous surface and depth neural activity recording with graphene transistor-based dual-modality probes.

    Science.gov (United States)

    Du, Mingde; Xu, Xianchen; Yang, Long; Guo, Yichuan; Guan, Shouliang; Shi, Jidong; Wang, Jinfen; Fang, Ying

    2018-05-15

    Subdural surface and penetrating depth probes are widely applied to record neural activities from the cortical surface and intracortical locations of the brain, respectively. Simultaneous surface and depth neural activity recording is essential to understand the linkage between the two modalities. Here, we develop flexible dual-modality neural probes based on graphene transistors. The neural probes exhibit stable electrical performance even under 90° bending because of the excellent mechanical properties of graphene, and thus allow multi-site recording from the subdural surface of rat cortex. In addition, finite element analysis was carried out to investigate the mechanical interactions between probe and cortex tissue during intracortical implantation. Based on the simulation results, a sharp tip angle of π/6 was chosen to facilitate tissue penetration of the neural probes. Accordingly, the graphene transistor-based dual-modality neural probes have been successfully applied for simultaneous surface and depth recording of epileptiform activity of rat brain in vivo. Our results show that graphene transistor-based dual-modality neural probes can serve as a facile and versatile tool to study tempo-spatial patterns of neural activities. Copyright © 2018 Elsevier B.V. All rights reserved.

  12. Application of artificial neural networks for versatile preprocessing of electrocardiogram recordings.

    Science.gov (United States)

    Mateo, J; Rieta, J J

    2012-02-01

    The electrocardiogram (ECG) is the most widely used method for diagnosis of heart diseases, where a good quality of recordings allows the proper interpretation and identification of physiological and pathological phenomena. However, ECG recordings often have interference from noises including thermal, muscle, baseline and powerline noises. These signals severely limit ECG recording utility and, hence, have to be removed. To deal with this problem, the present paper proposes an artificial neural network (ANN) as a filter to remove all kinds of noise in just one step. The method is based on a growing ANN which optimizes both the number of nodes in the hidden layer and the coefficient matrices, which are optimized by means of the Widrow-Hoff delta algorithm. The ANN has been trained with a database comprising all kinds of noise, both from synthesized and real ECG recordings, in order to handle any noise signal present in the ECG. The proposed system improves results yielded by conventional techniques of ECG filtering, such as FIR-based systems, adaptive filtering and wavelet filtering. Therefore, the algorithm could serve as an effective framework to substantially reduce noise in ECG recordings. In addition, the resulting ECG signal distortion is notably more reduced in comparison with conventional methodologies. In summary, the current contribution introduces a new method which is able to suppress all ECG interference signals in only one step with low ECG distortion and a high noise reduction.

  13. An Integrated Circuit for Simultaneous Extracellular Electrophysiology Recording and Optogenetic Neural Manipulation.

    Science.gov (United States)

    Chen, Chang Hao; McCullagh, Elizabeth A; Pun, Sio Hang; Mak, Peng Un; Vai, Mang I; Mak, Pui In; Klug, Achim; Lei, Tim C

    2017-03-01

    The ability to record and to control action potential firing in neuronal circuits is critical to understand how the brain functions. The objective of this study is to develop a monolithic integrated circuit (IC) to record action potentials and simultaneously control action potential firing using optogenetics. A low-noise and high input impedance (or low input capacitance) neural recording amplifier is combined with a high current laser/light-emitting diode (LED) driver in a single IC. The low input capacitance of the amplifier (9.7 pF) was achieved by adding a dedicated unity gain stage optimized for high impedance metal electrodes. The input referred noise of the amplifier is [Formula: see text], which is lower than the estimated thermal noise of the metal electrode. Thus, the action potentials originating from a single neuron can be recorded with a signal-to-noise ratio of at least 6.6. The LED/laser current driver delivers a maximum current of 330 mA, which is adequate for optogenetic control. The functionality of the IC was tested with an anesthetized Mongolian gerbil and auditory stimulated action potentials were recorded from the inferior colliculus. Spontaneous firings of fifth (trigeminal) nerve fibers were also inhibited using the optogenetic protein Halorhodopsin. Moreover, a noise model of the system was derived to guide the design. A single IC to measure and control action potentials using optogenetic proteins is realized so that more complicated behavioral neuroscience research and the translational neural disorder treatments become possible in the future.

  14. Neural neworks in a management information systems

    OpenAIRE

    Jana Weinlichová; Michael Štencl

    2009-01-01

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

  15. Neural control of magnetic suspension systems

    Science.gov (United States)

    Gray, W. Steven

    1993-01-01

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

  16. Application of neural networks in CRM systems

    Directory of Open Access Journals (Sweden)

    Bojanowska Agnieszka

    2017-01-01

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

  17. Tracking Single Units in Chronic, Large Scale, Neural Recordings for Brain Machine Interface Applications

    Directory of Open Access Journals (Sweden)

    Ahmed eEleryan

    2014-07-01

    Full Text Available In the study of population coding in neurobiological systems, tracking unit identity may be critical to assess possible changes in the coding properties of neuronal constituents over prolonged periods of time. Ensuring unit stability is even more critical for reliable neural decoding of motor variables in intra-cortically controlled brain-machine interfaces (BMIs. Variability in intrinsic spike patterns, tuning characteristics, and single-unit identity over chronic use is a major challenge to maintaining this stability, requiring frequent daily calibration of neural decoders in BMI sessions by an experienced human operator. Here, we report on a unit-stability tracking algorithm that efficiently and autonomously identifies putative single-units that are stable across many sessions using a relatively short duration recording interval at the start of each session. The algorithm first builds a database of features extracted from units' average spike waveforms and firing patterns across many days of recording. It then uses these features to decide whether spike occurrences on the same channel on one day belong to the same unit recorded on another day or not. We assessed the overall performance of the algorithm for different choices of features and classifiers trained using human expert judgment, and quantified it as a function of accuracy and execution time. Overall, we found a trade-off between accuracy and execution time with increasing data volumes from chronically implanted rhesus macaques, with an average of 12 seconds processing time per channel at ~90% classification accuracy. Furthermore, 77% of the resulting putative single-units matched those tracked by human experts. These results demonstrate that over the span of a few months of recordings, automated unit tracking can be performed with high accuracy and used to streamline the calibration phase during BMI sessions.

  18. Long-term neural recordings using MEMS based moveable microelectrodes in the brain

    Directory of Open Access Journals (Sweden)

    Nathan Jackson

    2010-06-01

    Full Text Available One of the critical requirements of the emerging class of neural prosthetic devices is to maintain good quality neural recordings over long time periods. We report here a novel (Micro-ElectroMechanical Systems based technology that can move microelectrodes in the event of deterioration in neural signal to sample a new set of neurons. Microscale electro-thermal actuators are used to controllably move microelectrodes post-implantation in steps of approximately 9 µm. In this study, a total of 12 moveable microelectrode chips were individually implanted in adult rats. Two of the 12 moveable microelectrode chips were not moved over a period of 3 weeks and were treated as control experiments. During the first three weeks of implantation, moving the microelectrodes led to an improvement in the average SNR from 14.61 ± 5.21 dB before movement to 18.13 ± 4.99 dB after movement across all microelectrodes and all days. However, the average RMS values of noise amplitudes were similar at 2.98 ± 1.22 µV and 3.01 ± 1.16 µV before and after microelectrode movement. Beyond three weeks, the primary observed failure mode was biological rejection of the PMMA (dental cement based skull mount resulting in the device loosening and eventually falling from the skull. Additionally, the average SNR for functioning devices beyond three weeks was 11.88 ± 2.02 dB before microelectrode movement and was significantly different (p<0.01 from the average SNR of 13.34 ± 0.919 dB after movement. The results of this study demonstrate that MEMS based technologies can move microelectrodes in rodent brains in long-term experiments resulting in improvements in signal quality. Further improvements in packaging and surgical techniques will potentially enable movable microelectrodes to record cortical neuronal activity in chronic experiments.

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

    Science.gov (United States)

    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

    2018-02-01

    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

  20. Techniques for extracting single-trial activity patterns from large-scale neural recordings

    Science.gov (United States)

    Churchland, Mark M; Yu, Byron M; Sahani, Maneesh; Shenoy, Krishna V

    2008-01-01

    Summary Large, chronically-implanted arrays of microelectrodes are an increasingly common tool for recording from primate cortex, and can provide extracellular recordings from many (order of 100) neurons. While the desire for cortically-based motor prostheses has helped drive their development, such arrays also offer great potential to advance basic neuroscience research. Here we discuss the utility of array recording for the study of neural dynamics. Neural activity often has dynamics beyond that driven directly by the stimulus. While governed by those dynamics, neural responses may nevertheless unfold differently for nominally identical trials, rendering many traditional analysis methods ineffective. We review recent studies – some employing simultaneous recording, some not – indicating that such variability is indeed present both during movement generation, and during the preceding premotor computations. In such cases, large-scale simultaneous recordings have the potential to provide an unprecedented view of neural dynamics at the level of single trials. However, this enterprise will depend not only on techniques for simultaneous recording, but also on the use and further development of analysis techniques that can appropriately reduce the dimensionality of the data, and allow visualization of single-trial neural behavior. PMID:18093826

  1. Spiking neural P systems with multiple channels.

    Science.gov (United States)

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

    2017-11-01

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

  2. Evaluating neural networks and artificial intelligence systems

    Science.gov (United States)

    Alberts, David S.

    1994-02-01

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

  3. Integrated Neural Flight and Propulsion Control System

    Science.gov (United States)

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

    2001-01-01

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

  4. Records Center Program Billing System

    Data.gov (United States)

    National Archives and Records Administration — RCPBS supports the Records center programs (RCP) in producing invoices for the storage (NARS-5) and servicing of National Archives and Records Administration’s...

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

    Science.gov (United States)

    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

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

    Science.gov (United States)

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

    2016-09-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2010-02-26

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

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

    Science.gov (United States)

    Curtis, Steven A. (Inventor)

    2011-01-01

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

  9. An externally head-mounted wireless neural recording device for laboratory animal research and possible human clinical use.

    Science.gov (United States)

    Yin, Ming; Li, Hao; Bull, Christopher; Borton, David A; Aceros, Juan; Larson, Lawrence; Nurmikko, Arto V

    2013-01-01

    In this paper we present a new type of head-mounted wireless neural recording device in a highly compact package, dedicated for untethered laboratory animal research and designed for future mobile human clinical use. The device, which takes its input from an array of intracortical microelectrode arrays (MEA) has ninety-seven broadband parallel neural recording channels and was integrated on to two custom designed printed circuit boards. These house several low power, custom integrated circuits, including a preamplifier ASIC, a controller ASIC, plus two SAR ADCs, a 3-axis accelerometer, a 48MHz clock source, and a Manchester encoder. Another ultralow power RF chip supports an OOK transmitter with the center frequency tunable from 3GHz to 4GHz, mounted on a separate low loss dielectric board together with a 3V LDO, with output fed to a UWB chip antenna. The IC boards were interconnected and packaged in a polyether ether ketone (PEEK) enclosure which is compatible with both animal and human use (e.g. sterilizable). The entire system consumes 17mA from a 1.2Ahr 3.6V Li-SOCl2 1/2AA battery, which operates the device for more than 2 days. The overall system includes a custom RF receiver electronics which are designed to directly interface with any number of commercial (or custom) neural signal processors for multi-channel broadband neural recording. Bench-top measurements and in vivo testing of the device in rhesus macaques are presented to demonstrate the performance of the wireless neural interface.

  10. A Wireless Fully Passive Neural Recording Device for Unobtrusive Neuropotential Monitoring.

    Science.gov (United States)

    Kiourti, Asimina; Lee, Cedric W L; Chae, Junseok; Volakis, John L

    2016-01-01

    We propose a novel wireless fully passive neural recording device for unobtrusive neuropotential monitoring. Previous work demonstrated the feasibility of monitoring emulated brain signals in a wireless fully passive manner. In this paper, we propose a novel realistic recorder that is significantly smaller and much more sensitive. The proposed recorder utilizes a highly efficient microwave backscattering method and operates without any formal power supply or regulating elements. Also, no intracranial wires or cables are required. In-vitro testing is performed inside a four-layer head phantom (skin, bone, gray matter, and white matter). Compared to our former implementation, the neural recorder proposed in this study has the following improved features: 1) 59% smaller footprint, 2) up to 20-dB improvement in neuropotential detection sensitivity, and 3) encapsulation in biocompatible polymer. For the first time, temporal emulated neuropotentials as low as 63 μVpp can be detected in a wireless fully passive manner. Remarkably, the high-sensitivity achieved in this study implies reading of most neural signals generated by the human brain. The proposed recorder brings forward transformational possibilities in wireless fully passive neural detection for a very wide range of applications (e.g., epilepsy, Alzheimer's, mental disorders, etc.).

  11. Analysis of complex systems using neural networks

    International Nuclear Information System (INIS)

    Uhrig, R.E.

    1992-01-01

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

  12. Mapping face categorization in the human ventral occipitotemporal cortex with direct neural intracranial recordings.

    Science.gov (United States)

    Rossion, Bruno; Jacques, Corentin; Jonas, Jacques

    2018-02-26

    The neural basis of face categorization has been widely investigated with functional magnetic resonance imaging (fMRI), identifying a set of face-selective local regions in the ventral occipitotemporal cortex (VOTC). However, indirect recording of neural activity with fMRI is associated with large fluctuations of signal across regions, often underestimating face-selective responses in the anterior VOTC. While direct recording of neural activity with subdural grids of electrodes (electrocorticography, ECoG) or depth electrodes (stereotactic electroencephalography, SEEG) offers a unique opportunity to fill this gap in knowledge, these studies rather reveal widely distributed face-selective responses. Moreover, intracranial recordings are complicated by interindividual variability in neuroanatomy, ambiguity in definition, and quantification of responses of interest, as well as limited access to sulci with ECoG. Here, we propose to combine SEEG in large samples of individuals with fast periodic visual stimulation to objectively define, quantify, and characterize face categorization across the whole VOTC. This approach reconciles the wide distribution of neural face categorization responses with their (right) hemispheric and regional specialization, and reveals several face-selective regions in anterior VOTC sulci. We outline the challenges of this research program to understand the neural basis of face categorization and high-level visual recognition in general. © 2018 New York Academy of Sciences.

  13. Collaborative Recurrent Neural Networks forDynamic Recommender Systems

    Science.gov (United States)

    2016-11-22

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

  14. IMPLEMENTATION OF NEURAL - CRYPTOGRAPHIC SYSTEM USING FPGA

    Directory of Open Access Journals (Sweden)

    KARAM M. Z. OTHMAN

    2011-08-01

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

  15. Simulating neural systems with Xyce.

    Energy Technology Data Exchange (ETDEWEB)

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

    2012-12-01

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

  16. Deficiencies in radiation protection record systems

    International Nuclear Information System (INIS)

    Martin, J.B.; Lyon, M.

    1991-01-01

    Radiation protection records are a fundamental part of any program for protecting radiation workers. Records are essential to epidemiological studies of radiation workers and are becoming increasingly important as the number of radiation exposure litigation cases increases. Ready retrievability of comprehensive records is also essential to the adequate defense of a radiation protection program. Appraisals of numerous radiation protection programs have revealed that few record-keeping systems comply with American National Standards Institute, Standard Practice N13.6-1972. Record-keeping requirements and types of deficiencies in radiation protection records systems are presented in this paper, followed by general recommendations for implementing a comprehensive radiation protection records system

  17. Deficiencies in radiation protection record systems

    International Nuclear Information System (INIS)

    Martin, J.B.; Lyon, M.

    1991-01-01

    Radiation protection records are a fundamental part of any program for protecting radiation workers. Records are essential to epidemiological studies of radiation workers and are becoming increasingly important as the number of radiation exposure litigation cases increases. Ready retrievability of comprehensive records is also essential to the adequate defense of a radiation protection program. Appraisals of numerous radiation protection programs have revealed that few record-keeping systems comply with American National Standards Institute, Standard Practice N13.6-1972. Record-keeping requirements and types of deficiencies in radiation protection records systems are presented in this paper, followed by general recommendations for implementing a comprehensive radiation protection records system. 8 refs

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

    Science.gov (United States)

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

    2015-12-01

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

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

    Science.gov (United States)

    1996-01-01

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

  20. Polymer SU-8 Based Microprobes for Neural Recording and Drug Delivery

    Science.gov (United States)

    Altuna, Ane; Fernandez, Luis; Berganzo, Javier

    2015-06-01

    This manuscript makes a reflection about SU-8 based microprobes for neural activity recording and drug delivery. By taking advantage of improvements in microfabrication technologies and using polymer SU-8 as the only structural material, we developed several microprobe prototypes aimed to: a) minimize injury in neural tissue, b) obtain high-quality electrical signals and c) deliver drugs at a micrometer precision scale. Dedicated packaging tools have been developed in parallel to fulfill requirements concerning electric and fluidic connections, size and handling. After these advances have been experimentally proven in brain using in vivo preparation, the technological concepts developed during consecutive prototypes are discussed in depth now.

  1. POLYMER SU-8 BASED MICROPROBES FOR NEURAL RECORDING AND DRUG DELIVERY

    Directory of Open Access Journals (Sweden)

    Ane eAltuna

    2015-06-01

    Full Text Available This manuscript makes a reflection about SU-8 based microprobes for neural activity recording and drug delivery. By taking advantage of improvements in microfabrication technologies and using polymer SU-8 as the only structural material, we developed several microprobe prototypes aimed to: a minimize injury in neural tissue, b obtain high-quality electrical signals and c deliver drugs at a micrometer precision scale. Dedicated packaging tools have been developed in parallel to fulfill requirements concerning electric and fluidic connections, size and handling. After these advances have been experimentally proven in brain using in vivo preparation, the technological concepts developed during consecutive prototypes are discussed in depth now.

  2. Dynamic artificial neural networks with affective systems.

    Directory of Open Access Journals (Sweden)

    Catherine D Schuman

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

  3. Dynamical systems, attractors, and neural circuits.

    Science.gov (United States)

    Miller, Paul

    2016-01-01

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

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

    Science.gov (United States)

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

    2010-06-01

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

  5. Simultaneous in vivo recording of local brain temperature and electrophysiological signals with a novel neural probe

    Science.gov (United States)

    Fekete, Z.; Csernai, M.; Kocsis, K.; Horváth, Á. C.; Pongrácz, A.; Barthó, P.

    2017-06-01

    Objective. Temperature is an important factor for neural function both in normal and pathological states, nevertheless, simultaneous monitoring of local brain temperature and neuronal activity has not yet been undertaken. Approach. In our work, we propose an implantable, calibrated multimodal biosensor that facilitates the complex investigation of thermal changes in both cortical and deep brain regions, which records multiunit activity of neuronal populations in mice. The fabricated neural probe contains four electrical recording sites and a platinum temperature sensor filament integrated on the same probe shaft within a distance of 30 µm from the closest recording site. The feasibility of the simultaneous functionality is presented in in vivo studies. The probe was tested in the thalamus of anesthetized mice while manipulating the core temperature of the animals. Main results. We obtained multiunit and local field recordings along with measurement of local brain temperature with accuracy of 0.14 °C. Brain temperature generally followed core body temperature, but also showed superimposed fluctuations corresponding to epochs of increased local neural activity. With the application of higher currents, we increased the local temperature by several degrees without observable tissue damage between 34-39 °C. Significance. The proposed multifunctional tool is envisioned to broaden our knowledge on the role of the thermal modulation of neuronal activity in both cortical and deeper brain regions.

  6. The development of a PZT-based microdrive for neural signal recording

    International Nuclear Information System (INIS)

    Park, Sangkyu; Yoon, Euisung; Park, Sukho; Lee, Sukchan; Shin, Hee-sup; Park, Hyunjun; Kim, Byungkyu; Kim, Daesoo; Park, Jongoh

    2008-01-01

    A hand-controlled microdrive has been used to obtain neural signals from rodents such as rats and mice. However, it places severe physical stress on the rodents during its manipulation, and this stress leads to alertness in the mice and low efficiency in obtaining neural signals from the mice. To overcome this issue, we developed a novel microdrive, which allows one to adjust the electrodes by a piezoelectric device (PZT) with high precision. Its mass is light enough to install on the mouse's head. The proposed microdrive has three H-type PZT actuators and their guiding structure. The operation principle of the microdrive is based on the well known inchworm mechanism. When the three PZT actuators are synchronized, linear motion of the electrode is produced along the guiding structure. The electrodes used for the recording of the neural signals from neuron cells were fixed at one of the PZT actuators. Our proposed microdrive has an accuracy of about 400 nm and a long stroke of about 5 mm. In response to formalin-induced pain, single unit activities are robustly measured at the thalamus with electrodes whose vertical depth is adjusted by the microdrive under urethane anesthesia. In addition, the microdrive was efficient in detecting neural signals from mice that were moving freely. Thus, the present study suggests that the PZT-based microdrive could be an alternative for the efficient detection of neural signals from mice during behavioral states without any stress to the mice. (technical note)

  7. Occupational Health Record-keeping System (OHRS)

    Data.gov (United States)

    Department of Veterans Affairs — Occupational Health Record-keeping System (OHRS) is part of the Clinical Information Support System (CISS) portal framework and the initial CISS partner system. OHRS...

  8. Neural Network Based Intrusion Detection System for Critical Infrastructures

    Energy Technology Data Exchange (ETDEWEB)

    Todd Vollmer; Ondrej Linda; Milos Manic

    2009-07-01

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

  9. Neural systems for preparatory control of imitation.

    Science.gov (United States)

    Cross, Katy A; Iacoboni, Marco

    2014-01-01

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

  10. Poly(3,4-ethylenedioxythiophene) (PEDOT) polymer coatings facilitate smaller neural recording electrodes

    Science.gov (United States)

    Ludwig, Kip A.; Langhals, Nicholas B.; Joseph, Mike D.; Richardson-Burns, Sarah M.; Hendricks, Jeffrey L.; Kipke, Daryl R.

    2011-02-01

    We investigated using poly(3,4-ethylenedioxythiophene) (PEDOT) to lower the impedance of small, gold recording electrodes with initial impedances outside of the effective recording range. Smaller electrode sites enable more densely packed arrays, increasing the number of input and output channels to and from the brain. Moreover, smaller electrode sizes promote smaller probe designs; decreasing the dimensions of the implanted probe has been demonstrated to decrease the inherent immune response, a known contributor to the failure of long-term implants. As expected, chronically implanted control electrodes were unable to record well-isolated unit activity, primarily as a result of a dramatically increased noise floor. Conversely, electrodes coated with PEDOT consistently recorded high-quality neural activity, and exhibited a much lower noise floor than controls. These results demonstrate that PEDOT coatings enable electrode designs 15 µm in diameter.

  11. System integration for radiation records

    International Nuclear Information System (INIS)

    Lawson, B.J.; Farrell, L.; Meacham, C.; Tapio, J.

    1994-01-01

    System integration is the process where through networking and/or software development, necessary business information is available in a common computing environment. System integration is becoming an important objective for many businesses. System integration can improve productivity and efficiency, reduce redundant stored information and errors, and improve availability of information. This paper will discuss the information flow in a radiation health environment, and how system integration can help. Information handled includes external dosimetry and internal dosimetry. The paper will focus on an ORACLE based system integration software product

  12. Theory of Neural Information Processing Systems

    International Nuclear Information System (INIS)

    Galla, Tobias

    2006-01-01

    It is difficult not to be amazed by the ability of the human brain to process, to structure and to memorize information. Even by the toughest standards the behaviour of this network of about 10 11 neurons qualifies as complex, and both the scientific community and the public take great interest in the growing field of neuroscience. The scientific endeavour to learn more about the function of the brain as an information processing system is here a truly interdisciplinary one, with important contributions from biology, computer science, physics, engineering and mathematics as the authors quite rightly point out in the introduction of their book. The role of the theoretical disciplines here is to provide mathematical models of information processing systems and the tools to study them. These models and tools are at the centre of the material covered in the book by Coolen, Kuehn and Sollich. The book is divided into five parts, providing basic introductory material on neural network models as well as the details of advanced techniques to study them. A mathematical appendix complements the main text. The range of topics is extremely broad, still the presentation is concise and the book well arranged. To stress the breadth of the book let me just mention a few keywords here: the material ranges from the basics of perceptrons and recurrent network architectures to more advanced aspects such as Bayesian learning and support vector machines; Shannon's theory of information and the definition of entropy are discussed, and a chapter on Amari's information geometry is not missing either. Finally the statistical mechanics chapters cover Gardner theory and the replica analysis of the Hopfield model, not without being preceded by a brief introduction of the basic concepts of equilibrium statistical physics. The book also contains a part on effective theories of the macroscopic dynamics of neural networks. Many dynamical aspects of neural networks are usually hard to find in the

  13. Identification of approximately duplicate material records in ERP systems

    Science.gov (United States)

    Zong, Wei; Wu, Feng; Chu, Lap-Keung; Sculli, Domenic

    2017-03-01

    The quality of master data is crucial for the accurate functioning of the various modules of an enterprise resource planning (ERP) system. This study addresses specific data problems arising from the generation of approximately duplicate material records in ERP databases. Such problems are mainly due to the firm's lack of unique and global identifiers for the material records, and to the arbitrary assignment of alternative names for the same material by various users. Traditional duplicate detection methods are ineffective in identifying such approximately duplicate material records because these methods typically rely on string comparisons of each field. To address this problem, a machine learning-based framework is developed to recognise semantic similarity between strings and to further identify and reunify approximately duplicate material records - a process referred to as de-duplication in this article. First, the keywords of the material records are extracted to form vectors of discriminating words. Second, a machine learning method using a probabilistic neural network is applied to determine the semantic similarity between these material records. The approach was evaluated using data from a real case study. The test results indicate that the proposed method outperforms traditional algorithms in identifying approximately duplicate material records.

  14. Neural Network for Optimization of Existing Control Systems

    DEFF Research Database (Denmark)

    Madsen, Per Printz

    1995-01-01

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

  15. Differential Covariance: A New Class of Methods to Estimate Sparse Connectivity from Neural Recordings.

    Science.gov (United States)

    Lin, Tiger W; Das, Anup; Krishnan, Giri P; Bazhenov, Maxim; Sejnowski, Terrence J

    2017-10-01

    With our ability to record more neurons simultaneously, making sense of these data is a challenge. Functional connectivity is one popular way to study the relationship of multiple neural signals. Correlation-based methods are a set of currently well-used techniques for functional connectivity estimation. However, due to explaining away and unobserved common inputs (Stevenson, Rebesco, Miller, & Körding, 2008 ), they produce spurious connections. The general linear model (GLM), which models spike trains as Poisson processes (Okatan, Wilson, & Brown, 2005 ; Truccolo, Eden, Fellows, Donoghue, & Brown, 2005 ; Pillow et al., 2008 ), avoids these confounds. We develop here a new class of methods by using differential signals based on simulated intracellular voltage recordings. It is equivalent to a regularized AR(2) model. We also expand the method to simulated local field potential recordings and calcium imaging. In all of our simulated data, the differential covariance-based methods achieved performance better than or similar to the GLM method and required fewer data samples. This new class of methods provides alternative ways to analyze neural signals.

  16. Differential Covariance: A New Class of Methods to Estimate Sparse Connectivity from Neural Recordings

    Science.gov (United States)

    Lin, Tiger W.; Das, Anup; Krishnan, Giri P.; Bazhenov, Maxim; Sejnowski, Terrence J.

    2017-01-01

    With our ability to record more neurons simultaneously, making sense of these data is a challenge. Functional connectivity is one popular way to study the relationship of multiple neural signals. Correlation-based methods are a set of currently well-used techniques for functional connectivity estimation. However, due to explaining away and unobserved common inputs (Stevenson, Rebesco, Miller, & Körding, 2008), they produce spurious connections. The general linear model (GLM), which models spike trains as Poisson processes (Okatan, Wilson, & Brown, 2005; Truccolo, Eden, Fellows, Donoghue, & Brown, 2005; Pillow et al., 2008), avoids these confounds. We develop here a new class of methods by using differential signals based on simulated intracellular voltage recordings. It is equivalent to a regularized AR(2) model. We also expand the method to simulated local field potential recordings and calcium imaging. In all of our simulated data, the differential covariance-based methods achieved performance better than or similar to the GLM method and required fewer data samples. This new class of methods provides alternative ways to analyze neural signals. PMID:28777719

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

    Directory of Open Access Journals (Sweden)

    Wilfredo Blanco

    2017-09-01

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

  18. Blue laser phase change recording system

    International Nuclear Information System (INIS)

    Hofmann, Holger; Dambach, S.Soeren; Richter, Hartmut

    2002-01-01

    The migration paths from DVD phase change recording with red laser to the next generation optical disk formats with blue laser and high NA optics are discussed with respect to optical aberration margins and disc capacities. A test system for the evaluation of phase change disks with more than 20 GB capacity is presented and first results of the recording performance are shown

  19. Connectivity inference from neural recording data: Challenges, mathematical bases and research directions.

    Science.gov (United States)

    Magrans de Abril, Ildefons; Yoshimoto, Junichiro; Doya, Kenji

    2018-06-01

    This article presents a review of computational methods for connectivity inference from neural activity data derived from multi-electrode recordings or fluorescence imaging. We first identify biophysical and technical challenges in connectivity inference along the data processing pipeline. We then review connectivity inference methods based on two major mathematical foundations, namely, descriptive model-free approaches and generative model-based approaches. We investigate representative studies in both categories and clarify which challenges have been addressed by which method. We further identify critical open issues and possible research directions. Copyright © 2018 The Author(s). Published by Elsevier Ltd.. All rights reserved.

  20. Neural mechanism of facilitation system during physical fatigue.

    Directory of Open Access Journals (Sweden)

    Masaaki Tanaka

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

  1. Implementation of an Electronic Medical Records System

    Science.gov (United States)

    2008-05-07

    Hartman, MAJ Roddex Barlow , CPT Christopher Besser and Capt Michael Emerson...thank you I am truly honored to call each of you my friends. Electronic... abnormal findings are addressed. 18 Electronic Medical Record Implementation Barriers of the Electronic Medical Records System There are several...examination findings • Psychological and social assessment findings N. The system provides a flexible mechanism for retrieval of encounter

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

    NARCIS (Netherlands)

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

    2014-01-01

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

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

    Science.gov (United States)

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

    2018-02-01

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

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

    Science.gov (United States)

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

    2018-02-01

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

  5. Spiking Neural P Systems with Communication on Request.

    Science.gov (United States)

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

    2017-12-01

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

  6. Assessing artificial neural networks and statistical methods for infilling missing soil moisture records

    Science.gov (United States)

    Dumedah, Gift; Walker, Jeffrey P.; Chik, Li

    2014-07-01

    Soil moisture information is critically important for water management operations including flood forecasting, drought monitoring, and groundwater recharge estimation. While an accurate and continuous record of soil moisture is required for these applications, the available soil moisture data, in practice, is typically fraught with missing values. There are a wide range of methods available to infilling hydrologic variables, but a thorough inter-comparison between statistical methods and artificial neural networks has not been made. This study examines 5 statistical methods including monthly averages, weighted Pearson correlation coefficient, a method based on temporal stability of soil moisture, and a weighted merging of the three methods, together with a method based on the concept of rough sets. Additionally, 9 artificial neural networks are examined, broadly categorized into feedforward, dynamic, and radial basis networks. These 14 infilling methods were used to estimate missing soil moisture records and subsequently validated against known values for 13 soil moisture monitoring stations for three different soil layer depths in the Yanco region in southeast Australia. The evaluation results show that the top three highest performing methods are the nonlinear autoregressive neural network, rough sets method, and monthly replacement. A high estimation accuracy (root mean square error (RMSE) of about 0.03 m/m) was found in the nonlinear autoregressive network, due to its regression based dynamic network which allows feedback connections through discrete-time estimation. An equally high accuracy (0.05 m/m RMSE) in the rough sets procedure illustrates the important role of temporal persistence of soil moisture, with the capability to account for different soil moisture conditions.

  7. Bayesian neural network modeling of tree-ring temperature variability record from the Western Himalayas

    Directory of Open Access Journals (Sweden)

    R. K. Tiwari

    2011-08-01

    Full Text Available A novel technique based on the Bayesian neural network (BNN theory is developed and employed to model the temperature variation record from the Western Himalayas. In order to estimate an a posteriori probability function, the BNN is trained with the Hybrid Monte Carlo (HMC/Markov Chain Monte Carlo (MCMC simulations algorithm. The efficacy of the new algorithm is tested on the well known chaotic, first order autoregressive (AR and random models and then applied to model the temperature variation record decoded from the tree-ring widths of the Western Himalayas for the period spanning over 1226–2000 AD. For modeling the actual tree-ring temperature data, optimum network parameters are chosen appropriately and then cross-validation test is performed to ensure the generalization skill of the network on the new data set. Finally, prediction result based on the BNN model is compared with the conventional artificial neural network (ANN and the AR linear models results. The comparative results show that the BNN based analysis makes better prediction than the ANN and the AR models. The new BNN modeling approach provides a viable tool for climate studies and could also be exploited for modeling other kinds of environmental data.

  8. Bifurcation and chaos in neural excitable system

    International Nuclear Information System (INIS)

    Jing Zhujun; Yang Jianping; Feng Wei

    2006-01-01

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

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

    Science.gov (United States)

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

    2013-01-01

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

  10. A new radiation exposure record system

    International Nuclear Information System (INIS)

    Lyon, M.; Berndt, V.L.; Trevino, G.W.; Oakley, B.M.

    1993-04-01

    The Hanford Radiological Records Program (HRRP) serves all Hanford contractors as the single repository for radiological exposure for all Hanford employees, subcontractors, and visitors. The program administers and preserves all Hanford radiation exposure records. The program also maintains a Radiation Protection Historical File which is a historical file of Hanford radiation protection and dosimetry procedures and practices. Several years ago DOE declared the existing UNIVAC mainframe computer obsolete and the existing Occupational Radiation Exposure (ORE) system was slated to be redeveloped. The new system named the Radiological Exposure (REX) System is described in this document

  11. National Recordal System IK holder catalogue process

    CSIR Research Space (South Africa)

    Pretorius, R

    2011-11-01

    Full Text Available The aim of this poster is to illustrate the South African National Recordal Systems (NRS) process whereby Indigenous Knowledge (IK) holder’s information is captured in a registry, as specified in South Africa’s Indigenous Knowledge Systems Policy...

  12. Health record systems that meet clinical needs

    Directory of Open Access Journals (Sweden)

    Gabriella Negrini

    2012-10-01

    Full Text Available Introduction Increased attention has recently been focused on health record systems as a result of accreditation programs, a growing emphasis on patient safety, and the increase in lawsuits involving allegations of malpractice. Health-care professionals frequently express dissatisfaction with the health record systems and complain that the data included are neither informative nor useful for clinical decision making. This article reviews the main objectives of a hospital health record system, with emphasis on its roles in communication and exchange among clinicians, patient safety, and continuity of care, and asks whether current systems have responded to the recent changes in the Italian health-care system.Discussion If health records are to meet the expectations of all health professionals, the overall information need must be carefully analyzed, a common data set must be created, and essential specialist contributions must be defined. Working with health-care professionals, the hospital management should define how clinical information is to be displayed and organized, identify a functionally optimal layout, define the characteristics of ongoing patient assessment in terms of who will be responsible for these activities and how often they will be performed. Internet technology can facilitate data retrieval and meet the general requirements of a paper-based health record system, but it must also ensure focus on clinical information, business continuity, integrity, security, and privacy.Conclusions The current health records system needs to be thoroughly revised to increase its accessibility, streamline the work of health-care professionals who consult it, and render it more useful for clinical decision making—a challenging task that will require the active involvement of the many professional classes involved.

  13. PWR system simulation and parameter estimation with neural networks

    International Nuclear Information System (INIS)

    Akkurt, Hatice; Colak, Uener

    2002-01-01

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

  14. PWR system simulation and parameter estimation with neural networks

    Energy Technology Data Exchange (ETDEWEB)

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

    2002-11-01

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

  15. A fast, robust algorithm for power line interference cancellation in neural recording

    Science.gov (United States)

    Keshtkaran, Mohammad Reza; Yang, Zhi

    2014-04-01

    Objective. Power line interference may severely corrupt neural recordings at 50/60 Hz and harmonic frequencies. The interference is usually non-stationary and can vary in frequency, amplitude and phase. To retrieve the gamma-band oscillations at the contaminated frequencies, it is desired to remove the interference without compromising the actual neural signals at the interference frequency bands. In this paper, we present a robust and computationally efficient algorithm for removing power line interference from neural recordings. Approach. The algorithm includes four steps. First, an adaptive notch filter is used to estimate the fundamental frequency of the interference. Subsequently, based on the estimated frequency, harmonics are generated by using discrete-time oscillators, and then the amplitude and phase of each harmonic are estimated by using a modified recursive least squares algorithm. Finally, the estimated interference is subtracted from the recorded data. Main results. The algorithm does not require any reference signal, and can track the frequency, phase and amplitude of each harmonic. When benchmarked with other popular approaches, our algorithm performs better in terms of noise immunity, convergence speed and output signal-to-noise ratio (SNR). While minimally affecting the signal bands of interest, the algorithm consistently yields fast convergence (30 dB) in different conditions of interference strengths (input SNR from -30 to 30 dB), power line frequencies (45-65 Hz) and phase and amplitude drifts. In addition, the algorithm features a straightforward parameter adjustment since the parameters are independent of the input SNR, input signal power and the sampling rate. A hardware prototype was fabricated in a 65 nm CMOS process and tested. Software implementation of the algorithm has been made available for open access at https://github.com/mrezak/removePLI. Significance. The proposed algorithm features a highly robust operation, fast adaptation to

  16. 78 FR 43258 - Privacy Act; System of Records: Human Resources Records, State-31

    Science.gov (United States)

    2013-07-19

    ... DEPARTMENT OF STATE [Public Notice 8384] Privacy Act; System of Records: Human Resources Records... system of records, Human Resources Records, State- 31, pursuant to the provisions of the Privacy Act of... State proposes that the current system will retain the name ``Human Resources Records'' (previously...

  17. On the robustness of EC-PC spike detection method for online neural recording.

    Science.gov (United States)

    Zhou, Yin; Wu, Tong; Rastegarnia, Amir; Guan, Cuntai; Keefer, Edward; Yang, Zhi

    2014-09-30

    Online spike detection is an important step to compress neural data and perform real-time neural information decoding. An unsupervised, automatic, yet robust signal processing is strongly desired, thus it can support a wide range of applications. We have developed a novel spike detection algorithm called "exponential component-polynomial component" (EC-PC) spike detection. We firstly evaluate the robustness of the EC-PC spike detector under different firing rates and SNRs. Secondly, we show that the detection Precision can be quantitatively derived without requiring additional user input parameters. We have realized the algorithm (including training) into a 0.13 μm CMOS chip, where an unsupervised, nonparametric operation has been demonstrated. Both simulated data and real data are used to evaluate the method under different firing rates (FRs), SNRs. The results show that the EC-PC spike detector is the most robust in comparison with some popular detectors. Moreover, the EC-PC detector can track changes in the background noise due to the ability to re-estimate the neural data distribution. Both real and synthesized data have been used for testing the proposed algorithm in comparison with other methods, including the absolute thresholding detector (AT), median absolute deviation detector (MAD), nonlinear energy operator detector (NEO), and continuous wavelet detector (CWD). Comparative testing results reveals that the EP-PC detection algorithm performs better than the other algorithms regardless of recording conditions. The EC-PC spike detector can be considered as an unsupervised and robust online spike detection. It is also suitable for hardware implementation. Copyright © 2014 Elsevier B.V. All rights reserved.

  18. 77 FR 65564 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2012-10-29

    ... Medical, Health and Billing Records system is the authoritative source of patients' IHS medical records... authoritative records, nor are they considered part of the IHS Medical, Health and Billing Records system of...

  19. National Recordal System IK holder catalogue process

    CSIR Research Space (South Africa)

    Pretorius, R

    2012-10-01

    Full Text Available good, based on IK.? Subsequently, the South African Department of Science and Technology (DST) created a National Indigenous Knowledge Systems Office (NIKSO) that is taking the lead on interfacing IKS with other recognised knowledge holders... is to illustrate the South African National Recordal Systems (NRS) process whereby Indigenous Knowledge (IK) holder?s information is captured in a registry, as specified in South Africa?s Indigenous Knowledge Systems (IKS) Policy, through an IK cataloguing...

  20. A Wireless and Batteryless Microsystem with Implantable Grid Electrode/3-Dimensional Probe Array for ECoG and Extracellular Neural Recording in Rats

    Directory of Open Access Journals (Sweden)

    Chih-Wei Chang

    2013-04-01

    Full Text Available This paper presents the design and implementation of an integrated wireless microsystem platform that provides the possibility to support versatile implantable neural sensing devices in free laboratory rats. Inductive coupled coils with low dropout regulator design allows true long-term recording without limitation of battery capacity. A 16-channel analog front end chip located on the headstage is designed for high channel account neural signal conditioning with low current consumption and noise. Two types of implantable electrodes including grid electrode and 3D probe array are also presented for brain surface recording and 3D biopotential acquisition in the implanted target volume of tissue. The overall system consumes less than 20 mA with small form factor, 3.9 × 3.9 cm2 mainboard and 1.8 × 3.4 cm2 headstage, is packaged into a backpack for rats. Practical in vivo recordings including auditory response, brain resection tissue and PZT-induced seizures recording demonstrate the correct function of the proposed microsystem. Presented achievements addressed the aforementioned properties by combining MEMS neural sensors, low-power circuit designs and commercial chips into system-level integration.

  1. Dopamine system: Manager of neural pathways

    Directory of Open Access Journals (Sweden)

    Simon eHong

    2013-12-01

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

  2. Diagnostic Neural Network Systems for the Electronic Circuits

    International Nuclear Information System (INIS)

    Mohamed, A.H.

    2014-01-01

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

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

    Science.gov (United States)

    Noori Hoshyar, Azadeh; Sulaiman, Riza

    2011-10-01

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

  4. 77 FR 65370 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2012-10-26

    ... respective personnel management and finance systems.'' Routine uses of records maintained in the system...; System of Records AGENCY: Defense Logistics Agency, DoD. ACTION: Notice to Alter a System of Records. SUMMARY: The Defense Logistics Agency proposes to alter a system of records in its inventory of record...

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

    International Nuclear Information System (INIS)

    Uhrig, R.E.

    1992-01-01

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

  6. Genetic learning in rule-based and neural systems

    Science.gov (United States)

    Smith, Robert E.

    1993-01-01

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

  7. A Web Browsing Behavior Recording System

    OpenAIRE

    Ohmura, Hayato; Kitasuka, Teruaki; Aritsugi, Masayoshi; オオムラ, ハヤト; キタスカ, テルアキ; アリツギ, マサヨシ; 大村, 勇人; 北須賀, 輝明; 有次, 正義

    2011-01-01

    In this paper, we introduce a Web browsing behavior recording system for research. Web browsing behavior data can help us to providesophisticated services for human activities, because the data must indicate characteristics ofWeb users.We discuss the necessity of the data with potential benefits, and develop a system for collecting the data as an add-on for Firefox. We also report some results of preliminary experiments to test its usefulness in analyses on human activities in this paper.

  8. Shades of grey; Assessing the contribution of the magno- and parvocellular systems to neural processing of the retinal input in the human visual system from the influence of neural population size and its discharge activity on the VEP.

    Science.gov (United States)

    Marcar, Valentine L; Baselgia, Silvana; Lüthi-Eisenegger, Barbara; Jäncke, Lutz

    2018-03-01

    Retinal input processing in the human visual system involves a phasic and tonic neural response. We investigated the role of the magno- and parvocellular systems by comparing the influence of the active neural population size and its discharge activity on the amplitude and latency of four VEP components. We recorded the scalp electric potential of 20 human volunteers viewing a series of dartboard images presented as a pattern reversing and pattern on-/offset stimulus. These patterns were designed to vary both neural population size coding the temporal- and spatial luminance contrast property and the discharge activity of the population involved in a systematic manner. When the VEP amplitude reflected the size of the neural population coding the temporal luminance contrast property of the image, the influence of luminance contrast followed the contrast response function of the parvocellular system. When the VEP amplitude reflected the size of the neural population responding to the spatial luminance contrast property the image, the influence of luminance contrast followed the contrast response function of the magnocellular system. The latencies of the VEP components examined exhibited the same behavior across our stimulus series. This investigation demonstrates the complex interplay of the magno- and parvocellular systems on the neural response as captured by the VEP. It also demonstrates a linear relationship between stimulus property, neural response, and the VEP and reveals the importance of feedback projections in modulating the ongoing neural response. In doing so, it corroborates the conclusions of our previous study.

  9. Robust record preservation system on geological repository

    International Nuclear Information System (INIS)

    Ohuchi, J.; Torata, S.; Tsuboya, T.

    2004-01-01

    Long-term record preservation system on geological disposal of High Level Radioactive Wastes (HLW) has been investigated as the institutional control by RWMC, Japan. Geological disposal of HLW, being based on the passive safe concept, has been considered not to necessitate the human controls to maintain its long-term safety. However how to complement the safety case on geological disposal is an important issue in each countries to progress the repository program with the step-wise decisions process during the long-term period up to several hundreds years. Although we cannot predict the future society, we need to realize the robust and redundant system for preserving records, which should be accessible, retrievable and understandable for the unpredicted future generations. First of all, we held a Rome workshop in January 2003 to exchange views on the matter, resulted in the suggestion directing the discussion on the record management and long-term preservation and retrieval of information regarding radioactive waste. Second, we considered the balance of active and passive system to strengthen the robustness. Another significance of long-term record preservation is to send current generation an implicit message, 'doing our best for future generations', in addition to aiming at both warning and their own decision-making. We call it 'meta-signal' to current generation. Thirdly, we demonstrated the laser-engraving technology to have converted five hundreds pages of an A4 sized report with human readable font sizes to 42 square silicon carbide plates, 10cm x10cm and 1mm in thickness. Silicon carbide would be an alternative to paper and might be possible to be an alternative to microfilm utilized as digital recording media. Another case study is the future generations' accessibility to the preserved records. (author)

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

    Science.gov (United States)

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

    2018-08-01

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

  11. Neural-network-directed alignment of optical systems using the laser-beam spatial filter as an example

    Science.gov (United States)

    Decker, Arthur J.; Krasowski, Michael J.; Weiland, Kenneth E.

    1993-01-01

    This report describes an effort at NASA Lewis Research Center to use artificial neural networks to automate the alignment and control of optical measurement systems. Specifically, it addresses the use of commercially available neural network software and hardware to direct alignments of the common laser-beam-smoothing spatial filter. The report presents a general approach for designing alignment records and combining these into training sets to teach optical alignment functions to neural networks and discusses the use of these training sets to train several types of neural networks. Neural network configurations used include the adaptive resonance network, the back-propagation-trained network, and the counter-propagation network. This work shows that neural networks can be used to produce robust sequencers. These sequencers can learn by example to execute the step-by-step procedures of optical alignment and also can learn adaptively to correct for environmentally induced misalignment. The long-range objective is to use neural networks to automate the alignment and operation of optical measurement systems in remote, harsh, or dangerous aerospace environments. This work also shows that when neural networks are trained by a human operator, training sets should be recorded, training should be executed, and testing should be done in a manner that does not depend on intellectual judgments of the human operator.

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

    Science.gov (United States)

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

    2015-10-01

    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.

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

    Science.gov (United States)

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

    2015-10-01

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

  14. A CMOS power-efficient low-noise current-mode front-end amplifier for neural signal recording.

    Science.gov (United States)

    Wu, Chung-Yu; Chen, Wei-Ming; Kuo, Liang-Ting

    2013-04-01

    In this paper, a new current-mode front-end amplifier (CMFEA) for neural signal recording systems is proposed. In the proposed CMFEA, a current-mode preamplifier with an active feedback loop operated at very low frequency is designed as the first gain stage to bypass any dc offset current generated by the electrode-tissue interface and to achieve a low high-pass cutoff frequency below 0.5 Hz. No reset signal or ultra-large pseudo resistor is required. The current-mode preamplifier has low dc operation current to enhance low-noise performance and decrease power consumption. A programmable current gain stage is adopted to provide adjustable gain for adaptive signal scaling. A following current-mode filter is designed to adjust the low-pass cutoff frequency for different neural signals. The proposed CMFEA is designed and fabricated in 0.18-μm CMOS technology and the area of the core circuit is 0.076 mm(2). The measured high-pass cutoff frequency is as low as 0.3 Hz and the low-pass cutoff frequency is adjustable from 1 kHz to 10 kHz. The measured maximum current gain is 55.9 dB. The measured input-referred current noise density is 153 fA /√Hz , and the power consumption is 13 μW at 1-V power supply. The fabricated CMFEA has been successfully applied to the animal test for recording the seizure ECoG of Long-Evan rats.

  15. In-plane silicon probes for simultaneous neural recording and drug delivery

    International Nuclear Information System (INIS)

    Seidl, K; Herwik, S; Paul, O; Ruther, P; Spieth, S; Zengerle, R; Steigert, J

    2010-01-01

    This paper reports on the design, fabrication and characterization of silicon-based microprobes for simultaneous neural recording and drug delivery. The fabrication technology is based on two-stage deep reactive ion etching combined with silicon wafer bonding and grinding to realize channel structures integrated in needle-like probe shafts. Liquids can be supplied to microfluidic devices via in-plane and out-of-plane ports. The liquid is dispensed at circular out-of-plane ports with a diameter of 25 µm and rectangular in-plane ports with dimensions of 50 × 50 µm 2 . Two-shaft probes with a pitch between shafts of 1.0 and 1.5 mm were realized. The probe shafts have a length of 8 mm and rectangular cross-sections of w × h (w = 250 µm and h = 200 or 250 µm). Each shaft contains one or two fluidic channels with a cross-section of 50 × 50 µm 2 . In addition, each probe shaft comprises four recording sites with diameters of 20 µm close to the outlet ports. Mechanical and fluidic characterization demonstrated the functionality of the probes. Typical infusion rates of 1.5 µL min −1 are achieved at a differential pressure of 1 kPa. The Pt-gray electrodes have an average electrode impedance of 260 ± 59 kΩ at 1 kHz

  16. 77 FR 16569 - Privacy Act of 1974: Systems of Records

    Science.gov (United States)

    2012-03-21

    ... two existing systems of records. The two existing systems of records are ``Administrative Audit System... existing systems of records, ``Administrative Audit System (SEC-14)'' and ``Fitness Center Membership... Audit System (SEC-14) records are used to ensure that all obligations and expenditures other than those...

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

    International Nuclear Information System (INIS)

    Whiteson, R.; Howell, J.A.

    1992-01-01

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

  18. Representation of neural networks as Lotka-Volterra systems

    International Nuclear Information System (INIS)

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

    1999-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Rami J Oweis

    2015-04-01

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

  20. Estimation of monthly wind power outputs of WECS with limited record period using artificial neural networks

    International Nuclear Information System (INIS)

    Tu, Yi-Long; Chang, Tsang-Jung; Chen, Cheng-Lung; Chang, Yu-Jung

    2012-01-01

    Highlights: ► ANN with short record training data is used to estimate power outputs in an existing station. ► The suitable numbers/parameters of input neurons for ANN are presented. ► Current wind speeds and previous power outputs are the most important input neurons. ► Choosing suitable input parameters is more important than choosing multiple parameters. - Abstract: For the brand new wind power industry, online recordings of wind power data are always in a relatively limited period. The aim of the study is to investigate the suitable numbers/parameters of input neurons for artificial neural networks under a short record of measured data. Measured wind speeds, wind directions (yaw angles) and power outputs with 10-min resolution at an existing wind power station, located at Jhongtun, Taiwan, are integrated to form three types of input neuron numbers and sixteen cases of input neurons. The first-10 days of each month in 2006 are used for data training to simulate the following 20-day power generation of the same month. The performance of various input neuron cases is evaluated. The simulated results show that using the first 10-day training data with adequate input neurons can estimate energy outputs well except the weak wind regime (May, June, and July). Among the input neuron parameters used, current wind speeds V(t) and previous power outputs P(t − 1) are the most important. Individually using one of them into input neurons can only provide satisfactory estimation. However, simultaneously using these two parameters into input neurons can give the best estimation. Thus, choosing suitable input parameters is more important than choosing multiple parameters.

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

    Science.gov (United States)

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

    2011-02-01

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

  2. Privacy Act System of Records: EPA Telecommunications Detail Records, EPA-32

    Science.gov (United States)

    Learn more about the EPA Telecommunications Detail Records System, including who is covered in the system, the purpose of data collection, routine uses for the system's records, and other security procedures.

  3. Privacy Act System of Records: Employee Counseling and Assistance Program Records, EPA-27

    Science.gov (United States)

    Learn about the Employee Counseling and Assistance Program Records System, including who is covered in the system, the purpose of data collection, routine uses for the system's records, and other security procedures.

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

    DEFF Research Database (Denmark)

    Sørensen, O.

    1997-01-01

    a Gauss-Newton search direction is applied. 3) Amongst numerous model types, often met in control applications, only the Non-linear ARMAX (NARMAX) model, representing input/output description, is examined. A simulated example confirms that a neural network has the potential to perform excellent System......The intention of this paper is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: 1) Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. 2) Amongst numerous training algorithms, only the Recursive Prediction Error Method using...

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

    International Nuclear Information System (INIS)

    Ciftcioglu, Oe.

    1996-03-01

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

  6. Thermal photovoltaic solar integrated system analysis using neural networks

    Energy Technology Data Exchange (ETDEWEB)

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

    2007-07-01

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

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

    Science.gov (United States)

    Patino, H D; Liu, D

    2000-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

    Lapedes, A.; Farber, R.

    1987-06-01

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

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

    Science.gov (United States)

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

    2016-07-05

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

  10. An integrated multichannel neural recording analog front-end ASIC with area-efficient driven right leg circuit.

    Science.gov (United States)

    Tao Tang; Wang Ling Goh; Lei Yao; Jia Hao Cheong; Yuan Gao

    2017-07-01

    This paper describes an integrated multichannel neural recording analog front end (AFE) with a novel area-efficient driven right leg (DRL) circuit to improve the system common mode rejection ratio (CMRR). The proposed AFE consists of an AC-coupled low-noise programmable-gain amplifier, an area-efficient DRL block and a 10-bit SAR ADC. Compared to conventional DRL circuit, the proposed capacitor-less DRL design achieves 90% chip area reduction with enhanced CMRR performance, making it ideal for multichannel biomedical recording applications. The AFE circuit has been designed in a standard 0.18-μm CMOS process. Post-layout simulation results show that the AFE provides two gain settings of 54dB/60dB while consuming 1 μA per channel under a supply voltage of 1 V. The input-referred noise of the AFE integrated from 1 Hz to 10k Hz is only 4 μVrms and the CMRR is 110 dB.

  11. 76 FR 52378 - Privacy Act; System of Records: State-76, Personal Services Contractor Records

    Science.gov (United States)

    2011-08-22

    ... Services Contractor Records SUMMARY: Notice is hereby given that the Department of State proposes to create a new system of records, Personal Services Contractor Records, State-76, pursuant to the provisions... July 20, 2011. It is proposed that the new system be named ``Personal Services Contractor Records.'' It...

  12. 77 FR 27756 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2012-05-11

    ...) deletes one system of records from its existing inventory of systems of records subject to the Privacy Act... inventory of record systems subject to the Privacy Act of 1974 (5 U.S.C. 552a), as amended. The deletion is... following system of records is deleted: 1. (18-04-03) ED Web Personalization Pilot Data Collection, 66 FR...

  13. 76 FR 114 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2011-01-03

    ...; System of Records AGENCY: Defense Contract Audit Agency, DoD. ACTION: Notice to amend a system of records. SUMMARY: The Defense Contract Audit Agency is proposing to amend a system of records notice in its... INFORMATION: The Defense Contract Audit Agency systems of records notices subject to the Privacy Act of 1974...

  14. 78 FR 14284 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2013-03-05

    ...; System of Records AGENCY: Defense Contract Audit Agency, DoD. ACTION: Notice to alter a System of Records. SUMMARY: The Defense Contract Audit Agency proposes to alter a system of records in its inventory of.... SUPPLEMENTARY INFORMATION: The Defense Contract Audit Agency notices for systems of records subject to the...

  15. 77 FR 75621 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2012-12-21

    ...; System of Records AGENCY: Defense Contract Audit Agency, DoD. ACTION: Notice to amend a System of Records. SUMMARY: The Defense Contract Audit Agency is amending a system of records notice in its existing.... SUPPLEMENTARY INFORMATION: The Defense Contract Audit Agency systems of records notices subject to the Privacy...

  16. 76 FR 115 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2011-01-03

    ...; System of Records AGENCY: Defense Contract Audit Agency, DoD. ACTION: Notice to amend a system of records. SUMMARY: The Defense Contract Audit Agency is proposing to amend a system of records notice in its... INFORMATION: The Defense Contract Audit Agency systems of records notices subject to the Privacy Act of 1974...

  17. 77 FR 77048 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2012-12-31

    ...; System of Records AGENCY: Defense Contract Audit Agency, DoD. ACTION: Notice to amend a System of Records. SUMMARY: The Defense Contract Audit Agency is amending a system of records notice in its existing.... SUPPLEMENTARY INFORMATION: The Defense Contract Audit Agency systems of records notices subject to the Privacy...

  18. 78 FR 14282 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2013-03-05

    ...; System of Records AGENCY: Defense Contract Audit Agency, DoD. ACTION: Notice to alter a System of Records. SUMMARY: The Defense Contract Audit Agency proposes to alter a system of records in its inventory of.... SUPPLEMENTARY INFORMATION: The Defense Contract Audit Agency notices for systems of records subject to the...

  19. 77 FR 65939 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2012-10-31

    ...) is amending the system of records currently entitled ``Veterans Health Information Systems and... Health Information Systems and Technology Architecture (VistA) Records-VA ROUTINE USES OF RECORDS... DEPARTMENT OF VETERANS AFFAIRS Privacy Act of 1974; System of Records AGENCY: Department of...

  20. 77 FR 3238 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2012-01-23

    ... Logistics Agency Primary Level Field Activities. Addresses may be obtained from the System manager below...; System of Records AGENCY: Defense Logistics Agency, DoD. ACTION: Notice To Amend a System of Records. SUMMARY: The Defense Logistics Agency is proposing to amend a system of records in its inventory of record...

  1. 78 FR 6078 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2013-01-29

    ...; System of Records AGENCY: Defense Finance and Accounting Service, DoD. ACTION: Notice to delete a system of records. SUMMARY: The Defense Finance and Accounting Service is deleting a system of records.... SUPPLEMENTARY INFORMATION: The Defense Finance and Accounting Service systems of records notices subject to the...

  2. 77 FR 65370 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2012-10-26

    ...; System of Records AGENCY: Defense Finance and Accounting Service, DoD. ACTION: Notice to delete a Systems of Records. SUMMARY: The Defense Finance and Accounting Service is deleting a system of records.... SUPPLEMENTARY INFORMATION: The Defense Finance and Accounting Service systems of records notices subject to the...

  3. 78 FR 27195 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2013-05-09

    ...; System of Records AGENCY: Defense Finance and Accounting Service, DoD. ACTION: Notice to delete two Systems of Records. SUMMARY: The Defense Finance and Accounting Service is deleting two systems of records...: The Defense Finance and Accounting Service systems of records notices subject to the Privacy Act of...

  4. 78 FR 27194 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2013-05-09

    ...; System of Records AGENCY: Defense Finance and Accounting Service, DoD. ACTION: Notice to delete two Systems of Records. SUMMARY: The Defense Finance and Accounting Service is deleting two systems of records...: The Defense Finance and Accounting Service systems of records notices subject to the Privacy Act of...

  5. 78 FR 69392 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2013-11-19

    ...; System of Records AGENCY: Defense Finance and Accounting Service, DoD. ACTION: Notice to delete a system of records. SUMMARY: The Defense Finance and Accounting Service is deleting a system of records... INFORMATION: The Defense Finance and Accounting Service systems of records notices subject to the Privacy Act...

  6. 77 FR 60400 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2012-10-03

    ...; System of Records AGENCY: Defense Finance and Accounting Service. ACTION: Notice to delete two systems of records. SUMMARY: The Defense Finance and Accounting Service is deleting two systems of records notices in.... SUPPLEMENTARY INFORMATION: The Defense Finance and Accounting Service systems of records notices subject to the...

  7. 78 FR 47309 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2013-08-05

    ...; System of Records AGENCY: Defense Finance and Accounting Service, DoD. ACTION: Notice to amend a System of Records. SUMMARY: The Defense Finance and Accounting Service proposes to alter a system of records... Defense Finance and Accounting Service systems of records notices subject to the Privacy Act of 1974 (5 U...

  8. Identification of input variables for feature based artificial neural networks-saccade detection in EOG recordings.

    Science.gov (United States)

    Tigges, P; Kathmann, N; Engel, R R

    1997-07-01

    Though artificial neural networks (ANN) are excellent tools for pattern recognition problems when signal to noise ratio is low, the identification of decision relevant features for ANN input data is still a crucial issue. The experience of the ANN designer and the existing knowledge and understanding of the problem seem to be the only links for a specific construction. In the present study a backpropagation ANN based on modified raw data inputs showed encouraging results. Investigating the specific influences of prototypical input patterns on a specially designed ANN led to a new sparse and efficient input data presentation. This data coding obtained by a semiautomatic procedure combining existing expert knowledge and the internal representation structures of the raw data based ANN yielded a list of feature vectors, each representing the relevant information for saccade identification. The feature based ANN produced a reduction of the error rate of nearly 40% compared with the raw data ANN. An overall correct classification of 92% of so far unknown data was realized. The proposed method of extracting internal ANN knowledge for the production of a better input data representation is not restricted to EOG recordings, and could be used in various fields of signal analysis.

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

    Science.gov (United States)

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

    2012-01-01

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

  10. Estimation of the non records logs from existing logs using artificial neural networks

    Directory of Open Access Journals (Sweden)

    Mehdi Mohammad Salehi

    2017-12-01

    Full Text Available Finding the information of the hydrocarbon reservoirs from well logs is one of the main objectives of the engineers. But, missing the log records (due to many reasons such as broken instruments, unsuitable borehole and etc. is a major challenge to achieve it. Prediction of the density and resistivity logs (Rt, DT and LLS from the conventional wire-line logs in one of the Iranian southwest oil fields is the main purpose of this study. Multilayer neural network was applied to develop an intelligent predictive model for prediction of the logs. A total of 3000 data sets from 3 wells (A, B and C of the studied field were used. Among them, the data of A, B and C wells were used to constructing and testing the model, respectively. To evaluate the performance of the model, the mean square error (MSE and correlation coefficient (R2 in the test data were calculated. A comparison between the MSE of the proposed model and recently intelligent models shows that the proposed model is more accurate than others. Acceptable accuracy and using conventional well logging data are the highlight advantages of the proposed intelligent model.

  11. 75 FR 81247 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2010-12-27

    ... Control Records system of records is also covered by the Defense Finance and Accounting System T7330a... Defense Finance and Accounting System T7332, Defense Debt Management System (February 17, 2009, 74 FR 7665...

  12. Adaptive Synchronization of Memristor-based Chaotic Neural Systems

    Directory of Open Access Journals (Sweden)

    Xiaofang Hu

    2014-11-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    1999-06-01

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

  14. Low-Gain, Low-Noise Integrated Neuronal Amplifier for Implantable Artifact-Reduction Recording System

    OpenAIRE

    Zbrzeski, Adeline; Lewis, Noëlle; Rummens, Francois; Jung, Ranu; N'Kaoua, Gilles; Benazzouz, Abdelhamid; Renaud, Sylvie

    2013-01-01

    Brain neuroprostheses for neuromodulation are being designed to monitor the neural activity of the brain in the vicinity of the region being stimulated using a single macro-electrode. Using a single macro-electrode, recent neuromodulation studies show that recording systems with a low gain neuronal amplifier and successive amplifier stages can reduce or reject stimulation artifacts. These systems were made with off-the-shelf components that are not amendable for future implant design. A low-g...

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

    Science.gov (United States)

    Uluyol, Onder

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

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

    Science.gov (United States)

    Lee, Andrew; Casasent, David

    1990-01-01

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

  17. 77 FR 65049 - Privacy Act; System of Records: Translator and Interpreter Records, State-37

    Science.gov (United States)

    2012-10-24

    ... DEPARTMENT OF STATE [Public Notice 8066] Privacy Act; System of Records: Translator and... an existing system of records, Translator and Interpreter Records, State-37, pursuant to the... INFORMATION: The Department of State proposes that the current system will retain the name ``Translator and...

  18. 77 FR 11063 - Privacy Act of 1974, System of Records

    Science.gov (United States)

    2012-02-24

    ... signature and encryption certificates for documents and email and to add biometric authentication... National Archives Records Administration--Schedules for the type of record being maintained. System manager...

  19. 28 CFR 16.51 - Security of systems of records.

    Science.gov (United States)

    2010-07-01

    ... 28 Judicial Administration 1 2010-07-01 2010-07-01 false Security of systems of records. 16.51... Security of systems of records. (a) Each component shall establish administrative and physical controls to prevent unauthorized access to its systems of records, to prevent unauthorized disclosure of records, and...

  20. 8 CFR 103.34 - Security of records systems.

    Science.gov (United States)

    2010-01-01

    ... 8 Aliens and Nationality 1 2010-01-01 2010-01-01 false Security of records systems. 103.34 Section 103.34 Aliens and Nationality DEPARTMENT OF HOMELAND SECURITY IMMIGRATION REGULATIONS POWERS AND DUTIES; AVAILABILITY OF RECORDS § 103.34 Security of records systems. The security of records systems...

  1. RIMS [Records Inventory Management System] Handbook

    International Nuclear Information System (INIS)

    1989-03-01

    The Records Inventory Management System (RIMS) is a computer library of abstracted documents relating to low-level radioactive waste. The documents are of interest to state governments, regional compacts, and the Department of Energy, especially as they relate to the Low-Level Radioactive Waste Policy Act requiring states or compacts of states to establish and operate waste disposal facilities. RIMS documents are primarily regulatory, policy, or technical documents, published by the various states and compacts of the United States; however, RIMS contains key international publications as well. The system has two sections: a document retrieval section and a document update section. The RIMS mainframe can be accessed through a PC or modem. Also, each state and compact may request a PC version of RIMS, which allows a user to enter documents off line and then upload the documents to the mainframe data base

  2. 75 FR 21260 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2010-04-23

    ... records in the system: Storage: Paper records and electronic storage media. Retrievability: Name, Social... include name, Social Security Number (SSN), enlisted service number, date of birth, rate, rank, record... and motivation, and other career related matters to meet [[Page 21261

  3. 75 FR 78688 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2010-12-16

    ... automated records maintained by Space and Warfare Systems Center Atlantic.'' * * * * * Retention and... Education and Training Records.'' * * * * * N07220-1 System name: Navy Standard Integrated Personnel System...), date of birth, education, training and qualifications, professional history, assignments, performance...

  4. 77 FR 15360 - Privacy Act of 1974; Systems of Records

    Science.gov (United States)

    2012-03-15

    ... a System of Records. SUMMARY: The National Security Agency (NSA) is amending a system of records... Officer, Department of Defense. GNSA 06 System Name: NSA/CSS Health, Medical and Safety Files (February 10... of the NSA/CSS compilation of system of records notices apply to this system.'' * * * * * Storage...

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

    DEFF Research Database (Denmark)

    Lehmann, Torsten

    1998-01-01

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

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

    Indian Academy of Sciences (India)

    2016-08-26

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

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

    African Journals Online (AJOL)

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

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

    Science.gov (United States)

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

    2011-01-01

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

  9. A CMOS frontend chip for implantable neural recording with wide voltage supply range

    International Nuclear Information System (INIS)

    Liu Jialin; Zhang Xu; Hu Xiaohui; Li Peng; Liu Ming; Chen Hongda; Guo Yatao; Li Bin

    2015-01-01

    A design for a CMOS frontend integrated circuit (chip) for neural signal acquisition working at wide voltage supply range is presented in this paper. The chip consists of a preamplifier, a serial instrumental amplifier (IA) and a cyclic analog-to-digital converter (CADC). The capacitive-coupled and capacitive-feedback topology combined with MOS-bipolar pseudo-resistor element is adopted in the preamplifier to create a −3 dB upper cut-off frequency less than 1 Hz without using a ponderous discrete device. A dual-amplifier instrumental amplifier is used to provide a low output impedance interface for ADC as well as to boost the gain. The preamplifier and the serial instrumental amplifier together provide a midband gain of 45.8 dB and have an input-referred noise of 6.7 μV rms integrated from 1 Hz to 5 kHz. The ADC digitizes the amplified signal at 12-bits precision with a highest sampling rate of 130 kS/s. The measured effective number of bits (ENOB) of the ADC is 8.7 bits. The entire circuit draws 165 to 216 μA current from the supply voltage varied from 1.34 to 3.3 V. The prototype chip is fabricated in the 0.18-μm CMOS process and occupies an area of 1.23 mm 2 (including pads). In-vitro recording was successfully carried out by the proposed frontend chip. (paper)

  10. A CMOS frontend chip for implantable neural recording with wide voltage supply range

    Science.gov (United States)

    Jialin, Liu; Xu, Zhang; Xiaohui, Hu; Yatao, Guo; Peng, Li; Ming, Liu; Bin, Li; Hongda, Chen

    2015-10-01

    A design for a CMOS frontend integrated circuit (chip) for neural signal acquisition working at wide voltage supply range is presented in this paper. The chip consists of a preamplifier, a serial instrumental amplifier (IA) and a cyclic analog-to-digital converter (CADC). The capacitive-coupled and capacitive-feedback topology combined with MOS-bipolar pseudo-resistor element is adopted in the preamplifier to create a -3 dB upper cut-off frequency less than 1 Hz without using a ponderous discrete device. A dual-amplifier instrumental amplifier is used to provide a low output impedance interface for ADC as well as to boost the gain. The preamplifier and the serial instrumental amplifier together provide a midband gain of 45.8 dB and have an input-referred noise of 6.7 μVrms integrated from 1 Hz to 5 kHz. The ADC digitizes the amplified signal at 12-bits precision with a highest sampling rate of 130 kS/s. The measured effective number of bits (ENOB) of the ADC is 8.7 bits. The entire circuit draws 165 to 216 μA current from the supply voltage varied from 1.34 to 3.3 V. The prototype chip is fabricated in the 0.18-μm CMOS process and occupies an area of 1.23 mm2 (including pads). In-vitro recording was successfully carried out by the proposed frontend chip. Project supported by the National Natural Science Foundation of China (Nos. 61474107, 61372060, 61335010, 61275200, 61178051) and the Key Program of the Chinese Academy of Sciences (No. KJZD-EW-L11-01).

  11. A stereo-compound hybrid microscope for combined intracellular and optical recording of invertebrate neural network activity

    OpenAIRE

    Frost, William N.; Wang, Jean; Brandon, Christopher J.

    2007-01-01

    Optical recording studies of invertebrate neural networks with voltage-sensitive dyes seldom employ conventional intracellular electrodes. This may in part be due to the traditional reliance on compound microscopes for such work. While such microscopes have high light-gathering power, they do not provide depth of field, making working with sharp electrodes difficult. Here we describe a hybrid microscope design, with switchable compound and stereo objectives, that eases the use of conventional...

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

    Science.gov (United States)

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

    2017-08-01

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

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

    International Nuclear Information System (INIS)

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

    2008-01-01

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

  14. 76 FR 76215 - Privacy Act; System of Records: State-78, Risk Analysis and Management Records

    Science.gov (United States)

    2011-12-06

    ... a system of records, Risk Analysis and Management Records, State-78, pursuant to the provisions of... INFORMATION: The Department of State proposes that the new system will be ``Risk Analysis and Management.... These standard routine uses apply to State-78, Risk Analysis and Management Records. POLICIES AND...

  15. 77 FR 15086 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2012-03-14

    ... Defense Intelligence Information System (DoDIIS) Customer Relationship Management System. The records will... instructions for submitting comments. * Mail: Federal Docket Management System Office, 4800 Mark Center Drive...

  16. Identifying digital records in business systems: the definition of a ...

    African Journals Online (AJOL)

    Before digital records can be preserved or managed, they need to be identified first. However, records identification is not a clearly defined process. Given the multi-faceted information system environment in organisations, large quantities of potential records are created and stored in systems not designed for records ...

  17. 75 FR 52513 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2010-08-26

    ... practices data, and employee health hazard educational data. Records include the name, Social Security... system of records notice. Note 2: Personal identity, diagnosis, prognosis of any patient maintained in... Records in the System: Storage: Paper records and electronic storage media. Retrievability: By individual...

  18. 75 FR 42719 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2010-07-22

    ... characteristics (such as name, last four digits of Social Security Number (SSN), grade, Unit Identification Code...) organization. CATEGORIES OF RECORDS IN THE SYSTEM: Name, last four digits of Social Security Number (SSN), duty... RECORDS IN THE SYSTEM: STORAGE: Records are stored on electronic storage media. RETRIEVABILITY: Records...

  19. 77 FR 58816 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2012-09-24

    ... obsolete. The records are now covered by the Defense Finance and Accounting Service system of records... Defense Finance and Accounting Service system of records notice, T-7206, Non-appropriated Funds Central... Cards (February 22, 1993, 58 FR 10562). Reason: These records are now covered by the Defense Finance and...

  20. 75 FR 65457 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2010-10-25

    ... number.'' CONTESTING RECORD PROCEDURES: Delete entry and replace with ``The NSA/CSS rules for contesting...' published at the beginning of the NSA/CSS's compilation of record systems also apply to this record system... number. CONTESTING RECORD PROCEDURES: The NSA/CSS rules for contesting contents and appealing initial...

  1. 75 FR 70342 - Privacy Act; System of Records: Equal Employment Opportunity Records

    Science.gov (United States)

    2010-11-17

    ... users are given cyber security awareness training which covers the procedures for handling Sensitive but.... State-09 SYSTEM NAME: Equal Employment Opportunity Records. SECURITY CLASSIFICATION: Unclassified... apply to the Equal Employment Opportunity Records, State-09. DISCLOSURE TO CONSUMER REPORTING AGENCIES...

  2. 29 CFR 1611.3 - Procedures for requests pertaining to individual records in a record system.

    Science.gov (United States)

    2010-07-01

    ... or be supplied with copies by mail. (b) Requests pertaining to records contained in a system of... Financial Disclosure Reports and Other Ethics Program Records), OGE/GOVT-2 (Confidential Statements of...

  3. 77 FR 77047 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2012-12-31

    ...; System of Records AGENCY: Defense Contract Audit Agency, DoD. ACTION: Notice to delete a Systems of Records. SUMMARY: The Defense Contract Audit Agency (DCAA) is deleting a system of records in its existing... at (703) 767-1022. SUPPLEMENTARY INFORMATION: The Defense Contract Audit Agency (DCAA) systems of...

  4. 75 FR 10473 - Privacy Act of 1974; Systems of Records

    Science.gov (United States)

    2010-03-08

    ... testing, or permanent implementation, as applicable. System manager(s) and address: Defense Logistics...; Systems of Records AGENCY: Defense Logistics Agency, DoD. ACTION: Notice to amend a system of records. SUMMARY: The Defense Logistics Agency proposes to amend a system of records notice in its existing...

  5. 76 FR 28002 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2011-05-13

    ... determination is made. System manager(s) and address: Personnel Security Specialists, Defense Logistics Agency...; System of Records AGENCY: Defense Logistics Agency, DoD. ACTION: Notice to Amend Two Systems of Records. SUMMARY: The Defense Logistics Agency is proposing to amend two systems of records notices in its existing...

  6. 76 FR 39390 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2011-07-06

    .... System manager(s) and address: Director, DLA Human Resources, Headquarters, Defense Logistics Agency...; System of Records AGENCY: Defense Logistics Agency, DoD. ACTION: Notice to amend a system of records. SUMMARY: The Defense Logistics Agency is proposing to amend a system of records notice in its existing...

  7. 76 FR 73602 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2011-11-29

    ....'' * * * * * System manager(s) and address: Delete entry and replace with ``Director, Defense Logistics Agency Human...; System of Records AGENCY: Defense Logistics Agency, Department of Defense (DoD). ACTION: Notice to amend a system of records. SUMMARY: The Defense Logistics Agency is proposing to amend a system of records...

  8. 78 FR 33807 - Privacy Act New System of Records

    Science.gov (United States)

    2013-06-05

    ... DEPARTMENT OF COMMERCE [Docket No. 130520483-3483-01] Privacy Act New System of Records AGENCY... Department's proposal for a new system of records under the Privacy Act. The system is entitled ``Information... Department is creating a new system of records that will enable electronic registration, via the Internet...

  9. 77 FR 5313 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2012-02-02

    ... Affairs (VA) is amending the system of records entitled ``Alternative Dispute Resolution Tracking System... records entitled ``Alternative Dispute Resolution Tracking System-VA'' (116VA09) in 67 FR 49392-49395 (July 30, 2002). The system of records tracked alternative dispute resolution (ADR) activity within VA...

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

    Science.gov (United States)

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

    2016-08-01

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

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

    Science.gov (United States)

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

    2016-11-14

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

  12. Three neural network based sensor systems for environmental monitoring

    International Nuclear Information System (INIS)

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

    1994-05-01

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

  13. Neural multigrid for gauge theories and other disordered systems

    International Nuclear Information System (INIS)

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

    1992-09-01

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

  14. Neural computing thermal comfort index for HVAC systems

    International Nuclear Information System (INIS)

    Atthajariyakul, S.; Leephakpreeda, T.

    2005-01-01

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

  15. 75 FR 26201 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2010-05-11

    ... National Defense Intelligence College. Categories of records in the system: Name, date of birth and Social... States Commission on Higher Education Association Characteristics of Excellence in Higher Education... system: Storage: Electronic storage media. Retrievability: By last name. Safeguards: Records are...

  16. 78 FR 14287 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2013-03-05

    ... by Defense Finance and Accounting Service (DFAS) and is covered by DFAS Systems of Records Notice T5500b, Integrated Garnishment System (IGS) (September 19, 2012, 77 FR 58106). Paper records previously...

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

    Institute of Scientific and Technical Information of China (English)

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

    2003-01-01

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

  18. A wireless multi-channel recording system for freely behaving mice and rats.

    Science.gov (United States)

    Fan, David; Rich, Dylan; Holtzman, Tahl; Ruther, Patrick; Dalley, Jeffrey W; Lopez, Alberto; Rossi, Mark A; Barter, Joseph W; Salas-Meza, Daniel; Herwik, Stanislav; Holzhammer, Tobias; Morizio, James; Yin, Henry H

    2011-01-01

    To understand the neural basis of behavior, it is necessary to record brain activity in freely moving animals. Advances in implantable multi-electrode array technology have enabled researchers to record the activity of neuronal ensembles from multiple brain regions. The full potential of this approach is currently limited by reliance on cable tethers, with bundles of wires connecting the implanted electrodes to the data acquisition system while impeding the natural behavior of the animal. To overcome these limitations, here we introduce a multi-channel wireless headstage system designed for small animals such as rats and mice. A variety of single unit and local field potential signals were recorded from the dorsal striatum and substantia nigra in mice and the ventral striatum and prefrontal cortex simultaneously in rats. This wireless system could be interfaced with commercially available data acquisition systems, and the signals obtained were comparable in quality to those acquired using cable tethers. On account of its small size, light weight, and rechargeable battery, this wireless headstage system is suitable for studying the neural basis of natural behavior, eliminating the need for wires, commutators, and other limitations associated with traditional tethered recording systems.

  19. A wireless multi-channel recording system for freely behaving mice and rats.

    Directory of Open Access Journals (Sweden)

    David Fan

    Full Text Available To understand the neural basis of behavior, it is necessary to record brain activity in freely moving animals. Advances in implantable multi-electrode array technology have enabled researchers to record the activity of neuronal ensembles from multiple brain regions. The full potential of this approach is currently limited by reliance on cable tethers, with bundles of wires connecting the implanted electrodes to the data acquisition system while impeding the natural behavior of the animal. To overcome these limitations, here we introduce a multi-channel wireless headstage system designed for small animals such as rats and mice. A variety of single unit and local field potential signals were recorded from the dorsal striatum and substantia nigra in mice and the ventral striatum and prefrontal cortex simultaneously in rats. This wireless system could be interfaced with commercially available data acquisition systems, and the signals obtained were comparable in quality to those acquired using cable tethers. On account of its small size, light weight, and rechargeable battery, this wireless headstage system is suitable for studying the neural basis of natural behavior, eliminating the need for wires, commutators, and other limitations associated with traditional tethered recording systems.

  20. Parameter estimation in space systems using recurrent neural networks

    Science.gov (United States)

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

    1991-01-01

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

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

    International Nuclear Information System (INIS)

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

    1997-01-01

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

  2. 75 FR 35028 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2010-06-21

    ... FEDERAL HOUSING FINANCE AGENCY [No. 2010-N-07] Privacy Act of 1974; System of Records AGENCY: Federal Housing Finance Agency. ACTION: Notice of the establishment of a new system of records. SUMMARY: The Federal Housing Finance Agency (FHFA) is revising the proposed system of records notice that was...

  3. 28 CFR 700.24 - Security of systems of records.

    Science.gov (United States)

    2010-07-01

    ... 28 Judicial Administration 2 2010-07-01 2010-07-01 false Security of systems of records. 700.24... Records Under the Privacy Act of 1974 § 700.24 Security of systems of records. (a) The Office Administrator or Security Officer shall be responsible for issuing regulations governing the security of systems...

  4. 13 CFR 102.33 - Security of systems of records.

    Science.gov (United States)

    2010-01-01

    ... 13 Business Credit and Assistance 1 2010-01-01 2010-01-01 false Security of systems of records....33 Security of systems of records. (a) Each Program/Support Office Head or designee shall establish administrative and physical controls to prevent unauthorized access to its systems of records, to prevent...

  5. 10 CFR 1705.03 - Systems of records notification.

    Science.gov (United States)

    2010-01-01

    ... Energy DEFENSE NUCLEAR FACILITIES SAFETY BOARD PRIVACY ACT § 1705.03 Systems of records notification. (a) Public notice. The Board has published in the Federal Register its systems of records. The Office of the Federal Register biennially compiles and publishes all systems of records maintained by all Federal...

  6. 75 FR 34709 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2010-06-18

    ... government and the public. When not considered mandatory, patient identification data shall be eliminated...; System of Records AGENCY: Department of the Air Force, DoD. ACTION: Notice to amend a system of records. SUMMARY: The Department of the Air Force is proposing to amend a system of records notice in its existing...

  7. 78 FR 41916 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2013-07-12

    ... Defense Finance and Accounting Service, Freedom of Information/Privacy Act Program Manager, Corporate...; System of Records AGENCY: Defense Finance and Accounting Service, DoD. ACTION: Notice to alter a System of Records. SUMMARY: The Defense Finance and Accounting Service proposes to alter a system of records...

  8. 78 FR 37799 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2013-06-24

    ... Finance and Accounting Service, Freedom of Information/Privacy Act Program Manager, Corporate...; System of Records AGENCY: Defense Finance and Accounting Service, DoD. ACTION: Notice to amend a System of Records. SUMMARY: The Defense Finance and Accounting Service is amending a system of records...

  9. 77 FR 58106 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2012-09-19

    ... Finance and Accounting Service, Freedom of Information/Privacy Act Program Manager, Corporate...; System of Records AGENCY: Defense Finance and Accounting Service, DoD. ACTION: Notice to amend two Systems of Records. SUMMARY: The Defense Finance and Accounting Service is amending two systems of records...

  10. 78 FR 52518 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2013-08-23

    ... Finance and Accounting Service, Freedom of Information/Privacy Act Program Manager, Corporate...; System of Records AGENCY: Defense Finance and Accounting Service, DoD. ACTION: Notice to amend a System of Records. SUMMARY: The Defense Finance and Accounting Service is amending a system of records...

  11. 77 FR 69444 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2012-11-19

    ..., Defense Finance and Accounting Service, Freedom of Information/Privacy Act Program Manager, Corporate...; System of Records AGENCY: Defense Finance and Accounting Service, DoD. ACTION: Notice to amend a system of records. SUMMARY: The Defense Finance and Accounting Service is amending a system of records...

  12. 76 FR 46756 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2011-08-03

    ...; System of Records AGENCY: Missile Defense Agency, Department of Defense (DoD). ACTION: Notice to Delete a System of Records. SUMMARY: The Missile Defense Agency proposes to delete a system of records notice in.... Peter Shearston, Missile Defense Agency, MDA/DXCM, 730 Irwin Ave, Schriever AFB, CO 80912-2101, or by...

  13. 78 FR 41917 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2013-07-12

    ...; System of Records AGENCY: Defense Finance and Accounting Service, DoD. ACTION: Notice to amend a System of Records. SUMMARY: The Defense Finance and Accounting Service is amending a system of records...: Mr. Gregory Outlaw, (317) 510-4591. SUPPLEMENTARY INFORMATION: The Defense Finance and Accounting...

  14. 77 FR 72336 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2012-12-05

    ....regulations.gov . Follow the instructions for submitting comments. Mail: Federal Docket Management System... CFSC System name: Army Club Membership Files (June 21, 2001, 66 FR 33239). Reason: The records were..., Army Club Membership Files system of records notice can be deleted. Records have met the required...

  15. Implementation of an Electronic Medical Records System

    National Research Council Canada - National Science Library

    Fletcher, Chadwick B

    2008-01-01

    .... Substantial benefits are realized through routine use of electronic medical records include improved quality, safety, and efficiency, along with the increased ability to conduct education and research...

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

    Science.gov (United States)

    Mi, Yuanyuan; Lin, Xiaohan; Wu, Si

    2016-01-01

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

  17. Statistical Physics of Neural Systems with Nonadditive Dendritic Coupling

    Directory of Open Access Journals (Sweden)

    David Breuer

    2014-03-01

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

  18. 76 FR 35483 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2011-06-17

    ... Records: 500.050 HSPD-12: Identity Management System is being established to support implementation of...: USPS 500.050 SYSTEM NAME: HSPD-12: Identity Management System (IDMS). Accordingly, the Postal Service... Management System (IDMS). SYSTEM LOCATION: Records relating to the Identity Management System are maintained...

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

    International Nuclear Information System (INIS)

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

    1992-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Biaobiao Zhang

    2011-01-01

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

  1. Neural Mechanisms and Information Processing in Recognition Systems

    Directory of Open Access Journals (Sweden)

    Mamiko Ozaki

    2014-10-01

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

  2. 76 FR 43278 - Privacy Act; System of Records

    Science.gov (United States)

    2011-07-20

    ... the Privacy Act of 1974, 5 U.S.C. 552a, to publish a description of the systems of records containing... locked file cabinets, and electronic records are maintained on a password-protected desktop personal...'' published by the National Archives and Records Administration, Washington, DC. Paper records are shredded...

  3. Integration of silicon-based neural probes and micro-drive arrays for chronic recording of large populations of neurons in behaving animals.

    Science.gov (United States)

    Michon, Frédéric; Aarts, Arno; Holzhammer, Tobias; Ruther, Patrick; Borghs, Gustaaf; McNaughton, Bruce; Kloosterman, Fabian

    2016-08-01

    Understanding how neuronal assemblies underlie cognitive function is a fundamental question in system neuroscience. It poses the technical challenge to monitor the activity of populations of neurons, potentially widely separated, in relation to behaviour. In this paper, we present a new system which aims at simultaneously recording from a large population of neurons from multiple separated brain regions in freely behaving animals. The concept of the new device is to combine the benefits of two existing electrophysiological techniques, i.e. the flexibility and modularity of micro-drive arrays and the high sampling ability of electrode-dense silicon probes. Newly engineered long bendable silicon probes were integrated into a micro-drive array. The resulting device can carry up to 16 independently movable silicon probes, each carrying 16 recording sites. Populations of neurons were recorded simultaneously in multiple cortical and/or hippocampal sites in two freely behaving implanted rats. Current approaches to monitor neuronal activity either allow to flexibly record from multiple widely separated brain regions (micro-drive arrays) but with a limited sampling density or to provide denser sampling at the expense of a flexible placement in multiple brain regions (neural probes). By combining these two approaches and their benefits, we present an alternative solution for flexible and simultaneous recordings from widely distributed populations of neurons in freely behaving rats.

  4. 77 FR 65913 - Privacy Act of 1974: Systems of Records.

    Science.gov (United States)

    2012-10-31

    ... follows: SEC-66 SYSTEM NAME: Backup Care Employee and Family Records. SYSTEM LOCATION: Bright Horizons... Securities Rulemaking Board; the Securities Investor Protection Corporation; the Public Company Accounting...

  5. A stereo-compound hybrid microscope for combined intracellular and optical recording of invertebrate neural network activity.

    Science.gov (United States)

    Frost, William N; Wang, Jean; Brandon, Christopher J

    2007-05-15

    Optical recording studies of invertebrate neural networks with voltage-sensitive dyes seldom employ conventional intracellular electrodes. This may in part be due to the traditional reliance on compound microscopes for such work. While such microscopes have high light-gathering power, they do not provide depth of field, making working with sharp electrodes difficult. Here we describe a hybrid microscope design, with switchable compound and stereo objectives, that eases the use of conventional intracellular electrodes in optical recording experiments. We use it, in combination with a voltage-sensitive dye and photodiode array, to identify neurons participating in the swim motor program of the marine mollusk Tritonia. This microscope design should be applicable to optical recording studies in many preparations.

  6. Nonlinear dynamical system approaches towards neural prosthesis

    International Nuclear Information System (INIS)

    Torikai, Hiroyuki; Hashimoto, Sho

    2011-01-01

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

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

    Science.gov (United States)

    2010-09-24

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

  8. Sympathetic neural modulation of the immune system

    International Nuclear Information System (INIS)

    Madden, K.S.

    1989-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Bahita Mohamed

    2011-01-01

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

  10. 78 FR 14276 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2013-03-05

    ...; System of Records AGENCY: United States Marine Corps, DoD. ACTION: Notice to delete twenty-three Systems... Management Information System Records, NM01560-2 Department of Defense Voluntary Education System, and...; NM01560-2 Department of Defense Voluntary Education System, and NM01500-2 Department of the Navy Education...

  11. Open Ephys electroencephalography (Open Ephys  +  EEG): a modular, low-cost, open-source solution to human neural recording.

    Science.gov (United States)

    Black, Christopher; Voigts, Jakob; Agrawal, Uday; Ladow, Max; Santoyo, Juan; Moore, Christopher; Jones, Stephanie

    2017-06-01

    Electroencephalography (EEG) offers a unique opportunity to study human neural activity non-invasively with millisecond resolution using minimal equipment in or outside of a lab setting. EEG can be combined with a number of techniques for closed-loop experiments, where external devices are driven by specific neural signals. However, reliable, commercially available EEG systems are expensive, often making them impractical for individual use and research development. Moreover, by design, a majority of these systems cannot be easily altered to the specification needed by the end user. We focused on mitigating these issues by implementing open-source tools to develop a new EEG platform to drive down research costs and promote collaboration and innovation. Here, we present methods to expand the open-source electrophysiology system, Open Ephys (www.openephys.org), to include human EEG recordings. We describe the equipment and protocol necessary to interface various EEG caps with the Open Ephys acquisition board, and detail methods for processing data. We present applications of Open Ephys  +  EEG as a research tool and discuss how this innovative EEG technology lays a framework for improved closed-loop paradigms and novel brain-computer interface experiments. The Open Ephys  +  EEG system can record reliable human EEG data, as well as human EMG data. A side-by-side comparison of eyes closed 8-14 Hz activity between the Open Ephys  +  EEG system and the Brainvision ActiCHamp EEG system showed similar average power and signal to noise. Open Ephys  +  EEG enables users to acquire high-quality human EEG data comparable to that of commercially available systems, while maintaining the price point and extensibility inherent to open-source systems.

  12. Open Ephys electroencephalography (Open Ephys  +  EEG): a modular, low-cost, open-source solution to human neural recording

    Science.gov (United States)

    Black, Christopher; Voigts, Jakob; Agrawal, Uday; Ladow, Max; Santoyo, Juan; Moore, Christopher; Jones, Stephanie

    2017-06-01

    Objective. Electroencephalography (EEG) offers a unique opportunity to study human neural activity non-invasively with millisecond resolution using minimal equipment in or outside of a lab setting. EEG can be combined with a number of techniques for closed-loop experiments, where external devices are driven by specific neural signals. However, reliable, commercially available EEG systems are expensive, often making them impractical for individual use and research development. Moreover, by design, a majority of these systems cannot be easily altered to the specification needed by the end user. We focused on mitigating these issues by implementing open-source tools to develop a new EEG platform to drive down research costs and promote collaboration and innovation. Approach. Here, we present methods to expand the open-source electrophysiology system, Open Ephys (www.openephys.org), to include human EEG recordings. We describe the equipment and protocol necessary to interface various EEG caps with the Open Ephys acquisition board, and detail methods for processing data. We present applications of Open Ephys  +  EEG as a research tool and discuss how this innovative EEG technology lays a framework for improved closed-loop paradigms and novel brain-computer interface experiments. Main results. The Open Ephys  +  EEG system can record reliable human EEG data, as well as human EMG data. A side-by-side comparison of eyes closed 8-14 Hz activity between the Open Ephys  +  EEG system and the Brainvision ActiCHamp EEG system showed similar average power and signal to noise. Significance. Open Ephys  +  EEG enables users to acquire high-quality human EEG data comparable to that of commercially available systems, while maintaining the price point and extensibility inherent to open-source systems.

  13. Fuzzy-Neural Automatic Daylight Control System

    Directory of Open Access Journals (Sweden)

    Grif H. Şt.

    2011-12-01

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

  14. 6 CFR 5.31 - Security of systems of records.

    Science.gov (United States)

    2010-01-01

    ... 6 Domestic Security 1 2010-01-01 2010-01-01 false Security of systems of records. 5.31 Section 5.31 Domestic Security DEPARTMENT OF HOMELAND SECURITY, OFFICE OF THE SECRETARY DISCLOSURE OF RECORDS AND INFORMATION Privacy Act § 5.31 Security of systems of records. (a) In general. Each component...

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

    International Nuclear Information System (INIS)

    Habib, Aziz; Alsieidi, Ragab; Youssef, Ghada

    2009-01-01

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

  16. Neural networks for feedback feedforward nonlinear control systems.

    Science.gov (United States)

    Parisini, T; Zoppoli, R

    1994-01-01

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

  17. Dynamics of a neural system with a multiscale architecture

    Science.gov (United States)

    Breakspear, Michael; Stam, Cornelis J

    2005-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Chien-Sheng Chen

    2012-03-01

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

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

    Science.gov (United States)

    Chen, Chien-Sheng

    2012-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Mihail V. Egorchev

    2017-12-01

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

  1. 76 FR 76956 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2011-12-09

    ... manager. Record source categories: Individual, social workers, rehabilitation counselors, and/or health... mental impairments and applicants for employment with Washington Headquarters Services/Human Resources... of records in the system: Storage: Paper file folders and electronic storage media. Retrievability...

  2. 75 FR 43494 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2010-07-26

    ... Register Liaison Officer, Department of Defense. GNSA 01 System name: NSA/CSS Access, Authority and Release... records are covered by GNSA 11, NSA/CSS Key Accountability Records, GNSA 10, NSA/CSS Personnel Security...

  3. Coding and signal processing for magnetic recording systems

    CERN Document Server

    Vasic, Bane

    2004-01-01

    RECORDING SYSTEMSA BriefHistory of Magnetic Storage, Dean PalmerPhysics of Longitudinal and Perpendicular Recording, Hong Zhou, Tom Roscamp, Roy Gustafson, Eric Boernern, and Roy ChantrellThe Physics of Optical Recording, William A. Challener and Terry W. McDanielHead Design Techniques for Recording Devices, Robert E. RottmayerCOMMUNICATION AND INFORMATION THEORY OF MAGNETIC RECORDING CHANNELSModeling the Recording Channel, Jaekyun MoonSignal and Noise Generation for Magnetic Recording Channel Simulations, Xueshi Yang and Erozan M. KurtasStatistical Analysis of Digital Signals and Systems, Dra

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

    International Nuclear Information System (INIS)

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

    2004-01-01

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

  5. 76 FR 3115 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2011-01-19

    ... Integrated Results and Statistical Tracking.'' System Location: Delete entry and replace with ``Headquarters... replace with ``Records are accessed by person(s) responsible for servicing the record system in... need-to-know. The system additionally incorporates integrated system security features to protect data...

  6. 77 FR 41774 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2012-07-16

    ... DEPARTMENT OF EDUCATION Privacy Act of 1974; System of Records AGENCY: Office of Management...-19176 (April 12, 2004). Electronic Access to This Document: The official version of this document is the... Assistant Secretary of the Office of Management deletes the following system of records: System No. System...

  7. 76 FR 46767 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2011-08-03

    ...: The Department of the Army proposes to add a system of records to its inventory of record systems... provides a student management system that integrates Web-enabled courseware to support online certification... DEPARTMENT OF DEFENSE Department of the Army [Docket ID USA-2011-0019] Privacy Act of 1974; System...

  8. 75 FR 21248 - Privacy Act of 1974; Systems of Records

    Science.gov (United States)

    2010-04-23

    ...; Systems of Records AGENCY: Defense Finance and Accounting Service, DoD. ACTION: Notice to alter a system of records. SUMMARY: The Defense Finance and Accounting Service (DFAS) is proposing to alter a system...) 522-5225. SUPPLEMENTARY INFORMATION: The Defense Finance and Accounting Service notices for systems of...

  9. 77 FR 60401 - Privacy Act of 1974; Systems of Records

    Science.gov (United States)

    2012-10-03

    ... computer password protection.'' * * * * * System manager(s) and address: Delete entry and replace with...; Systems of Records AGENCY: National Security Agency/Central Security Service, DoD. ACTION: Notice to amend a system of records. SUMMARY: The National Security Agency (NSA) is proposing to amend a system of...

  10. 75 FR 36642 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2010-06-28

    ... Defense. GNSA 11 System name: NSA/CSS Key Accountability Records System location: National Security Agency... system: NSA/CSS civilian employees, personnel under contract or appointment and military assignees... `Blanket Routine Uses' set forth at the beginning of the NSA/CSS' compilation of systems of records notices...

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

    CERN Document Server

    Liu, Jinkun

    2013-01-01

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

  12. In Vivo Recording of Neural and Behavioral Correlates of Anesthesia Induction, Reversal, and Euthanasia in Cephalopod Molluscs

    Directory of Open Access Journals (Sweden)

    Hanna M. Butler-Struben

    2018-02-01

    Full Text Available Cephalopod molluscs are among the most behaviorally and neurologically complex invertebrates. As they are now included in research animal welfare regulations in many countries, humane and effective anesthesia is required during invasive procedures. However, currently there is no evidence that agents believed to act as anesthetics produce effects beyond immobility. In this study we demonstrate, for the first time, that two of the most commonly used agents in cephalopod general anesthesia, magnesium chloride and ethanol, are capable of producing strong and reversible blockade of afferent and efferent neural signal; thus they are genuine anesthetics, rather than simply sedating agents that render animals immobile but not insensible. Additionally, we demonstrate that injected magnesium chloride and lidocaine are effective local anesthetic agents. This represents a considerable advance for cephalopod welfare. Using a reversible, minimally invasive recording procedure, we measured activity in the pallial nerve of cuttlefish (Sepia bandensis and octopus (Abdopus aculeatus, Octopus bocki, during induction and reversal for five putative general anesthetic and two local anesthetic agents. We describe the temporal relationship between loss of behavioral responses (immobility, loss of efferent neural signal (loss of “consciousness” and loss of afferent neural signal (anesthesia for general anesthesia, and loss of afferent signal for local anesthesia. Both ethanol and magnesium chloride were effective as bath-applied general anesthetics, causing immobility, complete loss of behavioral responsiveness and complete loss of afferent and efferent neural signal. Cold seawater, diethyl ether, and MS-222 (tricaine were ineffective. Subcutaneous injection of either lidocaine or magnesium chloride blocked behavioral and neural responses to pinch in the injected area, and we conclude that both are effective local anesthetic agents for cephalopods. Lastly, we

  13. In Vivo Recording of Neural and Behavioral Correlates of Anesthesia Induction, Reversal, and Euthanasia in Cephalopod Molluscs.

    Science.gov (United States)

    Butler-Struben, Hanna M; Brophy, Samantha M; Johnson, Nasira A; Crook, Robyn J

    2018-01-01

    Cephalopod molluscs are among the most behaviorally and neurologically complex invertebrates. As they are now included in research animal welfare regulations in many countries, humane and effective anesthesia is required during invasive procedures. However, currently there is no evidence that agents believed to act as anesthetics produce effects beyond immobility. In this study we demonstrate, for the first time, that two of the most commonly used agents in cephalopod general anesthesia, magnesium chloride and ethanol, are capable of producing strong and reversible blockade of afferent and efferent neural signal; thus they are genuine anesthetics, rather than simply sedating agents that render animals immobile but not insensible. Additionally, we demonstrate that injected magnesium chloride and lidocaine are effective local anesthetic agents. This represents a considerable advance for cephalopod welfare. Using a reversible, minimally invasive recording procedure, we measured activity in the pallial nerve of cuttlefish ( Sepia bandensis ) and octopus ( Abdopus aculeatus, Octopus bocki ), during induction and reversal for five putative general anesthetic and two local anesthetic agents. We describe the temporal relationship between loss of behavioral responses (immobility), loss of efferent neural signal (loss of "consciousness") and loss of afferent neural signal (anesthesia) for general anesthesia, and loss of afferent signal for local anesthesia. Both ethanol and magnesium chloride were effective as bath-applied general anesthetics, causing immobility, complete loss of behavioral responsiveness and complete loss of afferent and efferent neural signal. Cold seawater, diethyl ether, and MS-222 (tricaine) were ineffective. Subcutaneous injection of either lidocaine or magnesium chloride blocked behavioral and neural responses to pinch in the injected area, and we conclude that both are effective local anesthetic agents for cephalopods. Lastly, we demonstrate that a

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

    Directory of Open Access Journals (Sweden)

    Yuanyuan Mi

    2016-09-01

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

  15. Neural network application to aircraft control system design

    Science.gov (United States)

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

    1991-01-01

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

  16. Neural network application to aircraft control system design

    Science.gov (United States)

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

    1991-01-01

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

  17. Neural Network Target Identification System for False Alarm Reduction

    Science.gov (United States)

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

    2009-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Lorey K. Takahashi

    2014-03-01

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

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

    Science.gov (United States)

    Takahashi, Lorey K.

    2014-01-01

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

  20. Adaptive quantization of local field potentials for wireless implants in freely moving animals: an open-source neural recording device

    Science.gov (United States)

    Martinez, Dominique; Clément, Maxime; Messaoudi, Belkacem; Gervasoni, Damien; Litaudon, Philippe; Buonviso, Nathalie

    2018-04-01

    Objective. Modern neuroscience research requires electrophysiological recording of local field potentials (LFPs) in moving animals. Wireless transmission has the advantage of removing the wires between the animal and the recording equipment but is hampered by the large number of data to be sent at a relatively high rate. Approach. To reduce transmission bandwidth, we propose an encoder/decoder scheme based on adaptive non-uniform quantization. Our algorithm uses the current transmitted codeword to adapt the quantization intervals to changing statistics in LFP signals. It is thus backward adaptive and does not require the sending of side information. The computational complexity is low and similar at the encoder and decoder sides. These features allow for real-time signal recovery and facilitate hardware implementation with low-cost commercial microcontrollers. Main results. As proof-of-concept, we developed an open-source neural recording device called NeRD. The NeRD prototype digitally transmits eight channels encoded at 10 kHz with 2 bits per sample. It occupies a volume of 2  ×  2  ×  2 cm3 and weighs 8 g with a small battery allowing for 2 h 40 min of autonomy. The power dissipation is 59.4 mW for a communication range of 8 m and transmission losses below 0.1%. The small weight and low power consumption offer the possibility of mounting the entire device on the head of a rodent without resorting to a separate head-stage and battery backpack. The NeRD prototype is validated in recording LFPs in freely moving rats at 2 bits per sample while maintaining an acceptable signal-to-noise ratio (>30 dB) over a range of noisy channels. Significance. Adaptive quantization in neural implants allows for lower transmission bandwidths while retaining high signal fidelity and preserving fundamental frequencies in LFPs.

  1. Developing a Mixed Neural Network Approach to Forecast the Residential Electricity Consumption Based on Sensor Recorded Data.

    Science.gov (United States)

    Oprea, Simona-Vasilica; Pîrjan, Alexandru; Căruțașu, George; Petroșanu, Dana-Mihaela; Bâra, Adela; Stănică, Justina-Lavinia; Coculescu, Cristina

    2018-05-05

    In this paper, we report a study having as a main goal the obtaining of a method that can provide an accurate forecast of the residential electricity consumption, refining it up to the appliance level, using sensor recorded data, for residential smart homes complexes that use renewable energy sources as a part of their consumed electricity, overcoming the limitations of not having available historical meteorological data and the unwillingness of the contractor to acquire such data periodically in the future accurate short-term forecasts from a specialized institute due to the implied costs. In this purpose, we have developed a mixed artificial neural network (ANN) approach using both non-linear autoregressive with exogenous input (NARX) ANNs and function fitting neural networks (FITNETs). We have used a large dataset containing detailed electricity consumption data recorded by sensors, monitoring a series of individual appliances, while in the NARX case we have also used timestamps datasets as exogenous variables. After having developed and validated the forecasting method, we have compiled it in view of incorporating it into a cloud solution, being delivered to the contractor that can provide it as a service for a monthly fee to both the operators and residential consumers.

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

    Science.gov (United States)

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

    2017-12-01

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

  3. Radiation scanning system for data recording media

    International Nuclear Information System (INIS)

    Gucza, E.

    1975-01-01

    The scanner of an encoded record support operates by the reflection principle. The record support has tracks brocken down into individual fields which are assigned light-dark markers for encoding purposes.The support consists of a light, non-transparent card which can be pulled over a slot by a guide attached to the scanner. The slot is arranged at an oblique angle relative to the card and emits radiation, for instance, light. This radiation is reflected by the tracks, the empty fields reflecting more radiation than the blackend ones, and then after having been transformed into signals, impinges upon phototransistors through openings. The number of openings corresponds to the number of tracks. The light can be made diffuse prior to exposure of the card by means of a red transparent plastic foil. (DG/RF) [de

  4. 78 FR 14295 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2013-03-05

    ... in the system: Storage: Delete entry and replace with ``Paper records maintained in file folders, binders, and electronic storage media.'' Retrievability: Delete entry and replace with ``Name, SSN and/or... Personnel Record Group for permanent retention. Paper records are destroyed after electronic copy has been...

  5. 78 FR 35610 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2013-06-13

    ... Defense. NM05720-1 System name: FOIA Request/Appeal Files and Tracking System (April 2, 2008, 73 FR 17961...: Delete entry and replace with ``Records are accessed by custodian of the record system and by persons... cabinets or rooms, which are not viewable by individuals who do not have a need to know. Computerized...

  6. 12 CFR 792.67 - Security of systems of records.

    Science.gov (United States)

    2010-01-01

    ... 12 Banks and Banking 6 2010-01-01 2010-01-01 false Security of systems of records. 792.67 Section... AND PRIVACY ACT, AND BY SUBPOENA; SECURITY PROCEDURES FOR CLASSIFIED INFORMATION The Privacy Act § 792.67 Security of systems of records. (a) Each system manager, with the approval of the head of that...

  7. 76 FR 11465 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2011-03-02

    ... separate systems of records: ``FHFA-OIG Audit Files Database,'' ``FHFA-OIG Investigative & Evaluative Files...-OIG-1 system name: FHFA-OIG Audit Files Database. security classification: Sensitive but unclassified.... categories of records in the system: (1) Audit reports; and (2) working papers, which may include copies of...

  8. 78 FR 19244 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2013-03-29

    .... This system of records maintains information related to recordation of assignments of property rights.... Categories of individuals covered by the system: Persons who have given or received property rights under an... system: Assignments, grants, mortgages, liens, encumbrances, licenses, and other instruments affecting...

  9. 32 CFR 505.3 - Privacy Act systems of records.

    Science.gov (United States)

    2010-07-01

    ... anticipated threats or hazards to the security or integrity of data, which could result in substantial harm... 32 National Defense 3 2010-07-01 2010-07-01 true Privacy Act systems of records. 505.3 Section 505... AND PUBLIC RELATIONS ARMY PRIVACY ACT PROGRAM § 505.3 Privacy Act systems of records. (a) Systems of...

  10. 76 FR 12076 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2011-03-04

    .... For a list of system managers at the Defense Logistics Agency Primary Level Field Activities write to... managers at the Defense Logistics Agency Primary Level Field Activities, write to the Project Manager...; System of Records AGENCY: Defense Logistics Agency, DoD. ACTION: Notice to amend a system of records...

  11. 77 FR 31606 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2012-05-29

    ... DEPARTMENT OF EDUCATION Privacy Act of 1974; System of Records AGENCY: Office of English Language Acquisition, Language Enhancement and Academic Achievement for Limited English Proficient Students, Department... Secretary of the Office of English Language Acquisition deletes the following system of records: System...

  12. 75 FR 3899 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2010-01-25

    ... media. Retrievability: Records are retrieved in the system by name, Social Security Number (SSN), NGA.... DATES: The system will be effective on February 24, 2010, unless comments are received that would result..., or badge to an NGA facility. Categories of records in the system: Names, Social Security Number (SSN...

  13. 76 FR 77846 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2011-12-14

    ... terrorism and national security threat screening, system back-up, and continuity of operations purposes... DEPARTMENT OF JUSTICE [CPCLO Order No. 004-2011] Privacy Act of 1974; System of Records AGENCY: Federal Bureau of Investigation, Department of Justice. ACTION: Notice to amend system of records. SUMMARY...

  14. 75 FR 61450 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2010-10-05

    ..., Corporate Communications, Defense Finance and Accounting Service, DFAS-HKC/IN, 8899 E. 56th Avenue...; System of Records AGENCY: Defense Finance and Accounting Service; DoD. ACTION: Notice to add a system of records. SUMMARY: The Defense Finance and Accounting Service (DFAS) is proposing to add a system of...

  15. Finding Trapped Miners by Using a Prototype Seismic Recording System Made from Music-Recording Hardware

    Science.gov (United States)

    Pratt, Thomas L.

    2009-01-01

    The goal of this project was to use off-the-shelf music recording equipment to build and test a prototype seismic system to listen for people trapped in underground chambers (mines, caves, collapsed buildings). Previous workers found that an array of geophones is effective in locating trapped miners; displaying the data graphically, as well as playing it back into an audio device (headphones) at high speeds, was found to be effective for locating underground tapping. The desired system should record the data digitally to allow for further analysis, be capable of displaying the data graphically, allow for rudimentary analysis (bandpass filter, deconvolution), and allow the user to listen to the data at varying speeds. Although existing seismic reflection systems are adequate to record, display and analyze the data, they are relatively expensive and difficult to use and do not have an audio playback option. This makes it difficult for individual mines to have a system waiting on the shelf for an emergency. In contrast, music recording systems, like the one I used to construct the prototype system, can be purchased for about 20 percent of the cost of a seismic reflection system and are designed to be much easier to use. The prototype system makes use of an ~$3,000, 16-channel music recording system made by Presonus, Inc., of Baton Rouge, Louisiana. Other manufacturers make competitive systems that would serve equally well. Connecting the geophones to the recording system required the only custom part of this system - a connector that takes the output from the geophone cable and breaks it into 16 microphone inputs to be connected to the music recording system. The connector took about 1 day of technician time to build, using about $300 in off-the-shelf parts. Comparisons of the music recording system and a standard seismic reflection system (A 24-channel 'Geode' system manufactured by Geometrics, Inc., of San Jose, California) were carried out at two locations. Initial

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

    International Nuclear Information System (INIS)

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

    1991-01-01

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

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

    International Nuclear Information System (INIS)

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

    1992-01-01

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

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

    Science.gov (United States)

    Alam, Monzurul; Chen, Xi; Fernandez, Eduardo

    2013-12-01

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

  19. 21 CFR 21.71 - Disclosure of records in Privacy Act Record Systems; accounting required.

    Science.gov (United States)

    2010-04-01

    ... accounting shall be made, in accordance with paragraph (e) of this section, of any disclosure under paragraph (a) of this section of a record that is not a disclosure under § 21.70. (e) Where an accounting is... of the disclosure. The accounting shall not be considered a Privacy Act Record System. (2) Retain the...

  20. Phase transitions in glassy systems via convolutional neural networks

    Science.gov (United States)

    Fang, Chao

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

  1. NEURAL NETWORK SYSTEM FOR DIAGNOSTICS OF AVIATION DESIGNATION PRODUCTS

    Directory of Open Access Journals (Sweden)

    В. Єременко

    2011-02-01

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

  2. Fault Tolerant Neural Network for ECG Signal Classification Systems

    Directory of Open Access Journals (Sweden)

    MERAH, M.

    2011-08-01

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

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

    International Nuclear Information System (INIS)

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

    2008-01-01

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

  4. Optical imaging of neuronal activity and visualization of fine neural structures in non-desheathed nervous systems.

    Directory of Open Access Journals (Sweden)

    Christopher John Goldsmith

    Full Text Available Locating circuit neurons and recording from them with single-cell resolution is a prerequisite for studying neural circuits. Determining neuron location can be challenging even in small nervous systems because neurons are densely packed, found in different layers, and are often covered by ganglion and nerve sheaths that impede access for recording electrodes and neuronal markers. We revisited the voltage-sensitive dye RH795 for its ability to stain and record neurons through the ganglion sheath. Bath-application of RH795 stained neuronal membranes in cricket, earthworm and crab ganglia without removing the ganglion sheath, revealing neuron cell body locations in different ganglion layers. Using the pyloric and gastric mill central pattern generating neurons in the stomatogastric ganglion (STG of the crab, Cancer borealis, we found that RH795 permeated the ganglion without major residue in the sheath and brightly stained somatic, axonal and dendritic membranes. Visibility improved significantly in comparison to unstained ganglia, allowing the identification of somata location and number of most STG neurons. RH795 also stained axons and varicosities in non-desheathed nerves, and it revealed the location of sensory cell bodies in peripheral nerves. Importantly, the spike activity of the sensory neuron AGR, which influences the STG motor patterns, remained unaffected by RH795, while desheathing caused significant changes in AGR activity. With respect to recording neural activity, RH795 allowed us to optically record membrane potential changes of sub-sheath neuronal membranes without impairing sensory activity. The signal-to-noise ratio was comparable with that previously observed in desheathed preparations and sufficiently high to identify neurons in single-sweep recordings and synaptic events after spike-triggered averaging. In conclusion, RH795 enabled staining and optical recording of neurons through the ganglion sheath and is therefore both a

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

    Science.gov (United States)

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

    2014-12-01

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

  6. Neural systems supporting and affecting economically relevant behavior

    Directory of Open Access Journals (Sweden)

    Braeutigam S

    2012-05-01

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

  7. An implantable two axis micromanipulator made with a 3D printer for recording neural activity in free-swimming fish.

    Science.gov (United States)

    Rogers, Loranzie S; Van Wert, Jacey C; Mensinger, Allen F

    2017-08-15

    Chronically implanted electrodes allow monitoring neural activity from free moving animals. While a wide variety of implanted headstages, microdrives and electrodes exist for terrestrial animals, few have been developed for use with aquatic animals. A two axis micromanipulator was fabricated with a Formlabs 3D printer for implanting electrodes into free-swimming oyster toadfish (Opsanus tau). The five piece manipulator consisted of a base, body, electrode holder, manual screw drive and locking nut. The manipulator measured approximately 25×20×30mm (l×w×h) and weighed 5.28g after hand assembly. Microwire electrodes were inserted successfully with the manipulator to record high fidelity signals from the anterior lateral line nerve of the toadfish. The micromanipulator allowed the chronically implanted electrodes to be repositioned numerous times to record from multiple sites and extended successful recording time in the toadfish by several days. Three dimensional printing allowed an inexpensive (<$US 5 material), two axis micromanipulator to be printed relatively rapidly (<2h) to successfully record from multiple sites in the anterior lateral line nerve of free-swimming toadfish. Copyright © 2017 Elsevier B.V. All rights reserved.

  8. Three-dimensional hydrogel cell culture systems for modeling neural tissue

    Science.gov (United States)

    Frampton, John

    designed for use as a tool to predict the transport and processing that occurs prior to drug uptake in the central nervous system (CNS), and to predict BBB permeability. Electrochemical techniques and immunohistochemistry were used to validate this model and provide detailed information about cellular organization and function. Electrochemical impedance spectroscopy (EIS) provided evidence that endothelial cells cultured in the presence of astrocytes formed tight junctions capable of occluding the flow of electrical current. In a second series of experiments, a microglia-astrocyte co-culture system was developed to assess the effects of glial cells on electrode impedance recorded from neural prosthetic devices in vitro. Impedance measurements were compared with confocal images to determine the effects of glial cell density and cell type on electrode performance. The results indicate that EIS data can be used to model components of the reactive cell responses in brain tissue, and that impedance measurements recorded in vitro can be compared to measurements recorded in vivo. Taken together, these results demonstrate that alginate hydrogels can be used for the creation of 3-D neural cell scaffolds, and that such cell scaffolds can be used to model a variety of three-dimensional neural tissues in vitro, that cannot be studied in 2-D cultures.

  9. 77 FR 28898 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2012-05-16

    .../COPS-002 SYSTEM NAME: COPS Online Ordering System. SECURITY CLASSIFICATION: Unclassified. SYSTEM... to economic or property interests, identity theft or fraud, or harm to the security or integrity of... system of records entitled, ``COPS Online Ordering System,'' (JUSTICE/COPS-002). The system collects...

  10. Design of double tape recorder data acquisition system

    International Nuclear Information System (INIS)

    Guo Tianrui; Du Yifei

    1995-01-01

    In the data acquisition system supported by the microcomputer tape recorder, as the acquisition speed is often limited by the low speed of tape recorder, so a double tape recorder system is designed. In this system, two tape recorders are used in on-line acquisition system simultaneously. One DMA channel used is one designed for soft disk driver, another DMA channel used is one retained for user. By this way, the speed of tape writing could be increased to nearly twice as much. In order to prevent the data confusion, the authors open two data buffers in system and write different mark in each buffer, then write the data block to two tape recorders according to the mark. The system complies with the principle: 'Double write, Double read'

  11. A Gamma Memory Neural Network for System Identification

    Science.gov (United States)

    Motter, Mark A.; Principe, Jose C.

    1992-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2011-07-01

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

  13. Speaker diarization system using HXLPS and deep neural network

    Directory of Open Access Journals (Sweden)

    V. Subba Ramaiah

    2018-03-01

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

  14. 76 FR 49755 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2011-08-11

    ... aircraft. Categories of records in the system: Name, last four of Social Security Number (SSN), squadron ID... of Appendix I to OMB Circular No. A-130, ``Federal Agency Responsibilities for Maintaining records... Military Flight Operations Quality Assurance System location: Primary databases (Enterprise Level Servers...

  15. 75 FR 20346 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2010-04-19

    ... DEPARTMENT OF EDUCATION Privacy Act of 1974; System of Records AGENCY: Federal Student Aid, Department of Education. ACTION: Notice of an altered system of records. SUMMARY: In accordance with the Privacy Act of 1974, as amended (Privacy Act), 5 United States Code (U.S.C.) 552a, the Chief Operating...

  16. 77 FR 32111 - Privacy Act System of Records

    Science.gov (United States)

    2012-05-31

    ... contacted in order to obtain that office's advice regarding obligations under the Privacy Act; 8. Breach... FEDERAL COMMUNICATIONS COMMISSION Privacy Act System of Records AGENCY: Federal Communications Commission. ACTION: Notice; one new Privacy Act system of records. SUMMARY: Pursuant to subsection (e)(4) of...

  17. Data compression systems for home-use digital video recording

    NARCIS (Netherlands)

    With, de P.H.N.; Breeuwer, M.; van Grinsven, P.A.M.

    1992-01-01

    The authors focus on image data compression techniques for digital recording. Image coding for storage equipment covers a large variety of systems because the applications differ considerably in nature. Video coding systems suitable for digital TV and HDTV recording and digital electronic still

  18. 75 FR 37842 - Privacy Act of 1974; Systems of Records

    Science.gov (United States)

    2010-06-30

    ...: Individuals who are consultants and vendors to PBGC. Categories of records in the systems: Payment vouchers..., Human Resources Department; Director, Benefits Administration and Payment Department. Correspondence... system of records is maintained for use in determining amounts to be paid and in effecting payments by...

  19. 75 FR 17906 - Privacy Act of 1974; Systems of Records

    Science.gov (United States)

    2010-04-08

    ... ``Program Manager, Law Enforcement Operations, Headquarters, Defense Logistics Agency, Office of Public... Manager, Law Enforcement Operations, Headquarters, Defense Logistics Agency, Office of Public Safety, 8725...; Systems of Records AGENCY: Defense Logistics Agency, DoD. ACTION: Notice to amend a system of records...

  20. 77 FR 74851 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2012-12-18

    ... FEDERAL DEPOSIT INSURANCE CORPORATION Privacy Act of 1974; System of Records AGENCY: Federal Deposit Insurance Corporation. ACTION: Notice to Delete a System of Records. SUMMARY: In accordance with the requirements of the Privacy Act of 1974, as amended (Privacy Act), the Federal Deposit Insurance...

  1. 78 FR 79412 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2013-12-30

    ... Defense Finance and Accounting Service proposes to alter a system of records, T7205, General Accounting... transaction-driven financial statements in support of Defense Finance and Accounting Service financial mission... INFORMATION: The Defense Finance and Accounting Service notices for systems of records subject to the Privacy...

  2. 78 FR 31905 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2013-05-28

    ... Records. SUMMARY: The Defense Finance and Accounting Service proposes to alter a system of records in its... information. FOR FURTHER INFORMATION CONTACT: Mr. Gregory L. Outlaw, Defense Finance and Accounting Service... name: General Accounting and Finance System--Defense Transaction Interface Module (June 4, 2007, 72 FR...

  3. Personal health record systems and their security protection.

    Science.gov (United States)

    Win, Khin Than; Susilo, Willy; Mu, Yi

    2006-08-01

    The objective of this study is to analyze the security protection of personal health record systems. To achieve this we have investigated different personal health record systems, their security functions, and security issues. We have noted that current security mechanisms are not adequate and we have proposed some security mechanisms to tackle these problems.

  4. 78 FR 23810 - Privacy Act System of Records

    Science.gov (United States)

    2013-04-22

    ... SMALL BUSINESS ADMINISTRATION Privacy Act System of Records AGENCY: Small Business Administration. ACTION: Notice of new Privacy Act system of records and request for comment. SUMMARY: The Small Business... the protected information collected from applicants and participants in the Small Business Innovation...

  5. A system for automatically recording weight changes in sapling trees

    Science.gov (United States)

    Harold F. Haupt; Bud L. Jeffers

    1967-01-01

    Describes an accurate and simple system for taking continuous weight records of sapling-size trees. Measurements obtained using this system have helped in describing the mechanism of interception storage in tree crowns during snowfall.

  6. 77 FR 9632 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2012-02-17

    .... Mail: Federal Docket Management System Office, 4800 Mark Center Drive, East Tower, 2nd Floor, Suite..., payroll office and payroll records, including automated payroll systems.'' * * * * * [FR Doc. 2012-3720...

  7. 28 CFR 25.6 - Accessing records in the system.

    Science.gov (United States)

    2010-07-01

    ... National Instant Criminal Background Check System § 25.6 Accessing records in the system. (a) FFLs may... through the NCIC communication network. Upon receiving a request for a background check from an FFL, a POC...

  8. Automated Records Management Systems in the ESARBICA Region

    African Journals Online (AJOL)

    Automated Records Management Systems in the ESARBICA Region. ... to organizations as human and financial resources and that their management is important. ... Latter day archivists work with other professionals such as auditors, systems ...

  9. Beyond the neuropsychology of dreaming: Insights into the neural basis of dreaming with new techniques of sleep recording and analysis.

    Science.gov (United States)

    Cipolli, Carlo; Ferrara, Michele; De Gennaro, Luigi; Plazzi, Giuseppe

    2017-10-01

    Recent advances in electrophysiological [e.g., surface high-density electroencephalographic (hd-EEG) and intracranial recordings], video-polysomnography (video-PSG), transcranial stimulation and neuroimaging techniques allow more in-depth and more accurate investigation of the neural correlates of dreaming in healthy individuals and in patients with brain-damage, neurodegenerative diseases, sleep disorders or parasomnias. Convergent evidence provided by studies using these techniques in healthy subjects has led to a reformulation of several unresolved issues of dream generation and recall [such as the inter- and intra-individual differences in dream recall and the predictivity of specific EEG rhythms, such as theta in rapid eye movement (REM) sleep, for dream recall] within more comprehensive models of human consciousness and its variations across sleep/wake states than the traditional models, which were largely based on the neurophysiology of REM sleep in animals. These studies are casting new light on the neural bases (in particular, the activity of dorsal medial prefrontal cortex regions and hippocampus and amygdala areas) of the inter- and intra-individual differences in dream recall, the temporal location of specific contents or properties (e.g., lucidity) of dream experience and the processing of memories accessed during sleep and incorporated into dream content. Hd-EEG techniques, used on their own or in combination with neuroimaging, appear able to provide further important insights into how the brain generates not only dreaming during sleep but also some dreamlike experiences in waking. Copyright © 2016 Elsevier Ltd. All rights reserved.

  10. 76 FR 42737 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2011-07-19

    ... in this system can range from UNCLASSIFIED to TOP SECRET. SYSTEM LOCATION: Office of the Director of... UNCLASSIFIED to TOP SECRET. SYSTEM LOCATION: Office of the Director of National Intelligence, Washington, DC... classification of records in this system can range from UNCLASSIFIED to TOP SECRET. SYSTEM LOCATION: Office of...

  11. 77 FR 43816 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2012-07-26

    ... Technology E-Learning System (December 8, 2008, 73 FR 74471). * * * * * CHANGES: SYSTEM ID: Delete entry and replace with ``F033 AFSPC C''. SYSTEM NAME: Delete entry and replace with ``AF e-Learning System.'' SYSTEM... OMB Circular No. A-130, ``Federal Agency Responsibilities for Maintaining Records About Individuals...

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

    International Nuclear Information System (INIS)

    Mohamed, A.H.

    2010-01-01

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

  13. Stability Analysis of Neural Networks-Based System Identification

    Directory of Open Access Journals (Sweden)

    Talel Korkobi

    2008-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2000-07-01

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

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

    DEFF Research Database (Denmark)

    Jouffroy, Guillaume; Jouffroy, Jerome

    Central Pattern Generators (CPG) are oscillatory systems that are responsible for generating rhythmic patterns at the origin of many biological activities such as for example locomotion or digestion. These systems are generally modelled as recurrent neural networks whose parameters are tuned so...... that the network oscillates in a suitable way, this tuning being a non trivial task. It also appears that the link with the physical body that these oscillatory entities control has a fundamental importance, and it seems that most bodies used for experimental validation in the literature (walking robots, lamprey...... a brief description of the Roller-Racer, we present as a preliminary study an RNN-based feed-forward controller whose parameters are obtained through the well-known teacher forcing learning algorithm, extended to learn signals with a continuous component....

  16. Electronic medical records system user acceptance

    CSIR Research Space (South Africa)

    Erasmus, L

    2015-06-01

    Full Text Available on literature and confirmed in this case study, if adoption of EMR systems is the ultimate goal, the implementation thereof should be properly managed with strong leadership and political backing at the highest level. Adoption is also supported by keeping...

  17. Selectivity of recording of neural signals with micromachined intraneural micro electrodes

    NARCIS (Netherlands)

    Rutten, Wim; Rozijn, T.H.; Rozijn, Tom H.; Meier, J.H.; Meier, Jan H.

    1993-01-01

    The number uf afferent fibers in a peripheral nerve fascicle and the electrical volume conduction in neuraI tissue determine the interelectrode spacing, needed for selective recording with mufticontact devices. These factors taking into account, and also assuming uniform distribution of fibers

  18. A low-power current-reuse dual-band analog front-end for multi-channel neural signal recording.

    Science.gov (United States)

    Sepehrian, H; Gosselin, B

    2014-01-01

    Thoroughly studying the brain activity of freely moving subjects requires miniature data acquisition systems to measure and wirelessly transmit neural signals in real time. In this application, it is mandatory to simultaneously record the bioelectrical activity of a large number of neurons to gain a better knowledge of brain functions. However, due to limitations in transferring the entire raw data to a remote base station, employing dedicated data reduction techniques to extract the relevant part of neural signals is critical to decrease the amount of data to transfer. In this work, we present a new dual-band neural amplifier to separate the neuronal spike signals (SPK) and the local field potential (LFP) simultaneously in the analog domain, immediately after the pre-amplification stage. By separating these two bands right after the pre-amplification stage, it is possible to process LFP and SPK separately. As a result, the required dynamic range of the entire channel, which is determined by the signal-to-noise ratio of the SPK signal of larger bandwidth, can be relaxed. In this design, a new current-reuse low-power low-noise amplifier and a new dual-band filter that separates SPK and LFP while saving capacitors and pseudo resistors. A four-channel dual-band (SPK, LFP) analog front-end capable of simultaneously separating SPK and LFP is implemented in a TSMC 0.18 μm technology. Simulation results present a total power consumption per channel of 3.1 μw for an input referred noise of 3.28 μV and a NEF for 2.07. The cutoff frequency of the LFP band is fc=280 Hz, and fL=725 Hz and fL=11.2 KHz for SPK, with 36 dB gain for LFP band 46 dB gain for SPK band.

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

    Science.gov (United States)

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

    2012-07-01

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

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

    Science.gov (United States)

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

    2001-07-01

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

  1. Dedicated data recording video system for Spacelab experiments

    Science.gov (United States)

    Fukuda, Toshiyuki; Tanaka, Shoji; Fujiwara, Shinji; Onozuka, Kuniharu

    1984-04-01

    A feasibility study of video tape recorder (VTR) modification to add the capability of data recording etc. was conducted. This system is an on-broad system to support Spacelab experiments as a dedicated video system and a dedicated data recording system to operate independently of the normal operation of the Orbiter, Spacelab and the other experiments. It continuously records the video image signals with the acquired data, status and operator's voice at the same time on one cassette video tape. Such things, the crews' actions, animals' behavior, microscopic views and melting materials in furnace, etc. are recorded. So, it is expected that experimenters can make a very easy and convenient analysis of the synchronized video, voice and data signals in their post flight analysis.

  2. Closed-Loop Real-Time Imaging Enables Fully Automated Cell-Targeted Patch-Clamp Neural Recording In Vivo.

    Science.gov (United States)

    Suk, Ho-Jun; van Welie, Ingrid; Kodandaramaiah, Suhasa B; Allen, Brian; Forest, Craig R; Boyden, Edward S

    2017-08-30

    Targeted patch-clamp recording is a powerful method for characterizing visually identified cells in intact neural circuits, but it requires skill to perform. We previously developed an algorithm that automates "blind" patching in vivo, but full automation of visually guided, targeted in vivo patching has not been demonstrated, with currently available approaches requiring human intervention to compensate for cell movement as a patch pipette approaches a targeted neuron. Here we present a closed-loop real-time imaging strategy that automatically compensates for cell movement by tracking cell position and adjusting pipette motion while approaching a target. We demonstrate our system's ability to adaptively patch, under continuous two-photon imaging and real-time analysis, fluorophore-expressing neurons of multiple types in the living mouse cortex, without human intervention, with yields comparable to skilled human experimenters. Our "imagepatching" robot is easy to implement and will help enable scalable characterization of identified cell types in intact neural circuits. Copyright © 2017 Elsevier Inc. All rights reserved.

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

    International Nuclear Information System (INIS)

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

    1991-01-01

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

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

    Science.gov (United States)

    Lin, Chih-Min; Hsu, C F

    2003-01-01

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

  5. BOOK REVIEW: Theory of Neural Information Processing Systems

    Science.gov (United States)

    Galla, Tobias

    2006-04-01

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

  6. A control and recording system for a neutron diffractometer

    International Nuclear Information System (INIS)

    Czech, Z.; Turek, L.; Wierzewski, K.

    1982-01-01

    A digital system for automatic control and data recording being a part of a neutron diffractometer designed for measurement of the angular distribution of monochromatic neutrons is described. The system is built using digital TTL integrated circuits. Particular attention is drawn to the interesting design of the optimized cross-matrix which selects the elements subjected to recording. The system successfully works with the neutron diffractometer at the EWA reactor. (author)

  7. Low-Gain, Low-Noise Integrated Neuronal Amplifier for Implantable Artifact-Reduction Recording System

    Directory of Open Access Journals (Sweden)

    Abdelhamid Benazzouz

    2013-09-01

    Full Text Available Brain neuroprostheses for neuromodulation are being designed to monitor the neural activity of the brain in the vicinity of the region being stimulated using a single macro-electrode. Using a single macro-electrode, recent neuromodulation studies show that recording systems with a low gain neuronal amplifier and successive amplifier stages can reduce or reject stimulation artifacts. These systems were made with off-the-shelf components that are not amendable for future implant design. A low-gain, low-noise integrated neuronal amplifier (NA with the capability of recording local field potentials (LFP and spike activity is presented. In vitro and in vivo characterizations of the tissue/electrode interface, with equivalent impedance as an electrical model for recording in the LFP band using macro-electrodes for rodents, contribute to the NA design constraints. The NA occupies 0.15 mm2 and dissipates 6.73 µW, and was fabricated using a 0.35 µm CMOS process. Test-bench validation indicates that the NA provides a mid-band gain of 20 dB and achieves a low input-referred noise of 4 µVRMS. Ability of the NA to perform spike recording in test-bench experiments is presented. Additionally, an awake and freely moving rodent setup was used to illustrate the integrated NA ability to record LFPs, paving the pathway for future implantable systems for neuromodulation.

  8. Recording and surveillance systems for periodontal diseases

    DEFF Research Database (Denmark)

    Beltrán-Aguilar, Eugenio D; Eke, Paul I; Thornton-Evans, Gina

    2012-01-01

    This paper describes tools used to measure periodontal diseases and the integration of these tools into surveillance systems. Tools to measure periodontal diseases at the surveillance level have focussed on current manifestations of disease (e.g. gingival inflammation) or disease sequelae (e.......g. periodontal pocket depth or loss of attachment). All tools reviewed in this paper were developed based on the state of the science of the pathophysiology of periodontal disease at the time of their design and the need to provide valid and reliable measurements of the presence and severity of periodontal...... diseases. Therefore, some of these tools are no longer valid. Others, such as loss of periodontal attachment, are the current de-facto tools but demand many resources to undertake periodical assessment of the periodontal health of populations. Less complex tools such as the Community Periodontal Index...

  9. 77 FR 42593 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2012-07-19

    ... Administration (VBA). This system is a core system for VBA programs. This system of records does not directly... processing system designed to serve as the cornerstone of VBA's transition to paperless claims processing... attorney and any information concerning such individual which is relevant to a refusal to grant access...

  10. 78 FR 5787 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2013-01-28

    ... 0450 System name: Drug-Free Workplace Files (September 9, 2009, 74 FR 46418). Changes: * * * * * System... employment with the Defense Intelligence Agency.'' Categories of records in the system: Delete entry and... of the system: Delete entry and replace with ``E.O. 12564, Federal Drug Free Workplace; DoD Directive...

  11. 75 FR 136 - Privacy Act of 1974; Systems of Records

    Science.gov (United States)

    2010-01-04

    ... THE SYSTEM: STORAGE: Paper records and on electronic storage media. RETRIEVABILITY: Name and/or Social... Institute of Technology Management and Information System (AFITMIS) (February 21, 2008, 73 FR 9548). CHANGES: * * * * * SYSTEM NAME: Delete entry and replace with ``Air Force Institute of Technology Student Information System...

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

    Science.gov (United States)

    Brown, Ramsay A; Swanson, Larry W

    2013-09-01

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

  13. 76 FR 11213 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2011-03-01

    ..., ``Federal Agency Responsibilities for Maintaining Records About Individuals,'' dated February 8, 1996... system: Delete entry and replace with ``Records contain individual's name, Social Security Number (SSN....C. 8032, The Air Staff: general duties; Air Force Instruction 36-2102, Base-Level Relocation...

  14. 78 FR 14297 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2013-03-05

    ... name, address, phone number, birth date, and Social Security Number (SSN).'' Authority for maintenance... storage media.'' * * * * * System manager(s) and address: Delete entry and replace with ``Chief of... signature as a means of proving the identity of the individual requesting access to the records.'' Record...

  15. 76 FR 37329 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2011-06-27

    ... related to disciplinary actions, responses from subjects, and correspondence with Governmental agencies... responsibility and mismanagement; to take adverse action and appropriate disciplinary action against those found... of Records AGENCY: Department of the Army, DoD. ACTION: Notice to Amend a System of Records. SUMMARY...

  16. 76 FR 40343 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2011-07-08

    ... Responsibility Committee opinions, memoranda related to disciplinary actions, responses from subjects, and... professional responsibility and mismanagement; to take adverse action and appropriate disciplinary action... of Records AGENCY: Department of the Army, DoD. ACTION: Notice to amend a system of records. SUMMARY...

  17. 77 FR 66432 - Privacy Act of 1974, System of Records

    Science.gov (United States)

    2012-11-05

    ...;and investigations, committee meetings, agency decisions and rulings, #0;delegations of authority..., the United States Agency for International Development (USAID) is deleting the AID-15 Employee Payroll..., USAID is deleting the AID-15 Employee Payroll Records system of records because it is now covered under...

  18. 77 FR 74176 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2012-12-13

    ..., ``Federal Agency Responsibilities for Maintaining Records About Individuals,'' dated February 8, 1996... United States civilian personnel who are recommended for an award.'' Categories of Records in the System... Command (MAJCOM) are destroyed after 50 years. Decorations to individuals (Military and Civilian...

  19. 78 FR 68039 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2013-11-13

    ..., official title, subproject code, work address, home address, work phone, home phone, Social Security Number... system: Storage: Paper records and electronic storage media. Retrievability: Records may be retrieved by... duties and who have appropriate clearances and permissions. Usage of physical access controls, encryption...

  20. 76 FR 28757 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2011-05-18

    ... online tool helps the staff members at all four levels (school, district, area, and headquarters) apply... to paragraph 4c of Appendix I to OMB Circular No. A-130, ``Federal Agency Responsibilities for.... Categories of records in the system: Records consist of first name, last name, Social Security Number (SSN...

  1. 76 FR 10010 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2011-02-23

    ....'' * * * * * Storage: Delete entry and replace with ``Maintained on electronic storage media and/or in file binders... individual for consideration of continued service in the Air Force. Paper records are destroyed by tearing... records in the system: Storage: Maintained on electronic storage media and/or in file binders/ cabinets...

  2. 78 FR 5784 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2013-01-28

    ... records. SUMMARY: The Defense Finance and Accounting Service proposes to alter a system of records in its.... FOR FURTHER INFORMATION CONTACT: Mr. Gregory Outlaw, Defense Finance and Accounting Service, Freedom of Information/Privacy Act Program Manager, Corporate Communications, DFAS-HKC/IN, 8899 E. 56th...

  3. 78 FR 14280 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2013-03-05

    ... Records. SUMMARY: The Defense Finance and Accounting Service proposes to alter a system of records notice... information. FOR FURTHER INFORMATION CONTACT: Mr. Gregory L. Outlaw, Defense Finance and Accounting Service, Freedom of Information/Privacy Act Program Manager, Corporate Communications, DFAS-HKC/IN, 8899 E. 56th...

  4. 78 FR 14281 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2013-03-05

    ... Finance and Accounting Service, Freedom of Information/Privacy Act Program Manager, Corporate... Records. SUMMARY: The Defense Finance and Accounting Service proposes to amend a system of records in its.... FOR FURTHER INFORMATION CONTACT: Mr. Gregory L. Outlaw, Defense Finance and Accounting Service...

  5. 78 FR 14286 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2013-03-05

    ... Records. SUMMARY: The Defense Finance and Accounting Service proposes to amend a system of records in its... information. FOR FURTHER INFORMATION CONTACT: Mr. Gregory L. Outlaw, Defense Finance and Accounting Service, Freedom of Information/Privacy Act Program Manager, Corporate Communications, DFAS-HKC/IN, 8899 E. 56th...

  6. 78 FR 14285 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2013-03-05

    ... Records. SUMMARY: The Defense Finance and Accounting Service proposes to alter a system of records in its.... FOR FURTHER INFORMATION CONTACT: Mr. Gregory L. Outlaw, Defense Finance and Accounting Service, Freedom of Information/Privacy Act Program Manager, Corporate Communications, DFAS-HKC/IN, 8899 E. 56th...

  7. 76 FR 4880 - Privacy Act of 1974; Systems of Records

    Science.gov (United States)

    2011-01-27

    ... Management, the Office of Government Ethics, and the Department of Labor. Government-wide systems of records.../GOVT-1: Executive Branch Public Financial Disclosure Reports and Other Name-Retrieved Ethics Program... folders in locked file cabinets in Procurement and financial management staff offices. Electronic records...

  8. 78 FR 76897 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2013-12-19

    ... established as the Income Verification Match Center, has authority under section 8051 to verify Veterans' self...) is amending the system of records currently titled ``Income Verification Records--VA'' (89VA16) as... Reconciliation Act of 1990, provides VA the authority to verify Veterans' self-reported income to determine...

  9. 76 FR 13991 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2011-03-15

    ... identification and password or smart card technology protocols. Retention and disposal: Records are destroyed...: Records maintained include individual's name, home or business telephone number, e-mail and mailing..., Technology, and Logistics. Purpose(s): The system is used to produce subscription mailing lists for...

  10. 75 FR 6362 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2010-02-09

    ...; and E.O. 9397 (SSN), as amended.'' * * * * * RETENTION AND DISPOSAL: Delete entry and replace with ``Disposition is pending. Until the National Archives and Records Administration approve a retention and... limited access for job performance requirements. CATEGORIES OF RECORDS IN THE SYSTEM: Name, home address...

  11. 78 FR 64196 - Privacy Act Altered System of Records

    Science.gov (United States)

    2013-10-28

    ... records to COMMERCE/DEPARTMENT-20, Biographical Files and Social Networks. The amendment serves to modify... DEPARTMENT OF COMMERCE [Docket No. 130730666-3877-02] Privacy Act Altered System of Records AGENCY: Department of Commerce. ACTION: Notice; Commerce/Department-20, Biographical Files. SUMMARY: The Department...

  12. 76 FR 58786 - Privacy Act of 1974; Systems of Records

    Science.gov (United States)

    2011-09-22

    ... records, etc.) of employees and applicants for employment. Records contained in this system are also used.../Central Security Service (NSA/CSS) applicants for employment and employees tested for the use of illegal... Workplace; DoD Directive 1010.9, DoD Civilian Employee Drug Abuse Testing Program; and E.O. 9397 (SSN), as...

  13. 75 FR 62111 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2010-10-07

    ... DEPARTMENT OF DEFENSE Office of the Secretary [Docket ID: DOD-2010-OS-0140] Privacy Act of 1974... records notices in its existing inventory of record systems subject to the Privacy Act of 1974, (5 U.S.C... submissions available for public viewing on the Internet at http://www.regulations.gov as they are received...

  14. 75 FR 47797 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2010-08-09

    ... Officer, Department of Defense. Deletion: S330.40 CAHS System name: Employee Assistance Program Records (August 27, 1999; 64 FR 46889). Reason: This collection is covered under the existing DHHS/FOH EAP Privacy notice 09-90-0010, entitled ``Employee Assistance Program (EAP) Records, HHS/OS/ASAM/OHR.'' [FR Doc. 2010...

  15. 75 FR 22570 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2010-04-29

    ..., rank/grade, Social Security Number (SSN), promotion photos, date of birth, blood type, hair and eye... birth, home of record, records of emergency data. Personnel/Duty Related Information: Enlistment... Manpower Data Center (DMDC); and Department of Agriculture (DoA). Exemptions claimed for the system: None...

  16. 78 FR 36035 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2013-06-14

    ... Organization Assessment Subcommittee (OASC) Chair(s), based on recommendation of a professional committee of... studies is therefore evaluated by a professional committee: the Data Use Agreement committee of the OASC..., ACCESSING, RETAINING, AND DISPOSING OF RECORDS IN THE SYSTEM: STORAGE: Records are maintained on the server...

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

    KAUST Repository

    Bressloff, Paul C.

    2010-01-01

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

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

    Science.gov (United States)

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

    2018-06-01

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

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

    Science.gov (United States)

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

    2017-08-01

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

  20. 78 FR 25974 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2013-05-03

    ... specialty within each career management field. Routine uses of records maintained in the system, including..., distribution and utilization of all personnel in military duties, strength accounting and manpower management...

  1. 77 FR 13571 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2012-03-07

    ... be deleted. A0210-130 DALO Laundry Accounting Files (April 12, 1999, 64 FR 17641). Reason: The... schedule; therefore, the system of records notice can be deleted. A0600-8-104g AHRC Career Management...

  2. 77 FR 24242 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2012-04-23

    ... under the Freedom of Information Act (FOIA) or the Privacy Act. Categories of records in the system: The... allegation or complaint of discrimination based on race, color, religion, sex, national origin, age, or...

  3. 76 FR 33286 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2011-06-08

    .../GOVT-6 GSA SmartPay Purchase Charge Card Program; GSA/GOVT-8 Excluded Parties List System (EPLS); MSPB... Other Ethics Program Records; OGE/GOVT-2 Confidential Statements of Employment and Financial Interests...

  4. 76 FR 50721 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2011-08-16

    ... . Follow the instructions for submitting comments. Mail: Federal Docket Management System Office, 1160... of the Army, Privacy Office, U.S. Army Records Management and Declassification Agency, 7701 Telegraph...; laboratory reports, polygraph reports, documentary evidence, physical evidence, summary and administrative...

  5. 77 FR 56815 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2012-09-14

    ... and culture, learning ability, physical performance, combat readiness, discipline, motivation... education, work experience, motivation, knowledge of and attitude about the Army. When records show military... methods and systems aimed at improved group performance. (No decisions affecting an individual's rights or...

  6. 78 FR 38303 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2013-06-26

    .... FOR FURTHER INFORMATION CONTACT: Mr. Leroy Jones, Department of the Army, Privacy Office, U.S. Army.../army/index.html . The Department of the Army proposes to amend two systems of records notices in its...

  7. The design and implementation of online medical record system ...

    African Journals Online (AJOL)

    The design and implementation of online medical record system (OMRS) ... PROMOTING ACCESS TO AFRICAN RESEARCH. AFRICAN JOURNALS ONLINE (AJOL) ... International Journal of Natural and Applied Sciences. Journal Home ...

  8. 75 FR 33789 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2010-06-15

    ... categories: Individual, social workers, rehabilitation counselors, and/or health care personnel. Exemptions... mental impairments and applicants for employment with Washington Headquarters Services/Human Resources... of records in the system: Storage: Paper files folders and electronic storage media. Retrievability...

  9. NNETS - NEURAL NETWORK ENVIRONMENT ON A TRANSPUTER SYSTEM

    Science.gov (United States)

    Villarreal, J.

    1994-01-01

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

  10. A flexible time recording and time correlation analysis system

    International Nuclear Information System (INIS)

    Shenhav, N.J.; Leiferman, G.; Segal, Y.; Notea, A.

    1983-01-01

    A system was developed to digitize and record the time intervals between detection event pulses, feed to its input channels from a detection device. The accumulated data is transferred continuously in real time to a disc through a PDP 11/34 minicomputer. Even though the system was designed for a specific scope, i.e., the comparative study of passive neutron nondestructive assay methods, it can be characterized by its features as a general purpose time series recorder. The time correlation analysis is performed by software after completion of the data accumulation. The digitizing clock period is selectable and any value, larger than a minimum of 100 ns, may be selected. Bursts of up to 128 events with a frequency up to 10 MHz may be recorded. With the present recorder-minicomputer combination, the maximal average recording frequency is 40 kHz. (orig.)

  11. Symptom based diagnostic system using artificial neural networks

    International Nuclear Information System (INIS)

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

    2003-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Ruliang Wang

    2012-01-01

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

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

    DEFF Research Database (Denmark)

    Vinther, Kasper; Green, Torben; Østergaard, Søren

    2017-01-01

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

  14. A battery-free multichannel digital neural/EMG telemetry system for flying insects.

    Science.gov (United States)

    Thomas, Stewart J; Harrison, Reid R; Leonardo, Anthony; Reynolds, Matthew S

    2012-10-01

    This paper presents a digital neural/EMG telemetry system small enough and lightweight enough to permit recording from insects in flight. It has a measured flight package mass of only 38 mg. This system includes a single-chip telemetry integrated circuit (IC) employing RF power harvesting for battery-free operation, with communication via modulated backscatter in the UHF (902-928 MHz) band. An on-chip 11-bit ADC digitizes 10 neural channels with a sampling rate of 26.1 kSps and 4 EMG channels at 1.63 kSps, and telemeters this data wirelessly to a base station. The companion base station transceiver includes an RF transmitter of +36 dBm (4 W) output power to wirelessly power the telemetry IC, and a digital receiver with a sensitivity of -70 dBm for 10⁻⁵ BER at 5.0 Mbps to receive the data stream from the telemetry IC. The telemetry chip was fabricated in a commercial 0.35 μ m 4M1P (4 metal, 1 poly) CMOS process. The die measures 2.36 × 1.88 mm, is 250 μm thick, and is wire bonded into a flex circuit assembly measuring 4.6 × 6.8 mm.

  15. Algebraic and adaptive learning in neural control systems

    Science.gov (United States)

    Ferrari, Silvia

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

  16. 77 FR 21976 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2012-04-12

    ...: * * * * * System name: Delete entry and replace with ``DIA Military Recognition and Awards Files.'' System location... personnel recommended for recognition or awards while assigned or attached to DIA.'' Categories of records in the system: Delete entry and replace with ``Full name, rank, service affiliation, and Social...

  17. 75 FR 28240 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2010-05-20

    ... IN THE SYSTEM: Delete entry and replace with ``Identity information that includes Social Security... RECORDS IN THE SYSTEM: Identity information that includes Social Security Number (SSN), name, sex, race... SYSTEM: STORAGE: Delete entry and replace with ``Paper file folders and electronic storage media...

  18. 44 CFR 6.6 - Safeguarding systems of records.

    Science.gov (United States)

    2010-10-01

    ... 44 Emergency Management and Assistance 1 2010-10-01 2010-10-01 false Safeguarding systems of... maintained. (b) Personnel information contained in both manual and automated systems of records shall be... alternative storage system providing that it furnished an equivalent degree of physical security as storage in...

  19. 77 FR 38342 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2012-06-27

    ...), interested persons are invited to submit written data, views, or arguments on this proposal. A report of the... Tracking Activity USPS 830.000 SYSTEM NAME: Customer Service and Correspondence USPS 840.000 SYSTEM NAME... waiting to know if information about them is kept in this system of records must address inquiries in...

  20. 76 FR 67155 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2011-10-31

    ... inventory of record systems subject to the Privacy Act of 1974 (5 U.S.C. 552a), as amended. DATES: The... Federal Register Liaison Officer, Department of Defense. N05000-1 System name: OPNAV Headquarters Web... DEPARTMENT OF DEFENSE Department of the Navy [Docket ID USN-2011-0015] Privacy Act of 1974; System...

  1. 76 FR 35419 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2011-06-17

    ..., Alternate OSD Federal Register Liaison Officer, Department of Defense. DHA 10 System name: DoD Women... records, please contact the system manager.'' Categories of individuals covered by the system: Delete... members who are eligible for the DoD Women, Infants, and Children Overseas Program.'' Categories of...

  2. 77 FR 29619 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2012-05-18

    ... Administration (R-ADM). System location: United States Navy ships and submarines. Official mailing addresses are... by the system: United States Navy commissioned and enlisted personnel. Categories of records in the system: Name, Social Security Number (SSN), gender, race/ethnicity, birth date, place of birth, home...

  3. 76 FR 28964 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2011-05-19

    ... other than military members.'' Safeguards: Delete entry and replace with ``Mass Communication Tool (MCT... System name: Marine Corps Family Readiness Mass Communication Records. System location: Primary location.... Safeguards: Mass Communication Tool (MCT)--Password controlled system, file, and element access based on...

  4. A multi-channel high-resolution time recorder system

    International Nuclear Information System (INIS)

    Zhang Lingyun; Yang Xiaojun; Song Kezhu; Wang Yanfang

    2004-01-01

    This paper introduces a multi-channel and high-speed time recorder system, which was originally designed to work in the experiments of quantum cryptography research. The novelty of the system is that all the hardware logic is performed by only one FPGA. The system can achieve several desirable features, such as simplicity, high resolution and high processing speed. (authors)

  5. 76 FR 35197 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2011-06-16

    ... Information Architecture--Learning Management System (ATIA-LMS).'' * * * * * Authority for maintenance of the...). (Signature)'.'' * * * * * A0351b TRADOC DoD System name: Army Training Information Architecture--Learning... (703) 428-6185. SUPPLEMENTARY INFORMATION: Department of the Army notices for systems of records...

  6. 76 FR 9849 - Privacy Act of 1974: System of Records

    Science.gov (United States)

    2011-02-22

    ... system of records has been sent to the U.S. Congress and the Office of Management and Budget (OMB). V... Classification: Unclassified, Sensitive. System Location: Medical Exemption Program databases reside at the..., encryption, firewalls, and secured operating systems and to which only authorized personnel with a specific...

  7. 78 FR 73511 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2013-12-06

    ... DEPARTMENT OF DEFENSE [Docket ID: USN-2013-0046] Privacy Act of 1974; System of Records AGENCY... Privacy Act of 1974, as amended. The system being deleted is N01500-8, System Name: Personnel and Training... Internet at http://www.regulations.gov as they are received without change, including any personal...

  8. 75 FR 13575 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2010-03-22

    ..., proposes to modify the Accounting Systems for the Department of Justice, Justice/DOJ-001, to update the... the system description is set forth below. FOR FURTHER INFORMATION CONTACT: Robin Moss, Privacy... NAME: Accounting Systems for the Department of Justice (DOJ). * * * * * RECORD SOURCE CATEGORIES...

  9. 76 FR 81910 - Abolishment of Privacy Act System of Records

    Science.gov (United States)

    2011-12-29

    ... Program Manager, USDA, Forest Service, Office of Safety and Occupational and Health, 1400 Independence... Management Handbook (FSH) 6209.11. This system is abolished and removed from the inventory of the USDA System... DEPARTMENT OF AGRICULTURE Office of the Secretary Abolishment of Privacy Act System of Records...

  10. 78 FR 22525 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2013-04-16

    ... 10-0004 System name: Occupational, Safety, Health, and Environmental Management Records (July 2, 2010...: Delete entry and replace with ``This system will manage occupational, safety, health, and environmental... Management System Office, 4800 Mark Center Drive; East Tower, 2nd Floor, Suite 02G09, Alexandria, VA 22350...

  11. 77 FR 56625 - Privacy Act of 1974; Systems of Records

    Science.gov (United States)

    2012-09-13

    ... a System of Records. SUMMARY: The National Security Agency (NSA) is proposing to amend a system of..., Alternate OSD Federal Register Liaison Officer, Department of Defense. GNSA 25 System Name: NSA/CSS Travel... ``Primary location: National Security Agency/Central Security Service (NSA/CSS), 9800 Savage Road, Ft...

  12. 75 FR 70343 - Privacy Act; System of Records: Records of the Bureau of Public Affairs

    Science.gov (United States)

    2010-11-17

    ...: Electronic, hardcopy. RETRIEVABILITY: By individual name. SAFEGUARDS: All users are given cyber security...: Purpose and Disclosure to Consumer Reporting Agencies. Any persons interested in commenting on the amended.... State-22 SYSTEM NAME: Records of the Bureau of Public Affairs. SECURITY CLASSIFICATION: Unclassified...

  13. 77 FR 15026 - Privacy Act of 1974; Farm Records File (Automated) System of Records

    Science.gov (United States)

    2012-03-14

    ..., protect the environment, and enhance the marketing of agricultural products. The system of records covers... marketing data; Lease and transfer of allotments and quotas; Appeals; New grower applications; Conservation... boundaries and is recommended as the common location identifier for reporting acreage. Digital renditions of...

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

    DEFF Research Database (Denmark)

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

    2001-01-01

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

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

    DEFF Research Database (Denmark)

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

    2001-01-01

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

  16. 78 FR 37775 - Privacy Act Systems of Records; Phytosanitary Certificate Issuance and Tracking System

    Science.gov (United States)

    2013-06-24

    ... DEPARTMENT OF AGRICULTURE Office of the Secretary [Docket No. APHIS-2012-0104] Privacy Act Systems of Records; Phytosanitary Certificate Issuance and Tracking System AGENCY: Animal and Plant Health... Health Inspection Service proposes to add a system of records to its inventory of records systems subject...

  17. Implantable neurotechnologies: a review of integrated circuit neural amplifiers.

    Science.gov (United States)

    Ng, Kian Ann; Greenwald, Elliot; Xu, Yong Ping; Thakor, Nitish V

    2016-01-01

    Neural signal recording is critical in modern day neuroscience research and emerging neural prosthesis programs. Neural recording requires the use of precise, low-noise amplifier systems to acquire and condition the weak neural signals that are transduced through electrode interfaces. Neural amplifiers and amplifier-based systems are available commercially or can be designed in-house and fabricated using integrated circuit (IC) technologies, resulting in very large-scale integration or application-specific integrated circuit solutions. IC-based neural amplifiers are now used to acquire untethered/portable neural recordings, as they meet the requirements of a miniaturized form factor, light weight and low power consumption. Furthermore, such miniaturized and low-power IC neural amplifiers are now being used in emerging implantable neural prosthesis technologies. This review focuses on neural amplifier-based devices and is presented in two interrelated parts. First, neural signal recording is reviewed, and practical challenges are highlighted. Current amplifier designs with increased functionality and performance and without penalties in chip size and power are featured. Second, applications of IC-based neural amplifiers in basic science experiments (e.g., cortical studies using animal models), neural prostheses (e.g., brain/nerve machine interfaces) and treatment of neuronal diseases (e.g., DBS for treatment of epilepsy) are highlighted. The review concludes with future outlooks of this technology and important challenges with regard to neural signal amplification.

  18. HOSPITAL INFORMATION SYSTEMS: A STUDY OF ELECTRONIC PATIENT RECORDS

    Directory of Open Access Journals (Sweden)

    Pedro Luiz Cortês

    2011-05-01

    Full Text Available The importance of patient records, also known as medical records, is related to different needs and objectives, as they constitute permanent documents on the health of patients. With the advancement of information technologies and systems, patient records can be stored in databases, resulting in a positive impact on patient care. Based on these considerations, a research question that arises is “what are the benefits and problems that can be seen with the use of electronic versions of medical records?” This question leads to the formulation of the following hypothesis: although problems can be identified during the process of using electronic record systems, the benefits outweigh the difficulties, thereby justifying their use. To respond to the question and test the presented hypothesis, a research study was developed with users of the same electronic record system, consisting of doctors, nurses, and administrative personnel in three hospitals located in the city of São Paulo, Brazil. The results show that, despite some problems in their usage, the benefits of electronic patient records outweigh possible disadvantages.

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

    Science.gov (United States)

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

    2017-10-01

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

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

    Science.gov (United States)

    Song, Tao; Xu, Jinbang; Pan, Linqiang

    2015-12-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2010-07-01

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

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

    Science.gov (United States)

    Wai, Rong-Jong; Lee, Jeng-Dao

    2008-01-01

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

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

    Science.gov (United States)

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

    2017-11-01

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

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

    Directory of Open Access Journals (Sweden)

    S. Devaraju

    2014-04-01

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

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

    Science.gov (United States)

    Adams, Meghan Sara; Bronner-Fraser, Marianne

    2009-01-01

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

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

    International Nuclear Information System (INIS)

    Lan Hengyou; Cui Yishun

    2009-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Cipriani Christian

    2011-09-01

    Full Text Available Abstract 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.

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

    Science.gov (United States)

    Micera, Silvestro; Rossini, Paolo M; Rigosa, Jacopo; Citi, Luca; Carpaneto, Jacopo; Raspopovic, Stanisa; Tombini, Mario; Cipriani, Christian; Assenza, Giovanni; Carrozza, Maria C; Hoffmann, Klaus-Peter; Yoshida, Ken; Navarro, Xavier; Dario, Paolo

    2011-09-05

    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. 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. 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. These results open up new and promising possibilities for the development of a neuro-controlled hand prosthesis.

  9. Medical record management systems: criticisms and new perspectives.

    Science.gov (United States)

    Frénot, S; Laforest, F

    1999-06-01

    The first generation of computerized medical records stored the data as text, but these records did not bring any improvement in information manipulation. The use of a relational database management system (DBMS) has largely solved this problem as it allows for data requests by using SQL. However, this requires data structuring which is not very appropriate to medicine. Moreover, the use of templates and icon user interfaces has introduced a deviation from the paper-based record (still existing). The arrival of hypertext user interfaces has proven to be of interest to fill the gap between the paper-based medical record and its electronic version. We think that further improvement can be accomplished by using a fully document-based system. We present the architecture, advantages and disadvantages of classical DBMS-based and Web/DBMS-based solutions. We also present a document-based solution and explain its advantages, which include communication, security, flexibility and genericity.

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

    Science.gov (United States)

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

    2011-12-15

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

  11. Electronic health record standards, coding systems, frameworks, and infrastructures

    CERN Document Server

    Sinha, Pradeep K; Bendale, Prashant; Mantri, Manisha; Dande, Atreya

    2013-01-01

    Discover How Electronic Health Records Are Built to Drive the Next Generation of Healthcare Delivery The increased role of IT in the healthcare sector has led to the coining of a new phrase ""health informatics,"" which deals with the use of IT for better healthcare services. Health informatics applications often involve maintaining the health records of individuals, in digital form, which is referred to as an Electronic Health Record (EHR). Building and implementing an EHR infrastructure requires an understanding of healthcare standards, coding systems, and frameworks. This book provides an

  12. National electronic medical records integration on cloud computing system.

    Science.gov (United States)

    Mirza, Hebah; El-Masri, Samir

    2013-01-01

    Few Healthcare providers have an advanced level of Electronic Medical Record (EMR) adoption. Others have a low level and most have no EMR at all. Cloud computing technology is a new emerging technology that has been used in other industry and showed a great success. Despite the great features of Cloud computing, they haven't been utilized fairly yet in healthcare industry. This study presents an innovative Healthcare Cloud Computing system for Integrating Electronic Health Record (EHR). The proposed Cloud system applies the Cloud Computing technology on EHR system, to present a comprehensive EHR integrated environment.

  13. 77 FR 70136 - Privacy Act of 1974, System of Records

    Science.gov (United States)

    2012-11-23

    .... It is USAID's core financial management system and accounting system of record. Phoenix enables USAID..., which are required to perform necessary accounting operations. Phoenix falls under strict regulatory...: name, social security number, details of payroll transactions and work phone numbers. Phoenix imports...

  14. 76 FR 61679 - Privacy Act of 1974; Systems of Records

    Science.gov (United States)

    2011-10-05

    ... destruction of magnetic media. * * * * * GNSA 27 System name: Information Assurance Scholarship Program...). Categories of records in the system: Individual information to include: Title, full name, Social Security... information necessary to establish the identity of the individual, including name, address, and taxpayer...

  15. 75 FR 69642 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2010-11-15

    ... retirement data; civilian deployment information and adverse and disciplinary action data. Personnel...; civilian deployment information and adverse and disciplinary action data. Personnel information including...; System of Records AGENCY: Office of the Secretary of Defense, DoD. ACTION: Notice to Alter a System of...

  16. 75 FR 21264 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2010-04-23

    ... facilities. Categories of records in the system: Name, Social Security Number (SSN), and DoD Electronic Data... Army's Food Service Program. The system facilitates the ordering, receipt, warehousing, and issuance of... user name and password. Data is encrypted in its stored form and cannot be accessed except through the...

  17. 76 FR 21708 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2011-04-18

    ... Appendix I to OMB Circular No. A-130, ``Federal Agency Responsibilities for Maintaining Records About... Instruction (DoDI) 1400.25-V810, DoD Civilian Personnel Management System: Injury Compensation; DoDI 1400.25-V850, DoD Civilian Personnel Management System: Unemployment Compensation; DoD 1400.25-M, DoD Civilian...

  18. 77 FR 24925 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2012-04-26

    ... given that the United States Department of Agriculture (USDA) is revising two Privacy Act (PA) systems... Agriculture (NIFA), formerly the Cooperative State Research, Education, and Extension Service (CSREES). DATES... INFORMATION: I. Background 1. Two systems of records are being revised. A. State Cooperative Extension Service...

  19. 76 FR 55057 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2011-09-06

    ... Records. System location: U.S. Army Human Resources Command, Education Incentives Branch, 1600 Spearhead... Reserve; Army Regulation 621-5, Army Continuing Education System; Army Regulations 621-202, Education... accordance with 28 U.S.C. 1746, in the following format: If executed outside the United States: `I declare...

  20. 78 FR 5792 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2013-01-28

    ..., Marine Corps; BUMED Note 6110, Tracking and Reporting Individual Medical Readiness Data; SECNAVINST 6120... persons that are properly screened, cleared, and trained. Access to this system of records and personal... available only to authorized personnel having a need-to-know.'' * * * * * System manager(s) and address...

  1. 75 FR 65405 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2010-10-22

    ... that its three existing systems of records know as ``Veterans and Armed Forces Personnel United States... technology, including the following: (a) Use of the Disability Outreach Tracking System (DOTS) which stores... preventing and detecting fraud or abuse by persons in their operations or programs. Routine use number 7 has...

  2. Archaen to Recent aeolian sand systems and their sedimentary record

    DEFF Research Database (Denmark)

    Rodríguez-López, Juan Pedro; Clemmensen, Lars B; Lancaster, Nick

    2014-01-01

    The sedimentary record of aeolian sand systems extends from the Archean to the Quaternary, yet current understanding of aeolian sedimentary processes and product remains limited. Most preserved aeolian successions represent inland sand-sea or dunefield (erg) deposits, whereas coastal systems are ...

  3. 77 FR 27859 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2012-05-11

    ..., during the comment period, comments may be viewed online through the Federal Docket Management System... (704) 245-2492. SUPPLEMENTARY INFORMATION: I. Description of Proposed Systems of Records Background The... Federal agencies may be made to assist such agencies in preventing and detecting possible fraud or abuse...

  4. 75 FR 19946 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2010-04-16

    ... of records in the system: Delete entry and replace with ``Individual's name, organizational and home..., contact listing files, organizational telephone directories, and listing of office personnel.'' [[Page... are used to control access to the system data, and procedures are in place to deter and detect...

  5. 75 FR 67697 - Privacy Act of 1974; Systems of Records

    Science.gov (United States)

    2010-11-03

    ... OF INDIVIDUALS COVERED BY THE SYSTEM: Individuals and institutions who apply for recruitment...). CATEGORIES OF RECORDS IN THE SYSTEM: Individual information to include: Title, full name, Social Security... awarding of recruitment scholarships, retention scholarships or grants under the DoD Information Assurance...

  6. 75 FR 54662 - Privacy Act of 1974: Systems of Records

    Science.gov (United States)

    2010-09-08

    ..., Chief Privacy Officer, Office of Information Technology, 202-551-7209. In the Federal Register of August... SECURITIES AND EXCHANGE COMMISSION [Release No. PA-44A; File No. S7-17-10] Privacy Act of 1974: Systems of Records AGENCY: Securities and Exchange Commission. ACTION: Notice to establish systems of...

  7. 75 FR 53262 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2010-08-31

    ... a new Privacy Act system of records, JUSTICE/FBI- 021, the Data Integration and Visualization System... provisions of the Privacy Act in order to avoid interference with the national security and criminal law...)(G), (H) and (I); (e)(5) and (8); (f) and (g) of the Privacy Act: (1) Data Integration and...

  8. 77 FR 25993 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2012-05-02

    ... records in the system: Name, Social Security Number (SSN), date of birth, home address, place of birth..., Performance Appraisal; 51, Classification; 53, Pay Rates and Systems; 55, Pay Administration; 61, Hours of... Group Statistics; SECNAV Instruction 12250.6, Civilian Human Resources Management in the Department of...

  9. 78 FR 14283 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2013-03-05

    ... Finance and Accounting Service, Freedom of Information/Privacy Act Program Manager, Corporate... a system of records. SUMMARY: The Defense Finance and Accounting Service proposes to alter a system... identifiers or contact information. FOR FURTHER INFORMATION CONTACT: Mr. Gregory L. Outlaw, Defense Finance...

  10. 78 FR 17386 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2013-03-21

    ... System name: Historical Airman Promotion Master Test File (MTF) (June 11, 1997, 62 FR 31793). Changes... of Senior Airman (E-4) to Senior Master Sergeant (E-8).'' Categories of records in the system: Delete... Infrastructure (PKI)/ Common Access Card (CAC) authentication to lock out unauthorized access. Access to the...

  11. 76 FR 7239 - Proposed Privacy Act Systems of Records

    Science.gov (United States)

    2011-02-09

    ... property interests, identity theft or fraud, or harm to the security or integrity of this system or other... notice of a proposed system of records entitled, ``Treasury/DO .225 --TARP Fraud Investigation...: Supervisory Fraud Specialist, Office of Financial Stability, 1500 Pennsylvania Avenue, NW., Washington, DC...

  12. 77 FR 29616 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2012-05-18

    .... DCIO 01 System name: Defense Industrial Base (DIB) Cyber Security/Information Assurance Records. System location: Director, Defense Industrial Base (DIB) Cyber Security/Information Assurance (CS/IA) Program... Infrastructure Program (DCIP) Management; and DoDI 5205.13, Defense Industrial Base (DIB) Cyber Security...

  13. 75 FR 77849 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2010-12-14

    ... Agency, Defense Threat Reduction Agency, Missile Defense Organization, Pentagon Force Protection Agency... DEPARTMENT OF DEFENSE Office of the Secretary [Docket ID: DOD-2010-OS-0160] Privacy Act of 1974; System of Records AGENCY: Office of the Secretary of Defense, DoD. ACTION: Notice to add a system of...

  14. 75 FR 21250 - Privacy Act of 1974; Systems of Records

    Science.gov (United States)

    2010-04-23

    ... permanently retained are destroyed. Visitor passes and campus access files are destroyed when 15 years old... used to pass through automated turnstile system, access office suites and other work areas; to track..., ACCESSING, RETAINING, AND DISPOSING OF RECORDS IN THE SYSTEM: STORAGE: Paper files and electronic storage...

  15. 76 FR 36903 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2011-06-23

    ... have a need-to-know. Individuals responsible for servicing the records in the performance of official... name, nick names, Social Security Number (SSN), gender, date of birth, personal cell phone number, home... address: Delete entry and replace with ``Automated Civil Engineer System/ Interim Work Management System...

  16. 78 FR 64031 - Privacy Act of 1974: Systems of Records

    Science.gov (United States)

    2013-10-25

    ....'' The proposed system of records is necessary to the functions performed by the Workplace Solutions... agreement, self-certification home safety checklist, and supervisor-employee checklist; type of telework... practice. SYSTEM MANAGER(S) AND ADDRESS: Director, Financial Operations Department, Pension Benefit...

  17. 76 FR 52320 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2011-08-22

    ... & Privacy, and DoD Information Assurance Regulations. Auditing: Audit trail records from all available.../JS Privacy Office, Freedom of Information Directorate, Washington Headquarters Services, 1155 Defense... Defense. DHA 23 System name: Pharmacy Data Transaction Service (PDTS). System location: Primary: Emdeon...

  18. 77 FR 44207 - Privacy Act of 1974, System of Records

    Science.gov (United States)

    2012-07-27

    ... 10, 2012. ADDRESSES: You may submit comments: Paper Comments Fax: (703) 666-5670. Mail: Chief Privacy... the Privacy Act. It is USAID's core financial management system and accounting system of record... Budget Execution, which are required to perform necessary accounting operations. Phoenix falls under...

  19. 76 FR 65535 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2011-10-21

    ... the FBI BRUs expressly as part of this system notice because the entire notice is being republished. While the FBI BRUs provide necessary flexibility in disseminating records from the system, FBI notes...: Elizabeth Withnell, Supervisory Attorney-Advisor, Privacy and Civil Liberties Unit, Office of the General...

  20. 77 FR 27740 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2012-05-11

    ... storage media''. Retrievability: Delete ``and Social Security Number''. Safeguards: Delete last sentence...: * * * * * Categories of individuals covered by the system: Delete entry and replace with ``Children and their sponsors... admission to, DLA-managed day care facilities.'' Categories of records in the system: Delete ``Social...

  1. 75 FR 67703 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2010-11-03

    ... DEPARTMENT OF DEFENSE Department of the Army [Docket ID USA-2010-0024] Privacy Act of 1974; System... record systems subject to the Privacy Act of 1974, (5 U.S.C. 552a), as amended. DATES: This proposed... the public is to make these submissions available for public viewing on the Internet at http://www...

  2. 76 FR 60008 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2011-09-28

    ... DEPARTMENT OF DEFENSE Department of the Army [Docket ID USA-2011-0023] Privacy Act of 1974; System... existing inventory of record systems subject to the Privacy Act of 1974, (5 U.S.C. 552a), as amended... submissions available for public viewing on the Internet at http://www.regulations.gov as they are received...

  3. 76 FR 82286 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2011-12-30

    ... DEPARTMENT OF DEFENSE Department of the Army [Docket ID USA-2011-0028] Privacy Act of 1974; System... record systems subject to the Privacy Act of 1974 (5 U.S.C. 552a), as amended. DATES: This proposed... available for public viewing on the Internet at http://www.regulations.gov as they are received without...

  4. 77 FR 31851 - Privacy Act System of Records

    Science.gov (United States)

    2012-05-30

    ... establishing this new system of records, FCC/OMD-30, ``FCC Visitors Database,'' is for the FCC's Security...-30 SYSTEM NAME: FCC Visitors Database. SECURITY CLASSIFICATION: The FCC's Security Operations Center... databases are protected by the FCC's security protocols, which include controlled access, passwords, and...

  5. 76 FR 66752 - Privacy Act of 1974; System of Records

    Science.gov (United States)

    2011-10-27

    ... SYSTEMS: STORAGE: Information is stored manually in file jackets and electronically in office automation.... SAFEGUARDS: The information is stored in safes, locked filing cabinets, and office automation equipment in a... AND DISPOSAL: Records in the system are retained and disposed of in accordance with NARA Job NCI-60-77...

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

    Directory of Open Access Journals (Sweden)

    A. R Tahavvor

    2016-09-01

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

  7. A low noise remotely controllable wireless telemetry system for single-unit recording in rats navigating in a vertical maze.

    Science.gov (United States)

    Chen, Hsin-Yung; Wu, Jin-Shang; Hyland, Brian; Lu, Xiao-Dong; Chen, Jia Jin Jason

    2008-08-01

    The use of cables for recording neural activity limits the scope of behavioral tests used in conscious free-moving animals. Particularly, cable attachments make it impossible to record in three-dimensional (3D) mazes where levels are vertically stacked or in enclosed spaces. Such environments are of particular interest in investigations of hippocampal place cells, in which neural activity is correlated with spatial position in the environment. We developed a flexible miniaturized Bluetooth-based wireless data acquisition system. The wireless module included an 8-channel analogue front end, digital controller, and Bluetooth transceiver mounted on a backpack. Our bidirectional wireless design allowed all data channels to be previewed at 1 kHz sample rate, and one channel, selected by remote control, to be sampled at 10 kHz. Extracellular recordings of neuronal activity are highly susceptible to ambient electrical noise due to the high electrode impedance. Through careful design of appropriate shielding and hardware configuration to avoid ground loops, mains power and Bluetooth hopping frequency noise were reduced sufficiently to yield signal quality comparable to those recorded by wired systems. With this system we were able to obtain single-unit recordings of hippocampal place cells in rats running an enclosed vertical maze, over a range of 5 m.

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

    Science.gov (United States)

    Gao, Rong

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

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

    Directory of Open Access Journals (Sweden)

    Mhd Saeed Sharif

    2010-01-01

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

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

    Science.gov (United States)

    Yao, X; Liu, Y

    1997-01-01

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

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

    International Nuclear Information System (INIS)

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

    1994-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2009-09-15

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

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

    Directory of Open Access Journals (Sweden)

    David Overbye

    2005-06-01

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

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

    International Nuclear Information System (INIS)

    Karami, A.; Mohammadi, M.S.

    2008-01-01

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

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

    Science.gov (United States)

    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.

  16. The NRPB automated thermoluminescent dosemeter and dose record keeping system

    International Nuclear Information System (INIS)

    Dennis, J.A.; Marshall, T.O.; Shaw, K.B.

    1975-03-01

    This report describes the thermoluminescent personal radiation dosemeter and its associated automated processing equipment, which are being developed by the National Radiological Protection Board, together with the operation of a computerised dosemeter issue and record keeping system. The main justifications for introducing these systems are improvements in the organizational efficiency of the maintenance of individual dose records, a more flexible and accurate dosimetry system, and economics in operational costs. The dosemeter is based on a numbered aluminium plate containing two lithium fluoride in polytetrafluorethylene disks for the measurement of surface and body dose. This dosemeter is wrapped in thin plastic and labelled with the wearer's name and address. On return, the dosemeter is checked automatically for radioactive contamination; it is unwrapped and evaluated; the dose readings are included in the wearer's stored dose record; the dosemeter is annealed and is then available for re-issue to another wearer. Dose reports and warnings are automatically issued to the wearer or his employer. (author)

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

    OpenAIRE

    Xiangyu He; Shanghong He

    2014-01-01

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

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

    International Nuclear Information System (INIS)

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

    2006-01-01

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

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

    Science.gov (United States)

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

    2018-04-01

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

  20. Disease Recording Systems and Herd Health Schemes for Production Diseases

    Directory of Open Access Journals (Sweden)

    Østerås O

    2001-03-01

    Full Text Available Disease recording of cattle is compulsory in Sweden and Norway. Sweden and Denmark also have mandatory disease recording for swine, whereas Finland and Norway only have compulsory recording of infectious diseases. Both compulsory and voluntary systems are databased, the first ones developed in the 1970's. Disease recording at pig slaughtering is somewhat older. The veterinary practitioner, and often also the farmer, can report treated cases as well as fertility disturbances to the systems. Disease recording at slaughter is carried out by veterinarians and inspection officers. The databases are handled by the veterinary authorities or the agricultural organisations in each country. Costs are defrayed by the authorities and/or the agricultural industry. The farmers receive periodic reports. Data are stored for three to ten years, often longer. Affiliation to animal health schemes for cattle or swine is voluntary. In Sweden and Denmark (cattle they are run within the scope of government regulations. Affiliation to animal health programmes may also be demanded by organisations within the agricultural industry. These organisations are also responsible for the administration of the programmes. Costs to take part in herd health schemes are covered by the farmers themselves. In certain cases, grants are received from agricultural organisations, authorities, or the European Union. Recording of diseases and the format of animal health schemes in the Nordic countries are described here in order to illustrate the possibilities to compare data between countries.