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Sample records for chronic multi-electrode neural

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

    Science.gov (United States)

    Kim, Tae Gyo

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

  2. Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays.

    Science.gov (United States)

    Mena, Gonzalo E; Grosberg, Lauren E; Madugula, Sasidhar; Hottowy, Paweł; Litke, Alan; Cunningham, John; Chichilnisky, E J; Paninski, Liam

    2017-11-01

    Simultaneous electrical stimulation and recording using multi-electrode arrays can provide a valuable technique for studying circuit connectivity and engineering neural interfaces. However, interpreting these measurements is challenging because the spike sorting process (identifying and segregating action potentials arising from different neurons) is greatly complicated by electrical stimulation artifacts across the array, which can exhibit complex and nonlinear waveforms, and overlap temporarily with evoked spikes. Here we develop a scalable algorithm based on a structured Gaussian Process model to estimate the artifact and identify evoked spikes. The effectiveness of our methods is demonstrated in both real and simulated 512-electrode recordings in the peripheral primate retina with single-electrode and several types of multi-electrode stimulation. We establish small error rates in the identification of evoked spikes, with a computational complexity that is compatible with real-time data analysis. This technology may be helpful in the design of future high-resolution sensory prostheses based on tailored stimulation (e.g., retinal prostheses), and for closed-loop neural stimulation at a much larger scale than currently possible.

  3. Braided multi-electrode probes: mechanical compliance characteristics and recordings from spinal cords

    Science.gov (United States)

    Kim, Taegyo; Branner, Almut; Gulati, Tanuj; Giszter, Simon F.

    2013-08-01

    Objective. To test a novel braided multi-electrode probe design with compliance exceeding that of a 50 µm microwire, thus reducing micromotion- and macromotion-induced tissue stress. Approach. We use up to 24 ultra-fine wires interwoven into a tubular braid to obtain a highly flexible multi-electrode probe. The tether-portion wires are simply non-braided extensions of the braid structure, allowing the microprobe to follow gross neural tissue movements. Mechanical calculation and direct measurements evaluated bending stiffness and axial compression forces in the probe and tether system. These were compared to 50 µm nichrome microwire standards. Recording tests were performed in decerebrate animals. Main results. Mechanical bending tests on braids comprising 9.6 or 12.7 µm nichrome wires showed that implants (braided portions) had 4 to 21 times better mechanical compliance than a single 50 µm wire and non-braided tethers were 6 to 96 times better. Braided microprobes yielded robust neural recordings from animals' spinal cords throughout cord motions. Significance. Microwire electrode arrays that can record and withstand tissue micro- and macromotion of spinal cord tissues are demonstrated. This technology may provide a stable chronic neural interface into spinal cords of freely moving animals, is extensible to various applications, and may reduce mechanical tissue stress.

  4. Improving Impedance of Implantable Microwire Multi-Electrode Arrays by Ultrasonic Electroplating of Durable Platinum Black

    Science.gov (United States)

    Desai, Sharanya Arcot; Rolston, John D.; Guo, Liang; Potter, Steve M.

    2010-01-01

    Implantable microelectrode arrays (MEAs) have been a boon for neural stimulation and recording experiments. Commercially available MEAs have high impedances, due to their low surface area and small tip diameters, which are suitable for recording single unit activity. Lowering the electrode impedance, but preserving the small diameter, would provide a number of advantages, including reduced stimulation voltages, reduced stimulation artifacts and improved signal-to-noise ratio. Impedance reductions can be achieved by electroplating the MEAs with platinum (Pt) black, which increases the surface area but has little effect on the physical extent of the electrodes. However, because of the low durability of Pt black plating, this method has not been popular for chronic use. Sonicoplating (i.e. electroplating under ultrasonic agitation) has been shown to improve the durability of Pt black on the base metals of macro-electrodes used for cyclic voltammetry. This method has not previously been characterized for MEAs used in chronic neural implants. We show here that sonicoplating can lower the impedances of microwire multi-electrode arrays (MMEA) by an order of magnitude or more (depending on the time and voltage of electroplating), with better durability compared to pulsed plating or traditional DC methods. We also show the improved stimulation and recording performance that can be achieved in an in vivo implantation study with the sonicoplated low-impedance MMEAs, compared to high-impedance unplated electrodes. PMID:20485478

  5. A split microdrive for simultaneous multi-electrode recordings from two brain areas in awake small animals.

    NARCIS (Netherlands)

    Lansink, C.S.; Bakker, M.; Buster, W.; Lankelma, J.; van der Blom, R.; Westdorp, R.; Joosten, R.N.J.M.A.; Mc.Naughton, B.L.; Pennartz, C.M.A.

    2007-01-01

    Complex cognitive operations such as memory formation and decision-making are thought to be mediated not by single, isolated brain structures but by multiple, connected brain areas. To facilitate studies on the neural communication between connected brain structures, we developed a multi-electrode

  6. Extraction of network topology from multi-electrode recordings: Is there a small-world effect?

    Directory of Open Access Journals (Sweden)

    Felipe eGerhard

    2011-02-01

    Full Text Available The simultaneous recording of the activity of many neurons poses challenges for multivariate data analysis. Here, we propose a general scheme of reconstruction of the functional network from spike train recordings. Effective, causal interactions are estimated by fitting Generalized Linear Models (GLMs on the neural responses, incorporating effects of the neurons' self-history, of input from other neurons in the recorded network and of modulation by an external stimulus. The coupling terms arising from synaptic input can be transformed by thresholding into a binary connectivity matrix which is directed. Each link between two neurons represents a causal influence from one neuron to the other, given the observation of all other neurons from the population. The resulting graph is analyzed with respect to small-world and scale-free properties using quantitative measures for directed networks. Such graph-theoretic analyses have been performed on many complex dynamic networks, including the connectivity structure between different brain areas. Only few studies have attempted to look at the structure of cortical neural networks on the level of individual neurons. Here, using multi-electrode recordings from the visual system of the awake monkey, we find that cortical networks lack scale-free behavior, but show a small, but significant small-world structure. Assuming a simple distance-dependent probabilistic wiring between neurons, we find that this connectivity structure can account for all of the networks' observed small-world-ness. Moreover, for multi-electrode recordings the sampling of neurons is not uniform across the population. We show that the small-world-ness obtained by such a localized sub-sampling overestimates the strength of the true small-world-structure of the network. This bias is likely to be present in all previous experiments based on multi-electrode recordings.

  7. Evaluation and use of regenerative multi electrode interfaces in peripheral nerves

    Science.gov (United States)

    Desai, Vidhi

    Peripheral nerves offer unique accessibility to the innate motor and sensory pathways that can be interfaced with high degree of selectivity for intuitive and bidirectional control of advanced upper extremity prosthetic limbs. Several peripheral nerve interfaces have been proposed and investigated over the last few decades with significant progress made in the area of sensory feedback. However, clinical translation still remains a formidable challenge due to the lack of long term recordings. Prominent causes include signal degradation, eventual interface failures, and lack of specificity in the low amplitude nerve signals. This dissertation evaluates the capabilities of the newly developed Regenerative Multi-electrode Interface (REMI) by the characterization of signal quality progression, the identification of interfaced axon types, and the demonstration of "functional linkage" between acquired signals and target organs. Chapter 2 details the chronic recording of high quality signals from REMI in sciatic nerve which remained stable over a 120 day implantation period indicative of minimal ongoing tissue response with no detrimental effects on the recording ability. The dominant cause of failures was attributable to abiotic factors pertaining to the connector/wire breakage, observed in 76% of REMI implants. Also, the REMI implants had 20% higher success rate and significantly larger Signal to Noise Ratio (SNR) in comparison to the Utah Slanted Electrode Array (USEA). Chapter 3 describes the successful feasibility of interfacing with motor and sensory axons by REMI implantation in the tibial and sural fascicles of the sciatic nerve. A characteristic sampling bias towards recording signals from medium-to-large diameter axons that are primarily involved in mechanoception and proprioception sensory functions was uncovered. Specific bursting units (Inter Spike Interval of 30-70ms) were observed most frequently from the tibial fascicle during bipedal locomotion. Chapter 4

  8. Symplicity multi-electrode radiofrequency renal denervation system feasibility study.

    Science.gov (United States)

    Whitbourn, Robert; Harding, Scott A; Walton, Antony

    2015-05-01

    The aim of this study was to test the safety and performance of the Symplicity™ multi-electrode radio-frequency renal denervation system which was designed to reduce procedure time during renal denervation. The multi-electrode radiofrequency renal denervation system feasibility study is a prospective, non-randomised, open label, feasibility study that enrolled 50 subjects with hypertension. The study utilises a new renal denervation catheter which contains an array of four electrodes mounted in a helical configuration at 90 degrees from each other to deliver radiofrequency energy simultaneously to all four renal artery quadrants for 60 seconds. The protocol specified one renal denervation treatment towards the distal end of each main renal artery with radiofrequency energy delivered for 60 seconds per treatment. Total treatment time for both renal arteries was two minutes. The 12-month change in office systolic blood pressure (SBP) and 24-hour SBP was -19.2±25.2 mmHg, prenal artery stenosis or hypertensive emergencies occurred. The Symplicity multi-electrode radiofrequency renal denervation system was associated with a significant reduction in SBP at 12 months and minimal complications whilst it also reduced procedure time. NCT01699529.

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

    OpenAIRE

    Simons, Laura; Elman, Igor; Borsook, David

    2013-01-01

    Our understanding of chronic pain involves complex brain circuits that include sensory, emotional, cognitive and interoceptive processing. The feed-forward interactions between physical (e.g., trauma) and emotional pain and the consequences of altered psychological status on the expression of pain have made the evaluation and treatment of chronic pain a challenge in the clinic. By understanding the neural circuits involved in psychological processes, a mechanistic approach to the implementati...

  10. Multi-Electrode Impedance Method for Detection of Regional Ventilation

    International Nuclear Information System (INIS)

    Furuya, Norio; Sakamoto, Katsuyuki

    2013-01-01

    By means of computer simulation and experiment, we investigated the feasibility of simultaneously measuring the transfer impedance changes in the right apex, left apex, right base and left base of the lungs using the multi-electrode impedance method. To obtain the transfer impedance in each region, while suppressing the effects of other regions, changing the amplitude and polarity of the applied current must localize the high sensitivity areas in the interest region. Twelve current and eight voltage electrodes were equidistantly arranged on the anterior and posterior chest walls. The amplitudes and polarities of the currents that were simultaneously applied to the current electrodes, and which provided the appropriate sensitivity distribution, were theoretically obtained. The effects of the localized sensitivity distribution were verified by comparing the simulation results of the investigated method with the results of the conventional four-electrode method. From the results of the computer simulation, we developed a multi-electrode impedance pneumography and applied it to healthy adult volunteers who were both in sitting position and in left decubitus. We found that the measurement results were physiologically reasonable.

  11. Compensatory recruitment of neural resources in chronic alcoholism.

    Science.gov (United States)

    Chanraud, Sandra; Sullivan, Edith V

    2014-01-01

    Functional recovery occurs with sustained sobriety, but the neural mechanisms enabling recovery are only now emerging. Theories about promising mechanisms involve concepts of neuroadaptation, where excessive alcohol consumption results in untoward structural and functional brain changes which are subsequently candidates for reversal with sobriety. Views on functional adaptation in chronic alcoholism have expanded with results from neuroimaging studies. Here, we first describe and define the concept of neuroadaptation according to emerging theories based on the growing literature in aging-related cognitive functioning. Then we describe findings as they apply to chronic alcoholism and factors that could influence compensation, such as functional brain reserve and the integrity of brain structure. Finally, we review brain plasticity based on physiologic mechanisms that could underlie mechanisms of neural compensation. Where possible, we provide operational criteria to define functional and neural compensation. © 2014 Elsevier B.V. All rights reserved.

  12. Conformally encapsulated multi-electrode arrays with seamless insulation

    Energy Technology Data Exchange (ETDEWEB)

    Tabada, Phillipe J.; Shah, Kedar G.; Tolosa, Vanessa; Pannu, Satinderall S.; Tooker, Angela; Delima, Terri; Sheth, Heeral; Felix, Sarah

    2016-11-22

    Thin-film multi-electrode arrays (MEA) having one or more electrically conductive beams conformally encapsulated in a seamless block of electrically insulating material, and methods of fabricating such MEAs using reproducible, microfabrication processes. One or more electrically conductive traces are formed on scaffold material that is subsequently removed to suspend the traces over a substrate by support portions of the trace beam in contact with the substrate. By encapsulating the suspended traces, either individually or together, with a single continuous layer of an electrically insulating material, a seamless block of electrically insulating material is formed that conforms to the shape of the trace beam structure, including any trace backings which provide suspension support. Electrical contacts, electrodes, or leads of the traces are exposed from the encapsulated trace beam structure by removing the substrate.

  13. Complexity optimization and high-throughput low-latency hardware implementation of a multi-electrode spike-sorting algorithm.

    Science.gov (United States)

    Dragas, Jelena; Jackel, David; Hierlemann, Andreas; Franke, Felix

    2015-03-01

    Reliable real-time low-latency spike sorting with large data throughput is essential for studies of neural network dynamics and for brain-machine interfaces (BMIs), in which the stimulation of neural networks is based on the networks' most recent activity. However, the majority of existing multi-electrode spike-sorting algorithms are unsuited for processing high quantities of simultaneously recorded data. Recording from large neuronal networks using large high-density electrode sets (thousands of electrodes) imposes high demands on the data-processing hardware regarding computational complexity and data transmission bandwidth; this, in turn, entails demanding requirements in terms of chip area, memory resources and processing latency. This paper presents computational complexity optimization techniques, which facilitate the use of spike-sorting algorithms in large multi-electrode-based recording systems. The techniques are then applied to a previously published algorithm, on its own, unsuited for large electrode set recordings. Further, a real-time low-latency high-performance VLSI hardware architecture of the modified algorithm is presented, featuring a folded structure capable of processing the activity of hundreds of neurons simultaneously. The hardware is reconfigurable “on-the-fly” and adaptable to the nonstationarities of neuronal recordings. By transmitting exclusively spike time stamps and/or spike waveforms, its real-time processing offers the possibility of data bandwidth and data storage reduction.

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

    Science.gov (United States)

    Guo, Liang

    2016-01-01

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

  15. Cell growth characterization using multi-electrode bioimpedance spectroscopy

    International Nuclear Information System (INIS)

    Lu, Yi-Yu; Huang, Yu-Jie; Cheng, Kuo-Sheng; Huang, Ji-Jer

    2013-01-01

    Cell growth characterization during culturing is an important issue in a variety of biomedical applications. In this study an electrical bioimpedance spectroscopy-based multi-electrode culture monitoring system was developed to characterize cell growth. A PC12 cell line was cultured for the cell growth study. The bioimpedance variations for PC12 cell growth within the initial 12 h were measured over a range between 1 kHz and 4 MHz at three different medium concentrations. Within this frequency range, the largest bioimpedance value was 1.9 times the smallest bioimpedance value. The phase angle decreased over the range from 1 to 10 kHz when cells were growing. Then, the phase angle approached a constant over the frequency range between 10 kHz and 2 MHz. Thereafter, the phase angle increased rapidly from 20 to 52 degrees during cell culturing between 8 and 12 h at 4 MHz. The maximum cell number after culturing for 12 h increased by 25.8% for the control sites with poly-D-lysine (PDL) pastes. For the normal growth factor, the cell number increased up to 4.78 times from 8 to 12 h, but only 0.96 and 1.60 times for the other two medium growth factors. The correlation coefficients between impedance and cell number were 0.868 (coating with PDL), and 0.836 (without PDL) for the normal concentration medium. Thus, impedance may be used as an index for cell growth characterization. (paper)

  16. Chronic stress and neural function: accounting for sex and age.

    Science.gov (United States)

    Luine, V N; Beck, K D; Bowman, R E; Frankfurt, M; Maclusky, N J

    2007-10-01

    Cognitive responses to stress follow the temporally dependent pattern originally established by Selye (1) wherein short-term stressors elicit adaptive responses whereas continued stress (chronic) results in maladaptive changes--deleterious effects on physiological systems and impaired cognition. However, this pattern for cognitive effects appears to apply to only half the population (males) and, more specifically, to young, adult males. Females show different cognitive responses to stress. In contrast to impaired cognition in males after chronic stress, female rodents show enhanced performance on the same memory tasks after the same stress. Not only cognition, but anxiety, shows sex-dependent changes following chronic stress--stress is anxiolytic in males and anxiogenic in females. Moreover, behavioral responses to chronic stress are different in developing as well as aging subjects (both sexes) as compared to adults. In aged rats, chronic stress enhances recognition memory in both sexes, does not alter spatial memory, and anxiety effects are opposite to young adults. When pregnant dams are exposed to chronic stress, at adulthood the offspring display yet different consequences of stress on anxiety and cognition, and, in contrast to adulthood when the behavioral effects of stress are reversible, prenatal stress effects appear enduring. Changing levels of estradiol in the sexes over the lifespan appear to contribute to the differences in response to stress. Thus, theories of stress dependent modulations in CNS function--developed solely in male models, focused on peripheral physiological processes and tested in adults--may require revision when applied to a more diverse population (age- and sex-wise) at least in relation to the neural functions of cognition and anxiety. Moreover, these results suggest that other stressors and neural functions should be investigated to determine whether age, sex and gonadal hormones also have an impact.

  17. Integrated circuit amplifiers for multi-electrode intracortical recording.

    Science.gov (United States)

    Jochum, Thomas; Denison, Timothy; Wolf, Patrick

    2009-02-01

    Significant progress has been made in systems that interpret the electrical signals of the brain in order to control an actuator. One version of these systems senses neuronal extracellular action potentials with an array of up to 100 miniature probes inserted into the cortex. The impedance of each probe is high, so environmental electrical noise is readily coupled to the neuronal signal. To minimize this noise, an amplifier is placed close to each probe. Thus, the need has arisen for many amplifiers to be placed near the cortex. Commercially available integrated circuits do not satisfy the area, power and noise requirements of this application, so researchers have designed custom integrated-circuit amplifiers. This paper presents a comprehensive survey of the neural amplifiers described in publications prior to 2008. Methods to achieve high input impedance, low noise and a large time-constant high-pass filter are reviewed. A tutorial on the biological, electrochemical, mechanical and electromagnetic phenomena that influence amplifier design is provided. Areas for additional research, including sub-nanoampere electrolysis and chronic cortical heating, are discussed. Unresolved design concerns, including teraohm circuitry, electrical overstress and component failure, are identified.

  18. Depression in chronic ketamine users: Sex differences and neural bases.

    Science.gov (United States)

    Li, Chiang-Shan R; Zhang, Sheng; Hung, Chia-Chun; Chen, Chun-Ming; Duann, Jeng-Ren; Lin, Ching-Po; Lee, Tony Szu-Hsien

    2017-11-30

    Chronic ketamine use leads to cognitive and affective deficits including depression. Here, we examined sex differences and neural bases of depression in chronic ketamine users. Compared to non-drug using healthy controls (HC), ketamine-using females but not males showed increased depression score as assessed by the Center of Epidemiological Studies Depression Scale (CES-D). We evaluated resting state functional connectivity (rsFC) of the subgenual anterior cingulate cortex (sgACC), a prefrontal structure consistently implicated in the pathogenesis of depression. Compared to HC, ketamine users (KU) did not demonstrate significant changes in sgACC connectivities at a corrected threshold. However, in KU, a linear regression against CES-D score showed less sgACC connectivity to the orbitofrontal cortex (OFC) with increasing depression severity. Examined separately, male and female KU showed higher sgACC connectivity to bilateral superior temporal gyrus and dorsomedial prefrontal cortex (dmPFC), respectively, in correlation with depression. The linear correlation of sgACC-OFC and sgACC-dmPFC connectivity with depression was significantly different in slope between KU and HC. These findings highlighted changes in rsFC of the sgACC as associated with depression and sex differences in these changes in chronic ketamine users. Copyright © 2017 Elsevier B.V. All rights reserved.

  19. Neural Consequences of Chronic Short Sleep: Reversible or Lasting?

    Directory of Open Access Journals (Sweden)

    Zhengqing Zhao

    2017-05-01

    Full Text Available Approximately one-third of adolescents and adults in developed countries regularly experience insufficient sleep across the school and/or work week interspersed with weekend catch up sleep. This common practice of weekend recovery sleep reduces subjective sleepiness, yet recent studies demonstrate that one weekend of recovery sleep may not be sufficient in all persons to fully reverse all neurobehavioral impairments observed with chronic sleep loss, particularly vigilance. Moreover, recent studies in animal models demonstrate persistent injury to and loss of specific neuron types in response to chronic short sleep (CSS with lasting effects on sleep/wake patterns. Here, we provide a comprehensive review of the effects of chronic sleep disruption on neurobehavioral performance and injury to neurons, astrocytes, microglia, and oligodendrocytes and discuss what is known and what is not yet established for reversibility of neural injury. Recent neurobehavioral findings in humans are integrated with animal model research examining long-term consequences of sleep loss on neurobehavioral performance, brain development, neurogenesis, neurodegeneration, and connectivity. While it is now clear that recovery of vigilance following short sleep requires longer than one weekend, less is known of the impact of CSS on cognitive function, mood, and brain health long term. From work performed in animal models, CSS in the young adult and short-term sleep loss in critical developmental windows can have lasting detrimental effects on neurobehavioral performance.

  20. Chronic stress disrupts neural coherence between cortico-limbic structures

    Directory of Open Access Journals (Sweden)

    João Filipe Oliveira

    2013-02-01

    Full Text Available Chronic stress impairs cognitive function, namely on tasks that rely on the integrity of cortico-limbic networks. To unravel the functional impact of progressive stress in cortico-limbic networks we measured neural activity and spectral coherences between the ventral hippocampus (vHIP and the medial prefrontal cortex (mPFC in rats subjected to short term (STS and chronic unpredictable stress (CUS. CUS exposure consistently disrupted the spectral coherence between both areas for a wide range of frequencies, whereas STS exposure failed to trigger such effect. The chronic stress-induced coherence decrease correlated inversely with the vHIP power spectrum, but not with the mPFC power spectrum, which supports the view that hippocampal dysfunction is the primary event after stress exposure. Importantly, we additionally show that the variations in vHIP-to-mPFC coherence and power spectrum in the vHIP correlated with stress-induced behavioral deficits in a spatial reference memory task. Altogether, these findings result in an innovative readout to measure, and follow, the functional events that underlie the stress-induced reference memory impairments.

  1. Effects of grid potentials and geometric dimensions on the multi-electrode probe measurements

    International Nuclear Information System (INIS)

    Elakshar, F.F.; Abdul El-Raoof, W.S.

    1986-01-01

    A hollow anode plasma source is used to produce low temperature plasma which is injected into a magnetic field. The effects of the grid potentials, collector potential and geometric dimensions on multi-electrode probe measurements, in the presence of a magnetic field, are investigated. It is found that the collector potential plays a substantial role in the measurement of temperatures and densities. The finite-size of the geometric dimensions of the probe influences the data and high values of temperature are obtained when a small ratio of the discriminator grid radius to the separation distance is used, providing that the repeller grid potentials is low. Reliable measurements can only be obtained if the multi-electrode probe is used in the presence of a magnetic field strong enough to reduce electron Larmor radii to less than the grid mesh radius. (author)

  2. Commercialisation of CMOS Integrated Circuit Technology in Multi-Electrode Arrays for Neuroscience and Cell-Based Biosensors

    Directory of Open Access Journals (Sweden)

    Chris R. Bowen

    2011-05-01

    Full Text Available The adaptation of standard integrated circuit (IC technology as a transducer in cell-based biosensors in drug discovery pharmacology, neural interface systems and electrophysiology requires electrodes that are electrochemically stable, biocompatible and affordable. Unfortunately, the ubiquitous Complementary Metal Oxide Semiconductor (CMOS IC technology does not meet the first of these requirements. For devices intended only for research, modification of CMOS by post-processing using cleanroom facilities has been achieved. However, to enable adoption of CMOS as a basis for commercial biosensors, the economies of scale of CMOS fabrication must be maintained by using only low-cost post-processing techniques. This review highlights the methodologies employed in cell-based biosensor design where CMOS-based integrated circuits (ICs form an integral part of the transducer system. Particular emphasis will be placed on the application of multi-electrode arrays for in vitro neuroscience applications. Identifying suitable IC packaging methods presents further significant challenges when considering specific applications. The various challenges and difficulties are reviewed and some potential solutions are presented.

  3. Ethylene Removal in Strong Electric Field Formed by Floating Multi-Electrode

    Science.gov (United States)

    Nagasawa, Takeshi

    Ethylene gas that contains the acetic acid ester element can be removed by applying the pulse voltage to the floating multi-electrode device. This phenomenon is caused in the weak discharge by the strong electric field between the narrow electrodes. This device is possible in very small electric power (apples, and 3.5ppm/30min for 2 melons. However, ethylene gas that doesn't contain the acetic acid ester cannot be removed (ex. ethylene pure gas and Japanese apricot).

  4. 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.

  5. Chronic Ankle Instability and Neural Excitability of the Lower Extremity.

    Science.gov (United States)

    McLeod, Michelle M; Gribble, Phillip A; Pietrosimone, Brian G

    2015-08-01

    Neuromuscular dysfunction of the leg and thigh musculature, including decreased strength and postural control, is common in patients with chronic ankle instability (CAI). Understanding how CAI affects specific neural pathways may provide valuable information for targeted therapies. To investigate differences in spinal reflexive and corticospinal excitability of the fibularis longus and vastus medialis between limbs in patients with unilateral CAI and between CAI patients and participants serving as healthy controls. Case-control study. Research laboratory. A total of 56 participants volunteered, and complete data for 21 CAI patients (9 men, 12 women; age = 20.81 ± 1.63 years, height = 171.57 ± 11.44 cm, mass = 68.84 ± 11.93 kg) and 24 healthy participants serving as controls (7 men, 17 women; age = 22.54 ± 2.92 years, height = 172.35 ± 10.85 cm, mass = 69.15 ± 12.30 kg) were included in the final analyses. Control participants were matched to CAI patients on sex, age, and limb dominance. We assigned "involved" limbs, which corresponded with the involved limbs of the CAI patients, to control participants. Spinal reflexive excitability was assessed via the Hoffmann reflex and normalized to a maximal muscle response. Corticospinal excitability was assessed using transcranial magnetic stimulation. Active motor threshold (AMT) was defined as the lowest transcranial magnetic stimulation intensity required to elicit motor-evoked potentials equal to or greater than 100 μV in 5 of 10 consecutive stimuli. We obtained motor-evoked potentials (MEPs) at percentages ranging from 100% to 140% of AMT. Fibularis longus MEP amplitudes were greater in control participants than in CAI patients bilaterally at 100% AMT (control involved limb: 0.023 ± 0.031; CAI involved limb: 0.014 ± 0.008; control uninvolved limb: 0.021 ± 0.022; CAI uninvolved limb: 0.015 ± 0.007; F1,41 = 4.551, P = .04) and 105% AMT (control involved limb: 0.029 ± 0.026; CAI involved limb: 0.021 ± 0

  6. An Investigation of Groundwater Flow on a Coastal Barrier using Multi Electrode Profiling

    DEFF Research Database (Denmark)

    Poulsen, Søren Erbs; Christensen, Steen; Rasmussen, Keld Rømer

    2008-01-01

    Preliminary geophysical and hydrogeological investigations indicate that multi-electrode profiling (MEP) can be used to monitor groundwater salinity on a coastal barrier where a shallow thin aquifer discharges to the North Sea. A monitoring system including five groups of piezometers and five MEP...... groundwater modeling we hope to be able to quantify how time varying recharge, tides, and storms hitting the barrier affect groundwater flow and discharge to the sea. At the conference we will present monitoring results from the winter and spring 2008....

  7. An automated online positioning system and simulation environment for multi-electrodes in extracellular recordings

    DEFF Research Database (Denmark)

    Franke, Felix; Natora, Michal; Meier, Philipp

    2010-01-01

    to tissue drifts and other sources of variability in the recording setup, the position of the electrodes with respect to the recorded neurons can change causing low recording quality. The contributions of this work are threefold. We introduce a quality measure for the recording position of the electrode...... which should be maximized during recordings and is especially suitable for the use of multi-electrodes. An automated positioning system based on this quality measure is proposed. The system is able to find favorable recording positions and adapts the electrode position smoothly to changes of the neuron...

  8. Chronic childhood peer rejection is associated with heightened neural responses to social exclusion during adolescence.

    NARCIS (Netherlands)

    Will, G.J.; Van, Lier P.A.; Crone, E.A.; Guroglu, B.

    2016-01-01

    This functional Magnetic Resonance Imaging (fMRI) study examined subjective and neural responses to social exclusion in adolescents (age 12-15) who either had a stable accepted (n = 27; 14 males) or a chronic rejected (n = 19; 12 males) status among peers from age 6 to 12. Both groups of adolescents

  9. Optogenetic Modulation and Multi-Electrode Analysis of Cerebellar Networks In Vivo

    Science.gov (United States)

    Kruse, Wolfgang; Krause, Martin; Aarse, Janna; Mark, Melanie D.; Manahan-Vaughan, Denise; Herlitze, Stefan

    2014-01-01

    The firing patterns of cerebellar Purkinje cells (PCs), as the sole output of the cerebellar cortex, determine and tune motor behavior. PC firing is modulated by various inputs from different brain regions and by cell-types including granule cells (GCs), climbing fibers and inhibitory interneurons. To understand how signal integration in PCs occurs and how subtle changes in the modulation of PC firing lead to adjustment of motor behaviors, it is important to precisely record PC firing in vivo and to control modulatory pathways in a spatio-temporal manner. Combining optogenetic and multi-electrode approaches, we established a new method to integrate light-guides into a multi-electrode system. With this method we are able to variably position the light-guide in defined regions relative to the recording electrode with micrometer precision. We show that PC firing can be precisely monitored and modulated by light-activation of channelrhodopsin-2 (ChR2) expressed in PCs, GCs and interneurons. Thus, this method is ideally suited to investigate the spatio/temporal modulation of PCs in anesthetized and in behaving mice. PMID:25144735

  10. Optogenetic modulation and multi-electrode analysis of cerebellar networks in vivo.

    Directory of Open Access Journals (Sweden)

    Wolfgang Kruse

    Full Text Available The firing patterns of cerebellar Purkinje cells (PCs, as the sole output of the cerebellar cortex, determine and tune motor behavior. PC firing is modulated by various inputs from different brain regions and by cell-types including granule cells (GCs, climbing fibers and inhibitory interneurons. To understand how signal integration in PCs occurs and how subtle changes in the modulation of PC firing lead to adjustment of motor behaviors, it is important to precisely record PC firing in vivo and to control modulatory pathways in a spatio-temporal manner. Combining optogenetic and multi-electrode approaches, we established a new method to integrate light-guides into a multi-electrode system. With this method we are able to variably position the light-guide in defined regions relative to the recording electrode with micrometer precision. We show that PC firing can be precisely monitored and modulated by light-activation of channelrhodopsin-2 (ChR2 expressed in PCs, GCs and interneurons. Thus, this method is ideally suited to investigate the spatio/temporal modulation of PCs in anesthetized and in behaving mice.

  11. Binaural release from masking with single- and multi-electrode stimulation in children with cochlear implants.

    Science.gov (United States)

    Todd, Ann E; Goupell, Matthew J; Litovsky, Ruth Y

    2016-07-01

    Cochlear implants (CIs) provide children with access to speech information from a young age. Despite bilateral cochlear implantation becoming common, use of spatial cues in free field is smaller than in normal-hearing children. Clinically fit CIs are not synchronized across the ears; thus binaural experiments must utilize research processors that can control binaural cues with precision. Research to date has used single pairs of electrodes, which is insufficient for representing speech. Little is known about how children with bilateral CIs process binaural information with multi-electrode stimulation. Toward the goal of improving binaural unmasking of speech, this study evaluated binaural unmasking with multi- and single-electrode stimulation. Results showed that performance with multi-electrode stimulation was similar to the best performance with single-electrode stimulation. This was similar to the pattern of performance shown by normal-hearing adults when presented an acoustic CI simulation. Diotic and dichotic signal detection thresholds of the children with CIs were similar to those of normal-hearing children listening to a CI simulation. The magnitude of binaural unmasking was not related to whether the children with CIs had good interaural time difference sensitivity. Results support the potential for benefits from binaural hearing and speech unmasking in children with bilateral CIs.

  12. Binaural release from masking with single- and multi-electrode stimulation in children with cochlear implantsa)

    Science.gov (United States)

    Todd, Ann E.; Goupell, Matthew J.; Litovsky, Ruth Y.

    2016-01-01

    Cochlear implants (CIs) provide children with access to speech information from a young age. Despite bilateral cochlear implantation becoming common, use of spatial cues in free field is smaller than in normal-hearing children. Clinically fit CIs are not synchronized across the ears; thus binaural experiments must utilize research processors that can control binaural cues with precision. Research to date has used single pairs of electrodes, which is insufficient for representing speech. Little is known about how children with bilateral CIs process binaural information with multi-electrode stimulation. Toward the goal of improving binaural unmasking of speech, this study evaluated binaural unmasking with multi- and single-electrode stimulation. Results showed that performance with multi-electrode stimulation was similar to the best performance with single-electrode stimulation. This was similar to the pattern of performance shown by normal-hearing adults when presented an acoustic CI simulation. Diotic and dichotic signal detection thresholds of the children with CIs were similar to those of normal-hearing children listening to a CI simulation. The magnitude of binaural unmasking was not related to whether the children with CIs had good interaural time difference sensitivity. Results support the potential for benefits from binaural hearing and speech unmasking in children with bilateral CIs. PMID:27475132

  13. Abnormal neural activation patterns underlying working memory impairment in chronic phencyclidine-treated mice.

    Directory of Open Access Journals (Sweden)

    Yosefu Arime

    Full Text Available Working memory impairment is a hallmark feature of schizophrenia and is thought be caused by dysfunctions in the prefrontal cortex (PFC and associated brain regions. However, the neural circuit anomalies underlying this impairment are poorly understood. The aim of this study is to assess working memory performance in the chronic phencyclidine (PCP mouse model of schizophrenia, and to identify the neural substrates of working memory. To address this issue, we conducted the following experiments for mice after withdrawal from chronic administration (14 days of either saline or PCP (10 mg/kg: (1 a discrete paired-trial variable-delay task in T-maze to assess working memory, and (2 brain-wide c-Fos mapping to identify activated brain regions relevant to this task performance either 90 min or 0 min after the completion of the task, with each time point examined under working memory effort and basal conditions. Correct responses in the test phase of the task were significantly reduced across delays (5, 15, and 30 s in chronic PCP-treated mice compared with chronic saline-treated controls, suggesting delay-independent impairments in working memory in the PCP group. In layer 2-3 of the prelimbic cortex, the number of working memory effort-elicited c-Fos+ cells was significantly higher in the chronic PCP group than in the chronic saline group. The main effect of working memory effort relative to basal conditions was to induce significantly increased c-Fos+ cells in the other layers of prelimbic cortex and the anterior cingulate and infralimbic cortex regardless of the different chronic regimens. Conversely, this working memory effort had a negative effect (fewer c-Fos+ cells in the ventral hippocampus. These results shed light on some putative neural networks relevant to working memory impairments in mice chronically treated with PCP, and emphasize the importance of the layer 2-3 of the prelimbic cortex of the PFC.

  14. Comparison of Efficacy of Neural Therapy and Physical Therapy in Chronic Low Back Pain

    OpenAIRE

    Atalay, Nilgun Simsir; Sahin, Fusun; Atalay, Ali; Akkaya, Nuray

    2013-01-01

    The aim of this prospective study was to evaluate the effects of neural therapy, and physical therapy on level of pain, disability, quality of life, and psychological status in patients with chronic low back pain. Patients admitted to the physical therapy and rehabilitation outpatient clinic with the complaint of low back pain of at least 3 months duration. Group 1 (n=27), physical therapy (PT, hotpack, ultrasound, TENS 15 sessions), group 2 (n=33), neural therapy (NT, 1:1 mixture of 20 mg/mL...

  15. Design and manufacture of multi-electrode ion chamber for absolute photon-flux measurements of soft x-rays

    International Nuclear Information System (INIS)

    Yoshigoe, Akitaka; Teraoka, Yuden

    2001-03-01

    In order to measure the absolute photon-flux of soft x-rays at the photon energy region from 500 eV to 1500 eV, a sealed gas ion chamber with multi-electrodes was designed and manufactured. Actually we succeeded in measuring the photon-flux at the soft x-ray beamline, BL23SU, in the SPring-8. This report concretely describes the design and the adjustment of the sealed gas ion chamber with multi-electrodes. (author)

  16. Effects of Chronic Low-Dose Radiation on Human Neural Progenitor Cells

    Science.gov (United States)

    Katsura, Mari; Cyou-Nakamine, Hiromasa; Zen, Qin; Zen, Yang; Nansai, Hiroko; Amagasa, Shota; Kanki, Yasuharu; Inoue, Tsuyoshi; Kaneki, Kiyomi; Taguchi, Akashi; Kobayashi, Mika; Kaji, Toshiyuki; Kodama, Tatsuhiko; Miyagawa, Kiyoshi; Wada, Youichiro; Akimitsu, Nobuyoshi; Sone, Hideko

    2016-01-01

    The effects of chronic low-dose radiation on human health have not been well established. Recent studies have revealed that neural progenitor cells are present not only in the fetal brain but also in the adult brain. Since immature cells are generally more radiosensitive, here we investigated the effects of chronic low-dose radiation on cultured human neural progenitor cells (hNPCs) derived from embryonic stem cells. Radiation at low doses of 31, 124 and 496 mGy per 72 h was administered to hNPCs. The effects were estimated by gene expression profiling with microarray analysis as well as morphological analysis. Gene expression was dose-dependently changed by radiation. By thirty-one mGy of radiation, inflammatory pathways involving interferon signaling and cell junctions were altered. DNA repair and cell adhesion molecules were affected by 124 mGy of radiation while DNA synthesis, apoptosis, metabolism, and neural differentiation were all affected by 496 mGy of radiation. These in vitro results suggest that 496 mGy radiation affects the development of neuronal progenitor cells while altered gene expression was observed at a radiation dose lower than 100 mGy. This study would contribute to the elucidation of the clinical and subclinical phenotypes of impaired neuronal development induced by chronic low-dose radiation.

  17. Chronic Childhood Peer Rejection is Associated with Heightened Neural Responses to Social Exclusion During Adolescence.

    Science.gov (United States)

    Will, Geert-Jan; van Lier, Pol A C; Crone, Eveline A; Güroğlu, Berna

    2016-01-01

    This functional Magnetic Resonance Imaging (fMRI) study examined subjective and neural responses to social exclusion in adolescents (age 12-15) who either had a stable accepted (n = 27; 14 males) or a chronic rejected (n = 19; 12 males) status among peers from age 6 to 12. Both groups of adolescents reported similar increases in distress after being excluded in a virtual ball-tossing game (Cyberball), but adolescents with a history of chronic peer rejection showed higher activity in brain regions previously linked to the detection of, and the distress caused by, social exclusion. Specifically, compared with stably accepted adolescents, chronically rejected adolescents displayed: 1) higher activity in the dorsal anterior cingulate cortex (dACC) during social exclusion and 2) higher activity in the dACC and anterior prefrontal cortex when they were incidentally excluded in a social interaction in which they were overall included. These findings demonstrate that chronic childhood peer rejection is associated with heightened neural responses to social exclusion during adolescence, which has implications for understanding the processes through which peer rejection may lead to adverse effects on mental health over time.

  18. The Neural Correlates of Chronic Symptoms of Vertigo Proneness in Humans.

    Science.gov (United States)

    Alsalman, Ola; Ost, Jan; Vanspauwen, Robby; Blaivie, Catherine; De Ridder, Dirk; Vanneste, Sven

    2016-01-01

    Vestibular signals are of significant importance for variable functions including gaze stabilization, spatial perception, navigation, cognition, and bodily self-consciousness. The vestibular network governs functions that might be impaired in patients affected with vestibular dysfunction. It is currently unclear how different brain regions/networks process vestibular information and integrate the information into a unified spatial percept related to somatosensory awareness and whether people with recurrent balance complaints have a neural signature as a trait affecting their development of chronic symptoms of vertigo. Pivotal evidence points to a vestibular-related brain network in humans that is widely distributed in nature. By using resting state source localized electroencephalography in non-vertiginous state, electrophysiological changes in activity and functional connectivity of 23 patients with balance complaints where chronic symptoms of vertigo and dizziness are among the most common reported complaints are analyzed and compared to healthy subjects. The analyses showed increased alpha2 activity within the posterior cingulate cortex and the precuneues/cuneus and reduced beta3 and gamma activity within the pregenual and subgenual anterior cingulate cortex for the subjects with balance complaints. These electrophysiological variations were correlated with reported chronic symptoms of vertigo intensity. A region of interest analysis found reduced functional connectivity for gamma activity within the vestibular cortex, precuneus, frontal eye field, intra-parietal sulcus, orbitofrontal cortex, and the dorsal anterior cingulate cortex. In addition, there was a positive correlation between chronic symptoms of vertigo intensity and increased alpha-gamma nesting in the left frontal eye field. When compared to healthy subjects, there is evidence of electrophysiological changes in the brain of patients with balance complaints even outside chronic symptoms of vertigo

  19. The Neural Correlates of Chronic Symptoms of Vertigo Proneness in Humans.

    Directory of Open Access Journals (Sweden)

    Ola Alsalman

    Full Text Available Vestibular signals are of significant importance for variable functions including gaze stabilization, spatial perception, navigation, cognition, and bodily self-consciousness. The vestibular network governs functions that might be impaired in patients affected with vestibular dysfunction. It is currently unclear how different brain regions/networks process vestibular information and integrate the information into a unified spatial percept related to somatosensory awareness and whether people with recurrent balance complaints have a neural signature as a trait affecting their development of chronic symptoms of vertigo. Pivotal evidence points to a vestibular-related brain network in humans that is widely distributed in nature. By using resting state source localized electroencephalography in non-vertiginous state, electrophysiological changes in activity and functional connectivity of 23 patients with balance complaints where chronic symptoms of vertigo and dizziness are among the most common reported complaints are analyzed and compared to healthy subjects. The analyses showed increased alpha2 activity within the posterior cingulate cortex and the precuneues/cuneus and reduced beta3 and gamma activity within the pregenual and subgenual anterior cingulate cortex for the subjects with balance complaints. These electrophysiological variations were correlated with reported chronic symptoms of vertigo intensity. A region of interest analysis found reduced functional connectivity for gamma activity within the vestibular cortex, precuneus, frontal eye field, intra-parietal sulcus, orbitofrontal cortex, and the dorsal anterior cingulate cortex. In addition, there was a positive correlation between chronic symptoms of vertigo intensity and increased alpha-gamma nesting in the left frontal eye field. When compared to healthy subjects, there is evidence of electrophysiological changes in the brain of patients with balance complaints even outside chronic

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

    NARCIS (Netherlands)

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

    2008-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Misako Komatsu

    2017-09-01

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

  2. Classification of functional interactions from multi-electrodes data using conditional modularity analysis

    Science.gov (United States)

    Makhtar, Siti Noormiza; Senik, Mohd Harizal

    2018-02-01

    The availability of massive amount of neuronal signals are attracting widespread interest in functional connectivity analysis. Functional interactions estimated by multivariate partial coherence analysis in the frequency domain represent the connectivity strength in this study. Modularity is a network measure for the detection of community structure in network analysis. The discovery of community structure for the functional neuronal network was implemented on multi-electrode array (MEA) signals recorded from hippocampal regions in isoflurane-anaesthetized Lister-hooded rats. The analysis is expected to show modularity changes before and after local unilateral kainic acid (KA)-induced epileptiform activity. The result is presented using color-coded graphic of conditional modularity measure for 19 MEA nodes. This network is separated into four sub-regions to show the community detection within each sub-region. The results show that classification of neuronal signals into the inter- and intra-modular nodes is feasible using conditional modularity analysis. Estimation of segregation properties using conditional modularity analysis may provide further information about functional connectivity from MEA data.

  3. An improved method of crafting a multi-electrode spiral cuff for the selective.

    Science.gov (United States)

    Rozman, Janez; Pečlin, Polona; Ribarič, Samo; Godec, Matjaž; Burja, Jaka

    2018-01-17

    This article reviews an improved methodology and technology for crafting a multi-electrode spiral cuff for the selective activation of nerve fibres in particular superficial regions of a peripheral nerve. The analysis, structural and mechanical properties of the spot welds used for the interconnections between the stimulating electrodes and stainless-steel lead wires are presented. The cuff consisted of 33 platinum electrodes embedded within a self-curling 17-mm-long silicone spiral sheet with a nominal internal diameter of 2.5 mm. The weld was analyzed using scanning electron microscopy and nanohardness tests, while the interconnection was investigated using destructive load tests. The functionality of the cuff was tested in an isolated porcine vagus nerve. The results of the scanning electron microscopy show good alloying and none of the typical welding defects that occur between the wire and the platinum foil. The results of the destructive load tests show that the breaking loads were between 3.22 and 5 N. The results of the nanohardness testing show that the hardness of the weld was different for the particular sites on the weld sample. Finally, the results of the functional testing show that for different stimulation intensities both the compound action potential deflection and the shape are modulated.

  4. Unified selective sorting approach to analyse multi-electrode extracellular data

    Science.gov (United States)

    Veerabhadrappa, R.; Lim, C. P.; Nguyen, T. T.; Berk, M.; Tye, S. J.; Monaghan, P.; Nahavandi, S.; Bhatti, A.

    2016-01-01

    Extracellular data analysis has become a quintessential method for understanding the neurophysiological responses to stimuli. This demands stringent techniques owing to the complicated nature of the recording environment. In this paper, we highlight the challenges in extracellular multi-electrode recording and data analysis as well as the limitations pertaining to some of the currently employed methodologies. To address some of the challenges, we present a unified algorithm in the form of selective sorting. Selective sorting is modelled around hypothesized generative model, which addresses the natural phenomena of spikes triggered by an intricate neuronal population. The algorithm incorporates Cepstrum of Bispectrum, ad hoc clustering algorithms, wavelet transforms, least square and correlation concepts which strategically tailors a sequence to characterize and form distinctive clusters. Additionally, we demonstrate the influence of noise modelled wavelets to sort overlapping spikes. The algorithm is evaluated using both raw and synthesized data sets with different levels of complexity and the performances are tabulated for comparison using widely accepted qualitative and quantitative indicators. PMID:27339770

  5. All-carbon multi-electrode array for real-time in vitro measurements of oxidizable neurotransmitters

    Science.gov (United States)

    Picollo, Federico; Battiato, Alfio; Bernardi, Ettore; Plaitano, Marilena; Franchino, Claudio; Gosso, Sara; Pasquarelli, Alberto; Carbone, Emilio; Olivero, Paolo; Carabelli, Valentina

    2016-02-01

    We report on the ion beam fabrication of all-carbon multi electrode arrays (MEAs) based on 16 graphitic micro-channels embedded in single-crystal diamond (SCD) substrates. The fabricated SCD-MEAs are systematically employed for the in vitro simultaneous amperometric detection of the secretory activity from populations of chromaffin cells, demonstrating a new sensing approach with respect to standard techniques. The biochemical stability and biocompatibility of the SCD-based device combined with the parallel recording of multi-electrodes array allow: i) a significant time saving in data collection during drug screening and/or pharmacological tests over a large number of cells, ii) the possibility of comparing altered cell functionality among cell populations, and iii) the repeatition of acquisition runs over many cycles with a fully non-toxic and chemically robust bio-sensitive substrate.

  6. Electrode impedance analysis of chronic tungsten microwire neural implants: understanding abiotic vs. biotic contributions

    Directory of Open Access Journals (Sweden)

    Viswanath eSankar

    2014-05-01

    Full Text Available Changes in biotic and abiotic factors can be reflected in the complex impedance spectrum of the microelectrodes chronically implanted into the neural tissue. The recording surface of the tungsten electrode in vivo undergoes abiotic changes due to recording site corrosion and insulation delamination as well as biotic changes due to tissue encapsulation as a result of the foreign body immune response. We reported earlier that large changes in electrode impedance measured at 1 kHz were correlated with poor electrode functional performance, quantified through electrophysiological recordings during the chronic lifetime of the electrode. There is a need to identity the factors that contribute to the chronic impedance variation. In this work, we use numerical simulation and regression to equivalent circuit models to evaluate both the abiotic and biotic contributions to the impedance response over chronic implant duration. COMSOL® simulation of abiotic electrode morphology changes provide a possible explanation for the decrease in the electrode impedance at long implant duration while biotic changes play an important role in the large increase in impedance observed initially.

  7. A review on mechanical considerations for chronically-implanted neural probes

    Science.gov (United States)

    Lecomte, Aziliz; Descamps, Emeline; Bergaud, Christian

    2018-06-01

    This review intends to present a comprehensive analysis of the mechanical considerations for chronically-implanted neural probes. Failure of neural electrical recordings or stimulation over time has shown to arise from foreign body reaction and device material stability. It seems that devices that match most closely with the mechanical properties of the brain would be more likely to reduce the mechanical stress at the probe/tissue interface, thus improving body acceptance. The use of low Young’s modulus polymers instead of hard substrates is one way to enhance this mechanical mimetism, though compliance can be achieved through a variety of means. The reduction of probe width and thickness in comparison to a designated length, the use of soft hydrogel coatings and the release in device tethering to the skull, can also improve device compliance. Paradoxically, the more compliant the device, the more likely it will fail during the insertion process in the brain. Strategies have multiplied this past decade to offer partial or temporary stiffness to the device to overcome this buckling effect. A detailed description of the probe insertion mechanisms is provided to analyze potential sources of implantation failure and the need for a mechanically-enhancing structure. This leads us to present an overview of the strategies that have been put in place over the last ten years to overcome buckling issues. Particularly, great emphasis is put on bioresorbable polymers and their assessment for neural applications. Finally, a discussion is provided on some of the key features for the design of mechanically-reliable, polymer-based next generation of chronic neuroprosthetic devices.

  8. Barbed channels enhance unidirectional connectivity between neuronal networks cultured on multi electrode arrays

    Science.gov (United States)

    le Feber, Joost; Postma, Wybren; de Weerd, Eddy; Weusthof, Marcel; Rutten, Wim L. C.

    2015-01-01

    Cultured neurons on multi electrode arrays (MEAs) have been widely used to study various aspects of neuronal (network) functioning. A possible drawback of this approach is the lack of structure in these networks. At the single cell level, several solutions have been proposed to enable directed connectivity, and promising results were obtained. At the level of connected sub-populations, a few attempts have been made with promising results. First assessment of the designs' functionality, however, suggested room for further improvement. We designed a two chamber MEA aiming to create a unidirectional connection between the networks in both chambers (“emitting” and “receiving”). To achieve this unidirectionality, all interconnecting channels contained barbs that hindered axon growth in the opposite direction (from receiving to emitting chamber). Visual inspection showed that axons predominantly grew through the channels in the promoted direction. This observation was confirmed by spontaneous activity recordings. Cross-correlation between the signals from two electrodes inside the channels suggested signal propagation at ≈2 m/s from emitting to receiving chamber. Cross-correlation between the firing patterns in both chambers indicated that most correlated activity was initiated in the emitting chamber, which was also reflected by a significantly lower fraction of partial bursts (i.e., a one-chamber-only burst) in the emitting chamber. Finally, electrical stimulation in the emitting chamber induced a fast response in that chamber, and a slower response in the receiving chamber. Stimulation in the receiving chamber evoked a fast response in that chamber, but no response in the emitting chamber. These results confirm the predominantly unidirectional nature of the connecting channels from emitting to receiving chamber. PMID:26578869

  9. Barbed channels enhance unidirectional connectivity between neuronal networks cultured on multi electrode arrays.

    Directory of Open Access Journals (Sweden)

    Joost eLe Feber

    2015-11-01

    Full Text Available Cultured neurons on multi electrode arrays (MEAs have been widely used to study various as-pects of neuronal (network functioning. A possible drawback of this approach is the lack of structure in these networks. At the single cell level, several solutions have been proposed to ena-ble directed connectivity, and promising results were obtained. At the level of connected sub-populations, a few attempts have been made with promising results. First assessment of the de-signs’ functionality, however, suggested room for further improvement.We designed a two chamber MEA aiming to create a unidirectional connection between the net-works in both chambers (‘emitting’ and ‘receiving’. To achieve this unidirectionality, all inter-connecting channels contained barbs that hindered axon growth in the opposite direction (from receiving to emitting chamber. Visual inspection showed that axons predominantly grew through the channels in the promoted direction . This observation was confirmed by spontaneous activity recordings. Cross-correlation between the signals from two electrodes inside the channels suggested signal propagation at ≈2 m/s from emitting to receiving chamber. Cross-correlation between the firing patterns in both chambers indicated that most correlated activity was initiated in the emitting chamber, which was also reflected by a significantly lower fraction of partial bursts (e. a one-chamber-only burst in the emitting chamber. Finally, electrical stimulation in the emitting chamber induced a fast response in that chamber, and a slower response in the receiving chamber. Stimulation in the receiving chamber evoked a fast response in that chamber, but no response in the emitting chamber. These results confirm the predominantly unidirectional nature of the connecting channels from emitting to receiving chamber.

  10. In vitro extracellular recording and stimulation performance of nanoporous gold-modified multi-electrode arrays

    Science.gov (United States)

    Kim, Yong Hee; Kim, Gook Hwa; Kim, Ah Young; Han, Young Hwan; Chung, Myung-Ae; Jung, Sang-Don

    2015-12-01

    Objective. Nanoporous gold (Au) structures can reduce the impedance and enhance the charge injection capability of multi-electrode arrays (MEAs) used for interfacing neuronal networks. Even though there are various nanoporous Au preparation techniques, fabrication of MEA based on low-cost electro-codeposition of Ag:Au has not been performed. In this work, we have modified a Au MEA via the electro-codeposition of Ag:Au alloy, followed by the chemical etching of Ag, and report on the in vitro extracellular recording and stimulation performance of the nanoporous Au-modified MEA. Approach. Ag:Au alloy was electro-codeposited on a bilayer lift-off resist sputter-deposition passivated Au MEA followed by chemical etching of Ag to form a porous Au structure. Main results. The porous Au structure was analyzed by scanning electron microscopy and tunneling electron microscopy and found to have an interconnected nanoporous Au structure. The impedance value of the nanoporous Au-modified MEA is 15.4 ± 0.55 kΩ at 1 kHz, accompanied by the base noise V rms of 2.4 ± 0.3 μV. The charge injection limit of the nanoporous Au-modified electrode estimated from voltage transient measurement is approximately 1 mC cm-2, which is comparable to roughened platinum and carbon nanotube electrodes. The charge injection capability of the nanoporous Au-modified MEA was confirmed by observing stimulus-induced spikes at above 0.2 V. The nanoporous Au-modified MEA showed mechanical durability upon ultrasonic treatment for up to an hour. Significance. Electro-codeposition of Ag:Au alloy combined with chemical etching Ag is a low-cost process for fabricating nanoporous Au-modified MEA suitable for establishing the stimulus-response relationship of cultured neuronal networks.

  11. In vitro extracellular recording and stimulation performance of nanoporous gold-modified multi-electrode arrays.

    Science.gov (United States)

    Kim, Yong Hee; Kim, Gook Hwa; Kim, Ah Young; Han, Young Hwan; Chung, Myung-Ae; Jung, Sang-Don

    2015-12-01

    Nanoporous gold (Au) structures can reduce the impedance and enhance the charge injection capability of multi-electrode arrays (MEAs) used for interfacing neuronal networks. Even though there are various nanoporous Au preparation techniques, fabrication of MEA based on low-cost electro-codeposition of Ag:Au has not been performed. In this work, we have modified a Au MEA via the electro-codeposition of Ag:Au alloy, followed by the chemical etching of Ag, and report on the in vitro extracellular recording and stimulation performance of the nanoporous Au-modified MEA. Ag:Au alloy was electro-codeposited on a bilayer lift-off resist sputter-deposition passivated Au MEA followed by chemical etching of Ag to form a porous Au structure. The porous Au structure was analyzed by scanning electron microscopy and tunneling electron microscopy and found to have an interconnected nanoporous Au structure. The impedance value of the nanoporous Au-modified MEA is 15.4 ± 0.55 kΩ at 1 kHz, accompanied by the base noise V rms of 2.4 ± 0.3 μV. The charge injection limit of the nanoporous Au-modified electrode estimated from voltage transient measurement is approximately 1 mC cm(-2), which is comparable to roughened platinum and carbon nanotube electrodes. The charge injection capability of the nanoporous Au-modified MEA was confirmed by observing stimulus-induced spikes at above 0.2 V. The nanoporous Au-modified MEA showed mechanical durability upon ultrasonic treatment for up to an hour. Electro-codeposition of Ag:Au alloy combined with chemical etching Ag is a low-cost process for fabricating nanoporous Au-modified MEA suitable for establishing the stimulus-response relationship of cultured neuronal networks.

  12. Silicon/SU8 multi-electrode micro-needle for in vivo neurochemical monitoring.

    Science.gov (United States)

    Vasylieva, Natalia; Marinesco, Stéphane; Barbier, Daniel; Sabac, Andrei

    2015-10-15

    Simultaneous monitoring of glucose and lactate is an important challenge for understanding brain energetics in physiological or pathological states. We demonstrate here a versatile method based on a minimally invasive single implantation in the rat brain. A silicon/SU8-polymer multi-sensing needle-shaped biosensor, was fabricated and tested. The multi-electrode array design comprises three platinum planar microelectrodes with a surface area of 40 × 200 µm(2) and a spacing of 200 µm, which were micromachined on a single 3mm long micro-needle having a 100 × 50 µm(2) cross-section for reduced tissue damage during implantation. Platinum micro-electrodes were aligned at the bottom of micro-wells obtained by photolithography on a SU8 photoresist layer. After clean room processing, each micro-electrode was functionalized inside the micro-wells by means of a micro-dispensing device, either with glucose oxidase or with lactate oxidase, which were cross-linked on the platinum electrodes. The third electrode covered with Bovine Serum Albumin (BSA) was used for the control of non-specific currents. The thick SU8 photoresist layer has revealed excellent electrical insulation of the micro-electrodes and between interconnection lines, and ensured a precise localization and packaging of the sensing enzymes on platinum micro-electrodes. During in vitro calibration with concentrations of analytes in the mM range, the micro-wells patterned in the SU8 photoresist proved to be highly effective in eliminating cross-talk signals, caused by H2O2 diffusion from closely spaced micro-electrodes. Moreover, our biosensor was successfully assayed in the rat cortex for simultaneous monitoring of both glucose and lactate during insulin and glucose administration. Copyright © 2015 Elsevier B.V. All rights reserved.

  13. Carrier-interleaved orthogonal multi-electrode multi-carrier resistivity-measurement tool

    International Nuclear Information System (INIS)

    Cai, Yu; Sha, Shuang

    2016-01-01

    This paper proposes a new carrier-interleaved orthogonal multi-electrode multi-carrier resistivity-measurement tool used in a cylindrical borehole environment during oil-based mud drilling processes. The new tool is an orthogonal frequency division multiplexing access-based contactless multi-measurand detection tool. The tool can measure formation resistivity in different azimuthal angles and elevational depths. It can measure many more measurands simultaneously in a specified bandwidth than the legacy frequency division multiplexing multi-measurand tool without a channel-select filter while avoiding inter-carrier interference. The paper also shows that formation resistivity is not sensitive to frequency in certain frequency bands. The average resistivity collected from N subcarriers can increase the measurement of the signal-to-noise ratio (SNR) by N times given no amplitude clipping in the current-injection electrode. If the clipping limit is taken into account, with the phase rotation of each single carrier, the amplitude peak-to-average ratio can be reduced by 3 times, and the SNR can achieve a 9/ N times gain over the single-carrier system. The carrier-interleaving technique is also introduced to counter the carrier frequency offset (CFO) effect, where the CFO will cause inter-pad interference. A qualitative analysis and simulations demonstrate that block-interleaving performs better than tone-interleaving when coping with a large CFO. The theoretical analysis also suggests that increasing the subcarrier number can increase the measurement speed or enhance elevational resolution without sacrificing receiver performance. The complex orthogonal multi-pad multi-carrier resistivity logging tool, in which all subcarriers are complex signals, can provide a larger available subcarrier pool than other types of transceivers. (paper)

  14. Action potential propagation recorded from single axonal arbors using multi-electrode arrays.

    Science.gov (United States)

    Tovar, Kenneth R; Bridges, Daniel C; Wu, Bian; Randall, Connor; Audouard, Morgane; Jang, Jiwon; Hansma, Paul K; Kosik, Kenneth S

    2018-04-11

    We report the presence of co-occurring extracellular action potentials (eAPs) from cultured mouse hippocampal neurons among groups of planar electrodes on multi-electrode arrays (MEAs). The invariant sequences of eAPs among co-active electrode groups, repeated co-occurrences and short inter-electrode latencies are consistent with action potential propagation in unmyelinated axons. Repeated eAP co-detection by multiple electrodes was widespread in all our data records. Co-detection of eAPs confirms they result from the same neuron and allows these eAPs to be isolated from all other spikes independently of spike sorting algorithms. We averaged co-occurring events and revealed additional electrodes with eAPs that would otherwise be below detection threshold. We used these eAP cohorts to explore the temperature sensitivity of action potential propagation and the relationship between voltage-gated sodium channel density and propagation velocity. The sequence of eAPs among co-active electrodes 'fingerprints' neurons giving rise to these events and identifies them within neuronal ensembles. We used this property and the non-invasive nature of extracellular recording to monitor changes in excitability at multiple points in single axonal arbors simultaneously over several hours, demonstrating independence of axonal segments. Over several weeks, we recorded changes in inter-electrode propagation latencies and ongoing changes in excitability in different regions of single axonal arbors. Our work illustrates how repeated eAP co-occurrences can be used to extract physiological data from single axons with low electrode density MEAs. However, repeated eAP co-occurrences leads to over-sampling spikes from single neurons and thus can confound traditional spike-train analysis.

  15. Chronic Pain and Mental Health Disorders: Shared Neural Mechanisms, Epidemiology, and Treatment.

    Science.gov (United States)

    Hooten, W Michael

    2016-07-01

    Chronic pain and mental health disorders are common in the general population, and epidemiological studies suggest that a bidirectional relationship exists between these 2 conditions. The observations from functional imaging studies suggest that this bidirectional relationship is due in part to shared neural mechanisms. In addition to depression, anxiety, and substance use disorders, individuals with chronic pain are at risk of other mental health problems including suicide and cigarette smoking and many have sustained sexual violence. Within the broader biopsychosocial model of pain, the fear-avoidance model explains how behavioral factors affect the temporal course of chronic pain and provides the framework for an array of efficacious behavioral interventions including cognitive-behavioral therapy, acceptance-based therapies, and multidisciplinary pain rehabilitation. Concomitant pain and mental health disorders often complicate pharmacological management, but several drug classes, including serotonin-norepinephrine reuptake inhibitors, tricyclic antidepressants, and anticonvulsants, have efficacy for both conditions and should be considered first-line treatment agents. Copyright © 2016 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.

  16. Defining the neural fulcrum for chronic vagus nerve stimulation: implications for integrated cardiac control.

    Science.gov (United States)

    Ardell, Jeffrey L; Nier, Heath; Hammer, Matthew; Southerland, E Marie; Ardell, Christopher L; Beaumont, Eric; KenKnight, Bruce H; Armour, J Andrew

    2017-11-15

    The evoked cardiac response to bipolar cervical vagus nerve stimulation (VNS) reflects a dynamic interaction between afferent mediated decreases in central parasympathetic drive and suppressive effects evoked by direct stimulation of parasympathetic efferent axons to the heart. The neural fulcrum is defined as the operating point, based on frequency-amplitude-pulse width, where a null heart rate response is reproducibly evoked during the on-phase of VNS. Cardiac control, based on the principal of the neural fulcrum, can be elicited from either vagus. Beta-receptor blockade does not alter the tachycardia phase to low intensity VNS, but can increase the bradycardia to higher intensity VNS. While muscarinic cholinergic blockade prevented the VNS-induced bradycardia, clinically relevant doses of ACE inhibitors, beta-blockade and the funny channel blocker ivabradine did not alter the VNS chronotropic response. While there are qualitative differences in VNS heart control between awake and anaesthetized states, the physiological expression of the neural fulcrum is maintained. Vagus nerve stimulation (VNS) is an emerging therapy for treatment of chronic heart failure and remains a standard of therapy in patients with treatment-resistant epilepsy. The objective of this work was to characterize heart rate (HR) responses (HRRs) during the active phase of chronic VNS over a wide range of stimulation parameters in order to define optimal protocols for bidirectional bioelectronic control of the heart. In normal canines, bipolar electrodes were chronically implanted on the cervical vagosympathetic trunk bilaterally with anode cephalad to cathode (n = 8, 'cardiac' configuration) or with electrode positions reversed (n = 8, 'epilepsy' configuration). In awake state, HRRs were determined for each combination of pulse frequency (2-20 Hz), intensity (0-3.5 mA) and pulse widths (130-750 μs) over 14 months. At low intensities and higher frequency VNS, HR increased during the

  17. Proposed method for reconstructing velocity profiles using a multi-electrode electromagnetic flow meter

    International Nuclear Information System (INIS)

    Kollár, László E; Lucas, Gary P; Zhang, Zhichao

    2014-01-01

    An analytical method is developed for the reconstruction of velocity profiles using measured potential distributions obtained around the boundary of a multi-electrode electromagnetic flow meter (EMFM). The method is based on the discrete Fourier transform (DFT), and is implemented in Matlab. The method assumes the velocity profile in a section of a pipe as a superposition of polynomials up to sixth order. Each polynomial component is defined along a specific direction in the plane of the pipe section. For a potential distribution obtained in a uniform magnetic field, this direction is not unique for quadratic and higher-order components; thus, multiple possible solutions exist for the reconstructed velocity profile. A procedure for choosing the optimum velocity profile is proposed. It is applicable for single-phase or two-phase flows, and requires measurement of the potential distribution in a non-uniform magnetic field. The potential distribution in this non-uniform magnetic field is also calculated for the possible solutions using weight values. Then, the velocity profile with the calculated potential distribution which is closest to the measured one provides the optimum solution. The reliability of the method is first demonstrated by reconstructing an artificial velocity profile defined by polynomial functions. Next, velocity profiles in different two-phase flows, based on results from the literature, are used to define the input velocity fields. In all cases, COMSOL Multiphysics is used to model the physical specifications of the EMFM and to simulate the measurements; thus, COMSOL simulations produce the potential distributions on the internal circumference of the flow pipe. These potential distributions serve as inputs for the analytical method. The reconstructed velocity profiles show satisfactory agreement with the input velocity profiles. The method described in this paper is most suitable for stratified flows and is not applicable to axisymmetric flows in

  18. Comparison of efficacy of neural therapy and physical therapy in chronic low back pain.

    Science.gov (United States)

    Atalay, Nilgun Simsir; Sahin, Fusun; Atalay, Ali; Akkaya, Nuray

    2013-01-01

    The aim of this prospective study was to evaluate the effects of neural therapy, and physical therapy on level of pain, disability, quality of life, and psychological status in patients with chronic low back pain. Patients admitted to the physical therapy and rehabilitation outpatient clinic with the complaint of low back pain of at least 3 months duration. Group 1 (n=27), physical therapy (PT, hotpack, ultrasound, TENS 15 sessions), group 2 (n=33), neural therapy (NT, 1:1 mixture of 20 mg/mL Lidocaine HCl (Jetokain simplex®) and saline for 5 sessions. For pain, Visual Analogue Scale (VAS), for disability Roland Morris Disability Questionnaire (RMDQ), for quality-of-life Nottingham-Health-Profile (NHP), for depression, and anxiety, Hospital Anxiety-Depression Scale (HADS) were used before and after the treatment. Mean age was 47.3±11.32 years, symptom time was 13.78±11.98 months. There were no differences for demographic variables between groups. Significant improvements were detected for VAS, RMDQ, NHP-Pain, NHP-Physical activity, HADS for both of two groups after treatment. In addition to these findings, significant improvements were found for NHP-Energy, NHP-Social isolation in NT group. The differences of pre- and post-treatment values of parameters were evaluated for each group. Although there were no differences for VAS, NHP-sleep, NHP-Emotional reaction, HADS between groups, RMDQ, NHP-Pain, NHP-Physical activity, NHP-Social isolation were higher in NT than PT before treatment, the improvements for these parameters were better in NT than PT. In conclusion both of NT and PT are effective on pain, function, quality of life, anxiety, and depression in patients with chronic low back pain.

  19. 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.

  20. Effectiveness of Slump Neural Mobilization Technique for the management of chronic radicular low back pain

    International Nuclear Information System (INIS)

    Ali, M.; Rehman, S.S.U.; Ahmad, S.; Farooq, M.N.

    2015-01-01

    Objective: To determine the effectiveness of slump neural mobilization technique compared with lumber stabilization exercise (LSE) and shortwave diathermy (SWD) in the physical therapy management of chronic radicular low back pain (CRLBP). Methodology: A sample of 40 patients with CRLBP was selected and randomly placed into two groups A and B. 22 patients were treated with slump neural mobilization technique (SNMT), lumbar stabilization exercise (LSE) and Short wave diathermy (SWD), while 18 patient of group B were treated with LSE and SWD. All the patients were assessed by four point pain scale and Oswestry disability index (ODI) at the baseline and at the completion of three weeks at 5 days per week and 30 minutes single session per day. The data was collected on specially designed Performa and was analyzed by SPSS and paired t test was applied to determine the probability value at 95 % level of significance. Results: Both groups demonstrated significant improvement in pain score and ODI score, although improvement was more significant in group A (p<0.001 for both pain and ODI score) as compared to group B (p=0.003 for pain score and 0.163 for ODI score).table-I-III) Conclusion: It is concluded that SNMTalong LSE and SWD improves pain and function more as compared with LSE and SWD alone during the physical therapy management of CRLBP. (author)

  1. Chronic exposure to graphene-based nanomaterials induces behavioral deficits and neural damage in Caenorhabditis elegans.

    Science.gov (United States)

    Li, Ping; Xu, Tiantian; Wu, Siyu; Lei, Lili; He, Defu

    2017-10-01

    Nanomaterials of graphene and its derivatives have been widely applied in recent years, but whose impacts on the environment and health are still not well understood. In the present study, the potential adverse effects of graphite (G), graphite oxide nanoplatelets (GO) and graphene quantum dots (GQDs) on the motor nervous system were investigated using nematode Caenorhabditis elegans as the assay system. After being characterized using TEM, SEM, XPS and PLE, three nanomaterials were chronically exposed to C. elegans for 6 days. In total, 50-100 mg l -1 GO caused a significant reduction in the survival rate, but G and GDDs showed low lethality on nematodes. After chronic exposure of sub-lethal dosages, three nanomaterials were observed to distribute primarily in the pharynx and intestine; but GQDs were widespread in nematode body. Three graphene-based nanomaterials resulted in significant declines in locomotor frequency of body bending, head thrashing and pharynx pumping. In addition, mean speed, bending angle-frequency and wavelength of the crawling movement were significantly reduced after exposure. Using transgenic nematodes, we found high concentrations of graphene-based nanomaterials induced down-expression of dat-1::GFP and eat-4::GFP, but no significant changes in unc-47::GFP. This indicates that graphene-based nanomaterials can lead to damages in the dopaminergic and glutamatergic neurons. The present data suggest that chronic exposure of graphene-based nanomaterials may cause neurotoxicity risks of inducing behavioral deficits and neural damage. These findings provide useful information to understand the toxicity and safe application of graphene-based nanomaterials. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  2. Transplantation of Neural Precursor Cells Attenuates Chronic Immune Environment in Cervical Spinal Cord Injury

    Directory of Open Access Journals (Sweden)

    Lennart Riemann

    2018-06-01

    Full Text Available Inflammation after traumatic spinal cord injury (SCI is non-resolving and thus still present in chronic injury stages. It plays a key role in the pathophysiology of SCI and has been associated with further neurodegeneration and development of neuropathic pain. Neural precursor cells (NPCs have been shown to reduce the acute and sub-acute inflammatory response after SCI. In the present study, we examined effects of NPC transplantation on the immune environment in chronic stages of SCI. SCI was induced in rats by clip-compression of the cervical spinal cord at the level C6-C7. NPCs were transplanted 10 days post-injury. The functional outcome was assessed weekly for 8 weeks using the Basso, Beattie, and Bresnahan scale, the CatWalk system, and the grid walk test. Afterwards, the rats were sacrificed, and spinal cord sections were examined for M1/M2 macrophages, T lymphocytes, astrogliosis, and apoptosis using immunofluorescence staining. Rats treated with NPCs had compared to the control group significantly fewer pro-inflammatory M1 macrophages and reduced immunodensity for inducible nitric oxide synthase (iNOS, their marker enzyme. Anti-inflammatory M2 macrophages were rarely present 8 weeks after the SCI. In this model, the sub-acute transplantation of NPCs did not support survival and proliferation of M2 macrophages. Post-traumatic apoptosis, however, was significantly reduced in the NPC group, which might be explained by the altered microenvironment following NPC transplantation. Corresponding to these findings, reactive astrogliosis was significantly reduced in NPC-transplanted animals. Furthermore, we could observe a trend toward smaller cavity sizes and functional improvement following NPC transplantation. Our data suggest that transplantation of NPCs following SCI might attenuate inflammation even in chronic injury stages. This might prevent further neurodegeneration and could also set a stage for improved neuroregeneration after SCI.

  3. Environmental enrichment may protect against neural and behavioural damage caused by withdrawal from chronic alcohol intake.

    Science.gov (United States)

    Nobre, Manoel Jorge

    2016-12-01

    Exposure to stress and prolonged exposure to alcohol leads to neuronal damages in several brain regions, being the medial prefrontal cortex (mPFC) one of the most affected. These changes presumably reduce the ability of the organism to cope with these stimuli and may underlie a series of maladaptive behaviours among which include drug addiction and withdrawal. Drug-addicted individuals show a pattern of behavior similar to patients with lesions of the mPFC. This impairment in the decision-making could be one of the mechanisms responsible for the transition from the casual to compulsive drug use. The environmental enrichment (EE) has a protective effect on the neural and cognitive impairments induced by psychoactive drugs, including ethyl alcohol. The present study aims to determine the influence of withdrawal from intermittent long-term alcohol exposure on alcohol preference, emotional reactivity and neural aspects of early isolated or grouped reared rats kept under standard or complex environments and the influence of social isolation on these measures, as well. Our results point out new insights on this matter showing that the EE can attenuate the adverse effects of withdrawal and social isolation on rat's behavior. This effect is probably due to its protective action on the mPFC integrity, including the cingulate area 1 (Cg1), and the prelimbic (PrL) and infralimbic cortex (IL), what could account for the absence of changes in the emotional reactivity in EE alcohol withdrawal rats. We argue that morphological changes at these cortical levels can afford the emotional, cognitive and behavioural dysregulations verified following withdrawal from chronic alcohol intake. Copyright © 2016 ISDN. Published by Elsevier Ltd. All rights reserved.

  4. Vitamin D and ferritin correlation with chronic neck pain using standard statistics and a novel artificial neural network prediction model.

    Science.gov (United States)

    Eloqayli, Haytham; Al-Yousef, Ali; Jaradat, Raid

    2018-02-15

    Despite the high prevalence of chronic neck pain, there is limited consensus about the primary etiology, risk factors, diagnostic criteria and therapeutic outcome. Here, we aimed to determine if Ferritin and Vitamin D are modifiable risk factors with chronic neck pain using slandered statistics and artificial intelligence neural network (ANN). Fifty-four patients with chronic neck pain treated between February 2016 and August 2016 in King Abdullah University Hospital and 54 patients age matched controls undergoing outpatient or minor procedures were enrolled. Patients and control demographic parameters, height, weight and single measurement of serum vitamin D, Vitamin B12, ferritin, calcium, phosphorus, zinc were obtained. An ANN prediction model was developed. The statistical analysis reveals that patients with chronic neck pain have significantly lower serum Vitamin D and Ferritin (p-value artificial neural network can be of future benefit in classification and prediction models for chronic neck pain. We hope this initial work will encourage a future larger cohort study addressing vitamin D and iron correction as modifiable factors and the application of artificial intelligence models in clinical practice.

  5. Choosing Money over Drugs: The Neural Underpinnings of Difficult Choice in Chronic Cocaine Users.

    Science.gov (United States)

    Wesley, Michael J; Lohrenz, Terry; Koffarnus, Mikhail N; McClure, Samuel M; De La Garza, Richard; Salas, Ramiro; Thompson-Lake, Daisy G Y; Newton, Thomas F; Bickel, Warren K; Montague, P Read

    2014-01-01

    Addiction is considered a disorder that drives individuals to choose drugs at the expense of healthier alternatives. However, chronic cocaine users (CCUs) who meet addiction criteria retain the ability to choose money in the presence of the opportunity to choose cocaine. The neural mechanisms that differentiate CCUs from non-cocaine using controls (Controls) while executing these preferred choices remain unknown. Thus, therapeutic strategies aimed at shifting preferences towards healthier alternatives remain somewhat uninformed. This study used BOLD neuroimaging to examine brain activity as fifty CCUs and Controls performed single- and cross-commodity intertemporal choice tasks for money and/or cocaine. Behavioral analyses revealed preferences for each commodity type. Imaging analyses revealed the brain activity that differentiated CCUs from Controls while choosing money over cocaine. We observed that CCUs devalued future commodities more than Controls. Choices for money as opposed to cocaine correlated with greater activity in dorsal striatum of CCUs, compared to Controls. In addition, choices for future money as opposed to immediate cocaine engaged the left dorsolateral prefrontal cortex (DLPFC) of CCUs more than Controls. These data suggest that the ability of CCUs to execute choices away from cocaine relies on activity in the dorsal striatum and left DLPFC.

  6. Performance assessments of vertically aligned carbon nanotubes multi-electrode arrays using Cath.a-differentiated (CAD) cells

    Science.gov (United States)

    Jeong, Du Won; Jung, Jongjin; Kim, Gook Hwa; Yang, Cheol-Soo; Kim, Ju Jin; Jung, Sang Don; Lee, Jeong-O.

    2015-08-01

    In this work, Cath.a-differentiated (CAD) cells were used in place of primary neuronal cells to assess the performance of vertically aligned carbon nanotubes (VACNTs) multi-electrode arrays (MEA). To fabricate high-performance MEA, VACNTs were directly grown on graphene/Pt electrodes via plasma enhanced chemical deposition technique. Here, graphene served as an intermediate layer lowering contact resistance between VACNTs and Pt electrode. In order to lower the electrode impedance and to enhance the cell adhesion, VACNTs-MEAs were treated with UV-ozone for 20 min. Impedance of VACNTs electrode at 1 kHz frequency exhibits a reasonable value (110 kΩ) for extracellular signal recording, and the signal to noise ratio the is good enough to measure low signal amplitude (15.7). Spontaneous firing events from CAD cells were successfully measured with VACNTs MEAs that were also found to be surprisingly robust toward the biological interactions.

  7. Performance assessments of vertically aligned carbon nanotubes multi-electrode arrays using Cath.a-differentiated (CAD) cells

    International Nuclear Information System (INIS)

    Jeong, Du Won; Jin Kim, Ju; Jung, Jongjin; Yang, Cheol-Soo; Lee, Jeong-O; Hwa Kim, Gook; Don Jung, Sang

    2015-01-01

    In this work, Cath.a-differentiated (CAD) cells were used in place of primary neuronal cells to assess the performance of vertically aligned carbon nanotubes (VACNTs) multi-electrode arrays (MEA). To fabricate high-performance MEA, VACNTs were directly grown on graphene/Pt electrodes via plasma enhanced chemical deposition technique. Here, graphene served as an intermediate layer lowering contact resistance between VACNTs and Pt electrode. In order to lower the electrode impedance and to enhance the cell adhesion, VACNTs-MEAs were treated with UV–ozone for 20 min. Impedance of VACNTs electrode at 1 kHz frequency exhibits a reasonable value (110 kΩ) for extracellular signal recording, and the signal to noise ratio the is good enough to measure low signal amplitude (15.7). Spontaneous firing events from CAD cells were successfully measured with VACNTs MEAs that were also found to be surprisingly robust toward the biological interactions. (paper)

  8. Interpretation of field potentials measured on a multi electrode array in pharmacological toxicity screening on primary and human pluripotent stem cell-derived cardiomyocytes

    NARCIS (Netherlands)

    Tertoolen, L.G.J.; Braam, S. R.; van Meer, B.J.; Passier, R.; Mummery, C. L.

    2018-01-01

    Multi electrode arrays (MEAs) are increasingly used to detect external field potentials in electrically active cells. Recently, in combination with cardiomyocytes derived from human (induced) pluripotent stem cells they have started to become a preferred tool to examine newly developed drugs for

  9. Functional connectivity and information flow of the respiratory neural network in chronic obstructive pulmonary disease.

    Science.gov (United States)

    Yu, Lianchun; De Mazancourt, Marine; Hess, Agathe; Ashadi, Fakhrul R; Klein, Isabelle; Mal, Hervé; Courbage, Maurice; Mangin, Laurence

    2016-08-01

    Breathing involves a complex interplay between the brainstem automatic network and cortical voluntary command. How these brain regions communicate at rest or during inspiratory loading is unknown. This issue is crucial for several reasons: (i) increased respiratory loading is a major feature of several respiratory diseases, (ii) failure of the voluntary motor and cortical sensory processing drives is among the mechanisms that precede acute respiratory failure, (iii) several cerebral structures involved in responding to inspiratory loading participate in the perception of dyspnea, a distressing symptom in many disease. We studied functional connectivity and Granger causality of the respiratory network in controls and patients with chronic obstructive pulmonary disease (COPD), at rest and during inspiratory loading. Compared with those of controls, the motor cortex area of patients exhibited decreased connectivity with their contralateral counterparts and no connectivity with the brainstem. In the patients, the information flow was reversed at rest with the source of the network shifted from the medulla towards the motor cortex. During inspiratory loading, the system was overwhelmed and the motor cortex became the sink of the network. This major finding may help to understand why some patients with COPD are prone to acute respiratory failure. Network connectivity and causality were related to lung function and illness severity. We validated our connectivity and causality results with a mathematical model of neural network. Our findings suggest a new therapeutic strategy involving the modulation of brain activity to increase motor cortex functional connectivity and improve respiratory muscles performance in patients. Hum Brain Mapp 37:2736-2754, 2016. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

  10. Long-Term Effects of Chronic Oral Ritalin Administration on Cognitive and Neural Development in Adolescent Wistar Kyoto Rats

    Directory of Open Access Journals (Sweden)

    Jennifer L. Cornish

    2012-09-01

    Full Text Available The diagnosis of Attention Deficit Hyperactivity Disorder (ADHD often results in chronic treatment with psychostimulants such as methylphenidate (MPH, Ritalin®. With increases in misdiagnosis of ADHD, children may be inappropriately exposed to chronic psychostimulant treatment during development. The aim of this study was to assess the effect of chronic Ritalin treatment on cognitive and neural development in misdiagnosed “normal” (Wistar Kyoto, WKY rats and in Spontaneously Hypertensive Rats (SHR, a model of ADHD. Adolescent male animals were treated for four weeks with oral Ritalin® (2 × 2 mg/kg/day or distilled water (dH2O. The effect of chronic treatment on delayed reinforcement tasks (DRT and tyrosine hydroxylase immunoreactivity (TH-ir in the prefrontal cortex was assessed. Two weeks following chronic treatment, WKY rats previously exposed to MPH chose the delayed reinforcer significantly less than the dH2O treated controls in both the DRT and extinction task. MPH treatment did not significantly alter cognitive performance in the SHR. TH-ir in the infralimbic cortex was significantly altered by age and behavioural experience in WKY and SHR, however this effect was not evident in WKY rats treated with MPH. These results suggest that chronic treatment with MPH throughout adolescence in “normal” WKY rats increased impulsive choice and altered catecholamine development when compared to vehicle controls.

  11. Neural glycoprotein M6a is released in extracellular vesicles and modulated by chronic stressors in blood

    OpenAIRE

    Monteleone, Melisa C.; Billi, Silvia C.; Brocco, Marcela A.; Frasch, Alberto C.

    2017-01-01

    Membrane neuronal glycoprotein M6a is highly expressed in the brain and contributes to neural plasticity promoting neurite growth and spine and synapse formation. We have previously showed that chronic stressors alter hippocampal M6a mRNA levels in rodents and tree shrews. We now show that M6a glycoprotein can be detected in mouse blood. M6a is a transmembrane glycoprotein and, as such, unlikely to be free in blood. Here we demonstrate that, in blood, M6a is transported in extracellular vesic...

  12. Chronic behavior evaluation of a micro-machined neural implant with optimized design based on an experimentally derived model.

    Science.gov (United States)

    Andrei, Alexandru; Welkenhuysen, Marleen; Ameye, Lieveke; Nuttin, Bart; Eberle, Wolfgang

    2011-01-01

    Understanding the mechanical interactions between implants and the surrounding tissue is known to have an important role for improving the bio-compatibility of such devices. Using a recently developed model, a particular micro-machined neural implant design aiming the reduction of insertion forces dependence on the insertion speed was optimized. Implantations with 10 and 100 μm/s insertion speeds showed excellent agreement with the predicted behavior. Lesion size, gliosis (GFAP), inflammation (ED1) and neuronal cells density (NeuN) was evaluated after 6 week of chronic implantation showing no insertion speed dependence.

  13. Development and Characterization of a Diamond-Insulated Graphitic Multi Electrode Array Realized with Ion Beam Lithography

    Directory of Open Access Journals (Sweden)

    Federico Picollo

    2014-12-01

    Full Text Available The detection of quantal exocytic events from neurons and neuroendocrine cells is a challenging task in neuroscience. One of the most promising platforms for the development of a new generation of biosensors is diamond, due to its biocompatibility, transparency and chemical inertness. Moreover, the electrical properties of diamond can be turned from a perfect insulator into a conductive material (resistivity ~mΩ·cm by exploiting the metastable nature of this allotropic form of carbon. A 16‑channels MEA (Multi Electrode Array suitable for cell culture growing has been fabricated by means of ion implantation. A focused 1.2 MeV He+ beam was scanned on a IIa single-crystal diamond sample (4.5 × 4.5 × 0.5 mm3 to cause highly damaged sub-superficial structures that were defined with micrometric spatial resolution. After implantation, the sample was annealed. This process provides the conversion of the sub-superficial highly damaged regions to a graphitic phase embedded in a highly insulating diamond matrix. Thanks to a three-dimensional masking technique, the endpoints of the sub-superficial channels emerge in contact with the sample surface, therefore being available as sensing electrodes. Cyclic voltammetry and amperometry measurements of solutions with increasing concentrations of adrenaline were performed to characterize the biosensor sensitivity. The reported results demonstrate that this new type of biosensor is suitable for in vitro detection of catecholamine release.

  14. Electrophysiological Analysis of human Pluripotent Stem Cell-derived Cardiomyocytes (hPSC-CMs) Using Multi-electrode Arrays (MEAs).

    Science.gov (United States)

    Sala, Luca; Ward-van Oostwaard, Dorien; Tertoolen, Leon G J; Mummery, Christine L; Bellin, Milena

    2017-05-12

    Cardiomyocytes can now be derived with high efficiency from both human embryonic and human induced-Pluripotent Stem Cells (hPSC). hPSC-derived cardiomyocytes (hPSC-CMs) are increasingly recognized as having great value for modeling cardiovascular diseases in humans, especially arrhythmia syndromes. They have also demonstrated relevance as in vitro systems for predicting drug responses, which makes them potentially useful for drug-screening and discovery, safety pharmacology and perhaps eventually for personalized medicine. This would be facilitated by deriving hPSC-CMs from patients or susceptible individuals as hiPSCs. For all applications, however, precise measurement and analysis of hPSC-CM electrical properties are essential for identifying changes due to cardiac ion channel mutations and/or drugs that target ion channels and can cause sudden cardiac death. Compared with manual patch-clamp, multi-electrode array (MEA) devices offer the advantage of allowing medium- to high-throughput recordings. This protocol describes how to dissociate 2D cell cultures of hPSC-CMs to small aggregates and single cells and plate them on MEAs to record their spontaneous electrical activity as field potential. Methods for analyzing the recorded data to extract specific parameters, such as the QT and the RR intervals, are also described here. Changes in these parameters would be expected in hPSC-CMs carrying mutations responsible for cardiac arrhythmias and following addition of specific drugs, allowing detection of those that carry a cardiotoxic risk.

  15. Multi-electrode double layer capacitor having single electrolyte seal and aluminum-impregnated carbon cloth electrodes

    Science.gov (United States)

    Farahmandi, C. Joseph; Dispennette, John M.; Blank, Edward; Kolb, Alan C.

    1999-01-19

    A single cell, multi-electrode high performance double layer capacitor includes first and second flat stacks of electrodes adapted to be housed in a closeable two-part capacitor case which includes only a single electrolyte seal. Each electrode stack has a plurality of electrodes connected in parallel, with the electrodes of one stack being interleaved with the electrodes of the other stack to form an interleaved stack, and with the electrodes of each stack being electrically connected to respective capacitor terminals. A porous separator sleeve is inserted over the electrodes of one stack before interleaving to prevent electrical shorts between the electrodes. The electrodes are made by folding a compressible, low resistance, aluminum-impregnated carbon cloth, made from activated carbon fibers, around a current collector foil, with a tab of the foils of each electrode of each stack being connected in parallel and connected to the respective capacitor terminal. The height of the interleaved stack is somewhat greater than the inside height of the closed capacitor case, thereby requiring compression of the interleaved electrode stack when placed inside of the case, and thereby maintaining the interleaved electrode stack under modest constant pressure. The closed capacitor case is filled with an electrolytic solution and sealed. A preferred electrolytic solution is made by dissolving an appropriate salt into acetonitrile (CH.sub.3 CN). In one embodiment, the two parts of the capacitor case are conductive and function as the capacitor terminals.

  16. Clinical thermometry, using the 27 MHz multi-electrode current-source interstitial hyperthermia system in brain tumours

    International Nuclear Information System (INIS)

    Kaatee, Robert S.J.P.; Nowak, Peter C.J.M.; Zee, Jacoba van der; Bree, Jacob de; Kanis, Bart P.; Crezee, Hans; Levendag, Peter C.; Visser, Andries G.

    2001-01-01

    Background and purpose: In interstitial hyperthermia, temperature measurements are mainly performed inside heating applicators, and therefore, give the maximum temperatures of a rather heterogeneous temperature distribution. The problem of how to estimate lesion temperatures using the multi-electrode current-source interstitial hyperthermia (MECS-IHT) system in the brain was studied. Materials and methods: Temperatures were measured within the electrodes and in an extra catheter at the edge of a 4x4x4.5 cm 3 glioblastoma multiforme resection cavity. From the temperature decays during a power-off period, information was obtained about local maximum and minimum tissue temperatures. The significance of these data was examined through model calculations. Results: Maximum tissue temperatures could be estimated roughly by switching off all electrodes for about 5 s. Model calculations showed that the minimum tissue temperatures near a certain afterloading catheter correspond well with the temperature of the applicator inside, about 1 min after this applicator was switched off. Conclusions: Although the electrode temperatures read during heating are not suitable to assess the temperature distribution, it is feasible to heat the brain adequately using the MECS-IHT system with extra sensors outside the electrodes and/or application of decay methods

  17. Targeted therapies using electrical and magnetic neural stimulation for the treatment of chronic pain in spinal cord injury.

    Science.gov (United States)

    Moreno-Duarte, Ingrid; Morse, Leslie R; Alam, Mahtab; Bikson, Marom; Zafonte, Ross; Fregni, Felipe

    2014-01-15

    Chronic neuropathic pain is one of the most common and disabling symptoms in individuals with spinal cord injury (SCI). Over two-thirds of subjects with SCI suffer from chronic pain influencing quality of life, rehabilitation, and recovery. Given the refractoriness of chronic pain to most pharmacological treatments, the majority of individuals with SCI report worsening of this condition over time. Moreover, only 4-6% of patients in this cohort report improvement. Novel treatments targeting mechanisms associated with pain-maladaptive plasticity, such as electromagnetic neural stimulation, may be desirable to improve outcomes. To date, few, small clinical trials have assessed the effects of invasive and noninvasive nervous system stimulation on pain after SCI. We aimed to review initial efficacy, safety and potential predictors of response by assessing the effects of neural stimulation techniques to treat SCI pain. A literature search was performed using the PubMed database including studies using the following targeted stimulation strategies: transcranial Direct Current Stimulation (tDCS), High Definition tDCS (HD-tDCS), repetitive Transcranial Magnetical Stimulation (rTMS), Cranial Electrotherapy Stimulation (CES), Transcutaneous Electrical Nerve Stimulation (TENS), Spinal Cord Stimulation (SCS) and Motor Cortex Stimulation (MCS), published prior to June of 2012. We included studies from 1998 to 2012. Eight clinical trials and one naturalistic observational study (nine studies in total) met the inclusion criteria. Among the clinical trials, three studies assessed the effects of tDCS, two of CES, two of rTMS and one of TENS. The naturalistic study investigated the analgesic effects of SCS. No clinical trials for epidural motor cortex stimulation (MCS) or HD-tDCS were found. Parameters of stimulation and also clinical characteristics varied significantly across studies. Three out of eight studies showed larger effects sizes (0.73, 0.88 and 1.86 respectively) for pain

  18. The Effect of a Short-term Glucose Deprivation on Neuron Net Functioning of Hippocampus Primary Culture on a Multi-electrode Matrix

    OpenAIRE

    Vedunova M.V.; Korotchenko S.A.; Balashova A.N.; Isakova A.O.; Khaspekov L.G.; Kazantsev V.B.; Mukhina I.V.

    2011-01-01

    There has been studied the effect of a short-term glucose deprivation on neuron net functioning of hippocampus primary culture developing within 32 days on a multi-electrode matrix MED64 (Alpha MED Sciences Company, Japan) in an early and remote periods after deprivation. A short-term glucose deprivation (20 min) has been shown to result in the increase of electrobiological activity of neuron net of hippocampus primary culture, with the cascade of metabolic reactions being activated leading t...

  19. Safety of atrial fibrillation ablation with novel multi-electrode array catheters on uninterrupted anticoagulation-a single-center experience.

    LENUS (Irish Health Repository)

    Hayes, Christopher Ruslan

    2012-02-01

    INTRODUCTION: A recent single-center report indicated that the performance of atrial fibrillation ablation in patients on uninterrupted warfarin using a conventional deflectable tip electrode ablation catheter may be as safe as periprocedural discontinuation of warfarin and bridging with heparin. Novel multi-electrode array catheters for atrial fibrillation ablation are currently undergoing clinical evaluation. While offering the possibility of more rapid atrial fibrillation ablation, they are stiffer and necessitate the deployment of larger deflectable transseptal sheaths, and it remains to be determined if they increase the risk of cardiac perforation and vascular injury. Such potential risks would have implications for a strategy of uninterrupted periprocedural anticoagulation. METHOD AND RESULTS: We audited the safety outcomes of our atrial fibrillation ablation procedures using multi-electrode array ablation catheters in patients on uninterrupted warfarin (CHADS2 score>or=2) and in patients not on warfarin (uninterrupted aspirin). Two bleeding complications occurred in 49 patients on uninterrupted warfarin, both of which were managed successfully without longterm sequelae, and no bleeding complication occurred in 32 patients not on warfarin (uninterrupted aspirin). There were no thromboembolic events or other complication with either anticoagulant regimen. CONCLUSION: Despite the larger diameter and increased stiffness of multi-electrode array catheters and their deflectable transseptal sheaths, their use for catheter ablation in patients with atrial fibrillation on uninterrupted warfarin in this single-center experience does not appear to be unsafe, and thus, an adequately powered multicenter prospective randomized controlled trial should be considered.

  20. 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

  1. Systemic Injection of Neural Stem/progenitor Cells in Mice With Chronic EAE

    OpenAIRE

    Donegà, Matteo; Giusto, Elena; Cossetti, Chiara; Schaeffer, Julia; Pluchino, Stefano

    2014-01-01

    Neural stem/precursor cells (NPCs) are a promising stem cell source for transplantation approaches aiming at brain repair or restoration in regenerative neurology. This directive has arisen from the extensive evidence that brain repair is achieved after focal or systemic NPC transplantation in several pre clinical models of neurological diseases.

  2. Redo renal denervation using a multi-electrode radiofrequency system in patients with persistent therapy-resistant hypertension.

    Science.gov (United States)

    Daemen, J; Feyz, L; Van Zandvoort, L; Van Mieghem, N M

    2017-06-01

    Renal sympathetic denervation has been studied as a potential therapeutic option for patients with therapy-resistant hypertension; however, a significant proportion of patients do not show a significant reduction in blood pressure and are classified as non-responders. The objective of the present study was to assess whether a redo renal denervation procedure increases response rates. We present a case series of three consecutive renal denervation non-responders treated with the multi-electrode radiofrequency St. Jude EnligHTN catheter after an average of 22 months. Patients were followed for 6 months. Mean age was 66 years and two patients were male. Patients were previously treated using either ReCor's Paradise system, the Vessix V2 system or the Covidien OneShot system. Mean office blood pressure one year after the initial procedure was 187/102 mm Hg with a mean 24 h ambulatory blood pressure of 166/102 mm Hg. All patients underwent a successful redo procedure using the EnligHTN system because of persistent therapy-resistant hypertension. At 6 months a significant drop in both office and ambulatory blood pressure of -27/-6 mm Hg and -15/-13 mm Hg, respectively, was observed. No significant renal artery stenosis was observed at 6 months. In patients with therapy-resistant hypertension who do not respond to an initial renal denervation procedure, a redo procedure using the St. Jude EnligHTN system may help to significantly improve blood pressure control.

  3. Multi electrode semiconductors detectors

    CERN Document Server

    Amendolia, S R; Bertolucci, Ennio; Bosisio, L; Bradaschia, C; Budinich, M; Fidecaro, F; Foà, L; Focardi, E; Giazotto, A; Giorgi, M A; Marrocchesi, P S; Menzione, A; Ristori, L; Rolandi, Luigi; Scribano, A; Stefanini, A; Vincelli, M L

    1981-01-01

    Detectors with very high space resolution have been built in this laboratory and tested at CERN in order to investigate their possible use in high energy physics experiments. These detectors consist of thin layers of silicon crystals acting as ionization chambers. Thin electrodes, structured in strips or in more fancy shapes are applied to their surfaces by metal coating. The space resolution which could be reached is of the order of a few microns. An interesting feature of these solid state detectors is that they can work under very high or low external pressure or at very low temperature. The use of these detectors would strongly reduce the dimensions and the cost of high energy experiments. (3 refs).

  4. Multi electrode semiconductor detectors

    International Nuclear Information System (INIS)

    Amendolia, S.R.; Batignani, G.; Bertolucci, E.; Bosisio, L.; Budinich, M.; Bradaschia, C.; Fidecaro, F.; Foa, L.; Focardi, E.; Giazotto, A.; Giorgi, M.A.; Marrocchesi, P.S.; Menzione, A.; Ristori, L.; Rolandi, L.; Scribano, A.; Stefanini, A.; Vincelli, M.L.

    1981-01-01

    Detectors with very high space resolution have been built in the laboratory and tested at CERN in order to investigate their possible use in high energy physics experiments. These detectors consist of thin layers of silicon crystals acting as ionization chambers. Thin electrodes, structured in strips or in more fancy shapes are applied to their surfaces by metal coating. The space resolution which could be reached is of the order of a few microns. An interesting feature of these solid state detectors is that they can work under very high or low external pressure or at very low temperature. The use of these detectors would strongly reduce the dimensions and the cost of high energy experiments. (Auth.)

  5. Early Detection of Peak Demand Days of Chronic Respiratory Diseases Emergency Department Visits Using Artificial Neural Networks.

    Science.gov (United States)

    Khatri, Krishan L; Tamil, Lakshman S

    2018-01-01

    Chronic respiratory diseases, mainly asthma and chronic obstructive pulmonary disease (COPD), affect the lives of people by limiting their activities in various aspects. Overcrowding of hospital emergency departments (EDs) due to respiratory diseases in certain weather and environmental pollution conditions results in the degradation of quality of medical care, and even limits its availability. A useful tool for ED managers would be to forecast peak demand days so that they can take steps to improve the availability of medical care. In this paper, we developed an artificial neural network based classifier using multilayer perceptron with back propagation algorithm that predicts peak event (peak demand days) of patients with respiratory diseases, mainly asthma and COPD visiting EDs in Dallas County of Texas in the United States. The precision and recall for peak event class were 77.1% and 78.0%, respectively, and those for nonpeak events were 83.9% and 83.2%, respectively. The overall accuracy of the system is 81.0%.

  6. Chronic lead intoxication affects glial and neural systems and induces hypoactivity in adult rat.

    Science.gov (United States)

    Sansar, Wafa; Ahboucha, Samir; Gamrani, Halima

    2011-10-01

    Lead is an environmental toxin and its effects are principally manifested in the brain. Glial and neuronal changes have been described during development following chronic or acute lead intoxication, however, little is known about the effects of chronic lead intoxication in adults. In this study we evaluated immunohistochemically the glial and dopaminergic systems in adult male Wistar rats. 0.5% (v/v) lead acetate in drinking water was administrated chronically over a 3-month period. Hypertrophic immunoreactive astrocytes were observed in the frontal cortex and other brain structures of the treated animals. Analysis of the astroglial features showed increased number of astrocyte cell bodies and processes in treated rats, an increase confirmed by Western blot. Particular distribution of glial fibrillary acidic protein immunoreactivity was observed within the blood vessel walls in which dense immunoreactive glial processes emanate from astrocytes. Glial changes in the frontal cortex were concomitant with reduced tyrosine hydroxylase immunoreactive neuronal processes, which seem to occur as a consequence of significantly reduced dopaminergic neurons within the nucleus of origin in the substantia nigra. These glial and neuronal changes following lead intoxication may affect animal behavior as evidenced by reduced locomotor activity in an open field test. These findings demonstrate that chronic lead exposure induces astroglial changes, which may compromise neuronal function and consequently animal behavior. Copyright © 2010 Elsevier GmbH. All rights reserved.

  7. Safety and efficacy of a multi-electrode renal sympathetic denervation system in resistant hypertension: the EnligHTN I trial.

    Science.gov (United States)

    Worthley, Stephen G; Tsioufis, Costas P; Worthley, Matthew I; Sinhal, Ajay; Chew, Derek P; Meredith, Ian T; Malaiapan, Yuvi; Papademetriou, Vasilios

    2013-07-01

    Catheter-based renal artery sympathetic denervation has emerged as a novel therapy for treatment of patients with drug-resistant hypertension. Initial studies were performed using a single electrode radiofrequency catheter, but recent advances in catheter design have allowed the development of multi-electrode systems that can deliver lesions with a pre-determined pattern. This study was designed to evaluate the safety and efficacy of the EnligHTN(™) multi-electrode system. We conducted the first-in-human, prospective, multi-centre, non-randomized study in 46 patients (67% male, mean age 60 years, and mean baseline office blood pressure 176/96 mmHg) with drug-resistant hypertension. The primary efficacy objective was change in office blood pressure from baseline to 6 months. Safety measures included all adverse events with a focus on the renal artery and other vascular complications and changes in renal function. Renal artery denervation, using the EnligHTN system significantly reduced the office blood pressure from baseline to 1, 3, and 6 months by -28/10, -27/10 and -26/10 mmHg, respectively (P renal artery injury or other serious vascular complications occurred. Small, non-clinically relevant, changes in average estimated glomerular filtration rate were reported from baseline (87 ± 19 mL/min/1.73 m2) to 6 months post-procedure (82 ± 20 mL/min/1.73 m2). Renal sympathetic denervation, using the EnligHTN multi-electrode catheter results in a rapid and significant office blood pressure reduction that was sustained through 6 months. The EnligHTN system delivers a promising therapy for the treatment of drug-resistant hypertension.

  8. Chronic Restraint Stress Induces an Isoform-Specific Regulation on the Neural Cell Adhesion Molecule in the Hippocampus

    Science.gov (United States)

    Touyarot, K.; Sandi, C.

    2002-01-01

    Existing evidence indicates that 21-days exposure of rats to restraint stress induces dendritic atrophy in pyramidal cells of the hippocampus. This phenomenon has been related to altered performance in hippocampal-dependent learning tasks. Prior studies have shown that hippocampal expression of cell adhesion molecules is modified by such stress treatment, with the neural cell adhesion molecule (NCAM) decreasing and L1 increasing, their expression, at both the mRNA and protein levels. Given that NCAM comprises several isoforms, we investigated here whether chronic stress might differentially affect the expression of the three major isoforms (NCAM-120, NCAM-140, NCAM-180) in the hippocampus. In addition, as glucocorticoids have been implicated in the deleterious effects induced by chronic stress, we also evaluated plasma corticosterone levels and the hippocampal expression of the corticosteroid mineralocorticoid receptor (MR) and glucocorticoid receptor (GR). The results showed that the protein concentration of the NCAM-140 isoform decreased in the hippoampus of stressed rats. This effect was isoform-specific, because NCAM-120 and NCAM-180 levels were not significantly modified. In addition, whereas basal levels of plasma corticosterone tended to be increased, MR and GR concentrations were not significantly altered. Although possible changes in NCAM-120, NCAM-180 and corticosteroid receptors at earlier time points of the stress period cannot be ignored; this study suggests that a down-regulation of NCAM-140 might be implicated in the structural alterations consistently shown to be induced in the hippocampus by chronic stress exposure. As NCAM-140 is involved in cell-cell adhesion and neurite outgrowth, these findings suggest that this molecule might be one of the molecular mechanisms involved in the complex interactions among neurodegeneration-related events. PMID:12757368

  9. Determination of bisphenol A in human serum by high-performance liquid chromatography with multi-electrode electrochemical detection.

    Science.gov (United States)

    Inoue, K; Kato, K; Yoshimura, Y; Makino, T; Nakazawa, H

    2000-11-10

    A simple and sensitive method using high-performance liquid chromatography with multi-electrode electrochemical detection (HPLC-ED) including a coulometric array of four electrochemical sensors has been developed for the determination of bisphenol A in water and human serum. For good separation and detection of bisphenol A, a CAPCELL PAK UG 120 C18 reversed-phase column and a mobile phase consisting of 0.3% phosphoric acid-acetonitrile (60:40) were used. The detection limit obtained by the HPLC-ED method was 0.01 ng/ml (0.5 pg), which was more than 3000-times higher than the detection limit obtained by the ultraviolet (UV) method, and more than 200-times higher than the detection limit obtained by the fluorescence (FL) method. Bisphenol A in water and serum samples was pretreated by solid-phase extraction (SPE) after removing possible contamination derived from a plastic SPE cartridges and water used for the pretreatment. A trace amount (ND approximately 0.013 ng/ml) of bisphenol A was detected from the parts of cartridges (filtration column, sorbent bed and frits) by extraction with methanol, and it was completely removed by washing with at least 15 ml of methanol in the operation process. The concentrations of bisphenol A in tap water and Milli-Q-purified water were found to be 0.01 and 0.02 ng/ml, respectively. For that reason, bisphenol A-free water was made to trap bisphenol A in water using an Empore disk. In every pretreatment, SPE methods using bisphenol A-free water and washing with 15 ml of methanol were done in water and serum samples. The yields obtained from the recovery tests using water to which 0.5 or 0.05 ng/ml of bisphenol A was added were 83.8 to 98.2%, and the RSDs were 3.4 to 6.1%, respectively. The yields obtained from the recovery tests by OASIS HLB using serum to which 1.0 ng/ml or 0.1 ng/ml of bisphenol A was added were 79.0% and 87.3%, and the RSDs were 5.1% and 13.5%, respectively. The limits of quantification in water and serum sample

  10. A multi-electrode and pre-deformed bilayer spring structure electrostatic attractive MEMS actuator with large stroke at low actuation voltage

    International Nuclear Information System (INIS)

    Hu, Fangrong; Li, Zhi; Xiong, Xianming; Niu, Junhao; Peng, Zhiyong; Qian, Yixian; Yao, Jun

    2012-01-01

    This paper presents a multi-electrode and pre-deformed bilayer spring structure electrostatic attractive microelectromechanical systems (MEMS) actuator; it has large stroke at relatively low actuation voltage. Generally, electrostatic-attractive-force-based actuators have small stroke due to the instability resulted from the electrostatic ‘pull-in’ phenomenon. However, in many applications, the electrostatic micro-actuator with large stroke at low voltage is more preferred. By introducing a multi-electrode and a pre-deformed bilayer spring structure, an electrostatic attractive MEMS actuator with large stroke at very low actuation voltage has been successfully demonstrated in this paper. The actuator contains a central plate with a size of 300 µm × 300 µm × 1.5 µm and it is supported by four L-shaped bilayer springs which are pre-deformed due to residual stresses. Each bilayer spring is simultaneously attracted by three adjacent fixed electrodes, and the factors affecting the electrostatic attractive force are analyzed by a finite element analysis method. The prototype of the actuator is fabricated by poly-multi-user-MEMS-process (PolyMUMP) and the static performance is tested using a white light interferometer. The measured stroke of the actuator reaches 2 µm at 13 V dc, and it shows a good agreement with the simulation. (paper)

  11. The measurement of gas–liquid two-phase flows in a small diameter pipe using a dual-sensor multi-electrode conductance probe

    International Nuclear Information System (INIS)

    Zhai, Lu-Sheng; Bian, Peng; Han, Yun-Feng; Gao, Zhong-Ke; Jin, Ning-De

    2016-01-01

    We design a dual-sensor multi-electrode conductance probe to measure the flow parameters of gas–liquid two-phase flows in a vertical pipe with an inner diameter of 20 mm. The designed conductance probe consists of a phase volume fraction sensor (PVFS) and a cross-correlation velocity sensor (CCVS). Through inserting an insulated flow deflector in the central part of the pipe, the gas–liquid two-phase flows are forced to pass through an annual space. The multiple electrodes of the PVFS and the CCVS are flush-mounted on the inside of the pipe wall and the outside of the flow deflector, respectively. The geometry dimension of the PVFS is optimized based on the distribution characteristics of the sensor sensitivity field. In the flow loop test of vertical upward gas–liquid two-phase flows, the output signals from the dual-sensor multi-electrode conductance probe are collected by a data acquisition device from the National Instruments (NI) Corporation. The information transferring characteristics of local flow structures in the annular space are investigated using the transfer entropy theory. Additionally, the kinematic wave velocity is measured based on the drift velocity model to investigate the propagation behavior of the stable kinematic wave in the annular space. Finally, according to the motion characteristics of the gas–liquid two-phase flows, the drift velocity model based on the flow patterns is constructed to measure the individual phase flow rate with higher accuracy. (paper)

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

    Science.gov (United States)

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

    2017-08-01

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

  13. A First-in-Human, Phase I Study of Neural Stem Cell Transplantation for Chronic Spinal Cord Injury.

    Science.gov (United States)

    Curtis, Erik; Martin, Joel R; Gabel, Brandon; Sidhu, Nikki; Rzesiewicz, Teresa K; Mandeville, Ross; Van Gorp, Sebastiaan; Leerink, Marjolein; Tadokoro, Takahiro; Marsala, Silvia; Jamieson, Catriona; Marsala, Martin; Ciacci, Joseph D

    2018-06-01

    We tested the feasibility and safety of human-spinal-cord-derived neural stem cell (NSI-566) transplantation for the treatment of chronic spinal cord injury (SCI). In this clinical trial, four subjects with T2-T12 SCI received treatment consisting of removal of spinal instrumentation, laminectomy, and durotomy, followed by six midline bilateral stereotactic injections of NSI-566 cells. All subjects tolerated the procedure well and there have been no serious adverse events to date (18-27 months post-grafting). In two subjects, one to two levels of neurological improvement were detected using ISNCSCI motor and sensory scores. Our results support the safety of NSI-566 transplantation into the SCI site and early signs of potential efficacy in three of the subjects warrant further exploration of NSI-566 cells in dose escalation studies. Despite these encouraging secondary data, we emphasize that this safety trial lacks statistical power or a control group needed to evaluate functional changes resulting from cell grafting. Copyright © 2018. Published by Elsevier Inc.

  14. Bumetanide promotes neural precursor cell regeneration and dendritic development in the hippocampal dentate gyrus in the chronic stage of cerebral ischemia

    Directory of Open Access Journals (Sweden)

    Wang-shu Xu

    2016-01-01

    Full Text Available Bumetanide has been shown to lessen cerebral edema and reduce the infarct area in the acute stage of cerebral ischemia. Few studies focus on the effects of bumetanide on neuroprotection and neurogenesis in the chronic stage of cerebral ischemia. We established a rat model of cerebral ischemia by injecting endothelin-1 in the left cortical motor area and left corpus striatum. Seven days later, bumetanide 200 µg/kg/day was injected into the lateral ventricle for 21 consecutive days with a mini-osmotic pump. Results demonstrated that the number of neuroblasts cells and the total length of dendrites increased, escape latency reduced, and the number of platform crossings increased in the rat hippocampal dentate gyrus in the chronic stage of cerebral ischemia. These findings suggest that bumetanide promoted neural precursor cell regeneration, dendritic development and the recovery of cognitive function, and protected brain tissue in the chronic stage of ischemia.

  15. Avalanche analysis from multi-electrode ensemble recordings in cat, monkey and human cerebral cortex during wakefulness and sleep.

    Directory of Open Access Journals (Sweden)

    Nima eDehghani

    2012-08-01

    Full Text Available Self-organized critical states are found in many natural systems, from earthquakes to forest fires, they have also been observed in neural systems, particularly, in neuronal cultures. However, the presence of critical states in the awake brain remains controversial. Here, we compared avalanche analyses performed on different in vivo preparations during wakefulness, slow-wave sleep and REM sleep, using high-density electrode arrays in cat motor cortex (96 electrodes, monkey motor cortex and premotor cortex and human temporal cortex (96 electrodes in epileptic patients. In neuronal avalanches defined from units (up to 160 single units, the size of avalanches never clearly scaled as power-law, but rather scaled exponentially or displayed intermediate scaling. We also analyzed the dynamics of local field potentials (LFPs and in particular LFP negative peaks (nLFPs among the different electrodes (up to 96 sites in temporal cortex or up to 128 sites in adjacent motor and pre-motor cortices. In this case, the avalanches defined from nLFPs displayed power-law scaling in double logarithmic representations, as reported previously in monkey. However, avalanche defined as positive LFP (pLFP peaks, which are less directly related to neuronal firing, also displayed apparent power-law scaling. Closer examination of this scaling using the more reliable cumulative distribution function (CDF and other rigorous statistical measures, did not confirm power-law scaling. The same pattern was seen for cats, monkey and human, as well as for different brain states of wakefulness and sleep. We also tested other alternative distributions. Multiple exponential fitting yielded optimal fits of the avalanche dynamics with bi-exponential distributions. Collectively, these results show no clear evidence for power-law scaling or self-organized critical states in the awake and sleeping brain of mammals, from cat to man.

  16. Deactivation of Escherichia coli in a post-discharge chamber coupled to an atmospheric pressure multi-electrode DBD plasma source

    International Nuclear Information System (INIS)

    Pérez-Ruiz, V H; López-Callejas, R; De la Piedad Beneitez, A; Peña-Eguiluz, R; Mercado-Cabrera, A; Muñoz-Castro, A E; Barocio, S R; Valencia-Alvarado, R; Rodríguez-Méndez, B G

    2012-01-01

    Experimental results from applying a room pressure RF multi-electrode DBD plasma source to the inhibition of the population growth of Gram negative Escherichia coli (E. coli) within a post-discharge reactor are reported. The sample to be treated is deposited in the post-discharge chamber at about 50 mm from the plasma source outlet. Thus, the active species generated by the source are conveyed toward the chamber by the working gas flow. The plasma characterization included the measurement of the axial temperature at different distances from the reactor outlet by means of a K-type thermocouple. The resulting 294 K to 322 K temperature interval corresponded to distances between 10 mm to 1 mm respectively. As the material under treatment is placed further away, any thermal damage of the sample by the plasma is prevented. The measurement and optimization of the ozone O 3 concentration has also been carried out, provided that this is an active specie with particularly high germicide power. The effectiveness treatment of the E. coli bacteria growth inhibition by the proposed plasma source reached 99% when a 10 3 CFU/mL concentration on an agar plate had been exposed during ten minutes.

  17. Multi-Electrode Resistivity Probe for Investigation of Local Temperature Inside Metal Shell Battery Cells via Resistivity: Experiments and Evaluation of Electrical Resistance Tomography

    Directory of Open Access Journals (Sweden)

    Xiaobin Hong

    2015-01-01

    Full Text Available Direct Current (DC electrical resistivity is a material property that is sensitive to temperature changes. In this paper, the relationship between resistivity and local temperature inside steel shell battery cells (two commercial 10 Ah and 4.5 Ah lithium-ion cells is innovatively studied by Electrical Resistance Tomography (ERT. The Schlumberger configuration in ERT is applied to divide the cell body into several blocks distributed in different levels, where the apparent resistivities are measured by multi-electrode surface probes. The investigated temperature ranges from −20 to 80 °C. Experimental results have shown that the resistivities mainly depend on temperature changes in each block of the two cells used and the function of the resistivity and temperature can be fitted to the ERT-measurement results in the logistical-plot. Subsequently, the dependence of resistivity on the state of charge (SOC is investigated, and the SOC range of 70%–100% has a remarkable impact on the resistivity at low temperatures. The proposed approach under a thermal cool down regime is demonstrated to monitor the local transient temperature.

  18. Method of making a multi-electrode double layer capacitor having single electrolyte seal and aluminum-impregnated carbon cloth electrodes

    Science.gov (United States)

    Farahmandi, C. Joseph; Dispennette, John M.; Blank, Edward; Kolb, Alan C.

    2002-09-17

    A single cell, multi-electrode high performance double layer capacitor includes first and second flat stacks of electrodes adapted to be housed in a closeable two-part capacitor case which includes only a single electrolyte seal. Each electrode stack has a plurality of electrodes connected in parallel, with the electrodes of one stack being interleaved with the electrodes of the other stack to form an interleaved stack, and with the electrodes of each stack being electrically connected to respective capacitor terminals. A porous separator is positioned against the electrodes of one stack before interleaving to prevent electrical shorts between the electrodes. The electrodes are made by folding a compressible, low resistance, aluminum-impregnated carbon cloth, made from activated carbon fibers, around a current collector foil, with a tab of the foils of each electrode of each stack being connected in parallel and connected to the respective capacitor terminal. The height of the interleaved stack is somewhat greater than the inside height of the closed capacitor case, thereby requiring compression of the interleaved electrode stack when placed inside of the case, and thereby maintaining the interleaved electrode stack under modest constant pressure. The closed capacitor case is filled with an electrolytic solution and sealed. A preferred electrolytic solution is made by dissolving an appropriate salt into acetonitrile (CH.sub.3 CN). In one embodiment, the two parts of the capacitor case are conductive and function as the capacitor terminals.

  19. 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.

  20. Novel modulatory effects of neurosteroids and benzodiazepines on excitatory and inhibitory neurons excitability: a multi-electrode array (MEA recording study"

    Directory of Open Access Journals (Sweden)

    Giulia ePuia

    2012-11-01

    Full Text Available The balance between glutamate- and GABA-mediated neurotransmission in the brain is fundamental in the nervous system, but it is regulated by the ‘tonic’ release of a variety of endogenous factors. One such important group of molecules are the neurosteroids (NSs which, similarly to benzodiazepines (BDZs, enhance GABAergic neurotransmission. The purpose of our work was to investigate, at in-vivo physiologically relevant concentrations, the effects of NSs and BDZs as GABA modulators on dissociated neocortical neuron networks grown in long-term culture. We used a multi-electrode array (MEA recording technique and a novel analysis that was able to both identify the action potentials of engaged excitatory and inhibitory neurons and to detect drug-induced network up-states (burst. We found that the NSs tetrahydrodeoxycorticosterone (THDOC and allopregnanolone (ALLO applied at low nM concentrations, produced different modulatory effects on the two neuronal clusters. Conversely, at high concentrations (1 µM, both NSs, decreased excitatory and inhibitory neuron cluster excitability; however, even several hours after washout, the excitability of inhibitory neurons continued to be depressed, leading to a network long term depression (LTD. The BDZs clonazepam (CLZ and midazolam (MDZ also decreased the network excitability, but only MDZ caused LTD of inhibitory neuron cluster. To investigate the origin of the LTD after MDZ application, we tested finasteride (FIN, an inhibitor of endogenous NSs synthesis. FIN did not prevent the LTD induced by MDZ, but surprisingly induced it after application of CLZ. The significance and possible mechanisms underlying these LTD effects of NSs and BDZs are discussed. Taken together, our results not only demonstrate that ex-vivo networks show a sensitivity to NSs and BDZs comparable to that expressed in vivo, but also provide a new global in-vitro description that can help in understanding their activity in more complex

  1. Human neural stem cells differentiate and promote locomotor recovery in an early chronic spinal cord injury NOD-scid mouse model.

    Directory of Open Access Journals (Sweden)

    Desirée L Salazar

    2010-08-01

    Full Text Available Traumatic spinal cord injury (SCI results in partial or complete paralysis and is characterized by a loss of neurons and oligodendrocytes, axonal injury, and demyelination/dysmyelination of spared axons. Approximately 1,250,000 individuals have chronic SCI in the U.S.; therefore treatment in the chronic stages is highly clinically relevant. Human neural stem cells (hCNS-SCns were prospectively isolated based on fluorescence-activated cell sorting for a CD133(+ and CD24(-/lo population from fetal brain, grown as neurospheres, and lineage restricted to generate neurons, oligodendrocytes and astrocytes. hCNS-SCns have recently been transplanted sub-acutely following spinal cord injury and found to promote improved locomotor recovery. We tested the ability of hCNS-SCns transplanted 30 days post SCI to survive, differentiate, migrate, and promote improved locomotor recovery.hCNS-SCns were transplanted into immunodeficient NOD-scid mice 30 days post spinal cord contusion injury. hCNS-SCns transplanted mice demonstrated significantly improved locomotor recovery compared to vehicle controls using open field locomotor testing and CatWalk gait analysis. Transplanted hCNS-SCns exhibited long-term engraftment, migration, limited proliferation, and differentiation predominantly to oligodendrocytes and neurons. Astrocytic differentiation was rare and mice did not exhibit mechanical allodynia. Furthermore, differentiated hCNS-SCns integrated with the host as demonstrated by co-localization of human cytoplasm with discrete staining for the paranodal marker contactin-associated protein.The results suggest that hCNS-SCns are capable of surviving, differentiating, and promoting improved locomotor recovery when transplanted into an early chronic injury microenvironment. These data suggest that hCNS-SCns transplantation has efficacy in an early chronic SCI setting and thus expands the "window of opportunity" for intervention.

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

    Science.gov (United States)

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

    2016-02-01

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

  3. Dietary-induced binge eating increases prefrontal cortex neural activation to restraint stress and increases binge food consumption following chronic guanfacine.

    Science.gov (United States)

    Bello, Nicholas T; Walters, Amy L; Verpeut, Jessica L; Caverly, Jonathan

    2014-10-01

    Binge eating is a prominent feature of bulimia nervosa and binge eating disorder. Stress or perceived stress is an often-cited reason for binge eating. One notion is that the neural pathways that overlap with stress reactivity and feeding behavior are altered by recurrent binge eating. Using young adult female rats in a dietary-induced binge eating model (30 min access to binge food with or without 24-h calorie restriction, twice a week, for 6 weeks) we measured the neural activation by c-Fos immunoreactivity to the binge food (vegetable shortening mixed with 10% sucrose) in bingeing and non-bingeing animals under acute stress (immobilization; 1 h) or no stress conditions. There was an increase in the number of immunopositive cells in the dorsal medial prefrontal cortex (mPFC) in stressed animals previously exposed to the binge eating feeding schedules. Because attention deficit hyperactive disorder (ADHD) medications target the mPFC and have some efficacy at reducing binge eating in clinical populations, we examined whether chronic (2 weeks; via IP osmotic mini-pumps) treatment with a selective alpha-2A adrenergic agonist (0.5 mg/kg/day), guanfacine, would reduce binge-like eating. In the binge group with only scheduled access to binge food (30 min; twice a week; 8 weeks), guanfacine increased total calories consumed during the 30-min access period from the 2-week pre-treatment baseline and increased binge food consumption compared with saline-treated animals. These experiments suggest that mPFC is differentially activated in response to an immobilization stress in animals under different dietary conditions and chronic guanfacine, at the dose tested, was ineffective at reducing binge-like eating. Copyright © 2014 Elsevier Inc. All rights reserved.

  4. Chronic mild stress impairs latent inhibition and induces region-specific neural activation in CHL1-deficient mice, a mouse model of schizophrenia.

    Science.gov (United States)

    Buhusi, Mona; Obray, Daniel; Guercio, Bret; Bartlett, Mitchell J; Buhusi, Catalin V

    2017-08-30

    Schizophrenia is a neurodevelopmental disorder characterized by abnormal processing of information and attentional deficits. Schizophrenia has a high genetic component but is precipitated by environmental factors, as proposed by the 'two-hit' theory of schizophrenia. Here we compared latent inhibition as a measure of learning and attention, in CHL1-deficient mice, an animal model of schizophrenia, and their wild-type littermates, under no-stress and chronic mild stress conditions. All unstressed mice as well as the stressed wild-type mice showed latent inhibition. In contrast, CHL1-deficient mice did not show latent inhibition after exposure to chronic stress. Differences in neuronal activation (c-Fos-positive cell counts) were noted in brain regions associated with latent inhibition: Neuronal activation in the prelimbic/infralimbic cortices and the nucleus accumbens shell was affected solely by stress. Neuronal activation in basolateral amygdala and ventral hippocampus was affected independently by stress and genotype. Most importantly, neural activation in nucleus accumbens core was affected by the interaction between stress and genotype. These results provide strong support for a 'two-hit' (genes x environment) effect on latent inhibition in CHL1-deficient mice, and identify CHL1-deficient mice as a model of schizophrenia-like learning and attention impairments. Copyright © 2017 Elsevier B.V. All rights reserved.

  5. Action research in rehabilitation with chronic stroke recovery: A case report with a focus on neural plasticity.

    Science.gov (United States)

    Pedersen, Malene; Bundgaard, Tina H; Zeeman, Peter; Jørgensen, Jørgen R; Sørensen, Peter M B; Berro, Hamza M; Larsson, Bodil W

    2016-06-27

    Chronic stroke patients are primarily referred to general rehabilitation, rather than to specific neurorehabilitation. Currently, there are no Danish clinical guidelines for chronic stroke, but recent research in neuroplasticity has contributed to possible rehabilitation interventions for these patients. The purpose of this project is to describe the use of a specialized neuroplastic approach in combination with an already existing training program. The project is designed as an action research project concerning four participants with chronic stroke. Through ten intervention, a neuroplastic focus has been added to their group training program including daily home training. Participants were tested before and after the intervention with MAS, DGI, 6MWT, SSQLS. All four participants improved their functional levels and their quality of life following the intervention. This report indicates that a specific neuroplastic focus in combination with action research has an impact on the participants with chronic stroke. However, there is still no clarity regarding what type of rehabilitation methods can be considered the most efficacious in promoting neuroplasticity. This case report serves as a pilot project for further studies of how to implement neuroplasticity in physical therapy.

  6. In vivo monitoring of glial scar proliferation on chronically implanted neural electrodes by fiber optical coherence tomography

    Science.gov (United States)

    Xie, Yijing; Martini, Nadja; Hassler, Christina; Kirch, Robert D.; Stieglitz, Thomas; Seifert, Andreas; Hofmann, Ulrich G.

    2014-01-01

    In neural prosthetics and stereotactic neurosurgery, intracortical electrodes are often utilized for delivering therapeutic electrical pulses, and recording neural electrophysiological signals. Unfortunately, neuroinflammation impairs the neuron-electrode-interface by developing a compact glial encapsulation around the implants in long term. At present, analyzing this immune reaction is only feasible with post-mortem histology; currently no means for specific in vivo monitoring exist and most applicable imaging modalities can not provide information in deep brain regions. Optical coherence tomography (OCT) is a well established imaging modality for in vivo studies, providing cellular resolution and up to 1.2 mm imaging depth in brain tissue. A fiber based spectral domain OCT was shown to be capable of minimally invasive brain imaging. In the present study, we propose to use a fiber based spectral domain OCT to monitor the progression of the tissue's immune response through scar encapsulation progress in a rat animal model. A fine fiber catheter was implanted in rat brain together with a flexible polyimide microelectrode in sight both of which acts as a foreign body and induces the brain tissue immune reaction. OCT signals were collected from animals up to 12 weeks after implantation and thus gliotic scarring in vivo monitored for that time. Preliminary data showed a significant enhancement of the OCT backscattering signal during the first 3 weeks after implantation, and increased attenuation factor of the sampled tissue due to the glial scar formation. PMID:25191264

  7. 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.

  8. Catheter-based renal denervation for resistant hypertension: Twenty-four month results of the EnligHTN I first-in-human study using a multi-electrode ablation system.

    Science.gov (United States)

    Tsioufis, Costas P; Papademetriou, Vasilios; Dimitriadis, Kyriakos S; Kasiakogias, Alexandros; Tsiachris, Dimitrios; Worthley, Matthew I; Sinhal, Ajay R; Chew, Derek P; Meredith, Ian T; Malaiapan, Yuvi; Thomopoulos, Costas; Kallikazaros, Ioannis; Tousoulis, Dimitrios; Worthley, Stephen G

    2015-12-15

    Long term safety and efficacy data of multi-electrode ablation system for renal denervation (RDN) in patients with drug resistant hypertension (dRHT) are limited. We studied 46 patients (age: 60 ± 10 years, 4.7 ± 1.0 antihypertensive drugs) with drug resistant hypertension (dRHT). Reduction in office BP at 24 months from baseline was -29/-13 mmHg, while the reduction in 24-hour ambulatory BP and in home BP at 24 months were -13/-7 mmHg and -11/-6 mmHg respectively (p<0.05 for all). A correlation analysis revealed that baseline office and ambulatory BP were related to the extent of office and ambulatory BP drop. Apart from higher body mass index (33.3 ± 4.7 vs 29.5 ± 6.2 kg/m(2), p<0.05), there were no differences in patients that were RDN responders defined as ≥10 mmHg decrease (74%, n=34) compared to non-responders. Stepwise logistic regression analysis revealed no prognosticators of RDN response (p=NS for all). At 24 months there were no new serious device or procedure related adverse events. The EnligHTN I study shows that the multi-electrode ablation system provides a safe method of RDN in dRHT accompanied by a clinically relevant and sustained BP reduction. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  9. Effect of cervical vs. thoracic spinal manipulation on peripheral neural features and grip strength in subjects with chronic mechanical neck pain: a randomized controlled trial.

    Science.gov (United States)

    Bautista-Aguirre, Francisco; Oliva-Pascual-Vaca, Ángel; Heredia-Rizo, Alberto M; Boscá-Gandía, Juan J; Ricard, François; Rodriguez-Blanco, Cleofás

    2017-06-01

    treatment session using cervical or thoracic thrust techniques is not enough to achieve clinically relevant changes on neural mechanosensitivity and grip strength in chronic non-specific mechanical neck pain.

  10. Effects of chronic furosemide on central neural hyperactivity and cochlear thresholds after cochlear trauma in guinea pig

    Directory of Open Access Journals (Sweden)

    Wilhelmina eMulders

    2014-08-01

    Full Text Available Increased neuronal spontaneous firing rates have been observed throughout the central auditory system after trauma to the cochlea and this hyperactivity is believed to be associated with the phantom perception of tinnitus. Previously we have shown in an animal model of hearing loss, that an acute injection with furosemide can significantly decrease hyperactivity after cochlear trauma and eliminate behavioural evidence of tinnitus of early onset. However, furosemide also has the potential to affect cochlear thresholds. In this paper we measured the effects of a chronic (daily injections for 7 days furosemide treatment on the spontaneous firing rate of inferior colliculus neurons and on cochlear thresholds in order to establish whether a beneficial effect on hyperactivity can be obtained without causing additional hearing loss. Guinea pigs were exposed to a 10 kHz, 124dB, 2 hour acoustic trauma, and after 5 days of recovery, were given daily i.p. injections of 80mg/kg furosemide or an equivalent amount of saline. The activity of single IC neurons was recorded 24 hours following the last injection. The furosemide treatment had no effect on cochlear thresholds compared to saline injections but did result in significant reductions in spontaneous firing rates recorded in inferior colliculus. These results that suggest a long term beneficial effect of furosemide on hyperactivity after cochlear trauma may be achievable without detrimental effects on hearing, which is important when considering therapeutic potential.

  11. Neural Patterns of Reorganization after Intensive Robot-Assisted Virtual Reality Therapy and Repetitive Task Practice in Patients with Chronic Stroke

    Directory of Open Access Journals (Sweden)

    Soha Saleh

    2017-09-01

    Full Text Available Several approaches to rehabilitation of the hand following a stroke have emerged over the last two decades. These treatments, including repetitive task practice (RTP, robotically assisted rehabilitation and virtual rehabilitation activities, produce improvements in hand function but have yet to reinstate function to pre-stroke levels—which likely depends on developing the therapies to impact cortical reorganization in a manner that favors or supports recovery. Understanding cortical reorganization that underlies the above interventions is therefore critical to inform how such therapies can be utilized and improved and is the focus of the current investigation. Specifically, we compare neural reorganization elicited in stroke patients participating in two interventions: a hybrid of robot-assisted virtual reality (RAVR rehabilitation training and a program of RTP training. Ten chronic stroke subjects participated in eight 3-h sessions of RAVR therapy. Another group of nine stroke subjects participated in eight sessions of matched RTP therapy. Functional magnetic resonance imaging (fMRI data were acquired during paretic hand movement, before and after training. We compared the difference between groups and sessions (before and after training in terms of BOLD intensity, laterality index of activation in sensorimotor areas, and the effective connectivity between ipsilesional motor cortex (iMC, contralesional motor cortex, ipsilesional primary somatosensory cortex (iS1, ipsilesional ventral premotor area (iPMv, and ipsilesional supplementary motor area. Last, we analyzed the relationship between changes in fMRI data and functional improvement measured by the Jebsen Taylor Hand Function Test (JTHFT, in an attempt to identify how neurophysiological changes are related to motor improvement. Subjects in both groups demonstrated motor recovery after training, but fMRI data revealed RAVR-specific changes in neural reorganization patterns. First, BOLD

  12. Neural Patterns of Reorganization after Intensive Robot-Assisted Virtual Reality Therapy and Repetitive Task Practice in Patients with Chronic Stroke.

    Science.gov (United States)

    Saleh, Soha; Fluet, Gerard; Qiu, Qinyin; Merians, Alma; Adamovich, Sergei V; Tunik, Eugene

    2017-01-01

    Several approaches to rehabilitation of the hand following a stroke have emerged over the last two decades. These treatments, including repetitive task practice (RTP), robotically assisted rehabilitation and virtual rehabilitation activities, produce improvements in hand function but have yet to reinstate function to pre-stroke levels-which likely depends on developing the therapies to impact cortical reorganization in a manner that favors or supports recovery. Understanding cortical reorganization that underlies the above interventions is therefore critical to inform how such therapies can be utilized and improved and is the focus of the current investigation. Specifically, we compare neural reorganization elicited in stroke patients participating in two interventions: a hybrid of robot-assisted virtual reality (RAVR) rehabilitation training and a program of RTP training. Ten chronic stroke subjects participated in eight 3-h sessions of RAVR therapy. Another group of nine stroke subjects participated in eight sessions of matched RTP therapy. Functional magnetic resonance imaging (fMRI) data were acquired during paretic hand movement, before and after training. We compared the difference between groups and sessions (before and after training) in terms of BOLD intensity, laterality index of activation in sensorimotor areas, and the effective connectivity between ipsilesional motor cortex (iMC), contralesional motor cortex, ipsilesional primary somatosensory cortex (iS1), ipsilesional ventral premotor area (iPMv), and ipsilesional supplementary motor area. Last, we analyzed the relationship between changes in fMRI data and functional improvement measured by the Jebsen Taylor Hand Function Test (JTHFT), in an attempt to identify how neurophysiological changes are related to motor improvement. Subjects in both groups demonstrated motor recovery after training, but fMRI data revealed RAVR-specific changes in neural reorganization patterns. First, BOLD signal in multiple

  13. Chronic stress in adulthood followed by intermittent stress impairs spatial memory and the survival of newborn hippocampal cells in aging animals: prevention by FGL, a peptide mimetic of neural cell adhesion molecule

    DEFF Research Database (Denmark)

    Borcel, Erika; Pérez-Alvarez, Laura; Herrero, Ana Isabel

    2008-01-01

    In this study, we examined whether chronic stress in adulthood can exert long-term effects on spatial-cognitive abilities and on the survival of newborn hippocampal cells in aging animals. Male Wistar rats were subjected to chronic unpredictable stress at midlife (12 months old) and then reexposed...... in the hippocampus. Interestingly, spatial-memory performance in the Morris water maze was positively correlated with the number of newborn cells that survived in the dentate gyrus: better spatial memory in the water maze was associated with more 5-bromo-2-deoxyuridine (BrdU)-labeled cells. Administration of FGL......, a peptide mimetic of neural cell adhesion molecule, during the 4 weeks of continuous stress not only prevented the deleterious effects of chronic stress on spatial memory, but also reduced the survival of the newly generated hippocampal cells in aging animals. FGL treatment did not, however, prevent...

  14. Development of a multi-electrode extrapolation chamber as a prototype of a primary standard for the realization of the unit of the absorbed dose to water for beta brachytherapy sources

    CERN Document Server

    Bambynek, M

    2002-01-01

    The prototype of a primary standard has been developed, built and tested, which enables the realization of the unit of the absorbed dose to water for beta brachytherapy sources. In the course of the development of the prototype, the recommendations of the American Association of Physicists in Medicine (AAPM) Task Group 60 (TG60) and the Deutsche Gesellschaft fuer Medizinische Physik (DGMP) Arbeitskreis 18 (AK18) were taken into account. The prototype is based on a new multi-electrode extrapolation chamber (MEC) which meets, in particular, the requirements on high spatial resolution and small uncertainty. The central part of the MEC is a segmented collecting electrode which was manufactured in the clean room center of PTB by means of electron beam lithography on a wafer. A precise displacement device consisting of three piezoelectric macrotranslators has been incorporated to move the wafer collecting electrode against the entrance window. For adjustment of the wafer collecting electrode parallel to the entranc...

  15. 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.

  16. 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.

  17. Neural substrate expansion for the restoration of brain function

    Directory of Open Access Journals (Sweden)

    Han-Chiao Isaac Chen

    2016-01-01

    Full Text Available Restoring neurological and cognitive function in individuals who have suffered brain damage is one of the principal objectives of modern translational neuroscience. Electrical stimulation approaches, such as deep-brain stimulation, have achieved the most clinical success, but they ultimately may be limited by the computational capacity of the residual cerebral circuitry. An alternative strategy is brain substrate expansion, in which the computational capacity of the brain is augmented through the addition of new processing units and the reconstitution of network connectivity. This latter approach has been explored to some degree using both biological and electronic means but thus far has not demonstrated the ability to reestablish the function of large-scale neuronal networks. In this review, we contend that fulfilling the potential of brain substrate expansion will require a significant shift from current methods that emphasize direct manipulations of the brain (e.g., injections of cellular suspensions and the implantation of multi-electrode arrays to the generation of more sophisticated neural tissues and neural-electric hybrids in vitro that are subsequently transplanted into the brain. Drawing from neural tissue engineering, stem cell biology, and neural interface technologies, this strategy makes greater use of the manifold techniques available in the laboratory to create biocompatible constructs that recapitulate brain architecture and thus are more easily recognized and utilized by brain networks.

  18. Psychological processes in chronic pain: Influences of reward and fear learning as key mechanisms - Behavioral evidence, neural circuits, and maladaptive changes.

    Science.gov (United States)

    Nees, Frauke; Becker, Susanne

    2017-09-07

    In the understanding of chronic pain, hypotheses derived from psychological theories, together with insights from physiological assessments and brain imaging, highlight the importance of mechanistically driven approaches. Physical system changes, for example following injury, can result in alterations of psychological processes and are accompanied by changes in corticolimbic circuits, which have been shown to be essential in emotional learning and memory, as well as reward processing and related behavior. In the present review, we thus highlight the importance of motivational, reward/pain relief, and fear learning processes in the context of chronic pain and discuss the potential of a mechanistic understanding of chronic pain within a clinical perspective, for example for the development of therapeutic strategies. We argue that changes in these mechanisms are not only characteristic for chronic pain, reflecting consequences of the disorder, but are also critically involved in the transition from acute to chronic pain states. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

  19. Neural Plasticity Associated with Hippocampal PKA-CREB and NMDA Signaling Is Involved in the Antidepressant Effect of Repeated Low Dose of Yueju Pill on Chronic Mouse Model of Learned Helplessness

    Directory of Open Access Journals (Sweden)

    Zhilu Zou

    2017-01-01

    Full Text Available Yueju pill is a traditional Chinese medicine formulated to treat syndromes of mood disorders. Here, we investigated the therapeutic effect of repeated low dose of Yueju in the animal model mimicking clinical long-term depression condition and the role of neural plasticity associated with PKA- (protein kinase A- CREB (cAMP response element binding protein and NMDA (N-methyl-D-aspartate signaling. We showed that a single low dose of Yueju demonstrated antidepressant effects in tests of tail suspension, forced swim, and novelty-suppressed feeding. A chronic learned helplessness (LH protocol resulted in a long-term depressive-like condition. Repeated administration of Yueju following chronic LH remarkably alleviated all of depressive-like symptoms measured, whereas conventional antidepressant fluoxetine only showed a minor improvement. In the hippocampus, Yueju and fluoxetine both normalized brain-derived neurotrophic factor (BDNF and PKA level. Only Yueju, not fluoxetine, rescued the deficits in CREB signaling. The chronic LH upregulated the expression of NMDA receptor subunits NR1, NR2A, and NR2B, which were all attenuated by Yueju. Furthermore, intracerebraventricular administration of NMDA blunted the antidepressant effect of Yueju. These findings supported the antidepressant efficacy of repeated routine low dose of Yueju in a long-term depression model and the critical role of CREB and NMDA signaling.

  20. Neural Plasticity Associated with Hippocampal PKA-CREB and NMDA Signaling Is Involved in the Antidepressant Effect of Repeated Low Dose of Yueju Pill on Chronic Mouse Model of Learned Helplessness.

    Science.gov (United States)

    Zou, Zhilu; Chen, Yin; Shen, Qinqin; Guo, Xiaoyan; Zhang, Yuxuan; Chen, Gang

    2017-01-01

    Yueju pill is a traditional Chinese medicine formulated to treat syndromes of mood disorders. Here, we investigated the therapeutic effect of repeated low dose of Yueju in the animal model mimicking clinical long-term depression condition and the role of neural plasticity associated with PKA- (protein kinase A-) CREB (cAMP response element binding protein) and NMDA (N-methyl-D-aspartate) signaling. We showed that a single low dose of Yueju demonstrated antidepressant effects in tests of tail suspension, forced swim, and novelty-suppressed feeding. A chronic learned helplessness (LH) protocol resulted in a long-term depressive-like condition. Repeated administration of Yueju following chronic LH remarkably alleviated all of depressive-like symptoms measured, whereas conventional antidepressant fluoxetine only showed a minor improvement. In the hippocampus, Yueju and fluoxetine both normalized brain-derived neurotrophic factor (BDNF) and PKA level. Only Yueju, not fluoxetine, rescued the deficits in CREB signaling. The chronic LH upregulated the expression of NMDA receptor subunits NR1, NR2A, and NR2B, which were all attenuated by Yueju. Furthermore, intracerebraventricular administration of NMDA blunted the antidepressant effect of Yueju. These findings supported the antidepressant efficacy of repeated routine low dose of Yueju in a long-term depression model and the critical role of CREB and NMDA signaling.

  1. Functional Recovery Secondary to Neural Stem/Progenitor Cells Transplantation Combined with Treadmill Training in Mice with Chronic Spinal Cord Injury

    DEFF Research Database (Denmark)

    Tashiro, Syoichi; Nishimura, Soraya; Iwai, Hiroki

    are chiefly developed to improve the effect of regenerative therapy for this refractory state, physical training also have attracted the attention as a desirable candidate to combine with cell transplantation. Recently, we have reported that the addition of treadmill training enhances the effect of NS...... in the combined therapy group. Further investigation revealed that NS/PC transplantation improved spinal conductivity and central pattern generator activity, and that training promoted the appropriate inhibitory motor control including spasticity. The combined therapy enhanced these independent effects of each......Rapid progress in stem cell medicine is being realized in neural regeneration also in spinal cord injury (SCI). Researchers have reported remarkable functional recovery with various cell sources including induced Pluripotent Stem cell derived neural stem/progenitor cells (NS/PCs), especially...

  2. Neural networks

    International Nuclear Information System (INIS)

    Denby, Bruce; Lindsey, Clark; Lyons, Louis

    1992-01-01

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

  3. Multimodal Examination of Atrial Fibrillation Substrate: Correlation of Left Atrial Bipolar Voltage Using Multi-Electrode Fast Automated Mapping, Point-by-Point Mapping, and Magnetic Resonance Image Intensity Ratio.

    Science.gov (United States)

    Zghaib, Tarek; Keramati, Ali; Chrispin, Jonathan; Huang, Dong; Balouch, Muhammad A; Ciuffo, Luisa; Berger, Ronald D; Marine, Joseph E; Ashikaga, Hiroshi; Calkins, Hugh; Nazarian, Saman; Spragg, David D

    2018-01-01

    Bipolar voltage mapping, as part of atrial fibrillation (AF) ablation, is traditionally performed in a point-by-point (PBP) approach using single-tip ablation catheters. Alternative techniques for fibrosis-delineation include fast-anatomical mapping (FAM) with multi-electrode circular catheters, and late gadolinium-enhanced magnetic-resonance imaging (LGE-MRI). The correlation between PBP, FAM, and LGE-MRI fibrosis assessment is unknown. In this study, we examined AF substrate using different modalities (PBP, FAM, and LGE-MRI mapping) in patients presenting for an AF ablation. LGE-MRI was performed pre-ablation in 26 patients (73% males, age 63±8years). Local image-intensity ratio (IIR) was used to normalize myocardial intensities. PBP- and FAM-voltage maps were acquired, in sinus rhythm, prior to ablation and co-registered to LGE-MRI. Mean bipolar voltage for all 19,087 FAM voltage points was 0.88±1.27mV and average IIR was 1.08±0.18. In an adjusted mixed-effects model, each unit increase in local IIR was associated with 57% decrease in bipolar voltage (p0.74 corresponded to bipolar voltage voltage was significantly associated with log-PBP bipolar voltage (ß=0.36, pvoltages, FAM-mapping distribution was shifted to the left compared to PBP-mapping; at intermediate voltages, FAM and PBP voltages were overlapping; and at high voltages, FAM exceeded PBP-voltages. LGE-MRI, FAM and PBP-mapping show good correlation in delineating electro-anatomical AF substrate. Each approach has fundamental technical characteristics, the awareness of which allows proper assessment of atrial fibrosis.

  4. Using principal component analysis to capture individual differences within a unified neuropsychological model of chronic post-stroke aphasia: Revealing the unique neural correlates of speech fluency, phonology and semantics.

    Science.gov (United States)

    Halai, Ajay D; Woollams, Anna M; Lambon Ralph, Matthew A

    2017-01-01

    Individual differences in the performance profiles of neuropsychologically-impaired patients are pervasive yet there is still no resolution on the best way to model and account for the variation in their behavioural impairments and the associated neural correlates. To date, researchers have generally taken one of three different approaches: a single-case study methodology in which each case is considered separately; a case-series design in which all individual patients from a small coherent group are examined and directly compared; or, group studies, in which a sample of cases are investigated as one group with the assumption that they are drawn from a homogenous category and that performance differences are of no interest. In recent research, we have developed a complementary alternative through the use of principal component analysis (PCA) of individual data from large patient cohorts. This data-driven approach not only generates a single unified model for the group as a whole (expressed in terms of the emergent principal components) but is also able to capture the individual differences between patients (in terms of their relative positions along the principal behavioural axes). We demonstrate the use of this approach by considering speech fluency, phonology and semantics in aphasia diagnosis and classification, as well as their unique neural correlates. PCA of the behavioural data from 31 patients with chronic post-stroke aphasia resulted in four statistically-independent behavioural components reflecting phonological, semantic, executive-cognitive and fluency abilities. Even after accounting for lesion volume, entering the four behavioural components simultaneously into a voxel-based correlational methodology (VBCM) analysis revealed that speech fluency (speech quanta) was uniquely correlated with left motor cortex and underlying white matter (including the anterior section of the arcuate fasciculus and the frontal aslant tract), phonological skills with

  5. Neural hyperactivity in the amygdala induced by chronic treatment of rats with analgesics may elucidate the mechanisms underlying psychiatric comorbidities associated with medication-overuse headache.

    Science.gov (United States)

    Wanasuntronwong, Aree; Jansri, Ukkrit; Srikiatkhachorn, Anan

    2017-01-03

    Patients with medication-overuse headache suffer not only from chronic headache, but often from psychiatric comorbidities, such as anxiety and depression. The mechanisms underlying these comorbidities are unclear, but the amygdala is likely to be involved in their pathogenesis. To investigate the mechanisms underlying the comorbidities we used elevated plus maze and open field tests to assess anxiety-like behavior in rats chronically treated with analgesics. We measured the electrical properties of neurons in the amygdala, and examined the cortical spreading depression (CSD)-evoked expression of Fos in the trigeminal nucleus caudalis (TNC) and amygdala of rats chronically treated with analgesics. CSD, an analog of aura, evokes Fos expression in the TNC of rodents suggesting trigeminal nociception, considered to be a model of migraine. Increased anxiety-like behavior was seen both in elevated plus maze and open field tests in a model of medication overuse produced in male rats by chronic treatment with aspirin or acetaminophen. The time spent in the open arms of the maze by aspirin- or acetaminophen-treated rats (53 ± 36.1 and 37 ± 29.5 s, respectively) was significantly shorter than that spent by saline-treated vehicle control rats (138 ± 22.6 s, P amygdala as indicated by their more negative threshold for action potential generation (-54.6 ± 5.01 mV for aspirin-treated, -55.2 ± 0.97 mV for acetaminophen-treated, and -31.50 ± 5.34 mV for saline-treated rats, P amygdala [18 ± 10.2 Fos-immunoreactive (IR) neurons per slide in the amygdala of rats treated with aspirin, 11 ± 5.4 IR neurons per slide in rats treated with acetaminophen, and 4 ± 3.7 IR neurons per slide in saline-treated control rats, P amygdala, which could underlie the anxiety seen in patients with medication-overuse headache.

  6. 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.

  7. Neural Networks

    International Nuclear Information System (INIS)

    Smith, Patrick I.

    2003-01-01

    Physicists use large detectors to measure particles created in high-energy collisions at particle accelerators. These detectors typically produce signals indicating either where ionization occurs along the path of the particle, or where energy is deposited by the particle. The data produced by these signals is fed into pattern recognition programs to try to identify what particles were produced, and to measure the energy and direction of these particles. Ideally, there are many techniques used in this pattern recognition software. One technique, neural networks, is particularly suitable for identifying what type of particle caused by a set of energy deposits. Neural networks can derive meaning from complicated or imprecise data, extract patterns, and detect trends that are too complex to be noticed by either humans or other computer related processes. To assist in the advancement of this technology, Physicists use a tool kit to experiment with several neural network techniques. The goal of this research is interface a neural network tool kit into Java Analysis Studio (JAS3), an application that allows data to be analyzed from any experiment. As the final result, a physicist will have the ability to train, test, and implement a neural network with the desired output while using JAS3 to analyze the results or output. Before an implementation of a neural network can take place, a firm understanding of what a neural network is and how it works is beneficial. A neural network is an artificial representation of the human brain that tries to simulate the learning process [5]. It is also important to think of the word artificial in that definition as computer programs that use calculations during the learning process. In short, a neural network learns by representative examples. Perhaps the easiest way to describe the way neural networks learn is to explain how the human brain functions. The human brain contains billions of neural cells that are responsible for processing

  8. 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.

  9. Disturbed neural circuits in a subtype of chronic catatonic schizophrenia demonstrated by F-18-FDG-PET and F-18-DOPA-PET

    International Nuclear Information System (INIS)

    Lauer, M.; Beckmann, H.; Stoeber, G.; Schirrmeister, H.; Gerhard, A.; Ellitok, E.; Reske, S.N.

    2001-01-01

    Permanent verbal, visual scenic and coenaestetic hallucinations are the most prominent psychopathological symptoms aside from psychomotor disorders in speech-sluggish catatonia, a subtype of chronic catatonic schizophrenia according to Karl Leonhard. These continuous hallucinations serve as an excellent paradigm for the investigation of the assumed functional disturbances of cortical circuits in schizophrenia. Data from positron emission tomography (F-18-FDG-PET and F-18-DOPA-PET) from three patients with this rare phenotype were available (two cases of simple speech-sluggish catatonia, one case of a combined speech-prompt/speech-sluggish subtype) and were compared with a control collective. During their permanent hallucinations, all catatonic patients showed a clear bitemporal hypometabolism in the F-18-FDG-PET. Both patients with the simple speech-sluggish catatonia showed an additional bilateral thalamic hypermetabolism and an additional bilateral hypometabolism of the frontal cortex, especially on the left side. In contrast, the patient with the combined speech-prompt/speech-sluggish catatonia showed a bilateral thalamic hypo-metabolism combined with a bifrontal cortical hypermetabolism. However, the left/right ratio of the frontal cortex also showed a lateralization effect with a clear relative hypometabolism of the left frontal cortex. The F-18-DOPA-PET of both schizophrenic patients with simple speech-sluggish catatonia showed a normal F-18-DOPA storage in the striatum, whereas in the right putamen of the patient with the combined form a higher right/left ratio in F-DOPA storage was discernible, indicating an additional lateralized influence of the dopaminergic system in this subtype of chronic catatonic schizophrenia. (author)

  10. Chronic pancreatitis

    Science.gov (United States)

    Chronic pancreatitis - chronic; Pancreatitis - chronic - discharge; Pancreatic insufficiency - chronic; Acute pancreatitis - chronic ... abuse over many years. Repeated episodes of acute pancreatitis can lead to chronic pancreatitis. Genetics may be ...

  11. Chronic neural probe for simultaneous recording of single-unit, multi-unit, and local field potential activity from multiple brain sites

    Science.gov (United States)

    Pothof, F.; Bonini, L.; Lanzilotto, M.; Livi, A.; Fogassi, L.; Orban, G. A.; Paul, O.; Ruther, P.

    2016-08-01

    Objective. Drug resistant focal epilepsy can be treated by resecting the epileptic focus requiring a precise focus localisation using stereoelectroencephalography (SEEG) probes. As commercial SEEG probes offer only a limited spatial resolution, probes of higher channel count and design freedom enabling the incorporation of macro and microelectrodes would help increasing spatial resolution and thus open new perspectives for investigating mechanisms underlying focal epilepsy and its treatment. This work describes a new fabrication process for SEEG probes with materials and dimensions similar to clinical probes enabling recording single neuron activity at high spatial resolution. Approach. Polyimide is used as a biocompatible flexible substrate into which platinum electrodes and leads are integrated with a minimal feature size of 5 μm. The polyimide foils are rolled into the cylindrical probe shape at a diameter of 0.8 mm. The resulting probe features match those of clinically approved devices. Tests in saline solution confirmed the probe stability and functionality. Probes were implanted into the brain of one monkey (Macaca mulatta), trained to perform different motor tasks. Suitable configurations including up to 128 electrode sites allow the recording of task-related neuronal signals. Main results. Probes with 32 and 64 electrode sites were implanted in the posterior parietal cortex. Local field potentials and multi-unit activity were recorded as early as one hour after implantation. Stable single-unit activity was achieved for up to 26 days after implantation of a 64-channel probe. All recorded signals showed modulation during task execution. Significance. With the novel probes it is possible to record stable biologically relevant data over a time span exceeding the usual time needed for epileptic focus localisation in human patients. This is the first time that single units are recorded along cylindrical polyimide probes chronically implanted 22 mm deep into the

  12. Neural Networks

    Directory of Open Access Journals (Sweden)

    Schwindling Jerome

    2010-04-01

    Full Text Available This course presents an overview of the concepts of the neural networks and their aplication in the framework of High energy physics analyses. After a brief introduction on the concept of neural networks, the concept is explained in the frame of neuro-biology, introducing the concept of multi-layer perceptron, learning and their use as data classifer. The concept is then presented in a second part using in more details the mathematical approach focussing on typical use cases faced in particle physics. Finally, the last part presents the best way to use such statistical tools in view of event classifers, putting the emphasis on the setup of the multi-layer perceptron. The full article (15 p. corresponding to this lecture is written in french and is provided in the proceedings of the book SOS 2008.

  13. 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.

  14. 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.

  15. Perda auditiva sensório-neural na otite média crônica supurativa em pacientes com e sem colesteatoma Sensorineural hearing loss in chronic suppurative otitis media with and without cholesteatoma

    Directory of Open Access Journals (Sweden)

    Alexandre Fernandes de Azevedo

    2007-10-01

    Full Text Available Perda auditiva sensório-neural (PASN relacionada a otite média crônica supurativa (OMCS foi estudada para esclarecer a participação do colesteatoma nesse contexto. OBJETIVO: Avaliar ocorrência de PASN na OMCS, correlacionando com colesteatoma, duração da doença e idade. CASUÍSTICA E MÉTODOS: Estudo retrospectivo de 115 pacientes com OMCS com e sem colesteatoma submetidos à cirurgia. Incluíram-se pacientes com doença unilateral, orelha contralateral normal e idade inferior a 60 anos. RESULTADOS: Idade média foi de 26 anos, sendo 58 homens e 57 mulheres. Tempo médio de duração da doença otológica de 12,4 anos. Limiar auditivo médio foi de 40 dB na orelha com OMCS e 22dB na orelha normal (P=0,002. Observou-se colesteatoma em 78 dos 115 casos. Na orelha com OMCS, ocorreram 15 (13% casos de PASN, sendo 7 associadas à colesteatoma e 8 não associadas. Seis casos de PASN foram severa/profunda, correlacionando-se com idade ajustada (P=0,003, ausência de colesteatoma (P=0,01, mas não com duração da doença (P=0,458. CONCLUSÃO: PASN ocorreu em 13% dos pacientes com OMCS, correlacionando-se com o aumento da idade, mas não com a presença de colesteatoma ou com maior duração da doença otológica.Sensorineural hearing loss (SNHL related to chronic suppurative otitis media (CSOM was studied to clarify the involvement of cholesteatomas in this context. AIM: to evaluate SNHL related to CSOM and its association with cholesteatomas, disease duration and patients’ ages. METHODS: Retrospective analysis of 115 patients with CSOM with and without cholesteatoma submitted to surgical treatment. Inclusion criteria were active unilateral disease, normal contralateral ear and age below 60 years. RESULTS: The average age was 26.3 years, 58 males and 57 females. The duration of ear disease was, in average, 12.4 years. The average threshold of hearing was 40 dB in CSOM ear and 22 dB in the normal contralateral ear (P=0.002. CSOM with

  16. Spatially pooled contrast responses predict neural and perceptual similarity of naturalistic image categories.

    Directory of Open Access Journals (Sweden)

    Iris I A Groen

    Full Text Available The visual world is complex and continuously changing. Yet, our brain transforms patterns of light falling on our retina into a coherent percept within a few hundred milliseconds. Possibly, low-level neural responses already carry substantial information to facilitate rapid characterization of the visual input. Here, we computationally estimated low-level contrast responses to computer-generated naturalistic images, and tested whether spatial pooling of these responses could predict image similarity at the neural and behavioral level. Using EEG, we show that statistics derived from pooled responses explain a large amount of variance between single-image evoked potentials (ERPs in individual subjects. Dissimilarity analysis on multi-electrode ERPs demonstrated that large differences between images in pooled response statistics are predictive of more dissimilar patterns of evoked activity, whereas images with little difference in statistics give rise to highly similar evoked activity patterns. In a separate behavioral experiment, images with large differences in statistics were judged as different categories, whereas images with little differences were confused. These findings suggest that statistics derived from low-level contrast responses can be extracted in early visual processing and can be relevant for rapid judgment of visual similarity. We compared our results with two other, well- known contrast statistics: Fourier power spectra and higher-order properties of contrast distributions (skewness and kurtosis. Interestingly, whereas these statistics allow for accurate image categorization, they do not predict ERP response patterns or behavioral categorization confusions. These converging computational, neural and behavioral results suggest that statistics of pooled contrast responses contain information that corresponds with perceived visual similarity in a rapid, low-level categorization task.

  17. Spatially Pooled Contrast Responses Predict Neural and Perceptual Similarity of Naturalistic Image Categories

    Science.gov (United States)

    Groen, Iris I. A.; Ghebreab, Sennay; Lamme, Victor A. F.; Scholte, H. Steven

    2012-01-01

    The visual world is complex and continuously changing. Yet, our brain transforms patterns of light falling on our retina into a coherent percept within a few hundred milliseconds. Possibly, low-level neural responses already carry substantial information to facilitate rapid characterization of the visual input. Here, we computationally estimated low-level contrast responses to computer-generated naturalistic images, and tested whether spatial pooling of these responses could predict image similarity at the neural and behavioral level. Using EEG, we show that statistics derived from pooled responses explain a large amount of variance between single-image evoked potentials (ERPs) in individual subjects. Dissimilarity analysis on multi-electrode ERPs demonstrated that large differences between images in pooled response statistics are predictive of more dissimilar patterns of evoked activity, whereas images with little difference in statistics give rise to highly similar evoked activity patterns. In a separate behavioral experiment, images with large differences in statistics were judged as different categories, whereas images with little differences were confused. These findings suggest that statistics derived from low-level contrast responses can be extracted in early visual processing and can be relevant for rapid judgment of visual similarity. We compared our results with two other, well- known contrast statistics: Fourier power spectra and higher-order properties of contrast distributions (skewness and kurtosis). Interestingly, whereas these statistics allow for accurate image categorization, they do not predict ERP response patterns or behavioral categorization confusions. These converging computational, neural and behavioral results suggest that statistics of pooled contrast responses contain information that corresponds with perceived visual similarity in a rapid, low-level categorization task. PMID:23093921

  18. Combining two open source tools for neural computation (BioPatRec and Netlab) improves movement classification for prosthetic control.

    Science.gov (United States)

    Prahm, Cosima; Eckstein, Korbinian; Ortiz-Catalan, Max; Dorffner, Georg; Kaniusas, Eugenijus; Aszmann, Oskar C

    2016-08-31

    Controlling a myoelectric prosthesis for upper limbs is increasingly challenging for the user as more electrodes and joints become available. Motion classification based on pattern recognition with a multi-electrode array allows multiple joints to be controlled simultaneously. Previous pattern recognition studies are difficult to compare, because individual research groups use their own data sets. To resolve this shortcoming and to facilitate comparisons, open access data sets were analysed using components of BioPatRec and Netlab pattern recognition models. Performances of the artificial neural networks, linear models, and training program components were compared. Evaluation took place within the BioPatRec environment, a Matlab-based open source platform that provides feature extraction, processing and motion classification algorithms for prosthetic control. The algorithms were applied to myoelectric signals for individual and simultaneous classification of movements, with the aim of finding the best performing algorithm and network model. Evaluation criteria included classification accuracy and training time. Results in both the linear and the artificial neural network models demonstrated that Netlab's implementation using scaled conjugate training algorithm reached significantly higher accuracies than BioPatRec. It is concluded that the best movement classification performance would be achieved through integrating Netlab training algorithms in the BioPatRec environment so that future prosthesis training can be shortened and control made more reliable. Netlab was therefore included into the newest release of BioPatRec (v4.0).

  19. Morphological neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Ritter, G.X.; Sussner, P. [Univ. of Florida, Gainesville, FL (United States)

    1996-12-31

    The theory of artificial neural networks has been successfully applied to a wide variety of pattern recognition problems. In this theory, the first step in computing the next state of a neuron or in performing the next layer neural network computation involves the linear operation of multiplying neural values by their synaptic strengths and adding the results. Thresholding usually follows the linear operation in order to provide for nonlinearity of the network. In this paper we introduce a novel class of neural networks, called morphological neural networks, in which the operations of multiplication and addition are replaced by addition and maximum (or minimum), respectively. By taking the maximum (or minimum) of sums instead of the sum of products, morphological network computation is nonlinear before thresholding. As a consequence, the properties of morphological neural networks are drastically different than those of traditional neural network models. In this paper we consider some of these differences and provide some particular examples of morphological neural network.

  20. Neural Tube Defects

    Science.gov (United States)

    Neural tube defects are birth defects of the brain, spine, or spinal cord. They happen in the ... that she is pregnant. The two most common neural tube defects are spina bifida and anencephaly. In ...

  1. Neural tissue-spheres

    DEFF Research Database (Denmark)

    Andersen, Rikke K; Johansen, Mathias; Blaabjerg, Morten

    2007-01-01

    By combining new and established protocols we have developed a procedure for isolation and propagation of neural precursor cells from the forebrain subventricular zone (SVZ) of newborn rats. Small tissue blocks of the SVZ were dissected and propagated en bloc as free-floating neural tissue...... content, thus allowing experimental studies of neural precursor cells and their niche...

  2. Neural electrical activity and neural network growth.

    Science.gov (United States)

    Gafarov, F M

    2018-05-01

    The development of central and peripheral neural system depends in part on the emergence of the correct functional connectivity in its input and output pathways. Now it is generally accepted that molecular factors guide neurons to establish a primary scaffold that undergoes activity-dependent refinement for building a fully functional circuit. However, a number of experimental results obtained recently shows that the neuronal electrical activity plays an important role in the establishing of initial interneuronal connections. Nevertheless, these processes are rather difficult to study experimentally, due to the absence of theoretical description and quantitative parameters for estimation of the neuronal activity influence on growth in neural networks. In this work we propose a general framework for a theoretical description of the activity-dependent neural network growth. The theoretical description incorporates a closed-loop growth model in which the neural activity can affect neurite outgrowth, which in turn can affect neural activity. We carried out the detailed quantitative analysis of spatiotemporal activity patterns and studied the relationship between individual cells and the network as a whole to explore the relationship between developing connectivity and activity patterns. The model, developed in this work will allow us to develop new experimental techniques for studying and quantifying the influence of the neuronal activity on growth processes in neural networks and may lead to a novel techniques for constructing large-scale neural networks by self-organization. Copyright © 2018 Elsevier Ltd. All rights reserved.

  3. Chronic Pancreatitis.

    Science.gov (United States)

    Stram, Michelle; Liu, Shu; Singhi, Aatur D

    2016-12-01

    Chronic pancreatitis is a debilitating condition often associated with severe abdominal pain and exocrine and endocrine dysfunction. The underlying cause is multifactorial and involves complex interaction of environmental, genetic, and/or other risk factors. The pathology is dependent on the underlying pathogenesis of the disease. This review describes the clinical, gross, and microscopic findings of the main subtypes of chronic pancreatitis: alcoholic chronic pancreatitis, obstructive chronic pancreatitis, paraduodenal ("groove") pancreatitis, pancreatic divisum, autoimmune pancreatitis, and genetic factors associated with chronic pancreatitis. As pancreatic ductal adenocarcinoma may be confused with chronic pancreatitis, the main distinguishing features between these 2 diseases are discussed. Copyright © 2016 Elsevier Inc. All rights reserved.

  4. Chaotic diagonal recurrent neural network

    International Nuclear Information System (INIS)

    Wang Xing-Yuan; Zhang Yi

    2012-01-01

    We propose a novel neural network based on a diagonal recurrent neural network and chaos, and its structure and learning algorithm are designed. The multilayer feedforward neural network, diagonal recurrent neural network, and chaotic diagonal recurrent neural network are used to approach the cubic symmetry map. The simulation results show that the approximation capability of the chaotic diagonal recurrent neural network is better than the other two neural networks. (interdisciplinary physics and related areas of science and technology)

  5. 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.

  6. Chronic Pericarditis

    Science.gov (United States)

    ... surgery) and is considered subacute. Causes Usually, the cause of chronic effusive pericarditis is unknown. However, it may be caused by cancer, tuberculosis , or an underactive thyroid gland ( hypothyroidism ), and it occasionally occurs in people with chronic ...

  7. Chronic Pain

    Science.gov (United States)

    ... pain. Psychotherapy, relaxation and medication therapies, biofeedback, and behavior modification may also be employed to treat chronic pain. × ... pain. Psychotherapy, relaxation and medication therapies, biofeedback, and behavior modification may also be employed to treat chronic pain. ...

  8. Estimation of neural energy in microelectrode signals

    Science.gov (United States)

    Gaumond, R. P.; Clement, R.; Silva, R.; Sander, D.

    2004-09-01

    We considered the problem of determining the neural contribution to the signal recorded by an intracortical electrode. We developed a linear least-squares approach to determine the energy fraction of a signal attributable to an arbitrary number of autocorrelation-defined signals buried in noise. Application of the method requires estimation of autocorrelation functions Rap(tgr) characterizing the action potential (AP) waveforms and Rn(tgr) characterizing background noise. This method was applied to the analysis of chronically implanted microelectrode signals from motor cortex of rat. We found that neural (AP) energy consisted of a large-signal component which grows linearly with the number of threshold-detected neural events and a small-signal component unrelated to the count of threshold-detected AP signals. The addition of pseudorandom noise to electrode signals demonstrated the algorithm's effectiveness for a wide range of noise-to-signal energy ratios (0.08 to 39). We suggest, therefore, that the method could be of use in providing a measure of neural response in situations where clearly identified spike waveforms cannot be isolated, or in providing an additional 'background' measure of microelectrode neural activity to supplement the traditional AP spike count.

  9. The neuro-glial properties of adipose-derived adult stromal (ADAS) cells are not regulated by Notch 1 and are not derived from neural crest lineage.

    Science.gov (United States)

    Wrage, Philip C; Tran, Thi; To, Khai; Keefer, Edward W; Ruhn, Kelly A; Hong, John; Hattangadi, Supriya; Treviño, Isaac; Tansey, Malú G

    2008-01-16

    We investigated whether adipose-derived adult stromal (ADAS) are of neural crest origin and the extent to which Notch 1 regulates their growth and differentiation. Mouse ADAS cells cultured in media formulated for neural stem cells (NSC) displayed limited capacity for self-renewal, clonogenicity, and neurosphere formation compared to NSC from the subventricular zone in the hippocampus. Although ADAS cells expressed Nestin, GFAP, NSE and Tuj1 in vitro, exposure to NSC differentiation supplements did not induce mature neuronal marker expression. In contrast, in mesenchymal stem cell (MSC) media, ADAS cells retained their ability to proliferate and differentiate beyond 20 passages and expressed high levels of Nestin. In neuritizing cocktails, ADAS cells extended processes, downregulated Nestin expression, and displayed depolarization-induced Ca(2+) transients but no spontaneous or evoked neural network activity on Multi-Electrode Arrays. Deletion of Notch 1 in ADAS cell cultures grown in NSC proliferation medium did not significantly alter their proliferative potential in vitro or the differentiation-induced downregulation of Nestin. Co-culture of ADAS cells with fibroblasts that stably expressed the Notch ligand Jagged 1 or overexpression of the Notch intracellular domain (NICD) did not alter ADAS cell growth, morphology, or cellular marker expression. ADAS cells did not display robust expression of neural crest transcription factors or genes (Sox, CRABP2, and TH); and lineage tracing analyses using Wnt1-Cre;Rosa26R-lacZ or -EYFP reporter mice confirmed that fewer than 2% of the ADAS cell population derived from a Wnt1-positive population during development. In summary, although media formulations optimized for MSCs or NSCs enable expansion of mouse ADAS cells in vitro, we find no evidence that these cells are of neural crest origin, that they can undergo robust terminal differentiation into functionally mature neurons, and that Notch 1 is likely to be a key

  10. DataHigh: graphical user interface for visualizing and interacting with high-dimensional neural activity

    Science.gov (United States)

    Cowley, Benjamin R.; Kaufman, Matthew T.; Butler, Zachary S.; Churchland, Mark M.; Ryu, Stephen I.; Shenoy, Krishna V.; Yu, Byron M.

    2013-12-01

    Objective. Analyzing and interpreting the activity of a heterogeneous population of neurons can be challenging, especially as the number of neurons, experimental trials, and experimental conditions increases. One approach is to extract a set of latent variables that succinctly captures the prominent co-fluctuation patterns across the neural population. A key problem is that the number of latent variables needed to adequately describe the population activity is often greater than 3, thereby preventing direct visualization of the latent space. By visualizing a small number of 2-d projections of the latent space or each latent variable individually, it is easy to miss salient features of the population activity. Approach. To address this limitation, we developed a Matlab graphical user interface (called DataHigh) that allows the user to quickly and smoothly navigate through a continuum of different 2-d projections of the latent space. We also implemented a suite of additional visualization tools (including playing out population activity timecourses as a movie and displaying summary statistics, such as covariance ellipses and average timecourses) and an optional tool for performing dimensionality reduction. Main results. To demonstrate the utility and versatility of DataHigh, we used it to analyze single-trial spike count and single-trial timecourse population activity recorded using a multi-electrode array, as well as trial-averaged population activity recorded using single electrodes. Significance. DataHigh was developed to fulfil a need for visualization in exploratory neural data analysis, which can provide intuition that is critical for building scientific hypotheses and models of population activity.

  11. DataHigh: graphical user interface for visualizing and interacting with high-dimensional neural activity.

    Science.gov (United States)

    Cowley, Benjamin R; Kaufman, Matthew T; Butler, Zachary S; Churchland, Mark M; Ryu, Stephen I; Shenoy, Krishna V; Yu, Byron M

    2013-12-01

    Analyzing and interpreting the activity of a heterogeneous population of neurons can be challenging, especially as the number of neurons, experimental trials, and experimental conditions increases. One approach is to extract a set of latent variables that succinctly captures the prominent co-fluctuation patterns across the neural population. A key problem is that the number of latent variables needed to adequately describe the population activity is often greater than 3, thereby preventing direct visualization of the latent space. By visualizing a small number of 2-d projections of the latent space or each latent variable individually, it is easy to miss salient features of the population activity. To address this limitation, we developed a Matlab graphical user interface (called DataHigh) that allows the user to quickly and smoothly navigate through a continuum of different 2-d projections of the latent space. We also implemented a suite of additional visualization tools (including playing out population activity timecourses as a movie and displaying summary statistics, such as covariance ellipses and average timecourses) and an optional tool for performing dimensionality reduction. To demonstrate the utility and versatility of DataHigh, we used it to analyze single-trial spike count and single-trial timecourse population activity recorded using a multi-electrode array, as well as trial-averaged population activity recorded using single electrodes. DataHigh was developed to fulfil a need for visualization in exploratory neural data analysis, which can provide intuition that is critical for building scientific hypotheses and models of population activity.

  12. DataHigh: Graphical user interface for visualizing and interacting with high-dimensional neural activity

    Science.gov (United States)

    Cowley, Benjamin R.; Kaufman, Matthew T.; Butler, Zachary S.; Churchland, Mark M.; Ryu, Stephen I.; Shenoy, Krishna V.; Yu, Byron M.

    2014-01-01

    Objective Analyzing and interpreting the activity of a heterogeneous population of neurons can be challenging, especially as the number of neurons, experimental trials, and experimental conditions increases. One approach is to extract a set of latent variables that succinctly captures the prominent co-fluctuation patterns across the neural population. A key problem is that the number of latent variables needed to adequately describe the population activity is often greater than three, thereby preventing direct visualization of the latent space. By visualizing a small number of 2-d projections of the latent space or each latent variable individually, it is easy to miss salient features of the population activity. Approach To address this limitation, we developed a Matlab graphical user interface (called DataHigh) that allows the user to quickly and smoothly navigate through a continuum of different 2-d projections of the latent space. We also implemented a suite of additional visualization tools (including playing out population activity timecourses as a movie and displaying summary statistics, such as covariance ellipses and average timecourses) and an optional tool for performing dimensionality reduction. Main results To demonstrate the utility and versatility of DataHigh, we used it to analyze single-trial spike count and single-trial timecourse population activity recorded using a multi-electrode array, as well as trial-averaged population activity recorded using single electrodes. Significance DataHigh was developed to fulfill a need for visualization in exploratory neural data analysis, which can provide intuition that is critical for building scientific hypotheses and models of population activity. PMID:24216250

  13. A neural flow estimator

    DEFF Research Database (Denmark)

    Jørgensen, Ivan Harald Holger; Bogason, Gudmundur; Bruun, Erik

    1995-01-01

    This paper proposes a new way to estimate the flow in a micromechanical flow channel. A neural network is used to estimate the delay of random temperature fluctuations induced in a fluid. The design and implementation of a hardware efficient neural flow estimator is described. The system...... is implemented using switched-current technique and is capable of estimating flow in the μl/s range. The neural estimator is built around a multiplierless neural network, containing 96 synaptic weights which are updated using the LMS1-algorithm. An experimental chip has been designed that operates at 5 V...

  14. 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...

  15. Parasympathetic neural activity accounts for the lowering of exercise heart rate at high altitude

    DEFF Research Database (Denmark)

    Boushel, Robert Christopher; Calbet, J A; Rådegran, G

    2001-01-01

    In chronic hypoxia, both heart rate (HR) and cardiac output (Q) are reduced during exercise. The role of parasympathetic neural activity in lowering HR is unresolved, and its influence on Q and oxygen transport at high altitude has never been studied.......In chronic hypoxia, both heart rate (HR) and cardiac output (Q) are reduced during exercise. The role of parasympathetic neural activity in lowering HR is unresolved, and its influence on Q and oxygen transport at high altitude has never been studied....

  16. Neural Networks: Implementations and Applications

    OpenAIRE

    Vonk, E.; Veelenturf, L.P.J.; Jain, L.C.

    1996-01-01

    Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulators and their applications in character recognition and other engineering areas

  17. Critical Branching Neural Networks

    Science.gov (United States)

    Kello, Christopher T.

    2013-01-01

    It is now well-established that intrinsic variations in human neural and behavioral activity tend to exhibit scaling laws in their fluctuations and distributions. The meaning of these scaling laws is an ongoing matter of debate between isolable causes versus pervasive causes. A spiking neural network model is presented that self-tunes to critical…

  18. Consciousness and neural plasticity

    DEFF Research Database (Denmark)

    changes or to abandon the strong identity thesis altogether. Were one to pursue a theory according to which consciousness is not an epiphenomenon to brain processes, consciousness may in fact affect its own neural basis. The neural correlate of consciousness is often seen as a stable structure, that is...

  19. Chronic, low-dose rotenone reproduces Lewy neurites found in early stages of Parkinson's disease, reduces mitochondrial movement and slowly kills differentiated SH-SY5Y neural cells

    Directory of Open Access Journals (Sweden)

    Liu Lei

    2008-12-01

    Full Text Available Abstract Background Parkinson's disease, the most common adult neurodegenerative movement disorder, demonstrates a brain-wide pathology that begins pre-clinically with alpha-synuclein aggregates ("Lewy neurites" in processes of gut enteric and vagal motor neurons. Rostral progression into substantia nigra with death of dopamine neurons produces the motor impairment phenotype that yields a clinical diagnosis. The vast majority of Parkinson's disease occurs sporadically, and current models of sporadic Parkinson's disease (sPD can utilize directly infused or systemic neurotoxins. Results We developed a differentiation protocol for human SH-SY5Y neuroblastoma that yielded non-dividing dopaminergic neural cells with long processes that we then exposed to 50 nM rotenone, a complex I inhibitor used in Parkinson's disease models. After 21 days of rotenone, ~60% of cells died. Their processes retracted and accumulated ASYN-(+ and UB-(+ aggregates that blocked organelle transport. Mitochondrial movement velocities were reduced by 8 days of rotenone and continued to decline over time. No cytoplasmic inclusions resembling Lewy bodies were observed. Gene microarray analyses showed that the majority of genes were under-expressed. qPCR analyses of 11 mtDNA-encoded and 10 nDNA-encoded mitochondrial electron transport chain RNAs' relative expressions revealed small increases in mtDNA-encoded genes and lesser regulation of nDNA-encoded ETC genes. Conclusion Subacute rotenone treatment of differentiated SH-SY5Y neuroblastoma cells causes process retraction and partial death over several weeks, slowed mitochondrial movement in processes and appears to reproduce the Lewy neuritic changes of early Parkinson's disease pathology but does not cause Lewy body inclusions. The overall pattern of transcriptional regulation is gene under-expression with minimal regulation of ETC genes in spite of rotenone's being a complex I toxin. This rotenone-SH-SY5Y model in a

  20. Multi-electrode array technologies for neuroscience and cardiology

    Science.gov (United States)

    Spira, Micha E.; Hai, Aviad

    2013-02-01

    At present, the prime methodology for studying neuronal circuit-connectivity, physiology and pathology under in vitro or in vivo conditions is by using substrate-integrated microelectrode arrays. Although this methodology permits simultaneous, cell-non-invasive, long-term recordings of extracellular field potentials generated by action potentials, it is 'blind' to subthreshold synaptic potentials generated by single cells. On the other hand, intracellular recordings of the full electrophysiological repertoire (subthreshold synaptic potentials, membrane oscillations and action potentials) are, at present, obtained only by sharp or patch microelectrodes. These, however, are limited to single cells at a time and for short durations. Recently a number of laboratories began to merge the advantages of extracellular microelectrode arrays and intracellular microelectrodes. This Review describes the novel approaches, identifying their strengths and limitations from the point of view of the end users -- with the intention to help steer the bioengineering efforts towards the needs of brain-circuit research.

  1. Recent progress in multi-electrode spike sorting methods.

    Science.gov (United States)

    Lefebvre, Baptiste; Yger, Pierre; Marre, Olivier

    2016-11-01

    In recent years, arrays of extracellular electrodes have been developed and manufactured to record simultaneously from hundreds of electrodes packed with a high density. These recordings should allow neuroscientists to reconstruct the individual activity of the neurons spiking in the vicinity of these electrodes, with the help of signal processing algorithms. Algorithms need to solve a source separation problem, also known as spike sorting. However, these new devices challenge the classical way to do spike sorting. Here we review different methods that have been developed to sort spikes from these large-scale recordings. We describe the common properties of these algorithms, as well as their main differences. Finally, we outline the issues that remain to be solved by future spike sorting algorithms. Copyright © 2017 Elsevier Ltd. All rights reserved.

  2. Dynamics of neural cryptography.

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

    2007-05-01

    Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible.

  3. Dynamics of neural cryptography

    International Nuclear Information System (INIS)

    Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

    2007-01-01

    Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible

  4. Dynamics of neural cryptography

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

    2007-05-01

    Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible.

  5. A fully implantable rodent neural stimulator

    Science.gov (United States)

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

    2012-02-01

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

  6. N-methyl-d-aspartate receptors, learning and memory: chronic intraventricular infusion of the NMDA receptor antagonist d-AP5 interacts directly with the neural mechanisms of spatial learning.

    Science.gov (United States)

    Morris, R G M; Steele, R J; Bell, J E; Martin, S J

    2013-03-01

    Three experiments were conducted to contrast the hypothesis that hippocampal N-methyl-d-aspartate (NMDA) receptors participate directly in the mechanisms of hippocampus-dependent learning with an alternative view that apparent impairments of learning induced by NMDA receptor antagonists arise because of drug-induced neuropathological and/or sensorimotor disturbances. In experiment 1, rats given a chronic i.c.v. infusion of d-AP5 (30 mm) at 0.5 μL/h were selectively impaired, relative to aCSF-infused animals, in place but not cued navigation learning when they were trained during the 14-day drug infusion period, but were unimpaired on both tasks if trained 11 days after the minipumps were exhausted. d-AP5 caused sensorimotor disturbances in the spatial task, but these gradually worsened as the animals failed to learn. Histological assessment of potential neuropathological changes revealed no abnormalities in d-AP5-treated rats whether killed during or after chronic drug infusion. In experiment 2, a deficit in spatial learning was also apparent in d-AP5-treated rats trained on a spatial reference memory task involving two identical but visible platforms, a task chosen and shown to minimise sensorimotor disturbances. HPLC was used to identify the presence of d-AP5 in selected brain areas. In Experiment 3, rats treated with d-AP5 showed a delay-dependent deficit in spatial memory in the delayed matching-to-place protocol for the water maze. These data are discussed with respect to the learning mechanism and sensorimotor accounts of the impact of NMDA receptor antagonists on brain function. We argue that NMDA receptor mechanisms participate directly in spatial learning. © 2013 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

  7. ANT Advanced Neural Tool

    Energy Technology Data Exchange (ETDEWEB)

    Labrador, I.; Carrasco, R.; Martinez, L.

    1996-07-01

    This paper describes a practical introduction to the use of Artificial Neural Networks. Artificial Neural Nets are often used as an alternative to the traditional symbolic manipulation and first order logic used in Artificial Intelligence, due the high degree of difficulty to solve problems that can not be handled by programmers using algorithmic strategies. As a particular case of Neural Net a Multilayer Perception developed by programming in C language on OS9 real time operating system is presented. A detailed description about the program structure and practical use are included. Finally, several application examples that have been treated with the tool are presented, and some suggestions about hardware implementations. (Author) 15 refs.

  8. ANT Advanced Neural Tool

    International Nuclear Information System (INIS)

    Labrador, I.; Carrasco, R.; Martinez, L.

    1996-01-01

    This paper describes a practical introduction to the use of Artificial Neural Networks. Artificial Neural Nets are often used as an alternative to the traditional symbolic manipulation and first order logic used in Artificial Intelligence, due the high degree of difficulty to solve problems that can not be handled by programmers using algorithmic strategies. As a particular case of Neural Net a Multilayer Perception developed by programming in C language on OS9 real time operating system is presented. A detailed description about the program structure and practical use are included. Finally, several application examples that have been treated with the tool are presented, and some suggestions about hardware implementations. (Author) 15 refs

  9. Hidden neural networks

    DEFF Research Database (Denmark)

    Krogh, Anders Stærmose; Riis, Søren Kamaric

    1999-01-01

    A general framework for hybrids of hidden Markov models (HMMs) and neural networks (NNs) called hidden neural networks (HNNs) is described. The article begins by reviewing standard HMMs and estimation by conditional maximum likelihood, which is used by the HNN. In the HNN, the usual HMM probability...... parameters are replaced by the outputs of state-specific neural networks. As opposed to many other hybrids, the HNN is normalized globally and therefore has a valid probabilistic interpretation. All parameters in the HNN are estimated simultaneously according to the discriminative conditional maximum...... likelihood criterion. The HNN can be viewed as an undirected probabilistic independence network (a graphical model), where the neural networks provide a compact representation of the clique functions. An evaluation of the HNN on the task of recognizing broad phoneme classes in the TIMIT database shows clear...

  10. Neural networks for aircraft control

    Science.gov (United States)

    Linse, Dennis

    1990-01-01

    Current research in Artificial Neural Networks indicates that networks offer some potential advantages in adaptation and fault tolerance. This research is directed at determining the possible applicability of neural networks to aircraft control. The first application will be to aircraft trim. Neural network node characteristics, network topology and operation, neural network learning and example histories using neighboring optimal control with a neural net are discussed.

  11. Active Neural Localization

    OpenAIRE

    Chaplot, Devendra Singh; Parisotto, Emilio; Salakhutdinov, Ruslan

    2018-01-01

    Localization is the problem of estimating the location of an autonomous agent from an observation and a map of the environment. Traditional methods of localization, which filter the belief based on the observations, are sub-optimal in the number of steps required, as they do not decide the actions taken by the agent. We propose "Active Neural Localizer", a fully differentiable neural network that learns to localize accurately and efficiently. The proposed model incorporates ideas of tradition...

  12. Neural cryptography with feedback.

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Shacham, Lanir; Kanter, Ido

    2004-04-01

    Neural cryptography is based on a competition between attractive and repulsive stochastic forces. A feedback mechanism is added to neural cryptography which increases the repulsive forces. Using numerical simulations and an analytic approach, the probability of a successful attack is calculated for different model parameters. Scaling laws are derived which show that feedback improves the security of the system. In addition, a network with feedback generates a pseudorandom bit sequence which can be used to encrypt and decrypt a secret message.

  13. Impaired theta phase-resetting underlying auditory N1 suppression in chronic alcoholism.

    Science.gov (United States)

    Fuentemilla, Lluis; Marco-Pallarés, Josep; Gual, Antoni; Escera, Carles; Polo, Maria Dolores; Grau, Carles

    2009-02-18

    It has been suggested that chronic alcoholism may lead to altered neural mechanisms related to inhibitory processes. Here, we studied auditory N1 suppression phenomena (i.e. amplitude reduction with repetitive stimuli) in chronic alcoholic patients as an early-stage information-processing brain function involving inhibition by the analysis of the N1 event-related potential and time-frequency computation (spectral power and phase-resetting). Our results showed enhanced neural theta oscillatory phase-resetting underlying N1 generation in suppressed N1 event-related potential. The present findings suggest that chronic alcoholism alters neural oscillatory synchrony dynamics at very early stages of information processing.

  14. Parallel consensual neural networks.

    Science.gov (United States)

    Benediktsson, J A; Sveinsson, J R; Ersoy, O K; Swain, P H

    1997-01-01

    A new type of a neural-network architecture, the parallel consensual neural network (PCNN), is introduced and applied in classification/data fusion of multisource remote sensing and geographic data. The PCNN architecture is based on statistical consensus theory and involves using stage neural networks with transformed input data. The input data are transformed several times and the different transformed data are used as if they were independent inputs. The independent inputs are first classified using the stage neural networks. The output responses from the stage networks are then weighted and combined to make a consensual decision. In this paper, optimization methods are used in order to weight the outputs from the stage networks. Two approaches are proposed to compute the data transforms for the PCNN, one for binary data and another for analog data. The analog approach uses wavelet packets. The experimental results obtained with the proposed approach show that the PCNN outperforms both a conjugate-gradient backpropagation neural network and conventional statistical methods in terms of overall classification accuracy of test data.

  15. Chronic pancreatitis.

    Science.gov (United States)

    Kleeff, Jorg; Whitcomb, David C; Shimosegawa, Tooru; Esposito, Irene; Lerch, Markus M; Gress, Thomas; Mayerle, Julia; Drewes, Asbjørn Mohr; Rebours, Vinciane; Akisik, Fatih; Muñoz, J Enrique Domínguez; Neoptolemos, John P

    2017-09-07

    Chronic pancreatitis is defined as a pathological fibro-inflammatory syndrome of the pancreas in individuals with genetic, environmental and/or other risk factors who develop persistent pathological responses to parenchymal injury or stress. Potential causes can include toxic factors (such as alcohol or smoking), metabolic abnormalities, idiopathic mechanisms, genetics, autoimmune responses and obstructive mechanisms. The pathophysiology of chronic pancreatitis is fairly complex and includes acinar cell injury, acinar stress responses, duct dysfunction, persistent or altered inflammation, and/or neuro-immune crosstalk, but these mechanisms are not completely understood. Chronic pancreatitis is characterized by ongoing inflammation of the pancreas that results in progressive loss of the endocrine and exocrine compartment owing to atrophy and/or replacement with fibrotic tissue. Functional consequences include recurrent or constant abdominal pain, diabetes mellitus (endocrine insufficiency) and maldigestion (exocrine insufficiency). Diagnosing early-stage chronic pancreatitis is challenging as changes are subtle, ill-defined and overlap those of other disorders. Later stages are characterized by variable fibrosis and calcification of the pancreatic parenchyma; dilatation, distortion and stricturing of the pancreatic ducts; pseudocysts; intrapancreatic bile duct stricturing; narrowing of the duodenum; and superior mesenteric, portal and/or splenic vein thrombosis. Treatment options comprise medical, radiological, endoscopic and surgical interventions, but evidence-based approaches are limited. This Primer highlights the major progress that has been made in understanding the pathophysiology, presentation, prevalence and management of chronic pancreatitis and its complications.

  16. Neural Architectures for Control

    Science.gov (United States)

    Peterson, James K.

    1991-01-01

    The cerebellar model articulated controller (CMAC) neural architectures are shown to be viable for the purposes of real-time learning and control. Software tools for the exploration of CMAC performance are developed for three hardware platforms, the MacIntosh, the IBM PC, and the SUN workstation. All algorithm development was done using the C programming language. These software tools were then used to implement an adaptive critic neuro-control design that learns in real-time how to back up a trailer truck. The truck backer-upper experiment is a standard performance measure in the neural network literature, but previously the training of the controllers was done off-line. With the CMAC neural architectures, it was possible to train the neuro-controllers on-line in real-time on a MS-DOS PC 386. CMAC neural architectures are also used in conjunction with a hierarchical planning approach to find collision-free paths over 2-D analog valued obstacle fields. The method constructs a coarse resolution version of the original problem and then finds the corresponding coarse optimal path using multipass dynamic programming. CMAC artificial neural architectures are used to estimate the analog transition costs that dynamic programming requires. The CMAC architectures are trained in real-time for each obstacle field presented. The coarse optimal path is then used as a baseline for the construction of a fine scale optimal path through the original obstacle array. These results are a very good indication of the potential power of the neural architectures in control design. In order to reach as wide an audience as possible, we have run a seminar on neuro-control that has met once per week since 20 May 1991. This seminar has thoroughly discussed the CMAC architecture, relevant portions of classical control, back propagation through time, and adaptive critic designs.

  17. Sacred or Neural?

    DEFF Research Database (Denmark)

    Runehov, Anne Leona Cesarine

    Are religious spiritual experiences merely the product of the human nervous system? Anne L.C. Runehov investigates the potential of contemporary neuroscience to explain religious experiences. Following the footsteps of Michael Persinger, Andrew Newberg and Eugene d'Aquili she defines...... the terminological bounderies of "religious experiences" and explores the relevant criteria for the proper evaluation of scientific research, with a particular focus on the validity of reductionist models. Runehov's theis is that the perspectives looked at do not necessarily exclude each other but can be merged....... The question "sacred or neural?" becomes a statement "sacred and neural". The synergies thus produced provide manifold opportunities for interdisciplinary dialogue and research....

  18. Deconvolution using a neural network

    Energy Technology Data Exchange (ETDEWEB)

    Lehman, S.K.

    1990-11-15

    Viewing one dimensional deconvolution as a matrix inversion problem, we compare a neural network backpropagation matrix inverse with LMS, and pseudo-inverse. This is a largely an exercise in understanding how our neural network code works. 1 ref.

  19. Introduction to Artificial Neural Networks

    DEFF Research Database (Denmark)

    Larsen, Jan

    1999-01-01

    The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks.......The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks....

  20. Memory Consolidation and Neural Substrate of Reward

    Directory of Open Access Journals (Sweden)

    Redolar-Ripoll, Diego

    2012-08-01

    Full Text Available The aim of this report is to analyze the relationships between reward and learning and memory processes. Different studies have described how information about rewards influences behavior and how the brain uses this reward information to control learning and memory processes. Reward nature seems to be processed in different ways by neurons in different brain structures, ranging from the detection and perception of rewards to the use of information about predicted rewards for the control of goal-directed behavior. The neural substrate underling this processing of reward information is a reliable way of improving learning and memory processes. Evidence from several studies indicates that this neural system can facilitate memory consolidation in a wide variety of learning tasks. From a molecular perspective, certain cardinal features of reward have been described as forms of memory. Studies of human addicts and studies in animal models of addiction show that chronic drug exposure produces stable changes in the brain at the cellular and molecular levels that underlie the long-lasting behavioral plasticity associated with addiction. These molecular and cellular adaptations involved in addiction are also implicated in learning and memory processes. Dopamine seems to be a critical common signal to activate different genetic mechanisms that ultimately remodel synapses and circuits. Despite memory is an active and complex process mediated by different brain areas, the neural substrate of reward is able to improve memory consolidation in a several paradigms. We believe that there are many equivalent traits between reward and learning and memory processes.

  1. Effects of pudendal neuromodulation on bladder function in chronic spinal cord-injured rats

    Directory of Open Access Journals (Sweden)

    Yin-Tsong Lin

    2016-09-01

    Conclusion: This study demonstrates the feasibility of using pudendal neuromodulation in chronic SCI rats. These results could aid in developing an advanced neural prosthesis to restore bladder function in clinical settings.

  2. [Chronic diarrhea].

    Science.gov (United States)

    Stelzer, Teresa; Heuss, Ludwig Theodor

    2014-09-01

    Defined by lasting more than four weeks - is a common but often challenging clinical scenario. It is important to be aware that diarrhoea means different things to different patients. The evaluation of chronic diarrhoea depends on taking an excellent history and careful physical examination as well as planning investigations thoughtfully. Functional diarrhea ist the most common cause of chronic diarrhea in the developed countries and motility disorders are more common than inflammatory, osmotic or secretory causes. In some cases categorizing patients by their stool characteristics can be helpful in directing further evaluation.

  3. Neural correlates of pediatric obesity.

    Science.gov (United States)

    Bruce, Amanda S; Martin, Laura E; Savage, Cary R

    2011-06-01

    Childhood obesity rates have increased over the last 40 years and have a detrimental impact on public health. While the causes of the obesity epidemic are complex, obesity ultimately arises from chronic imbalances between energy intake and expenditure. An emerging area of research in obesity has focused on the role of the brain in evaluating the rewarding properties of food and making decisions about what and how much to eat. This article reviews recent scientific literature regarding the brain's role in pediatric food motivation and childhood obesity. The article will begin by reviewing some of the recent literature discussing challenges associated with neuroimaging in children and the relevant developmental brain changes that occur in childhood and adolescence. The article will then review studies regarding neural mechanisms of food motivation and the ability to delay gratification in children and how these responses differ in obese compared to healthy weight children. Increasing our understanding about how brain function and behavior may differ in children will inform future research, obesity prevention, and interventions targeting childhood obesity. Copyright © 2011. Published by Elsevier Inc.

  4. Chronic myelogenous leukemia (CML)

    Science.gov (United States)

    CML; Chronic myeloid leukemia; Chronic granulocytic leukemia; Leukemia - chronic granulocytic ... nuclear disaster. It takes many years to develop leukemia from radiation exposure. Most people treated for cancer ...

  5. Chronic Meningococcaemia

    African Journals Online (AJOL)

    clinical features, complications, laboratory findings and treatment of this condition are discussed. The resemblance, both clinically and histologically, to allergic vasculitis is stressed. S. Air. Med. J., 48, 2154 (1974). Chronic meningococcaemia is an uncommon condition today, but was well recognised in the early decades of.

  6. Chronic gastritis.

    Science.gov (United States)

    Sipponen, Pentti; Maaroos, Heidi-Ingrid

    2015-06-01

    Prevalence of chronic gastritis has markedly declined in developed populations during the past decades. However, chronic gastritis is still one of the most common serious pandemic infections with such severe killing sequelae as peptic ulcer or gastric cancer. Globally, on average, even more than half of people may have a chronic gastritis at present. Helicobacter pylori infection in childhood is the main cause of chronic gastritis, which microbial origin is the key for the understanding of the bizarre epidemiology and course of the disease. A life-long and aggressive inflammation in gastritis results in destruction (atrophic gastritis) of stomach mucosa with time (years and decades). The progressive worsening of atrophic gastritis results subsequently in dysfunctions of stomach mucosa. Atrophic gastritis will finally end up in a permanently acid-free stomach in the most extreme cases. Severe atrophic gastritis and acid-free stomach are the highest independent risk conditions for gastric cancer known so far. In addition to the risks of malignancy and peptic ulcer, acid-free stomach and severe forms of atrophic gastritis may associate with failures in absorption of essential vitamins, like vitamin B12, micronutrients (like iron, calcium, magnesium and zinc), diet and medicines.

  7. Chronic Pancreatitis

    International Nuclear Information System (INIS)

    Betancur, Jorge

    2002-01-01

    It is presented a case of a man with alcoholic chronic pancreatitis, whose marked dilatation of the ducts reasoned the issue. The severe untreatable pain was the surgery indication, which was practiced without complications either during or after the surgery. By the way, a shallow revision of the literature is made, by mentioning classification, physiopatholoy, clinical square, medical, surgical and endoscopic treatment

  8. Neural Network Ensembles

    DEFF Research Database (Denmark)

    Hansen, Lars Kai; Salamon, Peter

    1990-01-01

    We propose several means for improving the performance an training of neural networks for classification. We use crossvalidation as a tool for optimizing network parameters and architecture. We show further that the remaining generalization error can be reduced by invoking ensembles of similar...... networks....

  9. Neural correlates of consciousness

    African Journals Online (AJOL)

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

  10. Neural Networks and Micromechanics

    Science.gov (United States)

    Kussul, Ernst; Baidyk, Tatiana; Wunsch, Donald C.

    The title of the book, "Neural Networks and Micromechanics," seems artificial. However, the scientific and technological developments in recent decades demonstrate a very close connection between the two different areas of neural networks and micromechanics. The purpose of this book is to demonstrate this connection. Some artificial intelligence (AI) methods, including neural networks, could be used to improve automation system performance in manufacturing processes. However, the implementation of these AI methods within industry is rather slow because of the high cost of conducting experiments using conventional manufacturing and AI systems. To lower the cost, we have developed special micromechanical equipment that is similar to conventional mechanical equipment but of much smaller size and therefore of lower cost. This equipment could be used to evaluate different AI methods in an easy and inexpensive way. The proved methods could be transferred to industry through appropriate scaling. In this book, we describe the prototypes of low cost microequipment for manufacturing processes and the implementation of some AI methods to increase precision, such as computer vision systems based on neural networks for microdevice assembly and genetic algorithms for microequipment characterization and the increase of microequipment precision.

  11. Introduction to neural networks

    International Nuclear Information System (INIS)

    Pavlopoulos, P.

    1996-01-01

    This lecture is a presentation of today's research in neural computation. Neural computation is inspired by knowledge from neuro-science. It draws its methods in large degree from statistical physics and its potential applications lie mainly in computer science and engineering. Neural networks models are algorithms for cognitive tasks, such as learning and optimization, which are based on concepts derived from research into the nature of the brain. The lecture first gives an historical presentation of neural networks development and interest in performing complex tasks. Then, an exhaustive overview of data management and networks computation methods is given: the supervised learning and the associative memory problem, the capacity of networks, the Perceptron networks, the functional link networks, the Madaline (Multiple Adalines) networks, the back-propagation networks, the reduced coulomb energy (RCE) networks, the unsupervised learning and the competitive learning and vector quantization. An example of application in high energy physics is given with the trigger systems and track recognition system (track parametrization, event selection and particle identification) developed for the CPLEAR experiment detectors from the LEAR at CERN. (J.S.). 56 refs., 20 figs., 1 tab., 1 appendix

  12. Learning from neural control.

    Science.gov (United States)

    Wang, Cong; Hill, David J

    2006-01-01

    One of the amazing successes of biological systems is their ability to "learn by doing" and so adapt to their environment. In this paper, first, a deterministic learning mechanism is presented, by which an appropriately designed adaptive neural controller is capable of learning closed-loop system dynamics during tracking control to a periodic reference orbit. Among various neural network (NN) architectures, the localized radial basis function (RBF) network is employed. A property of persistence of excitation (PE) for RBF networks is established, and a partial PE condition of closed-loop signals, i.e., the PE condition of a regression subvector constructed out of the RBFs along a periodic state trajectory, is proven to be satisfied. Accurate NN approximation for closed-loop system dynamics is achieved in a local region along the periodic state trajectory, and a learning ability is implemented during a closed-loop feedback control process. Second, based on the deterministic learning mechanism, a neural learning control scheme is proposed which can effectively recall and reuse the learned knowledge to achieve closed-loop stability and improved control performance. The significance of this paper is that the presented deterministic learning mechanism and the neural learning control scheme provide elementary components toward the development of a biologically-plausible learning and control methodology. Simulation studies are included to demonstrate the effectiveness of the approach.

  13. 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...

  14. Neural underpinnings of music

    DEFF Research Database (Denmark)

    Vuust, Peter; Gebauer, Line K; Witek, Maria A G

    2014-01-01

    . According to this theory, perception and learning is manifested through the brain’s Bayesian minimization of the error between the input to the brain and the brain’s prior expectations. Fourth, empirical studies of neural and behavioral effects of syncopation, polyrhythm and groove will be reported, and we...

  15. Neural mechanisms underlying morphine withdrawal in addicted patients: a review

    Directory of Open Access Journals (Sweden)

    Nima Babhadiashar

    2015-06-01

    Full Text Available Morphine is one of the most potent alkaloid in opium, which has substantial medical uses and needs and it is the first active principle purified from herbal source. Morphine has commonly been used for relief of moderate to severe pain as it acts directly on the central nervous system; nonetheless, its chronic abuse increases tolerance and physical dependence, which is commonly known as opiate addiction. Morphine withdrawal syndrome is physiological and behavioral symptoms that stem from prolonged exposure to morphine. A majority of brain regions are hypofunctional over prolonged abstinence and acute morphine withdrawal. Furthermore, several neural mechanisms are likely to contribute to morphine withdrawal. The present review summarizes the literature pertaining to neural mechanisms underlying morphine withdrawal. Despite the fact that morphine withdrawal is a complex process, it is suggested that neural mechanisms play key roles in morphine withdrawal.

  16. Bioprinting for Neural Tissue Engineering.

    Science.gov (United States)

    Knowlton, Stephanie; Anand, Shivesh; Shah, Twisha; Tasoglu, Savas

    2018-01-01

    Bioprinting is a method by which a cell-encapsulating bioink is patterned to create complex tissue architectures. Given the potential impact of this technology on neural research, we review the current state-of-the-art approaches for bioprinting neural tissues. While 2D neural cultures are ubiquitous for studying neural cells, 3D cultures can more accurately replicate the microenvironment of neural tissues. By bioprinting neuronal constructs, one can precisely control the microenvironment by specifically formulating the bioink for neural tissues, and by spatially patterning cell types and scaffold properties in three dimensions. We review a range of bioprinted neural tissue models and discuss how they can be used to observe how neurons behave, understand disease processes, develop new therapies and, ultimately, design replacement tissues. Copyright © 2017 Elsevier Ltd. All rights reserved.

  17. Chronic Myelogenous Leukemia

    Science.gov (United States)

    Chronic myelogenous leukemia Overview Chronic myelogenous leukemia (CML) is an uncommon type of cancer of the blood cells. The term "chronic" in chronic myelogenous leukemia indicates that this cancer ...

  18. Chronic motor tic disorder

    Science.gov (United States)

    Chronic vocal tic disorder; Tic - chronic motor tic disorder ... Chronic motor tic disorder is more common than Tourette syndrome . Chronic tics may be forms of Tourette syndrome. Tics usually start ...

  19. Chronic thyroiditis (Hashimoto disease)

    Science.gov (United States)

    Hashimoto thyroiditis; Chronic lymphocytic thyroiditis; Autoimmune thyroiditis; Chronic autoimmune thyroiditis; Lymphadenoid goiter - Hashimoto; Hypothyroidism - Hashimoto; Type 2 polyglandular autoimmune ...

  20. Analysis of neural data

    CERN Document Server

    Kass, Robert E; Brown, Emery N

    2014-01-01

    Continual improvements in data collection and processing have had a huge impact on brain research, producing data sets that are often large and complicated. By emphasizing a few fundamental principles, and a handful of ubiquitous techniques, Analysis of Neural Data provides a unified treatment of analytical methods that have become essential for contemporary researchers. Throughout the book ideas are illustrated with more than 100 examples drawn from the literature, ranging from electrophysiology, to neuroimaging, to behavior. By demonstrating the commonality among various statistical approaches the authors provide the crucial tools for gaining knowledge from diverse types of data. Aimed at experimentalists with only high-school level mathematics, as well as computationally-oriented neuroscientists who have limited familiarity with statistics, Analysis of Neural Data serves as both a self-contained introduction and a reference work.

  1. Deep Neural Yodelling

    OpenAIRE

    Pfäffli, Daniel (Autor/in)

    2018-01-01

    Yodel music differs from most other genres by exercising the transition from chest voice to falsetto with an audible glottal stop which is recognised even by laymen. Yodel often consists of a yodeller with a choir accompaniment. In Switzerland, it is differentiated between the natural yodel and yodel songs. Today's approaches to music generation with machine learning algorithms are based on neural networks, which are best described by stacked layers of neurons which are connected with neurons...

  2. Neural networks for triggering

    International Nuclear Information System (INIS)

    Denby, B.; Campbell, M.; Bedeschi, F.; Chriss, N.; Bowers, C.; Nesti, F.

    1990-01-01

    Two types of neural network beauty trigger architectures, based on identification of electrons in jets and recognition of secondary vertices, have been simulated in the environment of the Fermilab CDF experiment. The efficiencies for B's and rejection of background obtained are encouraging. If hardware tests are successful, the electron identification architecture will be tested in the 1991 run of CDF. 10 refs., 5 figs., 1 tab

  3. Artificial neural network modelling

    CERN Document Server

    Samarasinghe, Sandhya

    2016-01-01

    This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .

  4. Rotation Invariance Neural Network

    OpenAIRE

    Li, Shiyuan

    2017-01-01

    Rotation invariance and translation invariance have great values in image recognition tasks. In this paper, we bring a new architecture in convolutional neural network (CNN) named cyclic convolutional layer to achieve rotation invariance in 2-D symbol recognition. We can also get the position and orientation of the 2-D symbol by the network to achieve detection purpose for multiple non-overlap target. Last but not least, this architecture can achieve one-shot learning in some cases using thos...

  5. Neural Mechanisms of Foraging

    OpenAIRE

    Kolling, Nils; Behrens, Timothy EJ; Mars, Rogier B; Rushworth, Matthew FS

    2012-01-01

    Behavioural economic studies, involving limited numbers of choices, have provided key insights into neural decision-making mechanisms. By contrast, animals’ foraging choices arise in the context of sequences of encounters with prey/food. On each encounter the animal chooses to engage or whether the environment is sufficiently rich that searching elsewhere is merited. The cost of foraging is also critical. We demonstrate humans can alternate between two modes of choice, comparative decision-ma...

  6. Partially flexible MEMS neural probe composed of polyimide and sucrose gel for reducing brain damage during and after implantation

    International Nuclear Information System (INIS)

    Jeon, Myounggun; Yoon, Eui-Sung; Cho, Il-Joo; Cho, Jeiwon; Jung, Dahee; Kim, Yun Kyung; Shin, Sehyun

    2014-01-01

    This paper presents a flexible microelectromechanical systems (MEMS) neural probe that minimizes neuron damage and immune response, suitable for chronic recording applications. MEMS neural probes with various features such as high electrode densities have been actively investigated for neuron stimulation and recording to study brain functions. However, successful recording of neural signals in chronic application using rigid silicon probes still remains challenging because of cell death and macrophages accumulated around the electrodes over time from continuous brain movement. Thus, in this paper, we propose a new flexible MEMS neural probe that consists of two segments: a polyimide-based, flexible segment for connection and a rigid segment composed of thin silicon for insertion. While the flexible connection segment is designed to reduce the long-term chronic neuron damage, the thin insertion segment is designed to minimize the brain damage during the insertion process. The proposed flexible neural probe was successfully fabricated using the MEMS process on a silicon on insulator wafer. For a successful insertion, a biodegradable sucrose gel is coated on the flexible segment to temporarily increase the probe stiffness to prevent buckling. After the insertion, the sucrose gel dissolves inside the brain exposing the polyimide probe. By performing an insertion test, we confirm that the flexible probe has enough stiffness. In addition, by monitoring immune responses and brain histology, we successfully demonstrate that the proposed flexible neural probe incurs fivefold less neural damage than that incurred by a conventional silicon neural probe. Therefore, the presented flexible neural probe is a promising candidate for recording stable neural signals for long-time chronic applications. (paper)

  7. Neural Based Orthogonal Data Fitting The EXIN Neural Networks

    CERN Document Server

    Cirrincione, Giansalvo

    2008-01-01

    Written by three leaders in the field of neural based algorithms, Neural Based Orthogonal Data Fitting proposes several neural networks, all endowed with a complete theory which not only explains their behavior, but also compares them with the existing neural and traditional algorithms. The algorithms are studied from different points of view, including: as a differential geometry problem, as a dynamic problem, as a stochastic problem, and as a numerical problem. All algorithms have also been analyzed on real time problems (large dimensional data matrices) and have shown accurate solutions. Wh

  8. Trimaran Resistance Artificial Neural Network

    Science.gov (United States)

    2011-01-01

    11th International Conference on Fast Sea Transportation FAST 2011, Honolulu, Hawaii, USA, September 2011 Trimaran Resistance Artificial Neural Network Richard...Trimaran Resistance Artificial Neural Network 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e... Artificial Neural Network and is restricted to the center and side-hull configurations tested. The value in the parametric model is that it is able to

  9. Chronic Pancreatitis

    International Nuclear Information System (INIS)

    Vavrecka, A.; Bilicky, J.

    2011-01-01

    Chronic pancreatitis is an ongoing inflammatory process that may over time lead to mal digestion, malabsorption and diabetic syndrome. Identification of risk (etiological) factors based on classifications TIGAR-O or later M-ANNHEIM. These factors (environmental and / or genetic) leads to failure of the stability of the digestive and lysosomal enzymes in the acinar cells, resulting in premature activation of digestive enzymes in the pancreas, and repeated nekroinflamation and fibrosis. The incidence has of the upward trend. Clinically the disease manifests itself in most cases with pain and possibly with nonspecific dyspeptic troubles. Decisive role in the diagnosis playing imaging methods, trans abdominal ultrasonography, computed tomography, magnetic resonance imaging, magnetic cholangiopancretography and foremost endoscopic ultrasonography, which has the highest sensitivity and specificity. Endoscopic retrograde cholangiopancreatography is currently regarded as a method for therapy, not for diagnosis. Less importance is now attached to a functional test. Symptomatic treatment is usually conservative. Abstinence is necessary, easily digestible, but calorie-rich diet with reduced fat. Most patients needed treatment with analgesics. In case of insufficient effect of analgesics is necessary to consider endoscopic therapy or surgery. If the external secretory insufficiency is present are served pancreatic extracts. Diabetic syndrome requires insulin delivery. Generally, chronic pancreatitis is a disease treatable but incurable. Proportion of patients are also dying of pancreatic cancer. (author)

  10. A central neural circuit for itch sensation.

    Science.gov (United States)

    Mu, Di; Deng, Juan; Liu, Ke-Fei; Wu, Zhen-Yu; Shi, Yu-Feng; Guo, Wei-Min; Mao, Qun-Quan; Liu, Xing-Jun; Li, Hui; Sun, Yan-Gang

    2017-08-18

    Although itch sensation is an important protective mechanism for animals, chronic itch remains a challenging clinical problem. Itch processing has been studied extensively at the spinal level. However, how itch information is transmitted to the brain and what central circuits underlie the itch-induced scratching behavior remain largely unknown. We found that the spinoparabrachial pathway was activated during itch processing and that optogenetic suppression of this pathway impaired itch-induced scratching behaviors. Itch-mediating spinal neurons, which express the gastrin-releasing peptide receptor, are disynaptically connected to the parabrachial nucleus via glutamatergic spinal projection neurons. Blockade of synaptic output of glutamatergic neurons in the parabrachial nucleus suppressed pruritogen-induced scratching behavior. Thus, our studies reveal a central neural circuit that is critical for itch signal processing. Copyright © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

  11. Optics in neural computation

    Science.gov (United States)

    Levene, Michael John

    In all attempts to emulate the considerable powers of the brain, one is struck by both its immense size, parallelism, and complexity. While the fields of neural networks, artificial intelligence, and neuromorphic engineering have all attempted oversimplifications on the considerable complexity, all three can benefit from the inherent scalability and parallelism of optics. This thesis looks at specific aspects of three modes in which optics, and particularly volume holography, can play a part in neural computation. First, holography serves as the basis of highly-parallel correlators, which are the foundation of optical neural networks. The huge input capability of optical neural networks make them most useful for image processing and image recognition and tracking. These tasks benefit from the shift invariance of optical correlators. In this thesis, I analyze the capacity of correlators, and then present several techniques for controlling the amount of shift invariance. Of particular interest is the Fresnel correlator, in which the hologram is displaced from the Fourier plane. In this case, the amount of shift invariance is limited not just by the thickness of the hologram, but by the distance of the hologram from the Fourier plane. Second, volume holography can provide the huge storage capacity and high speed, parallel read-out necessary to support large artificial intelligence systems. However, previous methods for storing data in volume holograms have relied on awkward beam-steering or on as-yet non- existent cheap, wide-bandwidth, tunable laser sources. This thesis presents a new technique, shift multiplexing, which is capable of very high densities, but which has the advantage of a very simple implementation. In shift multiplexing, the reference wave consists of a focused spot a few millimeters in front of the hologram. Multiplexing is achieved by simply translating the hologram a few tens of microns or less. This thesis describes the theory for how shift

  12. Neural mechanisms of sequence generation in songbirds

    Science.gov (United States)

    Langford, Bruce

    Animal models in research are useful for studying more complex behavior. For example, motor sequence generation of actions requiring good muscle coordination such as writing with a pen, playing an instrument, or speaking, may involve the interaction of many areas in the brain, each a complex system in itself; thus it can be difficult to determine causal relationships between neural behavior and the behavior being studied. Birdsong, however, provides an excellent model behavior for motor sequence learning, memory, and generation. The song consists of learned sequences of notes that are spectrographically stereotyped over multiple renditions of the song, similar to syllables in human speech. The main areas of the songbird brain involve in singing are known, however, the mechanisms by which these systems store and produce song are not well understood. We used a custom built, head-mounted, miniature motorized microdrive to chronically record the neural firing patterns of identified neurons in HVC, a pre-motor cortical nucleus which has been shown to be important in song timing. These were done in Bengalese finch which generate a song made up of stereotyped notes but variable note sequences. We observed song related bursting in neurons projecting to Area X, a homologue to basal ganglia, and tonic firing in HVC interneurons. Interneuron had firing rate patterns that were consistent over multiple renditions of the same note sequence. We also designed and built a light-weight, low-powered wireless programmable neural stimulator using Bluetooth Low Energy Protocol. It was able to generate perturbations in the song when current pulses were administered to RA, which projects to the brainstem nucleus responsible for syringeal muscle control.

  13. Analysis of neural networks

    CERN Document Server

    Heiden, Uwe

    1980-01-01

    The purpose of this work is a unified and general treatment of activity in neural networks from a mathematical pOint of view. Possible applications of the theory presented are indica­ ted throughout the text. However, they are not explored in de­ tail for two reasons : first, the universal character of n- ral activity in nearly all animals requires some type of a general approach~ secondly, the mathematical perspicuity would suffer if too many experimental details and empirical peculiarities were interspersed among the mathematical investigation. A guide to many applications is supplied by the references concerning a variety of specific issues. Of course the theory does not aim at covering all individual problems. Moreover there are other approaches to neural network theory (see e.g. Poggio-Torre, 1978) based on the different lev­ els at which the nervous system may be viewed. The theory is a deterministic one reflecting the average be­ havior of neurons or neuron pools. In this respect the essay is writt...

  14. Neural Synchronization and Cryptography

    Science.gov (United States)

    Ruttor, Andreas

    2007-11-01

    Neural networks can synchronize by learning from each other. In the case of discrete weights full synchronization is achieved in a finite number of steps. Additional networks can be trained by using the inputs and outputs generated during this process as examples. Several learning rules for both tasks are presented and analyzed. In the case of Tree Parity Machines synchronization is much faster than learning. Scaling laws for the number of steps needed for full synchronization and successful learning are derived using analytical models. They indicate that the difference between both processes can be controlled by changing the synaptic depth. In the case of bidirectional interaction the synchronization time increases proportional to the square of this parameter, but it grows exponentially, if information is transmitted in one direction only. Because of this effect neural synchronization can be used to construct a cryptographic key-exchange protocol. Here the partners benefit from mutual interaction, so that a passive attacker is usually unable to learn the generated key in time. The success probabilities of different attack methods are determined by numerical simulations and scaling laws are derived from the data. They show that the partners can reach any desired level of security by just increasing the synaptic depth. Then the complexity of a successful attack grows exponentially, but there is only a polynomial increase of the effort needed to generate a key. Further improvements of security are possible by replacing the random inputs with queries generated by the partners.

  15. Neural Networks for Optimal Control

    DEFF Research Database (Denmark)

    Sørensen, O.

    1995-01-01

    Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process.......Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process....

  16. Neural networks at the Tevatron

    International Nuclear Information System (INIS)

    Badgett, W.; Burkett, K.; Campbell, M.K.; Wu, D.Y.; Bianchin, S.; DeNardi, M.; Pauletta, G.; Santi, L.; Caner, A.; Denby, B.; Haggerty, H.; Lindsey, C.S.; Wainer, N.; Dall'Agata, M.; Johns, K.; Dickson, M.; Stanco, L.; Wyss, J.L.

    1992-10-01

    This paper summarizes neural network applications at the Fermilab Tevatron, including the first online hardware application in high energy physics (muon tracking): the CDF and DO neural network triggers; offline quark/gluon discrimination at CDF; ND a new tool for top to multijets recognition at CDF

  17. Neural Networks for the Beginner.

    Science.gov (United States)

    Snyder, Robin M.

    Motivated by the brain, neural networks are a right-brained approach to artificial intelligence that is used to recognize patterns based on previous training. In practice, one would not program an expert system to recognize a pattern and one would not train a neural network to make decisions from rules; but one could combine the best features of…

  18. Neural fields theory and applications

    CERN Document Server

    Graben, Peter; Potthast, Roland; Wright, James

    2014-01-01

    With this book, the editors present the first comprehensive collection in neural field studies, authored by leading scientists in the field - among them are two of the founding-fathers of neural field theory. Up to now, research results in the field have been disseminated across a number of distinct journals from mathematics, computational neuroscience, biophysics, cognitive science and others. Starting with a tutorial for novices in neural field studies, the book comprises chapters on emergent patterns, their phase transitions and evolution, on stochastic approaches, cortical development, cognition, robotics and computation, large-scale numerical simulations, the coupling of neural fields to the electroencephalogram and phase transitions in anesthesia. The intended readership are students and scientists in applied mathematics, theoretical physics, theoretical biology, and computational neuroscience. Neural field theory and its applications have a long-standing tradition in the mathematical and computational ...

  19. Artificial neural networks in NDT

    International Nuclear Information System (INIS)

    Abdul Aziz Mohamed

    2001-01-01

    Artificial neural networks, simply known as neural networks, have attracted considerable interest in recent years largely because of a growing recognition of the potential of these computational paradigms as powerful alternative models to conventional pattern recognition or function approximation techniques. The neural networks approach is having a profound effect on almost all fields, and has been utilised in fields Where experimental inter-disciplinary work is being carried out. Being a multidisciplinary subject with a broad knowledge base, Nondestructive Testing (NDT) or Nondestructive Evaluation (NDE) is no exception. This paper explains typical applications of neural networks in NDT/NDE. Three promising types of neural networks are highlighted, namely, back-propagation, binary Hopfield and Kohonen's self-organising maps. (Author)

  20. Atypical Chronic Myelogenous Leukemia

    Science.gov (United States)

    ... myeloproliferative neoplasms, leukemia , and other conditions . Chronic Myelomonocytic Leukemia Key Points Chronic myelomonocytic leukemia is a disease ... chance of recovery) and treatment options. Chronic myelomonocytic leukemia is a disease in which too many myelocytes ...

  1. Chronic lymphocytic leukemia (CLL)

    Science.gov (United States)

    ... is used for painful and enlarged lymph nodes. Blood transfusions or platelet transfusions may be required if blood ... unexplained fatigue, bruising, excessive sweating, or weight loss. Alternative ... Leukemia - chronic lymphocytic (CLL); Blood cancer - chronic lymphocytic leukemia; Bone marrow cancer - chronic ...

  2. Chronic Pancreatitis in Children

    Science.gov (United States)

    ... E-News Sign-Up Home Patient Information Children/Pediatric Chronic Pancreatitis in Children Chronic Pancreatitis in Children What symptoms would my child have? Frequent or chronic abdominal pain is the most common symptom of pancreatitis. The ...

  3. Neural correlates of executive functions in patients with obesity.

    Science.gov (United States)

    Ho, Ming-Chou; Chen, Vincent Chin-Hung; Chao, Seh-Huang; Fang, Ching-Tzu; Liu, Yi-Chun; Weng, Jun-Cheng

    2018-01-01

    Obesity is one of the most challenging problems in human health and is recognized as an important risk factor for many chronic diseases. It remains unclear how the neural systems (e.g., the mesolimbic "reward" and the prefrontal "control" neural systems) are correlated with patients' executive function (EF), conceptualized as the integration of "cool" EF and "hot" EF. "Cool" EF refers to relatively abstract, non-affective operations such as inhibitory control and mental flexibility. "Hot" EF refers to motivationally significant affective operations such as affective decision-making. We tried to find the correlation between structural and functional neuroimaging indices and EF in obese patients. The study population comprised seventeen patients with obesity (seven males and 10 females, BMI = 37.99 ± 5.40, age = 31.82 ± 8.75 year-old) preparing to undergo bariatric surgery. We used noninvasive diffusion tensor imaging, generalized q-sampling imaging, and resting-state functional magnetic resonance imaging to examine the neural correlations between structural and functional neuroimaging indices and EF performances in patients with obesity. We reported that many brain areas are correlated to the patients' EF performances. More interestingly, some correlations may implicate the possible associations of EF and the incentive motivational effects of food. The neural correlation between the left precuneus and middle occipital gyrus and inhibitory control may suggest that patients with a better ability to detect appetitive food may have worse inhibitory control. Also, the neural correlation between the superior frontal blade and affective decision-making may suggest that patients' affective decision-making may be associated with the incentive motivational effects of food. Our results provide evidence suggesting neural correlates of EF in patients with obesity.

  4. Ultra-low noise miniaturized neural amplifier with hardware averaging.

    Science.gov (United States)

    Dweiri, Yazan M; Eggers, Thomas; McCallum, Grant; Durand, Dominique M

    2015-08-01

    Peripheral nerves carry neural signals that could be used to control hybrid bionic systems. Cuff electrodes provide a robust and stable interface but the recorded signal amplitude is small (concept of hardware averaging to nerve recordings obtained with cuff electrodes. An optimization procedure is developed to minimize noise and power simultaneously. The novel design was based on existing neural amplifiers (Intan Technologies, LLC) and is validated with signals obtained from the FINE in chronic dog experiments. We showed that hardware averaging leads to a reduction in the total recording noise by a factor of 1/√N or less depending on the source resistance. Chronic recording of physiological activity with FINE using the presented design showed significant improvement on the recorded baseline noise with at least two parallel operation transconductance amplifiers leading to a 46.1% reduction at N = 8. The functionality of these recordings was quantified by the SNR improvement and shown to be significant for N = 3 or more. The present design was shown to be capable of generating hardware averaging on noise improvement for neural recording with cuff electrodes, and can accommodate the presence of high source impedances that are associated with the miniaturized contacts and the high channel count in electrode arrays. This technique can be adopted for other applications where miniaturized and implantable multichannel acquisition systems with ultra-low noise and low power are required.

  5. Chronic subdural hematoma

    Science.gov (United States)

    Subdural hemorrhage - chronic; Subdural hematoma - chronic; Subdural hygroma ... A subdural hematoma develops when bridging veins tear and leak blood. These are the tiny veins that run between the ...

  6. Chronic Diseases Overview

    Science.gov (United States)

    ... Plan Templates All Chronic Surveillance Systems Communications Center Social Media Press Room Press Release Archives Multimedia Communication Campaigns Publications Chronic Disease Overview 2016–2017 At A ...

  7. Chronic urticaria

    Directory of Open Access Journals (Sweden)

    Sandeep Sachdeva

    2011-01-01

    Full Text Available Chronic urticaria (CU is a disturbing allergic condition of the skin. Although frequently benign, it may sometimes be a red flag sign of a serious internal disease. A multitude of etiologies have been implicated in the causation of CU, including physical, infective, vasculitic, psychological and idiopathic. An autoimmune basis of most of the ′idiopathic′ forms is now hypothesized. Histamine released from mast cells is the major effector in pathogenesis and it is clinically characterized by wheals that have a tendency to recur. Laboratory investigations aimed at a specific etiology are not always conclusive, though may be suggestive of an underlying condition. A clinical search for associated systemic disease is strongly advocated under appropriate circumstances. The mainstay of treatment remains H1 antihistaminics. These may be combined with complementary pharmacopeia in the form of H2 blockers, doxepin, nifedipine and leukotriene inhibitors. More radical therapy in the form of immunoglobulins, plasmapheresis and cyclophosphamide may be required for recalcitrant cases. Autologous transfusion and alternative remedies like acupuncture have prospects for future. A stepwise management results in favorable outcomes. An update on CU based on our experience with patients at a tertiary care centre is presented.

  8. Changed Synaptic Plasticity in Neural Circuits of Depressive-Like and Escitalopram-Treated Rats

    Science.gov (United States)

    Li, Xiao-Li; Yuan, Yong-Gui; Xu, Hua; Wu, Di; Gong, Wei-Gang; Geng, Lei-Yu; Wu, Fang-Fang; Tang, Hao; Xu, Lin

    2015-01-01

    Background: Although progress has been made in the detection and characterization of neural plasticity in depression, it has not been fully understood in individual synaptic changes in the neural circuits under chronic stress and antidepressant treatment. Methods: Using electron microscopy and Western-blot analyses, the present study quantitatively examined the changes in the Gray’s Type I synaptic ultrastructures and the expression of synapse-associated proteins in the key brain regions of rats’ depressive-related neural circuit after chronic unpredicted mild stress and/or escitalopram administration. Meanwhile, their depressive behaviors were also determined by several tests. Results: The Type I synapses underwent considerable remodeling after chronic unpredicted mild stress, which resulted in the changed width of the synaptic cleft, length of the active zone, postsynaptic density thickness, and/or synaptic curvature in the subregions of medial prefrontal cortex and hippocampus, as well as the basolateral amygdaloid nucleus of the amygdala, accompanied by changed expression of several synapse-associated proteins. Chronic escitalopram administration significantly changed the above alternations in the chronic unpredicted mild stress rats but had little effect on normal controls. Also, there was a positive correlation between the locomotor activity and the maximal synaptic postsynaptic density thickness in the stratum radiatum of the Cornu Ammonis 1 region and a negative correlation between the sucrose preference and the length of the active zone in the basolateral amygdaloid nucleus region in chronic unpredicted mild stress rats. Conclusion: These findings strongly indicate that chronic stress and escitalopram can alter synaptic plasticity in the neural circuits, and the remodeled synaptic ultrastructure was correlated with the rats’ depressive behaviors, suggesting a therapeutic target for further exploration. PMID:25899067

  9. Interacting neural networks

    Science.gov (United States)

    Metzler, R.; Kinzel, W.; Kanter, I.

    2000-08-01

    Several scenarios of interacting neural networks which are trained either in an identical or in a competitive way are solved analytically. In the case of identical training each perceptron receives the output of its neighbor. The symmetry of the stationary state as well as the sensitivity to the used training algorithm are investigated. Two competitive perceptrons trained on mutually exclusive learning aims and a perceptron which is trained on the opposite of its own output are examined analytically. An ensemble of competitive perceptrons is used as decision-making algorithms in a model of a closed market (El Farol Bar problem or the Minority Game. In this game, a set of agents who have to make a binary decision is considered.); each network is trained on the history of minority decisions. This ensemble of perceptrons relaxes to a stationary state whose performance can be better than random.

  10. Neural circuitry and immunity

    Science.gov (United States)

    Pavlov, Valentin A.; Tracey, Kevin J.

    2015-01-01

    Research during the last decade has significantly advanced our understanding of the molecular mechanisms at the interface between the nervous system and the immune system. Insight into bidirectional neuroimmune communication has characterized the nervous system as an important partner of the immune system in the regulation of inflammation. Neuronal pathways, including the vagus nerve-based inflammatory reflex are physiological regulators of immune function and inflammation. In parallel, neuronal function is altered in conditions characterized by immune dysregulation and inflammation. Here, we review these regulatory mechanisms and describe the neural circuitry modulating immunity. Understanding these mechanisms reveals possibilities to use targeted neuromodulation as a therapeutic approach for inflammatory and autoimmune disorders. These findings and current clinical exploration of neuromodulation in the treatment of inflammatory diseases defines the emerging field of Bioelectronic Medicine. PMID:26512000

  11. Neural Darwinism and consciousness.

    Science.gov (United States)

    Seth, Anil K; Baars, Bernard J

    2005-03-01

    Neural Darwinism (ND) is a large scale selectionist theory of brain development and function that has been hypothesized to relate to consciousness. According to ND, consciousness is entailed by reentrant interactions among neuronal populations in the thalamocortical system (the 'dynamic core'). These interactions, which permit high-order discriminations among possible core states, confer selective advantages on organisms possessing them by linking current perceptual events to a past history of value-dependent learning. Here, we assess the consistency of ND with 16 widely recognized properties of consciousness, both physiological (for example, consciousness is associated with widespread, relatively fast, low amplitude interactions in the thalamocortical system), and phenomenal (for example, consciousness involves the existence of a private flow of events available only to the experiencing subject). While no theory accounts fully for all of these properties at present, we find that ND and its recent extensions fare well.

  12. Neural Alterations in Acquired Age-Related Hearing Loss

    Directory of Open Access Journals (Sweden)

    Raksha Anand Mudar

    2016-06-01

    Full Text Available Hearing loss is one of the most prevalent chronic health conditions in older adults. Growing evidence suggests that hearing loss is associated with reduced cognitive functioning and incident dementia. In this mini-review, we briefly examine literature on anatomical and functional alterations in the brains of adults with acquired age-associated hearing loss, which may underlie the cognitive consequences observed in this population, focusing on studies that have used structural and functional magnetic resonance imaging, diffusion tensor imaging, and event-related electroencephalography. We discuss structural and functional alterations observed in the temporal and frontal cortices and the limbic system. These neural alterations are discussed in the context of common cause, information-degradation, and sensory-deprivation hypotheses, and we suggest possible rehabilitation strategies. Although we are beginning to learn more about changes in neural architecture and functionality related to age-associated hearing loss, much work remains to be done. Understanding the neural alterations will provide objective markers for early identification of neural consequences of age-associated hearing loss and for evaluating benefits of intervention approaches.

  13. Sub-meninges implantation reduces immune response to neural implants.

    Science.gov (United States)

    Markwardt, Neil T; Stokol, Jodi; Rennaker, Robert L

    2013-04-15

    Glial scar formation around neural interfaces inhibits their ability to acquire usable signals from the surrounding neurons. To improve neural recording performance, the inflammatory response and glial scarring must be minimized. Previous work has indicated that meningeally derived cells participate in the immune response, and it is possible that the meninges may grow down around the shank of a neural implant, contributing to the formation of the glial scar. This study examines whether the glial scar can be reduced by placing a neural probe completely below the meninges. Rats were implanted with sets of loose microwire implants placed either completely below the meninges or implanted conventionally with the upper end penetrating the meninges, but not attached to the skull. Histological analysis was performed 4 weeks following surgical implantation to evaluate the glial scar. Our results found that sub-meninges implants showed an average reduction in reactive astrocyte activity of 63% compared to trans-meninges implants. Microglial activity was also reduced for sub-meninges implants. These results suggest that techniques that isolate implants from the meninges offer the potential to reduce the encapsulation response which should improve chronic recording quality and stability. Published by Elsevier B.V.

  14. Program Helps Simulate Neural Networks

    Science.gov (United States)

    Villarreal, James; Mcintire, Gary

    1993-01-01

    Neural Network Environment on Transputer System (NNETS) computer program provides users high degree of flexibility in creating and manipulating wide variety of neural-network topologies at processing speeds not found in conventional computing environments. Supports back-propagation and back-propagation-related algorithms. Back-propagation algorithm used is implementation of Rumelhart's generalized delta rule. NNETS developed on INMOS Transputer(R). Predefines back-propagation network, Jordan network, and reinforcement network to assist users in learning and defining own networks. Also enables users to configure other neural-network paradigms from NNETS basic architecture. Small portion of software written in OCCAM(R) language.

  15. 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

  16. Cooperating attackers in neural cryptography.

    Science.gov (United States)

    Shacham, Lanir N; Klein, Einat; Mislovaty, Rachel; Kanter, Ido; Kinzel, Wolfgang

    2004-06-01

    A successful attack strategy in neural cryptography is presented. The neural cryptosystem, based on synchronization of neural networks by mutual learning, has been recently shown to be secure under different attack strategies. The success of the advanced attacker presented here, called the "majority-flipping attacker," does not decay with the parameters of the model. This attacker's outstanding success is due to its using a group of attackers which cooperate throughout the synchronization process, unlike any other attack strategy known. An analytical description of this attack is also presented, and fits the results of simulations.

  17. Creative-Dynamics Approach To Neural Intelligence

    Science.gov (United States)

    Zak, Michail A.

    1992-01-01

    Paper discusses approach to mathematical modeling of artificial neural networks exhibiting complicated behaviors reminiscent of creativity and intelligence of biological neural networks. Neural network treated as non-Lipschitzian dynamical system - as described in "Non-Lipschitzian Dynamics For Modeling Neural Networks" (NPO-17814). System serves as tool for modeling of temporal-pattern memories and recognition of complicated spatial patterns.

  18. Neural components of altruistic punishment

    Directory of Open Access Journals (Sweden)

    Emily eDu

    2015-02-01

    Full Text Available Altruistic punishment, which occurs when an individual incurs a cost to punish in response to unfairness or a norm violation, may play a role in perpetuating cooperation. The neural correlates underlying costly punishment have only recently begun to be explored. Here we review the current state of research on the neural basis of altruism from the perspectives of costly punishment, emphasizing the importance of characterizing elementary neural processes underlying a decision to punish. In particular, we emphasize three cognitive processes that contribute to the decision to altruistically punish in most scenarios: inequity aversion, cost-benefit calculation, and social reference frame to distinguish self from others. Overall, we argue for the importance of understanding the neural correlates of altruistic punishment with respect to the core computations necessary to achieve a decision to punish.

  19. Neural complexity, dissociation, and schizophrenia

    Czech Academy of Sciences Publication Activity Database

    Bob, P.; Šusta, M.; Chládek, Jan; Glaslová, K.; Fedor-Ferybergh, P.

    2007-01-01

    Roč. 13, č. 10 (2007), HY1-5 ISSN 1234-1010 Institutional research plan: CEZ:AV0Z20650511 Keywords : neural complexity * dissociation * schizophrenia Subject RIV: FH - Neurology Impact factor: 1.607, year: 2007

  20. Neural Networks in Control Applications

    DEFF Research Database (Denmark)

    Sørensen, O.

    The intention of this report 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: - Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. - Amongst numerous training algorithms, only four algorithms are examined, all...... in a recursive form (sample updating). The simplest is the Back Probagation Error Algorithm, and the most complex is the recursive Prediction Error Method using a Gauss-Newton search direction. - Over-fitting is often considered to be a serious problem when training neural networks. This problem is specifically...

  1. Complex-Valued Neural Networks

    CERN Document Server

    Hirose, Akira

    2012-01-01

    This book is the second enlarged and revised edition of the first successful monograph on complex-valued neural networks (CVNNs) published in 2006, which lends itself to graduate and undergraduate courses in electrical engineering, informatics, control engineering, mechanics, robotics, bioengineering, and other relevant fields. In the second edition the recent trends in CVNNs research are included, resulting in e.g. almost a doubled number of references. The parametron invented in 1954 is also referred to with discussion on analogy and disparity. Also various additional arguments on the advantages of the complex-valued neural networks enhancing the difference to real-valued neural networks are given in various sections. The book is useful for those beginning their studies, for instance, in adaptive signal processing for highly functional sensing and imaging, control in unknown and changing environment, robotics inspired by human neural systems, and brain-like information processing, as well as interdisciplina...

  2. Artificial intelligence: Deep neural reasoning

    Science.gov (United States)

    Jaeger, Herbert

    2016-10-01

    The human brain can solve highly abstract reasoning problems using a neural network that is entirely physical. The underlying mechanisms are only partially understood, but an artificial network provides valuable insight. See Article p.471

  3. Optical Neural Network Classifier Architectures

    National Research Council Canada - National Science Library

    Getbehead, Mark

    1998-01-01

    We present an adaptive opto-electronic neural network hardware architecture capable of exploiting parallel optics to realize real-time processing and classification of high-dimensional data for Air...

  4. Memristor-based neural networks

    International Nuclear Information System (INIS)

    Thomas, Andy

    2013-01-01

    The synapse is a crucial element in biological neural networks, but a simple electronic equivalent has been absent. This complicates the development of hardware that imitates biological architectures in the nervous system. Now, the recent progress in the experimental realization of memristive devices has renewed interest in artificial neural networks. The resistance of a memristive system depends on its past states and exactly this functionality can be used to mimic the synaptic connections in a (human) brain. After a short introduction to memristors, we present and explain the relevant mechanisms in a biological neural network, such as long-term potentiation and spike time-dependent plasticity, and determine the minimal requirements for an artificial neural network. We review the implementations of these processes using basic electric circuits and more complex mechanisms that either imitate biological systems or could act as a model system for them. (topical review)

  5. Sequential neural models with stochastic layers

    DEFF Research Database (Denmark)

    Fraccaro, Marco; Sønderby, Søren Kaae; Paquet, Ulrich

    2016-01-01

    How can we efficiently propagate uncertainty in a latent state representation with recurrent neural networks? This paper introduces stochastic recurrent neural networks which glue a deterministic recurrent neural network and a state space model together to form a stochastic and sequential neural...... generative model. The clear separation of deterministic and stochastic layers allows a structured variational inference network to track the factorization of the model's posterior distribution. By retaining both the nonlinear recursive structure of a recurrent neural network and averaging over...

  6. Implantable Neural Interfaces for Sharks

    Science.gov (United States)

    2007-05-01

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

  7. What are artificial neural networks?

    DEFF Research Database (Denmark)

    Krogh, Anders

    2008-01-01

    Artificial neural networks have been applied to problems ranging from speech recognition to prediction of protein secondary structure, classification of cancers and gene prediction. How do they work and what might they be good for? Udgivelsesdato: 2008-Feb......Artificial neural networks have been applied to problems ranging from speech recognition to prediction of protein secondary structure, classification of cancers and gene prediction. How do they work and what might they be good for? Udgivelsesdato: 2008-Feb...

  8. Supporting Self-management of Chronic Pain

    Science.gov (United States)

    2018-04-04

    Chronic Pain Syndrome; Chronic Pain; Chronic Pain Due to Injury; Chronic Pain Due to Trauma; Chronic Pain Due to Malignancy (Finding); Chronic Pain Post-Procedural; Chronic Pain Hip; Chronic Pain, Widespread

  9. Neural correlates of hate.

    Directory of Open Access Journals (Sweden)

    Semir Zeki

    Full Text Available In this work, we address an important but unexplored topic, namely the neural correlates of hate. In a block-design fMRI study, we scanned 17 normal human subjects while they viewed the face of a person they hated and also faces of acquaintances for whom they had neutral feelings. A hate score was obtained for the object of hate for each subject and this was used as a covariate in a between-subject random effects analysis. Viewing a hated face resulted in increased activity in the medial frontal gyrus, right putamen, bilaterally in premotor cortex, in the frontal pole and bilaterally in the medial insula. We also found three areas where activation correlated linearly with the declared level of hatred, the right insula, right premotor cortex and the right fronto-medial gyrus. One area of deactivation was found in the right superior frontal gyrus. The study thus shows that there is a unique pattern of activity in the brain in the context of hate. Though distinct from the pattern of activity that correlates with romantic love, this pattern nevertheless shares two areas with the latter, namely the putamen and the insula.

  10. 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

  11. Treating Chronic Pain after Spinal Cord Injury

    Science.gov (United States)

    2016-09-01

    sensitive and reliable locomotor rating scale for open field testing in rats. J Neurotrauma 1995;12(1):1-21. [7] Bedi SS, Yang Q, Crook RJ, Du J, Wu...reveal novel insights to the pathophysiology of chronic SCI pain and whether NPCs can modify pain outcomes. This proposal will test whether neural...extensive loss of hindlimb function that was associated with a score ɛ on the 21 point BBB locomotor scale (Fig. 1A,B). In rats with T3 severe

  12. Influence of neural adaptation on dynamics and equilibrium state of neural activities in a ring neural network

    Science.gov (United States)

    Takiyama, Ken

    2017-12-01

    How neural adaptation affects neural information processing (i.e. the dynamics and equilibrium state of neural activities) is a central question in computational neuroscience. In my previous works, I analytically clarified the dynamics and equilibrium state of neural activities in a ring-type neural network model that is widely used to model the visual cortex, motor cortex, and several other brain regions. The neural dynamics and the equilibrium state in the neural network model corresponded to a Bayesian computation and statistically optimal multiple information integration, respectively, under a biologically inspired condition. These results were revealed in an analytically tractable manner; however, adaptation effects were not considered. Here, I analytically reveal how the dynamics and equilibrium state of neural activities in a ring neural network are influenced by spike-frequency adaptation (SFA). SFA is an adaptation that causes gradual inhibition of neural activity when a sustained stimulus is applied, and the strength of this inhibition depends on neural activities. I reveal that SFA plays three roles: (1) SFA amplifies the influence of external input in neural dynamics; (2) SFA allows the history of the external input to affect neural dynamics; and (3) the equilibrium state corresponds to the statistically optimal multiple information integration independent of the existence of SFA. In addition, the equilibrium state in a ring neural network model corresponds to the statistically optimal integration of multiple information sources under biologically inspired conditions, independent of the existence of SFA.

  13. [Chronic otitis mediaChronic Otitis Media].

    Science.gov (United States)

    Kohles, N; Schulz, T; Eßer, D

    2015-11-01

    There are 2 different kinds of chronic otitis media: Otitis media chronica mesotympanalis and otitis media chronica epitympanalis (cholesteatoma). The incidence of chronic otitis media as reported in literature differs in a wide range. The incidence rates vary between 0.45 and 46%. Both, otitis media chronica mesotympanalis and cholesteatoma, lead to eardrum perforation due to lengthy and recurring inflammations. Furthermore, chronic otitis media is characterized by frequently recurring otorrhea and conductive hearing loss. Georg Thieme Verlag KG Stuttgart · New York.

  14. Efficient universal computing architectures for decoding neural activity.

    Directory of Open Access Journals (Sweden)

    Benjamin I Rapoport

    Full Text Available The ability to decode neural activity into meaningful control signals for prosthetic devices is critical to the development of clinically useful brain- machine interfaces (BMIs. Such systems require input from tens to hundreds of brain-implanted recording electrodes in order to deliver robust and accurate performance; in serving that primary function they should also minimize power dissipation in order to avoid damaging neural tissue; and they should transmit data wirelessly in order to minimize the risk of infection associated with chronic, transcutaneous implants. Electronic architectures for brain- machine interfaces must therefore minimize size and power consumption, while maximizing the ability to compress data to be transmitted over limited-bandwidth wireless channels. Here we present a system of extremely low computational complexity, designed for real-time decoding of neural signals, and suited for highly scalable implantable systems. Our programmable architecture is an explicit implementation of a universal computing machine emulating the dynamics of a network of integrate-and-fire neurons; it requires no arithmetic operations except for counting, and decodes neural signals using only computationally inexpensive logic operations. The simplicity of this architecture does not compromise its ability to compress raw neural data by factors greater than [Formula: see text]. We describe a set of decoding algorithms based on this computational architecture, one designed to operate within an implanted system, minimizing its power consumption and data transmission bandwidth; and a complementary set of algorithms for learning, programming the decoder, and postprocessing the decoded output, designed to operate in an external, nonimplanted unit. The implementation of the implantable portion is estimated to require fewer than 5000 operations per second. A proof-of-concept, 32-channel field-programmable gate array (FPGA implementation of this portion

  15. Fractional Hopfield Neural Networks: Fractional Dynamic Associative Recurrent Neural Networks.

    Science.gov (United States)

    Pu, Yi-Fei; Yi, Zhang; Zhou, Ji-Liu

    2017-10-01

    This paper mainly discusses a novel conceptual framework: fractional Hopfield neural networks (FHNN). As is commonly known, fractional calculus has been incorporated into artificial neural networks, mainly because of its long-term memory and nonlocality. Some researchers have made interesting attempts at fractional neural networks and gained competitive advantages over integer-order neural networks. Therefore, it is naturally makes one ponder how to generalize the first-order Hopfield neural networks to the fractional-order ones, and how to implement FHNN by means of fractional calculus. We propose to introduce a novel mathematical method: fractional calculus to implement FHNN. First, we implement fractor in the form of an analog circuit. Second, we implement FHNN by utilizing fractor and the fractional steepest descent approach, construct its Lyapunov function, and further analyze its attractors. Third, we perform experiments to analyze the stability and convergence of FHNN, and further discuss its applications to the defense against chip cloning attacks for anticounterfeiting. The main contribution of our work is to propose FHNN in the form of an analog circuit by utilizing a fractor and the fractional steepest descent approach, construct its Lyapunov function, prove its Lyapunov stability, analyze its attractors, and apply FHNN to the defense against chip cloning attacks for anticounterfeiting. A significant advantage of FHNN is that its attractors essentially relate to the neuron's fractional order. FHNN possesses the fractional-order-stability and fractional-order-sensitivity characteristics.

  16. Neural network regulation driven by autonomous neural firings

    Science.gov (United States)

    Cho, Myoung Won

    2016-07-01

    Biological neurons naturally fire spontaneously due to the existence of a noisy current. Such autonomous firings may provide a driving force for network formation because synaptic connections can be modified due to neural firings. Here, we study the effect of autonomous firings on network formation. For the temporally asymmetric Hebbian learning, bidirectional connections lose their balance easily and become unidirectional ones. Defining the difference between reciprocal connections as new variables, we could express the learning dynamics as if Ising model spins interact with each other in magnetism. We present a theoretical method to estimate the interaction between the new variables in a neural system. We apply the method to some network systems and find some tendencies of autonomous neural network regulation.

  17. Chronic mucus hypersecretion

    DEFF Research Database (Denmark)

    Ulrik, Charlotte Suppli; von Linstow, Marie-Louise; Nepper-Christensen, Steen

    2005-01-01

    To investigate if chronic mucus hypersecretion (CMH) can be used as a marker of asthma in young adults.......To investigate if chronic mucus hypersecretion (CMH) can be used as a marker of asthma in young adults....

  18. Chronic tophaceous gout

    Directory of Open Access Journals (Sweden)

    Thappa D

    1993-01-01

    Full Text Available A rare case of chronic tophaceous gout, in a 27-year-old female on diuretics for chronic congestive cardiac failure with characteristic histopathological and radiological changes is reported.

  19. Chronic Kidney Diseases

    Science.gov (United States)

    ... Safe Videos for Educators Search English Español Chronic Kidney Diseases KidsHealth / For Kids / Chronic Kidney Diseases What's ... re talking about your kidneys. What Are the Kidneys? Your kidneys are tucked under your lower ribs ...

  20. Chronic Recurrent Multifocal Osteomyelitis

    African Journals Online (AJOL)

    Administrator

    OBJECTIVE: To present a case and review the literature on chronic ... Successful treatment is difficult to achieve, though some ... named the syndrome “subacute and chronic ... An assessment of acute ... scans can cause a significant radiation.

  1. Neural networks and statistical learning

    CERN Document Server

    Du, Ke-Lin

    2014-01-01

    Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardw...

  2. Neural networks in signal processing

    International Nuclear Information System (INIS)

    Govil, R.

    2000-01-01

    Nuclear Engineering has matured during the last decade. In research and design, control, supervision, maintenance and production, mathematical models and theories are used extensively. In all such applications signal processing is embedded in the process. Artificial Neural Networks (ANN), because of their nonlinear, adaptive nature are well suited to such applications where the classical assumptions of linearity and second order Gaussian noise statistics cannot be made. ANN's can be treated as nonparametric techniques, which can model an underlying process from example data. They can also adopt their model parameters to statistical change with time. Algorithms in the framework of Neural Networks in Signal processing have found new applications potentials in the field of Nuclear Engineering. This paper reviews the fundamentals of Neural Networks in signal processing and their applications in tasks such as recognition/identification and control. The topics covered include dynamic modeling, model based ANN's, statistical learning, eigen structure based processing and generalization structures. (orig.)

  3. Principles of neural information processing

    CERN Document Server

    Seelen, Werner v

    2016-01-01

    In this fundamental book the authors devise a framework that describes the working of the brain as a whole. It presents a comprehensive introduction to the principles of Neural Information Processing as well as recent and authoritative research. The books´ guiding principles are the main purpose of neural activity, namely, to organize behavior to ensure survival, as well as the understanding of the evolutionary genesis of the brain. Among the developed principles and strategies belong self-organization of neural systems, flexibility, the active interpretation of the world by means of construction and prediction as well as their embedding into the world, all of which form the framework of the presented description. Since, in brains, their partial self-organization, the lifelong adaptation and their use of various methods of processing incoming information are all interconnected, the authors have chosen not only neurobiology and evolution theory as a basis for the elaboration of such a framework, but also syst...

  4. Heredity of chronic bronchitis

    DEFF Research Database (Denmark)

    Meteran, Howraman; Backer, Vibeke; Kyvik, Kirsten Ohm

    2014-01-01

    BACKGROUND: Smoking is a major risk factor for lung diseases and lower respiratory symptoms, but since not all smokers develop chronic bronchitis and since chronic bronchitis is also diagnosed in never-smokers, it has been suggested that some individuals are more susceptible to develop chronic br...

  5. Neural Decoder for Topological Codes

    Science.gov (United States)

    Torlai, Giacomo; Melko, Roger G.

    2017-07-01

    We present an algorithm for error correction in topological codes that exploits modern machine learning techniques. Our decoder is constructed from a stochastic neural network called a Boltzmann machine, of the type extensively used in deep learning. We provide a general prescription for the training of the network and a decoding strategy that is applicable to a wide variety of stabilizer codes with very little specialization. We demonstrate the neural decoder numerically on the well-known two-dimensional toric code with phase-flip errors.

  6. Entropy Learning in Neural Network

    Directory of Open Access Journals (Sweden)

    Geok See Ng

    2017-12-01

    Full Text Available In this paper, entropy term is used in the learning phase of a neural network.  As learning progresses, more hidden nodes get into saturation.  The early creation of such hidden nodes may impair generalisation.  Hence entropy approach is proposed to dampen the early creation of such nodes.  The entropy learning also helps to increase the importance of relevant nodes while dampening the less important nodes.  At the end of learning, the less important nodes can then be eliminated to reduce the memory requirements of the neural network.

  7. The neural cell adhesion molecule

    DEFF Research Database (Denmark)

    Berezin, V; Bock, E; Poulsen, F M

    2000-01-01

    During the past year, the understanding of the structure and function of neural cell adhesion has advanced considerably. The three-dimensional structures of several of the individual modules of the neural cell adhesion molecule (NCAM) have been determined, as well as the structure of the complex...... between two identical fragments of the NCAM. Also during the past year, a link between homophilic cell adhesion and several signal transduction pathways has been proposed, connecting the event of cell surface adhesion to cellular responses such as neurite outgrowth. Finally, the stimulation of neurite...

  8. Antenna analysis using neural networks

    Science.gov (United States)

    Smith, William T.

    1992-01-01

    Conventional computing schemes have long been used to analyze problems in electromagnetics (EM). The vast majority of EM applications require computationally intensive algorithms involving numerical integration and solutions to large systems of equations. The feasibility of using neural network computing algorithms for antenna analysis is investigated. The ultimate goal is to use a trained neural network algorithm to reduce the computational demands of existing reflector surface error compensation techniques. Neural networks are computational algorithms based on neurobiological systems. Neural nets consist of massively parallel interconnected nonlinear computational elements. They are often employed in pattern recognition and image processing problems. Recently, neural network analysis has been applied in the electromagnetics area for the design of frequency selective surfaces and beam forming networks. The backpropagation training algorithm was employed to simulate classical antenna array synthesis techniques. The Woodward-Lawson (W-L) and Dolph-Chebyshev (D-C) array pattern synthesis techniques were used to train the neural network. The inputs to the network were samples of the desired synthesis pattern. The outputs are the array element excitations required to synthesize the desired pattern. Once trained, the network is used to simulate the W-L or D-C techniques. Various sector patterns and cosecant-type patterns (27 total) generated using W-L synthesis were used to train the network. Desired pattern samples were then fed to the neural network. The outputs of the network were the simulated W-L excitations. A 20 element linear array was used. There were 41 input pattern samples with 40 output excitations (20 real parts, 20 imaginary). A comparison between the simulated and actual W-L techniques is shown for a triangular-shaped pattern. Dolph-Chebyshev is a different class of synthesis technique in that D-C is used for side lobe control as opposed to pattern

  9. High Frequency Deep Brain Stimulation and Neural Rhythms in Parkinson's Disease.

    Science.gov (United States)

    Blumenfeld, Zack; Brontë-Stewart, Helen

    2015-12-01

    High frequency (HF) deep brain stimulation (DBS) is an established therapy for the treatment of Parkinson's disease (PD). It effectively treats the cardinal motor signs of PD, including tremor, bradykinesia, and rigidity. The most common neural target is the subthalamic nucleus, located within the basal ganglia, the region most acutely affected by PD pathology. Using chronically-implanted DBS electrodes, researchers have been able to record underlying neural rhythms from several nodes in the PD network as well as perturb it using DBS to measure the ensuing neural and behavioral effects, both acutely and over time. In this review, we provide an overview of the PD neural network, focusing on the pathophysiological signals that have been recorded from PD patients as well as the mechanisms underlying the therapeutic benefits of HF DBS. We then discuss evidence for the relationship between specific neural oscillations and symptoms of PD, including the aberrant relationships potentially underlying functional connectivity in PD as well as the use of different frequencies of stimulation to more specifically target certain symptoms. Finally, we briefly describe several current areas of investigation and how the ability to record neural data in ecologically-valid settings may allow researchers to explore the relationship between brain and behavior in an unprecedented manner, culminating in the future automation of neurostimulation therapy for the treatment of a variety of neuropsychiatric diseases.

  10. Arabic Handwriting Recognition Using Neural Network Classifier

    African Journals Online (AJOL)

    pc

    2018-03-05

    Mar 5, 2018 ... an OCR using Neural Network classifier preceded by a set of preprocessing .... Artificial Neural Networks (ANNs), which we adopt in this research, consist of ... advantage and disadvantages of each technique. In [9],. Khemiri ...

  11. Neural overlap in processing music and speech.

    Science.gov (United States)

    Peretz, Isabelle; Vuvan, Dominique; Lagrois, Marie-Élaine; Armony, Jorge L

    2015-03-19

    Neural overlap in processing music and speech, as measured by the co-activation of brain regions in neuroimaging studies, may suggest that parts of the neural circuitries established for language may have been recycled during evolution for musicality, or vice versa that musicality served as a springboard for language emergence. Such a perspective has important implications for several topics of general interest besides evolutionary origins. For instance, neural overlap is an important premise for the possibility of music training to influence language acquisition and literacy. However, neural overlap in processing music and speech does not entail sharing neural circuitries. Neural separability between music and speech may occur in overlapping brain regions. In this paper, we review the evidence and outline the issues faced in interpreting such neural data, and argue that converging evidence from several methodologies is needed before neural overlap is taken as evidence of sharing. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

  12. Application of neural networks in coastal engineering

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.

    the neural network attractive. A neural network is an information processing system modeled on the structure of the dynamic process. It can solve the complex/nonlinear problems quickly once trained by operating on problems using an interconnected number...

  13. Ocean wave forecasting using recurrent neural networks

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.; Prabaharan, N.

    , merchant vessel routing, nearshore construction, etc. more efficiently and safely. This paper describes an artificial neural network, namely recurrent neural network with rprop update algorithm and is applied for wave forecasting. Measured ocean waves off...

  14. Neural overlap in processing music and speech

    Science.gov (United States)

    Peretz, Isabelle; Vuvan, Dominique; Lagrois, Marie-Élaine; Armony, Jorge L.

    2015-01-01

    Neural overlap in processing music and speech, as measured by the co-activation of brain regions in neuroimaging studies, may suggest that parts of the neural circuitries established for language may have been recycled during evolution for musicality, or vice versa that musicality served as a springboard for language emergence. Such a perspective has important implications for several topics of general interest besides evolutionary origins. For instance, neural overlap is an important premise for the possibility of music training to influence language acquisition and literacy. However, neural overlap in processing music and speech does not entail sharing neural circuitries. Neural separability between music and speech may occur in overlapping brain regions. In this paper, we review the evidence and outline the issues faced in interpreting such neural data, and argue that converging evidence from several methodologies is needed before neural overlap is taken as evidence of sharing. PMID:25646513

  15. Neural correlates of central inhibition during physical fatigue.

    Directory of Open Access Journals (Sweden)

    Masaaki Tanaka

    Full Text Available Central inhibition plays a pivotal role in determining physical performance during physical fatigue. Classical conditioning of central inhibition is believed to be associated with the pathophysiology of chronic fatigue. We tried to determine whether classical conditioning of central inhibition can really occur and to clarify the neural mechanisms of central inhibition related to classical conditioning during physical fatigue using magnetoencephalography (MEG. Eight right-handed volunteers participated in this study. We used metronome sounds as conditioned stimuli and maximum handgrip trials as unconditioned stimuli to cause central inhibition. Participants underwent MEG recording during 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 next day, neural activities during imagery of maximum grips of the right hand guided by metronome sounds were measured for 10 min. Levels of fatigue sensation and sympathetic nerve activity on the second day were significantly higher relative to those of the first day. Equivalent current dipoles (ECDs in the posterior cingulated cortex (PCC, with latencies of approximately 460 ms, were observed in all the participants on the second day, although ECDs were not identified in any of the participants on the first day. We demonstrated that classical conditioning of central inhibition can occur and that the PCC is involved in the neural substrates of central inhibition related to classical conditioning during physical fatigue.

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

    Science.gov (United States)

    Foutz, Thomas J; Ackermann, D Michael; Kilgore, Kevin L; McIntyre, Cameron C

    2012-01-01

    The delivery of therapeutic levels of electrical current to neural tissue is a well-established treatment for numerous indications such as Parkinson's disease and chronic pain. While the neuromodulation medical device industry has experienced steady clinical growth over the last two decades, much of the core technology underlying implanted pulse generators remain unchanged. In this study we propose some new methods for achieving increased energy-efficiency during neural stimulation. The first method exploits the biophysical features of excitable tissue through the use of a centered-triangular stimulation waveform. Neural activation with this waveform is achieved with a statistically significant reduction in energy compared to traditional rectangular waveforms. The second method demonstrates energy savings that could be achieved by advanced circuitry design. We show that the traditional practice of using a fixed compliance voltage for constant-current stimulation results in substantial energy loss. A portion of this energy can be recuperated by adjusting the compliance voltage to real-time requirements. Lastly, we demonstrate the potential impact of axon fiber diameter on defining the energy-optimal pulse-width for stimulation. When designing implantable pulse generators for energy efficiency, we propose that the future combination of a variable compliance system, a centered-triangular stimulus waveform, and an axon diameter specific stimulation pulse-width has great potential to reduce energy consumption and prolong battery life in neuromodulation devices.

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

    Directory of Open Access Journals (Sweden)

    Thomas J Foutz

    Full Text Available The delivery of therapeutic levels of electrical current to neural tissue is a well-established treatment for numerous indications such as Parkinson's disease and chronic pain. While the neuromodulation medical device industry has experienced steady clinical growth over the last two decades, much of the core technology underlying implanted pulse generators remain unchanged. In this study we propose some new methods for achieving increased energy-efficiency during neural stimulation. The first method exploits the biophysical features of excitable tissue through the use of a centered-triangular stimulation waveform. Neural activation with this waveform is achieved with a statistically significant reduction in energy compared to traditional rectangular waveforms. The second method demonstrates energy savings that could be achieved by advanced circuitry design. We show that the traditional practice of using a fixed compliance voltage for constant-current stimulation results in substantial energy loss. A portion of this energy can be recuperated by adjusting the compliance voltage to real-time requirements. Lastly, we demonstrate the potential impact of axon fiber diameter on defining the energy-optimal pulse-width for stimulation. When designing implantable pulse generators for energy efficiency, we propose that the future combination of a variable compliance system, a centered-triangular stimulus waveform, and an axon diameter specific stimulation pulse-width has great potential to reduce energy consumption and prolong battery life in neuromodulation devices.

  18. Neural mechanisms of mindfulness and meditation: Evidence from neuroimaging studies

    Institute of Scientific and Technical Information of China (English)

    William; R; Marchand

    2014-01-01

    Mindfulness is the dispassionate,moment-by-moment awareness of sensations,emotions and thoughts.Mindfulness-based interventions are being increasingly used for stress,psychological well being,coping with chronic illness as well as adjunctive treatments for psychiatric disorders.However,the neural mechanisms associated with mindfulness have not been well characterized.Recent functional and structural neuroimaging studies are beginning to provide insights into neural processes associated with the practice of mindfulness.A review of this literature revealed compelling evidence that mindfulness impacts the function of the medial cortex and associated default mode network as well as insula and amygdala.Additionally,mindfulness practice appears to effect lateral frontal regions and basal ganglia,at least in some cases.Structural imaging studies are consistent with these findings and also indicate changes in the hippocampus.While many questions remain unanswered,the current literature provides evidence of brain regions and networks relevant for understanding neural processes associated with mindfulness.

  19. Neural Correlate of Anterograde Amnesia in Wernicke-Korsakoff Syndrome.

    Science.gov (United States)

    Nahum, Louis; Pignat, Jean-Michel; Bouzerda-Wahlen, Aurélie; Gabriel, Damien; Liverani, Maria Chiara; Lazeyras, François; Ptak, Radek; Richiardi, Jonas; Haller, Sven; Thorens, Gabriel; Zullino, Daniele F; Guggisberg, Adrian G; Schnider, Armin

    2015-09-01

    The neural correlate of anterograde amnesia in Wernicke-Korsakoff syndrome (WKS) is still debated. While the capacity to learn new information has been associated with integrity of the medial temporal lobe (MTL), previous studies indicated that the WKS is associated with diencephalic lesions, mainly in the mammillary bodies and anterior or dorsomedial thalamic nuclei. The present study tested the hypothesis that amnesia in WKS is associated with a disrupted neural circuit between diencephalic and hippocampal structures. High-density evoked potentials were recorded in four severely amnesic patients with chronic WKS, in five patients with chronic alcoholism without WKS, and in ten age matched controls. Participants performed a continuous recognition task of pictures previously shown to induce a left medial temporal lobe dependent positive potential between 250 and 350 ms. In addition, the integrity of the fornix was assessed using diffusion tensor imaging (DTI). WKS, but not alcoholic patients without WKS, showed absence of the early, left MTL dependent positive potential following immediate picture repetitions. DTI indicated disruption of the fornix, which connects diencephalic and hippocampal structures. The findings support an interpretation of anterograde amnesia in WKS as a consequence of a disconnection between diencephalic and MTL structures with deficient contribution of the MTL to rapid consolidation.

  20. MEMBRAIN NEURAL NETWORK FOR VISUAL PATTERN RECOGNITION

    Directory of Open Access Journals (Sweden)

    Artur Popko

    2013-06-01

    Full Text Available Recognition of visual patterns is one of significant applications of Artificial Neural Networks, which partially emulate human thinking in the domain of artificial intelligence. In the paper, a simplified neural approach to recognition of visual patterns is portrayed and discussed. This paper is dedicated for investigators in visual patterns recognition, Artificial Neural Networking and related disciplines. The document describes also MemBrain application environment as a powerful and easy to use neural networks’ editor and simulator supporting ANN.

  1. Neural network to diagnose lining condition

    Science.gov (United States)

    Yemelyanov, V. A.; Yemelyanova, N. Y.; Nedelkin, A. A.; Zarudnaya, M. V.

    2018-03-01

    The paper presents data on the problem of diagnosing the lining condition at the iron and steel works. The authors describe the neural network structure and software that are designed and developed to determine the lining burnout zones. The simulation results of the proposed neural networks are presented. The authors note the low learning and classification errors of the proposed neural networks. To realize the proposed neural network, the specialized software has been developed.

  2. Neural Control Mechanisms and Body Fluid Homeostasis

    Science.gov (United States)

    Johnson, Alan Kim

    1998-01-01

    The goal of the proposed research was to study the nature of afferent signals to the brain that reflect the status of body fluid balance and to investigate the central neural mechanisms that process this information for the activation of response systems which restore body fluid homeostasis. That is, in the face of loss of fluids from intracellular or extracellular fluid compartments, animals seek and ingest water and ionic solutions (particularly Na(+) solutions) to restore the intracellular and extracellular spaces. Over recent years, our laboratory has generated a substantial body of information indicating that: (1) a fall in systemic arterial pressure facilitates the ingestion of rehydrating solutions and (2) that the actions of brain amine systems (e.g., norepinephrine; serotonin) are critical for precise correction of fluid losses. Because both acute and chronic dehydration are associated with physiological stresses, such as exercise and sustained exposure to microgravity, the present research will aid in achieving a better understanding of how vital information is handled by the nervous system for maintenance of the body's fluid matrix which is critical for health and well-being.

  3. Neural plasticity lessons from disorders of consciousness

    Directory of Open Access Journals (Sweden)

    Athena eDemertzi

    2011-02-01

    Full Text Available Communication and intentional behavior are supported by the brain’s integrity at a structural and a functional level. When widespread loss of cerebral connectivity is brought about as a result of a severe brain injury, in many cases patients are not capable of conscious interactive behavior and are said to suffer from disorders of consciousness (e.g., coma, vegetative state /unresponsive wakefulness syndrome, minimally conscious states. This lesion paradigm has offered not only clinical insights, as how to improve diagnosis, prognosis and treatment, but also put forward scientific opportunities to study the brain’s plastic abilities. We here review interventional and observational studies performed in severely brain-injured patients with regards to recovery of consciousness. The study of the recovered conscious brain (spontaneous and/or after surgical or pharmacologic interventions, suggests a link between some specific brain areas and the capacity of the brain to sustain conscious experience, challenging at the same time the notion of fixed temporal boundaries in rehabilitative processes. Altered functional connectivity, cerebral structural reorganization as well as behavioral amelioration after invasive treatments will be discussed as the main indices for plasticity in these challenging patients. The study of patients with chronic disorders of consciousness may, thus, provide further insights not only at a clinical level (i.e., medical management and rehabilitation but also from a scientific-theoretical perspective (i.e., the brain’s plastic abilities and the pursuit of the neural correlate of consciousness.

  4. Simplified LQG Control with Neural Networks

    DEFF Research Database (Denmark)

    Sørensen, O.

    1997-01-01

    A new neural network application for non-linear state control is described. One neural network is modelled to form a Kalmann predictor and trained to act as an optimal state observer for a non-linear process. Another neural network is modelled to form a state controller and trained to produce...

  5. Analysis of neural networks through base functions

    NARCIS (Netherlands)

    van der Zwaag, B.J.; Slump, Cornelis H.; Spaanenburg, L.

    Problem statement. Despite their success-story, neural networks have one major disadvantage compared to other techniques: the inability to explain comprehensively how a trained neural network reaches its output; neural networks are not only (incorrectly) seen as a "magic tool" but possibly even more

  6. Genetic Algorithm Optimized Neural Networks Ensemble as ...

    African Journals Online (AJOL)

    NJD

    Improvements in neural network calibration models by a novel approach using neural network ensemble (NNE) for the simultaneous ... process by training a number of neural networks. .... Matlab® version 6.1 was employed for building principal component ... provide a fair simulation of calibration data set with some degree.

  7. Recycling signals in the neural crest

    OpenAIRE

    Taneyhill, Lisa A.; Bronner-Fraser, Marianne E.

    2006-01-01

    Vertebrate neural crest cells are multipotent and differentiate into structures that include cartilage and the bones of the face, as well as much of the peripheral nervous system. Understanding how different model vertebrates utilize signaling pathways reiteratively during various stages of neural crest formation and differentiation lends insight into human disorders associated with the neural crest.

  8. Recycling signals in the neural crest.

    Science.gov (United States)

    Taneyhill, Lisa A; Bronner-Fraser, Marianne

    2005-01-01

    Vertebrate neural crest cells are multipotent and differentiate into structures that include cartilage and the bones of the face, as well as much of the peripheral nervous system. Understanding how different model vertebrates utilize signaling pathways reiteratively during various stages of neural crest formation and differentiation lends insight into human disorders associated with the neural crest.

  9. Neural chips, neural computers and application in high and superhigh energy physics experiments

    International Nuclear Information System (INIS)

    Nikityuk, N.M.; )

    2001-01-01

    Architecture peculiarity and characteristics of series of neural chips and neural computes used in scientific instruments are considered. Tendency of development and use of them in high energy and superhigh energy physics experiments are described. Comparative data which characterize the efficient use of neural chips for useful event selection, classification elementary particles, reconstruction of tracks of charged particles and for search of hypothesis Higgs particles are given. The characteristics of native neural chips and accelerated neural boards are considered [ru

  10. Medical Imaging with Neural Networks

    International Nuclear Information System (INIS)

    Pattichis, C.; Cnstantinides, A.

    1994-01-01

    The objective of this paper is to provide an overview of the recent developments in the use of artificial neural networks in medical imaging. The areas of medical imaging that are covered include : ultrasound, magnetic resonance, nuclear medicine and radiological (including computerized tomography). (authors)

  11. Optoelectronic Implementation of Neural Networks

    Indian Academy of Sciences (India)

    neural networks, such as learning, adapting and copying by means of parallel ... to provide robust recognition of hand-printed English text. Engine idle and misfiring .... and s represents the bounded activation function of a neuron. It is typically ...

  12. Aphasia Classification Using Neural Networks

    DEFF Research Database (Denmark)

    Axer, H.; Jantzen, Jan; Berks, G.

    2000-01-01

    A web-based software model (http://fuzzy.iau.dtu.dk/aphasia.nsf) was developed as an example for classification of aphasia using neural networks. Two multilayer perceptrons were used to classify the type of aphasia (Broca, Wernicke, anomic, global) according to the results in some subtests...

  13. 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.

  14. Medical Imaging with Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Pattichis, C [Department of Computer Science, University of Cyprus, Kallipoleos 75, P.O.Box 537, Nicosia (Cyprus); Cnstantinides, A [Department of Electrical Engineering, Imperial College of Science, Technology and Medicine, London SW7 2BT (United Kingdom)

    1994-12-31

    The objective of this paper is to provide an overview of the recent developments in the use of artificial neural networks in medical imaging. The areas of medical imaging that are covered include : ultrasound, magnetic resonance, nuclear medicine and radiological (including computerized tomography). (authors). 61 refs, 4 tabs.

  15. Numerical experiments with neural networks

    International Nuclear Information System (INIS)

    Miranda, Enrique.

    1990-01-01

    Neural networks are highly idealized models which, in spite of their simplicity, reproduce some key features of the real brain. In this paper, they are introduced at a level adequate for an undergraduate computational physics course. Some relevant magnitudes are defined and evaluated numerically for the Hopfield model and a short term memory model. (Author)

  16. Serotonin, neural markers and memory

    Directory of Open Access Journals (Sweden)

    Alfredo eMeneses

    2015-07-01

    Full Text Available Diverse neuropsychiatric disorders present dysfunctional memory and no effective treatment exits for them; likely as result of the absence of neural markers associated to memory. Neurotransmitter systems and signaling pathways have been implicated in memory and dysfunctional memory; however, their role is poorly understood. Hence, neural markers and cerebral functions and dysfunctions are revised. To our knowledge no previous systematic works have been published addressing these issues. The interactions among behavioral tasks, control groups and molecular changes and/or pharmacological effects are mentioned. Neurotransmitter receptors and signaling pathways, during normal and abnormally functioning memory with an emphasis on the behavioral aspects of memory are revised. With focus on serotonin, since as it is a well characterized neurotransmitter, with multiple pharmacological tools, and well characterized downstream signaling in mammals’ species. 5-HT1A, 5-HT4, 5-HT5, 5-HT6 and 5-HT7 receptors as well as SERT (serotonin transporter seem to be useful neural markers and/or therapeutic targets. Certainly, if the mentioned evidence is replicated, then the translatability from preclinical and clinical studies to neural changes might be confirmed. Hypothesis and theories might provide appropriate limits and perspectives of evidence

  17. Neural correlates of viewing paintings

    DEFF Research Database (Denmark)

    Vartanian, Oshin; Skov, Martin

    2014-01-01

    Many studies involving functional magnetic resonance imaging (fMRI) have exposed participants to paintings under varying task demands. To isolate neural systems that are activated reliably across fMRI studies in response to viewing paintings regardless of variation in task demands, a quantitative...

  18. Neural Basis of Visual Distraction

    Science.gov (United States)

    Kim, So-Yeon; Hopfinger, Joseph B.

    2010-01-01

    The ability to maintain focus and avoid distraction by goal-irrelevant stimuli is critical for performing many tasks and may be a key deficit in attention-related problems. Recent studies have demonstrated that irrelevant stimuli that are consciously perceived may be filtered out on a neural level and not cause the distraction triggered by…

  19. Vestibular hearing and neural synchronization.

    Science.gov (United States)

    Emami, Seyede Faranak; Daneshi, Ahmad

    2012-01-01

    Objectives. Vestibular hearing as an auditory sensitivity of the saccule in the human ear is revealed by cervical vestibular evoked myogenic potentials (cVEMPs). The range of the vestibular hearing lies in the low frequency. Also, the amplitude of an auditory brainstem response component depends on the amount of synchronized neural activity, and the auditory nerve fibers' responses have the best synchronization with the low frequency. Thus, the aim of this study was to investigate correlation between vestibular hearing using cVEMPs and neural synchronization via slow wave Auditory Brainstem Responses (sABR). Study Design. This case-control survey was consisted of twenty-two dizzy patients, compared to twenty healthy controls. Methods. Intervention comprised of Pure Tone Audiometry (PTA), Impedance acoustic metry (IA), Videonystagmography (VNG), fast wave ABR (fABR), sABR, and cVEMPs. Results. The affected ears of the dizzy patients had the abnormal findings of cVEMPs (insecure vestibular hearing) and the abnormal findings of sABR (decreased neural synchronization). Comparison of the cVEMPs at affected ears versus unaffected ears and the normal persons revealed significant differences (P < 0.05). Conclusion. Safe vestibular hearing was effective in the improvement of the neural synchronization.

  20. Spin glasses and neural networks

    International Nuclear Information System (INIS)

    Parga, N.; Universidad Nacional de Cuyo, San Carlos de Bariloche

    1989-01-01

    The mean-field theory of spin glass models has been used as a prototype of systems with frustration and disorder. One of the most interesting related systems are models of associative memories. In these lectures we review the main concepts developed to solve the Sherrington-Kirkpatrick model and its application to neural networks. (orig.)

  1. Non-invasive neural stimulation

    Science.gov (United States)

    Tyler, William J.; Sanguinetti, Joseph L.; Fini, Maria; Hool, Nicholas

    2017-05-01

    Neurotechnologies for non-invasively interfacing with neural circuits have been evolving from those capable of sensing neural activity to those capable of restoring and enhancing human brain function. Generally referred to as non-invasive neural stimulation (NINS) methods, these neuromodulation approaches rely on electrical, magnetic, photonic, and acoustic or ultrasonic energy to influence nervous system activity, brain function, and behavior. Evidence that has been surmounting for decades shows that advanced neural engineering of NINS technologies will indeed transform the way humans treat diseases, interact with information, communicate, and learn. The physics underlying the ability of various NINS methods to modulate nervous system activity can be quite different from one another depending on the energy modality used as we briefly discuss. For members of commercial and defense industry sectors that have not traditionally engaged in neuroscience research and development, the science, engineering and technology required to advance NINS methods beyond the state-of-the-art presents tremendous opportunities. Within the past few years alone there have been large increases in global investments made by federal agencies, foundations, private investors and multinational corporations to develop advanced applications of NINS technologies. Driven by these efforts NINS methods and devices have recently been introduced to mass markets via the consumer electronics industry. Further, NINS continues to be explored in a growing number of defense applications focused on enhancing human dimensions. The present paper provides a brief introduction to the field of non-invasive neural stimulation by highlighting some of the more common methods in use or under current development today.

  2. Neural networks and applications tutorial

    Science.gov (United States)

    Guyon, I.

    1991-09-01

    The importance of neural networks has grown dramatically during this decade. While only a few years ago they were primarily of academic interest, now dozens of companies and many universities are investigating the potential use of these systems and products are beginning to appear. The idea of building a machine whose architecture is inspired by that of the brain has roots which go far back in history. Nowadays, technological advances of computers and the availability of custom integrated circuits, permit simulations of hundreds or even thousands of neurons. In conjunction, the growing interest in learning machines, non-linear dynamics and parallel computation spurred renewed attention in artificial neural networks. Many tentative applications have been proposed, including decision systems (associative memories, classifiers, data compressors and optimizers), or parametric models for signal processing purposes (system identification, automatic control, noise canceling, etc.). While they do not always outperform standard methods, neural network approaches are already used in some real world applications for pattern recognition and signal processing tasks. The tutorial is divided into six lectures, that where presented at the Third Graduate Summer Course on Computational Physics (September 3-7, 1990) on Parallel Architectures and Applications, organized by the European Physical Society: (1) Introduction: machine learning and biological computation. (2) Adaptive artificial neurons (perceptron, ADALINE, sigmoid units, etc.): learning rules and implementations. (3) Neural network systems: architectures, learning algorithms. (4) Applications: pattern recognition, signal processing, etc. (5) Elements of learning theory: how to build networks which generalize. (6) A case study: a neural network for on-line recognition of handwritten alphanumeric characters.

  3. Parameterization Of Solar Radiation Using Neural Network

    International Nuclear Information System (INIS)

    Jiya, J. D.; Alfa, B.

    2002-01-01

    This paper presents a neural network technique for parameterization of global solar radiation. The available data from twenty-one stations is used for training the neural network and the data from other ten stations is used to validate the neural model. The neural network utilizes latitude, longitude, altitude, sunshine duration and period number to parameterize solar radiation values. The testing data was not used in the training to demonstrate the performance of the neural network in unknown stations to parameterize solar radiation. The results indicate a good agreement between the parameterized solar radiation values and actual measured values

  4. Flexible poly(methyl methacrylate)-based neural probe: An affordable implementation

    Science.gov (United States)

    Gasemi, Pejman; Veladi, Hadi; Shahabi, Parviz; Khalilzadeh, Emad

    2018-03-01

    This research presents a novel technique used to fabricate a deep brain stimulation probe based on a commercial poly(methyl methacrylate) (PMMA) polymer. This technique is developed to overcome the high cost of available probes crucial for chronic stimulation and recording in neural disorders such as Parkinson’s disease and epilepsy. The probe is made of PMMA and its mechanical properties have been customized by controlling the reaction conditions. The polymer is adjusted to be stiff enough to be easily inserted and, on the other hand, soft enough to perform required movements. As cost is one of the issues in the use of neural probes, a simple process is proposed for the production of PMMA neural probes without using expensive equipment and operations, and without compromising performance and quality. An in vivo animal test was conducted to observe the recording capability of a PMMA probe.

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

    Science.gov (United States)

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

    2013-03-01

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

  6. Spike Neural Models Part II: Abstract Neural Models

    Directory of Open Access Journals (Sweden)

    Johnson, Melissa G.

    2018-02-01

    Full Text Available Neurons are complex cells that require a lot of time and resources to model completely. In spiking neural networks (SNN though, not all that complexity is required. Therefore simple, abstract models are often used. These models save time, use less computer resources, and are easier to understand. This tutorial presents two such models: Izhikevich's model, which is biologically realistic in the resulting spike trains but not in the parameters, and the Leaky Integrate and Fire (LIF model which is not biologically realistic but does quickly and easily integrate input to produce spikes. Izhikevich's model is based on Hodgkin-Huxley's model but simplified such that it uses only two differentiation equations and four parameters to produce various realistic spike patterns. LIF is based on a standard electrical circuit and contains one equation. Either of these two models, or any of the many other models in literature can be used in a SNN. Choosing a neural model is an important task that depends on the goal of the research and the resources available. Once a model is chosen, network decisions such as connectivity, delay, and sparseness, need to be made. Understanding neural models and how they are incorporated into the network is the first step in creating a SNN.

  7. Obsessive-compulsive disorder; chronic versus non-chronic symptoms

    NARCIS (Netherlands)

    Visser, H.A.; van Oppen, P.C.; van Megen, H.J.; Eikelenboom, M.; van Balkom, A.J.L.M.

    2014-01-01

    Objective Understanding chronicity in OCD is hampered by contradictory findings arising from dissimilar definitions of chronic OCD. The purpose of this study was to investigate the magnitude of chronicity in OCD and to examine if chronic OCD is critically different from non-chronic OCD, using a

  8. Stem Cells and Hydrogel Bridges for the Treatment of Acute and Chronic Spinal Cord Injury

    Czech Academy of Sciences Publication Activity Database

    Syková, Eva; Jendelová, Pavla; Hejčl, Aleš; Kozubenko, Nataliya; Amemori, Takashi

    2010-01-01

    Roč. 19, č. 3 (2010), s. 366-366 ISSN 0963-6897. [Annual Meeting of the American-Society-for- Neural - Therapy - and -Repair /17./. 29.04.2010-01.05.2010, Clearwater Beach] Institutional research plan: CEZ:AV0Z50390703 Keywords : stem cells * chronic spinal cord Subject RIV: FH - Neurology

  9. Brown tumor of lumber spint in patient with chronic renal failure ...

    African Journals Online (AJOL)

    Brown tumors are erosive bone lesions caused by increased osteoclastic activity. They usually occur in the severe forms of secondary hyperparathyroidism, as in patients with hemodialysis-dependent chronic renal disease. Involvement of the lumbar spine with this tumor causing neural compression is extremely rare.

  10. Optical resonators and neural networks

    Science.gov (United States)

    Anderson, Dana Z.

    1986-08-01

    It may be possible to implement neural network models using continuous field optical architectures. These devices offer the inherent parallelism of propagating waves and an information density in principle dictated by the wavelength of light and the quality of the bulk optical elements. Few components are needed to construct a relatively large equivalent network. Various associative memories based on optical resonators have been demonstrated in the literature, a ring resonator design is discussed in detail here. Information is stored in a holographic medium and recalled through a competitive processes in the gain medium supplying energy to the ring rsonator. The resonator memory is the first realized example of a neural network function implemented with this kind of architecture.

  11. Neural Network for Sparse Reconstruction

    Directory of Open Access Journals (Sweden)

    Qingfa Li

    2014-01-01

    Full Text Available We construct a neural network based on smoothing approximation techniques and projected gradient method to solve a kind of sparse reconstruction problems. Neural network can be implemented by circuits and can be seen as an important method for solving optimization problems, especially large scale problems. Smoothing approximation is an efficient technique for solving nonsmooth optimization problems. We combine these two techniques to overcome the difficulties of the choices of the step size in discrete algorithms and the item in the set-valued map of differential inclusion. In theory, the proposed network can converge to the optimal solution set of the given problem. Furthermore, some numerical experiments show the effectiveness of the proposed network in this paper.

  12. IMNN: Information Maximizing Neural Networks

    Science.gov (United States)

    Charnock, Tom; Lavaux, Guilhem; Wandelt, Benjamin D.

    2018-04-01

    This software trains artificial neural networks to find non-linear functionals of data that maximize Fisher information: information maximizing neural networks (IMNNs). As compressing large data sets vastly simplifies both frequentist and Bayesian inference, important information may be inadvertently missed. Likelihood-free inference based on automatically derived IMNN summaries produces summaries that are good approximations to sufficient statistics. IMNNs are robustly capable of automatically finding optimal, non-linear summaries of the data even in cases where linear compression fails: inferring the variance of Gaussian signal in the presence of noise, inferring cosmological parameters from mock simulations of the Lyman-α forest in quasar spectra, and inferring frequency-domain parameters from LISA-like detections of gravitational waveforms. In this final case, the IMNN summary outperforms linear data compression by avoiding the introduction of spurious likelihood maxima.

  13. Genetic attack on neural cryptography.

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Naeh, Rivka; Kanter, Ido

    2006-03-01

    Different scaling properties for the complexity of bidirectional synchronization and unidirectional learning are essential for the security of neural cryptography. Incrementing the synaptic depth of the networks increases the synchronization time only polynomially, but the success of the geometric attack is reduced exponentially and it clearly fails in the limit of infinite synaptic depth. This method is improved by adding a genetic algorithm, which selects the fittest neural networks. The probability of a successful genetic attack is calculated for different model parameters using numerical simulations. The results show that scaling laws observed in the case of other attacks hold for the improved algorithm, too. The number of networks needed for an effective attack grows exponentially with increasing synaptic depth. In addition, finite-size effects caused by Hebbian and anti-Hebbian learning are analyzed. These learning rules converge to the random walk rule if the synaptic depth is small compared to the square root of the system size.

  14. Neural Networks Methodology and Applications

    CERN Document Server

    Dreyfus, Gérard

    2005-01-01

    Neural networks represent a powerful data processing technique that has reached maturity and broad application. When clearly understood and appropriately used, they are a mandatory component in the toolbox of any engineer who wants make the best use of the available data, in order to build models, make predictions, mine data, recognize shapes or signals, etc. Ranging from theoretical foundations to real-life applications, this book is intended to provide engineers and researchers with clear methodologies for taking advantage of neural networks in industrial, financial or banking applications, many instances of which are presented in the book. For the benefit of readers wishing to gain deeper knowledge of the topics, the book features appendices that provide theoretical details for greater insight, and algorithmic details for efficient programming and implementation. The chapters have been written by experts ands seemlessly edited to present a coherent and comprehensive, yet not redundant, practically-oriented...

  15. Genetic attack on neural cryptography

    International Nuclear Information System (INIS)

    Ruttor, Andreas; Kinzel, Wolfgang; Naeh, Rivka; Kanter, Ido

    2006-01-01

    Different scaling properties for the complexity of bidirectional synchronization and unidirectional learning are essential for the security of neural cryptography. Incrementing the synaptic depth of the networks increases the synchronization time only polynomially, but the success of the geometric attack is reduced exponentially and it clearly fails in the limit of infinite synaptic depth. This method is improved by adding a genetic algorithm, which selects the fittest neural networks. The probability of a successful genetic attack is calculated for different model parameters using numerical simulations. The results show that scaling laws observed in the case of other attacks hold for the improved algorithm, too. The number of networks needed for an effective attack grows exponentially with increasing synaptic depth. In addition, finite-size effects caused by Hebbian and anti-Hebbian learning are analyzed. These learning rules converge to the random walk rule if the synaptic depth is small compared to the square root of the system size

  16. Genetic attack on neural cryptography

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Naeh, Rivka; Kanter, Ido

    2006-03-01

    Different scaling properties for the complexity of bidirectional synchronization and unidirectional learning are essential for the security of neural cryptography. Incrementing the synaptic depth of the networks increases the synchronization time only polynomially, but the success of the geometric attack is reduced exponentially and it clearly fails in the limit of infinite synaptic depth. This method is improved by adding a genetic algorithm, which selects the fittest neural networks. The probability of a successful genetic attack is calculated for different model parameters using numerical simulations. The results show that scaling laws observed in the case of other attacks hold for the improved algorithm, too. The number of networks needed for an effective attack grows exponentially with increasing synaptic depth. In addition, finite-size effects caused by Hebbian and anti-Hebbian learning are analyzed. These learning rules converge to the random walk rule if the synaptic depth is small compared to the square root of the system size.

  17. Scheduling with artificial neural networks

    OpenAIRE

    Gürgün, Burçkaan

    1993-01-01

    Ankara : Department of Industrial Engineering and The Institute of Engineering and Sciences of Bilkent Univ., 1993. Thesis (Master's) -- Bilkent University, 1993. Includes bibliographical references leaves 59-65. Artificial Neural Networks (ANNs) attempt to emulate the massively parallel and distributed processing of the human brain. They are being examined for a variety of problems that have been very difficult to solve. The objective of this thesis is to review the curren...

  18. Handbook on neural information processing

    CERN Document Server

    Maggini, Marco; Jain, Lakhmi

    2013-01-01

    This handbook presents some of the most recent topics in neural information processing, covering both theoretical concepts and practical applications. The contributions include:                         Deep architectures                         Recurrent, recursive, and graph neural networks                         Cellular neural networks                         Bayesian networks                         Approximation capabilities of neural networks                         Semi-supervised learning                         Statistical relational learning                         Kernel methods for structured data                         Multiple classifier systems                         Self organisation and modal learning                         Applications to ...

  19. Deep Gate Recurrent Neural Network

    Science.gov (United States)

    2016-11-22

    and Fred Cummins. Learning to forget: Continual prediction with lstm . Neural computation, 12(10):2451–2471, 2000. Alex Graves. Generating sequences...DSGU) and Simple Gated Unit (SGU), which are structures for learning long-term dependencies. Compared to traditional Long Short-Term Memory ( LSTM ) and...Gated Recurrent Unit (GRU), both structures require fewer parameters and less computation time in sequence classification tasks. Unlike GRU and LSTM

  20. Adaptive Graph Convolutional Neural Networks

    OpenAIRE

    Li, Ruoyu; Wang, Sheng; Zhu, Feiyun; Huang, Junzhou

    2018-01-01

    Graph Convolutional Neural Networks (Graph CNNs) are generalizations of classical CNNs to handle graph data such as molecular data, point could and social networks. Current filters in graph CNNs are built for fixed and shared graph structure. However, for most real data, the graph structures varies in both size and connectivity. The paper proposes a generalized and flexible graph CNN taking data of arbitrary graph structure as input. In that way a task-driven adaptive graph is learned for eac...

  1. Adaptive Regularization of Neural Classifiers

    DEFF Research Database (Denmark)

    Andersen, Lars Nonboe; Larsen, Jan; Hansen, Lars Kai

    1997-01-01

    We present a regularization scheme which iteratively adapts the regularization parameters by minimizing the validation error. It is suggested to use the adaptive regularization scheme in conjunction with optimal brain damage pruning to optimize the architecture and to avoid overfitting. Furthermo......, we propose an improved neural classification architecture eliminating an inherent redundancy in the widely used SoftMax classification network. Numerical results demonstrate the viability of the method...

  2. Central neural pathways for thermoregulation

    Science.gov (United States)

    Morrison, Shaun F.; Nakamura, Kazuhiro

    2010-01-01

    Central neural circuits orchestrate a homeostatic repertoire to maintain body temperature during environmental temperature challenges and to alter body temperature during the inflammatory response. This review summarizes the functional organization of the neural pathways through which cutaneous thermal receptors alter thermoregulatory effectors: the cutaneous circulation for heat loss, the brown adipose tissue, skeletal muscle and heart for thermogenesis and species-dependent mechanisms (sweating, panting and saliva spreading) for evaporative heat loss. These effectors are regulated by parallel but distinct, effector-specific neural pathways that share a common peripheral thermal sensory input. The thermal afferent circuits include cutaneous thermal receptors, spinal dorsal horn neurons and lateral parabrachial nucleus neurons projecting to the preoptic area to influence warm-sensitive, inhibitory output neurons which control thermogenesis-promoting neurons in the dorsomedial hypothalamus that project to premotor neurons in the rostral ventromedial medulla, including the raphe pallidus, that descend to provide the excitation necessary to drive thermogenic thermal effectors. A distinct population of warm-sensitive preoptic neurons controls heat loss through an inhibitory input to raphe pallidus neurons controlling cutaneous vasoconstriction. PMID:21196160

  3. Adaptive competitive learning neural networks

    Directory of Open Access Journals (Sweden)

    Ahmed R. Abas

    2013-11-01

    Full Text Available In this paper, the adaptive competitive learning (ACL neural network algorithm is proposed. This neural network not only groups similar input feature vectors together but also determines the appropriate number of groups of these vectors. This algorithm uses a new proposed criterion referred to as the ACL criterion. This criterion evaluates different clustering structures produced by the ACL neural network for an input data set. Then, it selects the best clustering structure and the corresponding network architecture for this data set. The selected structure is composed of the minimum number of clusters that are compact and balanced in their sizes. The selected network architecture is efficient, in terms of its complexity, as it contains the minimum number of neurons. Synaptic weight vectors of these neurons represent well-separated, compact and balanced clusters in the input data set. The performance of the ACL algorithm is evaluated and compared with the performance of a recently proposed algorithm in the literature in clustering an input data set and determining its number of clusters. Results show that the ACL algorithm is more accurate and robust in both determining the number of clusters and allocating input feature vectors into these clusters than the other algorithm especially with data sets that are sparsely distributed.

  4. Neural mechanisms of social dominance

    Science.gov (United States)

    Watanabe, Noriya; Yamamoto, Miyuki

    2015-01-01

    In a group setting, individuals' perceptions of their own level of dominance or of the dominance level of others, and the ability to adequately control their behavior based on these perceptions are crucial for living within a social environment. Recent advances in neural imaging and molecular technology have enabled researchers to investigate the neural substrates that support the perception of social dominance and the formation of a social hierarchy in humans. At the systems' level, recent studies showed that dominance perception is represented in broad brain regions which include the amygdala, hippocampus, striatum, and various cortical networks such as the prefrontal, and parietal cortices. Additionally, neurotransmitter systems such as the dopaminergic and serotonergic systems, modulate and are modulated by the formation of the social hierarchy in a group. While these monoamine systems have a wide distribution and multiple functions, it was recently found that the Neuropeptide B/W contributes to the perception of dominance and is present in neurons that have a limited projection primarily to the amygdala. The present review discusses the specific roles of these neural regions and neurotransmitter systems in the perception of dominance and in hierarchy formation. PMID:26136644

  5. Neural mechanisms of social dominance

    Directory of Open Access Journals (Sweden)

    Noriya eWatanabe

    2015-06-01

    Full Text Available In a group setting, individuals’ perceptions of their own level of dominance or of the dominance level of others, and the ability to adequately control their behavior based on these perceptions are crucial for living within a social environment. Recent advances in neural imaging and molecular technology have enabled researchers to investigate the neural substrates that support the perception of social dominance and the formation of a social hierarchy in humans. At the systems’ level, recent studies showed that dominance perception is represented in broad brain regions which include the amygdala, hippocampus, striatum, and various cortical networks such as the prefrontal, and parietal cortices. Additionally, neurotransmitter systems such as the dopaminergic and serotonergic systems, modulate and are modulated by the formation of the social hierarchy in a group. While these monoamine systems have a wide distribution and multiple functions, it was recently found that the Neuropeptide B/W contributes to the perception of dominance and is present in neurons that have a limited projection primarily to the amygdala. The present review discusses the specific roles of these neural regions and neurotransmitter systems in the perception of dominance and in hierarchy formation.

  6. Neural Representations of Physics Concepts.

    Science.gov (United States)

    Mason, Robert A; Just, Marcel Adam

    2016-06-01

    We used functional MRI (fMRI) to assess neural representations of physics concepts (momentum, energy, etc.) in juniors, seniors, and graduate students majoring in physics or engineering. Our goal was to identify the underlying neural dimensions of these representations. Using factor analysis to reduce the number of dimensions of activation, we obtained four physics-related factors that were mapped to sets of voxels. The four factors were interpretable as causal motion visualization, periodicity, algebraic form, and energy flow. The individual concepts were identifiable from their fMRI signatures with a mean rank accuracy of .75 using a machine-learning (multivoxel) classifier. Furthermore, there was commonality in participants' neural representation of physics; a classifier trained on data from all but one participant identified the concepts in the left-out participant (mean accuracy = .71 across all nine participant samples). The findings indicate that abstract scientific concepts acquired in an educational setting evoke activation patterns that are identifiable and common, indicating that science education builds abstract knowledge using inherent, repurposed brain systems. © The Author(s) 2016.

  7. Photon spectrometry utilizing neural networks

    International Nuclear Information System (INIS)

    Silveira, R.; Benevides, C.; Lima, F.; Vilela, E.

    2015-01-01

    Having in mind the time spent on the uneventful work of characterization of the radiation beams used in a ionizing radiation metrology laboratory, the Metrology Service of the Centro Regional de Ciencias Nucleares do Nordeste - CRCN-NE verified the applicability of artificial intelligence (artificial neural networks) to perform the spectrometry in photon fields. For this, was developed a multilayer neural network, as an application for the classification of patterns in energy, associated with a thermoluminescent dosimetric system (TLD-700 and TLD-600). A set of dosimeters was initially exposed to various well known medium energies, between 40 keV and 1.2 MeV, coinciding with the beams determined by ISO 4037 standard, for the dose of 10 mSv in the quantity Hp(10), on a chest phantom (ISO slab phantom) with the purpose of generating a set of training data for the neural network. Subsequently, a new set of dosimeters irradiated in unknown energies was presented to the network with the purpose to test the method. The methodology used in this work was suitable for application in the classification of energy beams, having obtained 100% of the classification performed. (authors)

  8. Wireless multi-channel single unit recording in freely moving and vocalizing primates.

    Science.gov (United States)

    Roy, Sabyasachi; Wang, Xiaoqin

    2012-01-15

    The ability to record well-isolated action potentials from individual neurons in naturally behaving animals is crucial for understanding neural mechanisms underlying natural behaviors. Traditional neurophysiology techniques, however, require the animal to be restrained which often restricts natural behavior. An example is the common marmoset (Callithrix jacchus), a highly vocal New World primate species, used in our laboratory to study the neural correlates of vocal production and sensory feedback. When restrained by traditional neurophysiological techniques marmoset vocal behavior is severely inhibited. Tethered recording systems, while proven effective in rodents pose limitations in arboreal animals such as the marmoset that typically roam in a three-dimensional environment. To overcome these obstacles, we have developed a wireless neural recording technique that is capable of collecting single-unit data from chronically implanted multi-electrodes in freely moving marmosets. A lightweight, low power and low noise wireless transmitter (headstage) is attached to a multi-electrode array placed in the premotor cortex of the marmoset. The wireless headstage is capable of transmitting 15 channels of neural data with signal-to-noise ratio (SNR) comparable to a tethered system. To minimize radio-frequency (RF) and electro-magnetic interference (EMI), the experiments were conducted within a custom designed RF/EMI and acoustically shielded chamber. The individual electrodes of the multi-electrode array were periodically advanced to densely sample the cortical layers. We recorded single-unit data over a period of several months from the frontal cortex of two marmosets. These recordings demonstrate the feasibility of using our wireless recording method to study single neuron activity in freely roaming primates. Copyright © 2011 Elsevier B.V. All rights reserved.

  9. 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

  10. 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

  11. An introduction to neural networks surgery, a field of neuromodulation which is based on advances in neural networks science and digitised brain imaging.

    Science.gov (United States)

    Sakas, D E; Panourias, I G; Simpson, B A

    2007-01-01

    Operative Neuromodulation is the field of altering electrically or chemically the signal transmission in the nervous system by implanted devices in order to excite, inhibit or tune the activities of neurons or neural networks and produce therapeutic effects. The present article reviews relevant literature on procedures or devices applied either in contact with the cerebral cortex or cranial nerves or in deep sites inside the brain in order to treat various refractory neurological conditions such as: a) chronic pain (facial, somatic, deafferentation, phantom limb), b) movement disorders (Parkinson's disease, dystonia, Tourette syndrome), c) epilepsy, d) psychiatric disease, e) hearing deficits, and f) visual loss. These data indicate that in operative neuromodulation, a new field emerges that is based on neural networks research and on advances in digitised stereometric brain imaging which allow precise localisation of cerebral neural networks and their relay stations; this field can be described as Neural networks surgery because it aims to act extrinsically or intrinsically on neural networks and to alter therapeutically the neural signal transmission with the use of implantable electrical or electronic devices. The authors also review neurotechnology literature relevant to neuroengineering, nanotechnologies, brain computer interfaces, hybrid cultured probes, neuromimetics, neuroinformatics, neurocomputation, and computational neuromodulation; the latter field is dedicated to the study of the biophysical and mathematical characteristics of electrochemical neuromodulation. The article also brings forward particularly interesting lines of research such as the carbon nanofibers electrode arrays for simultaneous electrochemical recording and stimulation, closed-loop systems for responsive neuromodulation, and the intracortical electrodes for restoring hearing or vision. The present review of cerebral neuromodulatory procedures highlights the transition from the

  12. Chronic pelvic pain.

    Science.gov (United States)

    Stein, Sharon L

    2013-12-01

    Chronic pelvic pain is pain lasting longer than 6 months and is estimated to occur in 15% of women. Causes of pelvic pain include disorders of gynecologic, urologic, gastroenterologic, and musculoskeletal systems. The multidisciplinary nature of chronic pelvic pain may complicate diagnosis and treatment. Treatments vary by cause but may include medicinal, neuroablative, and surgical treatments. Copyright © 2013 Elsevier Inc. All rights reserved.

  13. Battling Chronic Absenteeism

    Science.gov (United States)

    Nauer, Kim

    2016-01-01

    While the principal of a New York elementary school (P.S. 48) took on chronic absenteeism from 2011 to 2013, a research team at the Center for New York City Affairs followed her efforts. The school drove down chronic absenteeism almost 10 percentage points. School staff routinely touched base with students, outside "success mentors"…

  14. Microparticle Shedding from Neural Progenitor Cells and Vascular Compartment Cells Is Increased in Ischemic Stroke.

    Science.gov (United States)

    Chiva-Blanch, Gemma; Suades, Rosa; Crespo, Javier; Peña, Esther; Padró, Teresa; Jiménez-Xarrié, Elena; Martí-Fàbregas, Joan; Badimon, Lina

    2016-01-01

    Ischemic stroke has shown to induce platelet and endothelial microparticle shedding, but whether stroke induces microparticle shedding from additional blood and vascular compartment cells is unclear. Neural precursor cells have been shown to replace dying neurons at sites of brain injury; however, if neural precursor cell activation is associated to microparticle shedding, and whether this activation is maintained at long term and associates to stroke type and severity remains unknown. We analyzed neural precursor cells and blood and vascular compartment cells microparticle shedding after an acute ischemic stroke. Forty-four patients were included in the study within the first 48h after the onset of stroke. The cerebral lesion size was evaluated at 3-7 days of the stroke. Circulating microparticles from neural precursor cells and blood and vascular compartment cells (platelets, endothelial cells, erythrocytes, leukocytes, lymphocytes, monocytes and smooth muscle cells) were analyzed by flow cytometry at the onset of stroke and at 7 and 90 days. Forty-four age-matched high cardiovascular risk subjects without documented vascular disease were used as controls. Compared to high cardiovascular risk controls, patients showed higher number of neural precursor cell- and all blood and vascular compartment cell-derived microparticles at the onset of stroke, and after 7 and 90 days. At 90 days, neural precursor cell-derived microparticles decreased and smooth muscle cell-derived microparticles increased compared to levels at the onset of stroke, but only in those patients with the highest stroke-induced cerebral lesions. Stroke increases blood and vascular compartment cell and neural precursor cell microparticle shedding, an effect that is chronically maintained up to 90 days after the ischemic event. These results show that stroke induces a generalized blood and vascular cell activation and the initiation of neuronal cell repair process after stroke. Larger cerebral lesions

  15. Microparticle Shedding from Neural Progenitor Cells and Vascular Compartment Cells Is Increased in Ischemic Stroke.

    Directory of Open Access Journals (Sweden)

    Gemma Chiva-Blanch

    Full Text Available Ischemic stroke has shown to induce platelet and endothelial microparticle shedding, but whether stroke induces microparticle shedding from additional blood and vascular compartment cells is unclear. Neural precursor cells have been shown to replace dying neurons at sites of brain injury; however, if neural precursor cell activation is associated to microparticle shedding, and whether this activation is maintained at long term and associates to stroke type and severity remains unknown. We analyzed neural precursor cells and blood and vascular compartment cells microparticle shedding after an acute ischemic stroke.Forty-four patients were included in the study within the first 48h after the onset of stroke. The cerebral lesion size was evaluated at 3-7 days of the stroke. Circulating microparticles from neural precursor cells and blood and vascular compartment cells (platelets, endothelial cells, erythrocytes, leukocytes, lymphocytes, monocytes and smooth muscle cells were analyzed by flow cytometry at the onset of stroke and at 7 and 90 days. Forty-four age-matched high cardiovascular risk subjects without documented vascular disease were used as controls.Compared to high cardiovascular risk controls, patients showed higher number of neural precursor cell- and all blood and vascular compartment cell-derived microparticles at the onset of stroke, and after 7 and 90 days. At 90 days, neural precursor cell-derived microparticles decreased and smooth muscle cell-derived microparticles increased compared to levels at the onset of stroke, but only in those patients with the highest stroke-induced cerebral lesions.Stroke increases blood and vascular compartment cell and neural precursor cell microparticle shedding, an effect that is chronically maintained up to 90 days after the ischemic event. These results show that stroke induces a generalized blood and vascular cell activation and the initiation of neuronal cell repair process after stroke. Larger

  16. Architecture and biological applications of artificial neural networks: a tuberculosis perspective.

    Science.gov (United States)

    Darsey, Jerry A; Griffin, William O; Joginipelli, Sravanthi; Melapu, Venkata Kiran

    2015-01-01

    Advancement of science and technology has prompted researchers to develop new intelligent systems that can solve a variety of problems such as pattern recognition, prediction, and optimization. The ability of the human brain to learn in a fashion that tolerates noise and error has attracted many researchers and provided the starting point for the development of artificial neural networks: the intelligent systems. Intelligent systems can acclimatize to the environment or data and can maximize the chances of success or improve the efficiency of a search. Due to massive parallelism with large numbers of interconnected processers and their ability to learn from the data, neural networks can solve a variety of challenging computational problems. Neural networks have the ability to derive meaning from complicated and imprecise data; they are used in detecting patterns, and trends that are too complex for humans, or other computer systems. Solutions to the toughest problems will not be found through one narrow specialization; therefore we need to combine interdisciplinary approaches to discover the solutions to a variety of problems. Many researchers in different disciplines such as medicine, bioinformatics, molecular biology, and pharmacology have successfully applied artificial neural networks. This chapter helps the reader in understanding the basics of artificial neural networks, their applications, and methodology; it also outlines the network learning process and architecture. We present a brief outline of the application of neural networks to medical diagnosis, drug discovery, gene identification, and protein structure prediction. We conclude with a summary of the results from our study on tuberculosis data using neural networks, in diagnosing active tuberculosis, and predicting chronic vs. infiltrative forms of tuberculosis.

  17. Neurofeedback Control in Parkinsonian Patients Using Electrocorticography Signals Accessed Wirelessly With a Chronic, Fully Implanted Device.

    Science.gov (United States)

    Khanna, Preeya; Swann, Nicole C; de Hemptinne, Coralie; Miocinovic, Svjetlana; Miller, Andrew; Starr, Philip A; Carmena, Jose M

    2017-10-01

    Parkinson's disease (PD) is characterized by motor symptoms such as rigidity and bradykinesia that prevent normal movement. Beta band oscillations (13-30 Hz) in neural local field potentials (LFPs) have been associated with these motor symptoms. Here, three PD patients implanted with a therapeutic deep brain neural stimulator that can also record and wirelessly stream neural data played a neurofeedback game where they modulated their beta band power from sensorimotor cortical areas. Patients' beta band power was streamed in real-time to update the position of a cursor that they tried to drive into a cued target. After playing the game for 1-2 hours each, all three patients exhibited above chance-level performance regardless of subcortical stimulation levels. This study, for the first time, demonstrates using an invasive neural recording system for at-home neurofeedback training. Future work will investigate chronic neurofeedback training as a potentially therapeutic tool for patients with neurological disorders.

  18. Closed-Loop Deep Brain Stimulation for Refractory Chronic Pain

    Directory of Open Access Journals (Sweden)

    Prasad Shirvalkar

    2018-03-01

    Full Text Available Pain is a subjective experience that alerts an individual to actual or potential tissue damage. Through mechanisms that are still unclear, normal physiological pain can lose its adaptive value and evolve into pathological chronic neuropathic pain. Chronic pain is a multifaceted experience that can be understood in terms of somatosensory, affective, and cognitive dimensions, each with associated symptoms and neural signals. While there have been many attempts to treat chronic pain, in this article we will argue that feedback-controlled ‘closed-loop’ deep brain stimulation (DBS offers an urgent and promising route for treatment. Contemporary DBS trials for chronic pain use “open-loop” approaches in which tonic stimulation is delivered with fixed parameters to a single brain region. The impact of key variables such as the target brain region and the stimulation waveform is unclear, and long-term efficacy has mixed results. We hypothesize that chronic pain is due to abnormal synchronization between brain networks encoding the somatosensory, affective and cognitive dimensions of pain, and that multisite, closed-loop DBS provides an intuitive mechanism for disrupting that synchrony. By (1 identifying biomarkers of the subjective pain experience and (2 integrating these signals into a state-space representation of pain, we can create a predictive model of each patient's pain experience. Then, by establishing how stimulation in different brain regions influences individual neural signals, we can design real-time, closed-loop therapies tailored to each patient. While chronic pain is a complex disorder that has eluded modern therapies, rich historical data and state-of-the-art technology can now be used to develop a promising treatment.

  19. Testing for Chronic Diarrhea.

    Science.gov (United States)

    Raman, M

    Chronic diarrhea is a frequently encountered symptom in clinical practice. The etiologies for chronic diarrhea are diverse and broad with varying clinical implications. A useful method of categorizing chronic diarrhea to guide a diagnostic work-up is a pathophysiology-based framework. Chronic diarrhea may be categorized as malabsorptive, secretory, osmotic, and inflammatory or motility related. Frequently, overlap between categories may exist for any given diarrhea etiology and diagnostic testing must occur with an understanding of the differential diagnosis. Investigations to achieve a diagnosis for chronic diarrhea range from screening blood and stool tests to more directed testing such as diagnostic imaging, and endoscopic and histological evaluation. The pathophysiology-based framework proposed in this chapter will allow the clinician to select screening tests followed by targeted tests to minimize cost and complications to the patient, while providing a highly effective method to achieve an accurate diagnosis. © 2017 Elsevier Inc. All rights reserved.

  20. Chronic gastritis - an update.

    Science.gov (United States)

    Varbanova, Mariya; Frauenschläger, Katrin; Malfertheiner, Peter

    2014-12-01

    Helicobacter pylori is the main aetiologic factor for chronic gastritis worldwide. The degree of inflammation and the evolution of this form of chronic gastritis can vary largely depending on bacterial virulence factors, host susceptibility factors and environmental conditions. Autoimmune gastritis is another cause of chronic inflammation in the stomach, which can occur in all age groups. This disease presents typically with vitamin B12 deficiency and pernicious anaemia. The presence of anti-parietal cell antibodies is highly specific for the diagnosis. The role of H. pylori as a trigger for autoimmune gastritis remains uncertain. Other rare conditions for chronic gastritis are chronic inflammatory conditions such as Crohn's disease or on the background of lymphocytic or collagenous gastroenteropathies. Copyright © 2014. Published by Elsevier Ltd.

  1. Neural plasticity of development and learning.

    Science.gov (United States)

    Galván, Adriana

    2010-06-01

    Development and learning are powerful agents of change across the lifespan that induce robust structural and functional plasticity in neural systems. An unresolved question in developmental cognitive neuroscience is whether development and learning share the same neural mechanisms associated with experience-related neural plasticity. In this article, I outline the conceptual and practical challenges of this question, review insights gleaned from adult studies, and describe recent strides toward examining this topic across development using neuroimaging methods. I suggest that development and learning are not two completely separate constructs and instead, that they exist on a continuum. While progressive and regressive changes are central to both, the behavioral consequences associated with these changes are closely tied to the existing neural architecture of maturity of the system. Eventually, a deeper, more mechanistic understanding of neural plasticity will shed light on behavioral changes across development and, more broadly, about the underlying neural basis of cognition. (c) 2010 Wiley-Liss, Inc.

  2. Neurosecurity: security and privacy for neural devices.

    Science.gov (United States)

    Denning, Tamara; Matsuoka, Yoky; Kohno, Tadayoshi

    2009-07-01

    An increasing number of neural implantable devices will become available in the near future due to advances in neural engineering. This discipline holds the potential to improve many patients' lives dramatically by offering improved-and in some cases entirely new-forms of rehabilitation for conditions ranging from missing limbs to degenerative cognitive diseases. The use of standard engineering practices, medical trials, and neuroethical evaluations during the design process can create systems that are safe and that follow ethical guidelines; unfortunately, none of these disciplines currently ensure that neural devices are robust against adversarial entities trying to exploit these devices to alter, block, or eavesdrop on neural signals. The authors define "neurosecurity"-a version of computer science security principles and methods applied to neural engineering-and discuss why neurosecurity should be a critical consideration in the design of future neural devices.

  3. Mechanism of Chronic Pain in Rodent Brain Imaging

    Science.gov (United States)

    Chang, Pei-Ching

    Chronic pain is a significant health problem that greatly impacts the quality of life of individuals and imparts high costs to society. Despite intense research effort in understanding of the mechanism of pain, chronic pain remains a clinical problem that has few effective therapies. The advent of human brain imaging research in recent years has changed the way that chronic pain is viewed. To further extend the use of human brain imaging techniques for better therapies, the adoption of imaging technique onto the animal pain models is essential, in which underlying brain mechanisms can be systematically studied using various combination of imaging and invasive techniques. The general goal of this thesis is to addresses how brain develops and maintains chronic pain in an animal model using fMRI. We demonstrate that nucleus accumbens, the central component of mesolimbic circuitry, is essential in development of chronic pain. To advance our imaging technique, we develop an innovative methodology to carry out fMRI in awake, conscious rat. Using this cutting-edge technique, we show that allodynia is assoicated with shift brain response toward neural circuits associated nucleus accumbens and prefrontal cortex that regulate affective and cognitive component of pain. Taken together, this thesis provides a deeper understanding of how brain mediates pain. It builds on the existing body of knowledge through maximizing the depth of insight into brain imaging of chronic pain.

  4. The Neural Border: Induction, Specification and Maturation of the territory that generates Neural Crest cells.

    Science.gov (United States)

    Pla, Patrick; Monsoro-Burq, Anne H

    2018-05-28

    The neural crest is induced at the edge between the neural plate and the nonneural ectoderm, in an area called the neural (plate) border, during gastrulation and neurulation. In recent years, many studies have explored how this domain is patterned, and how the neural crest is induced within this territory, that also participates to the prospective dorsal neural tube, the dorsalmost nonneural ectoderm, as well as placode derivatives in the anterior area. This review highlights the tissue interactions, the cell-cell signaling and the molecular mechanisms involved in this dynamic spatiotemporal patterning, resulting in the induction of the premigratory neural crest. Collectively, these studies allow building a complex neural border and early neural crest gene regulatory network, mostly composed by transcriptional regulations but also, more recently, including novel signaling interactions. Copyright © 2018. Published by Elsevier Inc.

  5. Direct adaptive control using feedforward neural networks

    OpenAIRE

    Cajueiro, Daniel Oliveira; Hemerly, Elder Moreira

    2003-01-01

    ABSTRACT: This paper proposes a new scheme for direct neural adaptive control that works efficiently employing only one neural network, used for simultaneously identifying and controlling the plant. The idea behind this structure of adaptive control is to compensate the control input obtained by a conventional feedback controller. The neural network training process is carried out by using two different techniques: backpropagation and extended Kalman filter algorithm. Additionally, the conver...

  6. Introduction to Concepts in Artificial Neural Networks

    Science.gov (United States)

    Niebur, Dagmar

    1995-01-01

    This introduction to artificial neural networks summarizes some basic concepts of computational neuroscience and the resulting models of artificial neurons. The terminology of biological and artificial neurons, biological and machine learning and neural processing is introduced. The concepts of supervised and unsupervised learning are explained with examples from the power system area. Finally, a taxonomy of different types of neurons and different classes of artificial neural networks is presented.

  7. Mode Choice Modeling Using Artificial Neural Networks

    OpenAIRE

    Edara, Praveen Kumar

    2003-01-01

    Artificial intelligence techniques have produced excellent results in many diverse fields of engineering. Techniques such as neural networks and fuzzy systems have found their way into transportation engineering. In recent years, neural networks are being used instead of regression techniques for travel demand forecasting purposes. The basic reason lies in the fact that neural networks are able to capture complex relationships and learn from examples and also able to adapt when new data becom...

  8. Dynamic training algorithm for dynamic neural networks

    International Nuclear Information System (INIS)

    Tan, Y.; Van Cauwenberghe, A.; Liu, Z.

    1996-01-01

    The widely used backpropagation algorithm for training neural networks based on the gradient descent has a significant drawback of slow convergence. A Gauss-Newton method based recursive least squares (RLS) type algorithm with dynamic error backpropagation is presented to speed-up the learning procedure of neural networks with local recurrent terms. Finally, simulation examples concerning the applications of the RLS type algorithm to identification of nonlinear processes using a local recurrent neural network are also included in this paper

  9. Neural crest contributions to the lamprey head

    Science.gov (United States)

    McCauley, David W.; Bronner-Fraser, Marianne

    2003-01-01

    The neural crest is a vertebrate-specific cell population that contributes to the facial skeleton and other derivatives. We have performed focal DiI injection into the cranial neural tube of the developing lamprey in order to follow the migratory pathways of discrete groups of cells from origin to destination and to compare neural crest migratory pathways in a basal vertebrate to those of gnathostomes. The results show that the general pathways of cranial neural crest migration are conserved throughout the vertebrates, with cells migrating in streams analogous to the mandibular and hyoid streams. Caudal branchial neural crest cells migrate ventrally as a sheet of cells from the hindbrain and super-pharyngeal region of the neural tube and form a cylinder surrounding a core of mesoderm in each pharyngeal arch, similar to that seen in zebrafish and axolotl. In addition to these similarities, we also uncovered important differences. Migration into the presumptive caudal branchial arches of the lamprey involves both rostral and caudal movements of neural crest cells that have not been described in gnathostomes, suggesting that barriers that constrain rostrocaudal movement of cranial neural crest cells may have arisen after the agnathan/gnathostome split. Accordingly, neural crest cells from a single axial level contributed to multiple arches and there was extensive mixing between populations. There was no apparent filling of neural crest derivatives in a ventral-to-dorsal order, as has been observed in higher vertebrates, nor did we find evidence of a neural crest contribution to cranial sensory ganglia. These results suggest that migratory constraints and additional neural crest derivatives arose later in gnathostome evolution.

  10. Adaptive optimization and control using neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Mead, W.C.; Brown, S.K.; Jones, R.D.; Bowling, P.S.; Barnes, C.W.

    1993-10-22

    Recent work has demonstrated the ability of neural-network-based controllers to optimize and control machines with complex, non-linear, relatively unknown control spaces. We present a brief overview of neural networks via a taxonomy illustrating some capabilities of different kinds of neural networks. We present some successful control examples, particularly the optimization and control of a small-angle negative ion source.

  11. The quest for a Quantum Neural Network

    OpenAIRE

    Schuld, M.; Sinayskiy, I.; Petruccione, F.

    2014-01-01

    With the overwhelming success in the field of quantum information in the last decades, the "quest" for a Quantum Neural Network (QNN) model began in order to combine quantum computing with the striking properties of neural computing. This article presents a systematic approach to QNN research, which so far consists of a conglomeration of ideas and proposals. It outlines the challenge of combining the nonlinear, dissipative dynamics of neural computing and the linear, unitary dynamics of quant...

  12. Stages of Chronic Myelogenous Leukemia

    Science.gov (United States)

    ... ALL Treatment Childhood AML Treatment Research Chronic Myelogenous Leukemia Treatment (PDQ®)–Patient Version General Information About Chronic Myelogenous Leukemia Go to Health Professional Version Key Points Chronic ...

  13. NeuroMEMS: Neural Probe Microtechnologies

    Directory of Open Access Journals (Sweden)

    Sam Musallam

    2008-10-01

    Full Text Available Neural probe technologies have already had a significant positive effect on our understanding of the brain by revealing the functioning of networks of biological neurons. Probes are implanted in different areas of the brain to record and/or stimulate specific sites in the brain. Neural probes are currently used in many clinical settings for diagnosis of brain diseases such as seizers, epilepsy, migraine, Alzheimer’s, and dementia. We find these devices assisting paralyzed patients by allowing them to operate computers or robots using their neural activity. In recent years, probe technologies were assisted by rapid advancements in microfabrication and microelectronic technologies and thus are enabling highly functional and robust neural probes which are opening new and exciting avenues in neural sciences and brain machine interfaces. With a wide variety of probes that have been designed, fabricated, and tested to date, this review aims to provide an overview of the advances and recent progress in the microfabrication techniques of neural probes. In addition, we aim to highlight the challenges faced in developing and implementing ultralong multi-site recording probes that are needed to monitor neural activity from deeper regions in the brain. Finally, we review techniques that can improve the biocompatibility of the neural probes to minimize the immune response and encourage neural growth around the electrodes for long term implantation studies.

  14. Fuzzy neural network theory and application

    CERN Document Server

    Liu, Puyin

    2004-01-01

    This book systematically synthesizes research achievements in the field of fuzzy neural networks in recent years. It also provides a comprehensive presentation of the developments in fuzzy neural networks, with regard to theory as well as their application to system modeling and image restoration. Special emphasis is placed on the fundamental concepts and architecture analysis of fuzzy neural networks. The book is unique in treating all kinds of fuzzy neural networks and their learning algorithms and universal approximations, and employing simulation examples which are carefully designed to he

  15. Practical neural network recipies in C++

    CERN Document Server

    Masters

    2014-01-01

    This text serves as a cookbook for neural network solutions to practical problems using C++. It will enable those with moderate programming experience to select a neural network model appropriate to solving a particular problem, and to produce a working program implementing that network. The book provides guidance along the entire problem-solving path, including designing the training set, preprocessing variables, training and validating the network, and evaluating its performance. Though the book is not intended as a general course in neural networks, no background in neural works is assum

  16. Boolean Factor Analysis by Attractor Neural Network

    Czech Academy of Sciences Publication Activity Database

    Frolov, A. A.; Húsek, Dušan; Muraviev, I. P.; Polyakov, P.Y.

    2007-01-01

    Roč. 18, č. 3 (2007), s. 698-707 ISSN 1045-9227 R&D Projects: GA AV ČR 1ET100300419; GA ČR GA201/05/0079 Institutional research plan: CEZ:AV0Z10300504 Keywords : recurrent neural network * Hopfield-like neural network * associative memory * unsupervised learning * neural network architecture * neural network application * statistics * Boolean factor analysis * dimensionality reduction * features clustering * concepts search * information retrieval Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 2.769, year: 2007

  17. 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.

  18. Finite connectivity attractor neural networks

    International Nuclear Information System (INIS)

    Wemmenhove, B; Coolen, A C C

    2003-01-01

    We study a family of diluted attractor neural networks with a finite average number of (symmetric) connections per neuron. As in finite connectivity spin glasses, their equilibrium properties are described by order parameter functions, for which we derive an integral equation in replica symmetric approximation. A bifurcation analysis of this equation reveals the locations of the paramagnetic to recall and paramagnetic to spin-glass transition lines in the phase diagram. The line separating the retrieval phase from the spin-glass phase is calculated at zero temperature. All phase transitions are found to be continuous

  19. Chronic hypersensitivity pneumonitis.

    Science.gov (United States)

    Pereira, Carlos Ac; Gimenez, Andréa; Kuranishi, Lilian; Storrer, Karin

    2016-01-01

    Hypersensitivity pneumonitis (HSP) is a common interstitial lung disease resulting from inhalation of a large variety of antigens by susceptible individuals. The disease is best classified as acute and chronic. Chronic HSP can be fibrosing or not. Fibrotic HSP has a large differential diagnosis and has a worse prognosis. The most common etiologies for HSP are reviewed. Diagnostic criteria are proposed for both chronic forms based on exposure, lung auscultation, lung function tests, HRCT findings, bronchoalveolar lavage, and biopsies. Treatment options are limited, but lung transplantation results in greater survival in comparison to idiopathic pulmonary fibrosis. Randomized trials with new antifibrotic agents are necessary.

  20. Chronic neutrophilic leukemia.

    Science.gov (United States)

    Bredeweg, Arthur; Burch, Micah; Krause, John R

    2018-01-01

    Chronic neutrophilic leukemia is a rare myeloproliferative disorder characterized by a sustained peripheral blood neutrophilia, absence of the BCR/ABL oncoprotein, bone marrow hypercellularity with less than 5% myeloblasts and normal neutrophil maturation, and no dysplasia. This leukemia has been associated with mutations in the colony-stimulating factor 3 receptor (CSF3R) that may activate this receptor, leading to the proliferation of neutrophils that are the hallmark of chronic neutrophilic leukemia. We present a case of chronic neutrophilic leukemia and discuss the criteria for diagnosis and the significance of mutations found in this leukemia.

  1. Neural signature of behavioural inhibition in women with bulimia nervosa.

    Science.gov (United States)

    Skunde, Mandy; Walther, Stephan; Simon, Joe J; Wu, Mudan; Bendszus, Martin; Herzog, Wolfgang; Friederich, Hans-Christoph

    2016-08-01

    Impaired inhibitory control is considered a behavioural phenotype in patients with bulimia nervosa. However, the underlying neural correlates of impaired general and food-specific behavioural inhibition are largely unknown. Therefore, we investigated brain activation during the performance of behavioural inhibition to general and food-related stimuli in adults with bulimia nervosa. Women with bulimia and healthy control women underwent event-related fMRI while performing a general and a food-specific no-go task. We included 28 women with bulimia nervosa and 29 healthy control women in our study. On a neuronal level, we observed significant group differences in response to general no-go stimuli in women with bulimia nervosa with high symptom severity; compared with healthy controls, the patients showed reduced activation in the right sensorimotor area (postcentral gyrus, precentral gyrus) and right dorsal striatum (caudate nucleus, putamen). The present results are limited to adult women with bulimia nervosa. Furthermore, it remains unclear whether impaired behavioural inhibition in patients with this disorder are a cause or consequence of chronic illness. Our findings suggest that diminished frontostriatal brain activation in patients with bulimia nervosa contribute to the severity of binge eating symptoms. Gaining further insight into the neural mechanisms of behavioural inhibition problems in individuals with this disorder may inform brain-directed treatment approaches and the development of response inhibition training approaches to improve inhibitory control in patients with bulimia nervosa. The present study does not support greater behavioural and neural impairments to food-specific behavioural inhibition in these patients.

  2. Neural signature of behavioural inhibition in women with bulimia nervosa

    Science.gov (United States)

    Skunde, Mandy; Walther, Stephan; Simon, Joe J.; Wu, Mudan; Bendszus, Martin; Herzog, Wolfgang; Friederich, Hans-Christoph

    2016-01-01

    Background Impaired inhibitory control is considered a behavioural phenotype in patients with bulimia nervosa. However, the underlying neural correlates of impaired general and food-specific behavioural inhibition are largely unknown. Therefore, we investigated brain activation during the performance of behavioural inhibition to general and food-related stimuli in adults with bulimia nervosa. Methods Women with bulimia and healthy control women underwent event-related fMRI while performing a general and a food-specific no-go task. Results We included 28 women with bulimia nervosa and 29 healthy control women in our study. On a neuronal level, we observed significant group differences in response to general no-go stimuli in women with bulimia nervosa with high symptom severity; compared with healthy controls, the patients showed reduced activation in the right sensorimotor area (postcentral gyrus, precentral gyrus) and right dorsal striatum (caudate nucleus, putamen). Limitations The present results are limited to adult women with bulimia nervosa. Furthermore, it remains unclear whether impaired behavioural inhibition in patients with this disorder are a cause or consequence of chronic illness. Conclusion Our findings suggest that diminished frontostriatal brain activation in patients with bulimia nervosa contribute to the severity of binge eating symptoms. Gaining further insight into the neural mechanisms of behavioural inhibition problems in individuals with this disorder may inform brain-directed treatment approaches and the development of response inhibition training approaches to improve inhibitory control in patients with bulimia nervosa. The present study does not support greater behavioural and neural impairments to food-specific behavioural inhibition in these patients. PMID:27575858

  3. Neural Network Based Load Frequency Control for Restructuring ...

    African Journals Online (AJOL)

    Neural Network Based Load Frequency Control for Restructuring Power Industry. ... an artificial neural network (ANN) application of load frequency control (LFC) of a Multi-Area power system by using a neural network controller is presented.

  4. In-vitro differentiation induction of neural stem cells

    NARCIS (Netherlands)

    Balasubramaniyan, Veerakumar

    2006-01-01

    Neurale stamcellen maken de drie belangrijkste celtypes van ons zenuwstelsel aan. Veerakumar Balasubramaniyan onderzocht hoe neurale stamcellen kunnen worden aangezet tot het aanmaken van specifieke neurale celtypes. Met behulp van genetische technieken lukte het hem oligodendrocyten te verkrijgen:

  5. Neural and psychosocial mechanisms of pain sensitivity in fibromyalgia.

    Science.gov (United States)

    English, Brian

    2014-06-01

    Fibromyalgia is a chronic musculoskeletal pain disorder that affects an estimated 5 million adults in the U.S. The hallmark is burning, searing, tingling, shooting, stabbing, deep aching, or sharp pain. Fibromyalgia is generally considered to be a "central sensitivity syndrome" where central sensitization is regarded as the cause of pain in its own right. Nonetheless, the case continues to be made that all central and spatially distributed peripheral components of fibromyalgia pain would fade if the peripheral generators could be silenced. Although neural mechanisms are clearly important in pain sensitivity, cognitive and social mechanisms also need to be considered. The aim of this review is to examine four mechanisms responsible for heightened pain sensitivity in fibromyalgia: peripheral sensitization, central sensitization, cognitive-emotional sensitization, and interpersonal sensitization. The purpose of framing the review in terms of pain sensitivity in fibromyalgia is to highlight that different mechanisms of sensitization are appropriately regarded as intervening variables when it comes to understanding individual differences in the experience of pain. The paper concludes by considering the implications of the findings of the review for explanations of fibromyalgia pain by nurses working in multidisciplinary teams. The trend appears to be able to explain the cause of fibromyalgia pain in terms of sensitization per se. The recommended alternative is to explain fibromyalgia pain in terms of changes in pain sensitivity and the role of underlying neural and psychosocial mechanisms. Copyright © 2014 American Society for Pain Management Nursing. Published by Elsevier Inc. All rights reserved.

  6. Hydrogel-Electrospun Fiber Mat Composite Coatings for Neural Prostheses

    Directory of Open Access Journals (Sweden)

    Ning eHan

    2011-03-01

    Full Text Available Achieving stable, long-term performance of implanted neural prosthetic devices has been challenging because of implantation related neuron loss and a foreign body response that results in encapsulating glial scar formation. To improve neuron-prosthesis integration and form chronic, stable interfaces, we investigated the potential of neurotrophin-eluting hydrogel-electrospun fiber mat (EFM composite coatings. In particular, poly(ethylene glycol-poly(ε-caprolactone (PEGPCL hydrogel- poly(ε-caprolactone (PCL EFM composites were applied as coatings for multielectrode arrays (MEAs. Coatings were stable and persisted on electrode surfaces for over 1 month under an agarose gel tissue phantom and over 9 months in a PBS immersion bath. To demonstrate drug release, a neurotrophin, nerve growth factor (NGF, was loaded in the PEGPCL hydrogel layer, and coating cytotoxicity and sustained NGF release were evaluated using a PC12 cell culture model. Quantitative MTT assays showed that these coatings had no significant toxicity toward PC12 cells, and neurite extension at day 7 and 14 confirmed sustained release of NGF at biologically significant concentrations for at least 2 weeks. Our results demonstrate that hydrogel-EFM composite materials can be applied to neural prostheses as a means to improve neuron-electrode proximity and enhance long-term device performance and function.

  7. Progress in understanding mood disorders: optogenetic dissection of neural circuits.

    Science.gov (United States)

    Lammel, S; Tye, K M; Warden, M R

    2014-01-01

    Major depression is characterized by a cluster of symptoms that includes hopelessness, low mood, feelings of worthlessness and inability to experience pleasure. The lifetime prevalence of major depression approaches 20%, yet current treatments are often inadequate both because of associated side effects and because they are ineffective for many people. In basic research, animal models are often used to study depression. Typically, experimental animals are exposed to acute or chronic stress to generate a variety of depression-like symptoms. Despite its clinical importance, very little is known about the cellular and neural circuits that mediate these symptoms. Recent advances in circuit-targeted approaches have provided new opportunities to study the neuropathology of mood disorders such as depression and anxiety. We review recent progress and highlight some studies that have begun tracing a functional neuronal circuit diagram that may prove essential in establishing novel treatment strategies in mood disorders. First, we shed light on the complexity of mesocorticolimbic dopamine (DA) responses to stress by discussing two recent studies reporting that optogenetic activation of midbrain DA neurons can induce or reverse depression-related behaviors. Second, we describe the role of the lateral habenula circuitry in the pathophysiology of depression. Finally, we discuss how the prefrontal cortex controls limbic and neuromodulatory circuits in mood disorders. © 2013 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society.

  8. Chronic Conditions Dashboard

    Data.gov (United States)

    U.S. Department of Health & Human Services — The CMS Chronic Conditions Dashboard presents statistical views of information on the prevalence, utilization and Medicare spending for Medicare beneficiaries with...

  9. About Chronic Kidney Disease

    Science.gov (United States)

    ... detect CKD: blood pressure, urine albumin and serum creatinine. What causes CKD? The two main causes of chronic kidney disease are diabetes and high blood pressure , which are responsible for up to ...

  10. Low back pain - chronic

    Science.gov (United States)

    ... this page: //medlineplus.gov/ency/article/007422.htm Low back pain - chronic To use the sharing features on this page, please enable JavaScript. Low back pain refers to pain that you feel in your ...

  11. Chronic Condition Data Warehouse

    Data.gov (United States)

    U.S. Department of Health & Human Services — The CMS Chronic Condition Data Warehouse (CCW) provides researchers with Medicare and Medicaid beneficiary, claims, and assessment data linked by beneficiary across...

  12. People Experiencing Chronic Homelessness

    Science.gov (United States)

    ... People with Disabilities Share Ending Chronic Homelessness Among People with Disabilities Last updated on May 31, 2018 We can end homelessness for people with disabilities in our communities who experience recurring ...

  13. Dealing with chronic cancer

    Science.gov (United States)

    ... enjoying the present instead of worrying about the future. Focus on the small things that bring you ... E. The chronic leukemias. In: Goldman L, Schafer AI, eds. Goldman's Cecil Medicine . 25th ed. Philadelphia, PA: ...

  14. Chronic Conditions PUF

    Data.gov (United States)

    U.S. Department of Health & Human Services — The Chronic Conditions PUFs are aggregated files in which each record is a profile or cell defined by the characteristics of Medicare beneficiaries. A profile is...

  15. CHRONIC UNEXPLAINED OROFACIAL PAIN

    Directory of Open Access Journals (Sweden)

    Aleš Vesnaver

    2002-04-01

    Full Text Available Background. Chronic unexplained orofacial pain is frequently the cause of prolonged suffering for the patient and an unsolvable problem for the therapist. Pathophysiology of the onset of this type of pain is virtually unknown. Still, it is possible to divide chronic orofacial pain into several separate categories, according to its onset, symptoms and therapy. All forms of this type of pain have a strong psychological component.Methods. A retrograde review was conducted, in which patients’ records, treated in 1994 for chronic unexplained orofacial pain, were followed through a 5 year period. The modalities of treatment then and at present were compared.Conclusions. Except for trigeminal neuralgia, where carbamazepine remains the first choice drug, treatment of chronic facial pain has changed considerably.

  16. Chronic Kidney Disease

    Science.gov (United States)

    You have two kidneys, each about the size of your fist. Their main job is to filter your blood. They remove wastes and ... help control blood pressure, and make hormones. Chronic kidney disease (CKD) means that your kidneys are damaged ...

  17. Chronic inflammatory demyelinative polyneuropathy

    DEFF Research Database (Denmark)

    Said, Gérard; Krarup, Christian

    2013-01-01

    Chronic inflammatory demyelinative polyneuropathy (CIDP) is an acquired polyneuropathy presumably of immunological origin. It is characterized by a progressive or a relapsing course with predominant motor deficit. The diagnosis rests on the association of non-length-dependent predominantly motor...

  18. Chronic Hypertension in Pregnancy

    Science.gov (United States)

    ... org/ by guest on June 19, 2018 Chronic Hypertension in Pregnancy Ellen W. Seely, MD; Cynthia Maxwell, ... M any women have been diag- nosed with hypertension (blood pressure Ͼ 140/ 90 mm Hg) when ...

  19. Neural network modeling of emotion

    Science.gov (United States)

    Levine, Daniel S.

    2007-03-01

    This article reviews the history and development of computational neural network modeling of cognitive and behavioral processes that involve emotion. The exposition starts with models of classical conditioning dating from the early 1970s. Then it proceeds toward models of interactions between emotion and attention. Then models of emotional influences on decision making are reviewed, including some speculative (not and not yet simulated) models of the evolution of decision rules. Through the late 1980s, the neural networks developed to model emotional processes were mainly embodiments of significant functional principles motivated by psychological data. In the last two decades, network models of these processes have become much more detailed in their incorporation of known physiological properties of specific brain regions, while preserving many of the psychological principles from the earlier models. Most network models of emotional processes so far have dealt with positive and negative emotion in general, rather than specific emotions such as fear, joy, sadness, and anger. But a later section of this article reviews a few models relevant to specific emotions: one family of models of auditory fear conditioning in rats, and one model of induced pleasure enhancing creativity in humans. Then models of emotional disorders are reviewed. The article concludes with philosophical statements about the essential contributions of emotion to intelligent behavior and the importance of quantitative theories and models to the interdisciplinary enterprise of understanding the interactions of emotion, cognition, and behavior.

  20. Imaging Posture Veils Neural Signals

    Directory of Open Access Journals (Sweden)

    Robert T Thibault

    2016-10-01

    Full Text Available Whereas modern brain imaging often demands holding body positions incongruent with everyday life, posture governs both neural activity and cognitive performance. Humans commonly perform while upright; yet, many neuroimaging methodologies require participants to remain motionless and adhere to non-ecological comportments within a confined space. This inconsistency between ecological postures and imaging constraints undermines the transferability and generalizability of many a neuroimaging assay.Here we highlight the influence of posture on brain function and behavior. Specifically, we challenge the tacit assumption that brain processes and cognitive performance are comparable across a spectrum of positions. We provide an integrative synthesis regarding the increasingly prominent influence of imaging postures on autonomic function, mental capacity, sensory thresholds, and neural activity. Arguing that neuroimagers and cognitive scientists could benefit from considering the influence posture wields on both general functioning and brain activity, we examine existing imaging technologies and the potential of portable and versatile imaging devices (e.g., functional near infrared spectroscopy. Finally, we discuss ways that accounting for posture may help unveil the complex brain processes of everyday cognition.

  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 dynamics in reconfigurable silicon.

    Science.gov (United States)

    Basu, A; Ramakrishnan, S; Petre, C; Koziol, S; Brink, S; Hasler, P E

    2010-10-01

    A neuromorphic analog chip is presented that is capable of implementing massively parallel neural computations while retaining the programmability of digital systems. We show measurements from neurons with Hopf bifurcations and integrate and fire neurons, excitatory and inhibitory synapses, passive dendrite cables, coupled spiking neurons, and central pattern generators implemented on the chip. This chip provides a platform for not only simulating detailed neuron dynamics but also uses the same to interface with actual cells in applications such as a dynamic clamp. There are 28 computational analog blocks (CAB), each consisting of ion channels with tunable parameters, synapses, winner-take-all elements, current sources, transconductance amplifiers, and capacitors. There are four other CABs which have programmable bias generators. The programmability is achieved using floating gate transistors with on-chip programming control. The switch matrix for interconnecting the components in CABs also consists of floating-gate transistors. Emphasis is placed on replicating the detailed dynamics of computational neural models. Massive computational area efficiency is obtained by using the reconfigurable interconnect as synaptic weights, resulting in more than 50 000 possible 9-b accurate synapses in 9 mm(2).

  3. Chimera States in Neural Oscillators

    Science.gov (United States)

    Bahar, Sonya; Glaze, Tera

    2014-03-01

    Chimera states have recently been explored both theoretically and experimentally, in various coupled nonlinear oscillators, ranging from phase-oscillator models to coupled chemical reactions. In a chimera state, both coherent and incoherent (or synchronized and desynchronized) states occur simultaneously in populations of identical oscillators. We investigate chimera behavior in a population of neural oscillators using the Huber-Braun model, a Hodgkin-Huxley-like model originally developed to characterize the temperature-dependent bursting behavior of mammalian cold receptors. One population of neurons is allowed to synchronize, with each neuron receiving input from all the others in its group (global within-group coupling). Subsequently, a second population of identical neurons is placed under an identical global within-group coupling, and the two populations are also coupled to each other (between-group coupling). For certain values of the coupling constants, the neurons in the two populations exhibit radically different synchronization behavior. We will discuss the range of chimera activity in the model, and discuss its implications for actual neural activity, such as unihemispheric sleep.

  4. Improved transformer protection using probabilistic neural network ...

    African Journals Online (AJOL)

    user

    secure and dependable protection for power transformers. Owing to its superior learning and generalization capabilities Artificial. Neural Network (ANN) can considerably enhance the scope of WI method. ANN approach is faster, robust and easier to implement than the conventional waveform approach. The use of neural ...

  5. Neural network signal understanding for instrumentation

    DEFF Research Database (Denmark)

    Pau, L. F.; Johansen, F. S.

    1990-01-01

    understanding research is surveyed, and the selected implementation and its performance in terms of correct classification rates and robustness to noise are described. Formal results on neural net training time and sensitivity to weights are given. A theory for neural control using functional link nets is given...

  6. A Chip for an Implantable Neural Stimulator

    DEFF Research Database (Denmark)

    Gudnason, Gunnar; Bruun, Erik; Haugland, Morten

    2000-01-01

    This paper describes a chip for a multichannel neural stimulator for functional electrical stimulation (FES). The purpose of FES is to restore muscular control in disabled patients. The chip performs all the signal processing required in an implanted neural stimulator. The power and digital data...

  7. Hidden neural networks: application to speech recognition

    DEFF Research Database (Denmark)

    Riis, Søren Kamaric

    1998-01-01

    We evaluate the hidden neural network HMM/NN hybrid on two speech recognition benchmark tasks; (1) task independent isolated word recognition on the Phonebook database, and (2) recognition of broad phoneme classes in continuous speech from the TIMIT database. It is shown how hidden neural networks...

  8. Neural Network Classifier Based on Growing Hyperspheres

    Czech Academy of Sciences Publication Activity Database

    Jiřina Jr., Marcel; Jiřina, Marcel

    2000-01-01

    Roč. 10, č. 3 (2000), s. 417-428 ISSN 1210-0552. [Neural Network World 2000. Prague, 09.07.2000-12.07.2000] Grant - others:MŠMT ČR(CZ) VS96047; MPO(CZ) RP-4210 Institutional research plan: AV0Z1030915 Keywords : neural network * classifier * hyperspheres * big -dimensional data Subject RIV: BA - General Mathematics

  9. A high-speed analog neural processor

    NARCIS (Netherlands)

    Masa, P.; Masa, Peter; Hoen, Klaas; Hoen, Klaas; Wallinga, Hans

    1994-01-01

    Targeted at high-energy physics research applications, our special-purpose analog neural processor can classify up to 70 dimensional vectors within 50 nanoseconds. The decision-making process of the implemented feedforward neural network enables this type of computation to tolerate weight

  10. Neurophysiology and neural engineering: a review.

    Science.gov (United States)

    Prochazka, Arthur

    2017-08-01

    Neurophysiology is the branch of physiology concerned with understanding the function of neural systems. Neural engineering (also known as neuroengineering) is a discipline within biomedical engineering that uses engineering techniques to understand, repair, replace, enhance, or otherwise exploit the properties and functions of neural systems. In most cases neural engineering involves the development of an interface between electronic devices and living neural tissue. This review describes the origins of neural engineering, the explosive development of methods and devices commencing in the late 1950s, and the present-day devices that have resulted. The barriers to interfacing electronic devices with living neural tissues are many and varied, and consequently there have been numerous stops and starts along the way. Representative examples are discussed. None of this could have happened without a basic understanding of the relevant neurophysiology. I also consider examples of how neural engineering is repaying the debt to basic neurophysiology with new knowledge and insight. Copyright © 2017 the American Physiological Society.

  11. Neural Networks for Non-linear Control

    DEFF Research Database (Denmark)

    Sørensen, O.

    1994-01-01

    This paper describes how a neural network, structured as a Multi Layer Perceptron, is trained to predict, simulate and control a non-linear process.......This paper describes how a neural network, structured as a Multi Layer Perceptron, is trained to predict, simulate and control a non-linear process....

  12. Interpretable neural networks with BP-SOM

    NARCIS (Netherlands)

    Weijters, A.J.M.M.; Bosch, van den A.P.J.; Pobil, del A.P.; Mira, J.; Ali, M.

    1998-01-01

    Artificial Neural Networks (ANNS) are used successfully in industry and commerce. This is not surprising since neural networks are especially competitive for complex tasks for which insufficient domain-specific knowledge is available. However, interpretation of models induced by ANNS is often

  13. Deciphering the Cognitive and Neural Mechanisms Underlying ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Deciphering the Cognitive and Neural Mechanisms Underlying Auditory Learning. This project seeks to understand the brain mechanisms necessary for people to learn to perceive sounds. Neural circuits and learning. The research team will test people with and without musical training to evaluate their capacity to learn ...

  14. The neural network approach to parton fitting

    International Nuclear Information System (INIS)

    Rojo, Joan; Latorre, Jose I.; Del Debbio, Luigi; Forte, Stefano; Piccione, Andrea

    2005-01-01

    We introduce the neural network approach to global fits of parton distribution functions. First we review previous work on unbiased parametrizations of deep-inelastic structure functions with faithful estimation of their uncertainties, and then we summarize the current status of neural network parton distribution fits

  15. NEURAL METHODS FOR THE FINANCIAL PREDICTION

    OpenAIRE

    Jerzy Balicki; Piotr Dryja; Waldemar Korłub; Piotr Przybyłek; Maciej Tyszka; Marcin Zadroga; Marcin Zakidalski

    2016-01-01

    Artificial neural networks can be used to predict share investment on the stock market, assess the reliability of credit client or predicting banking crises. Moreover, this paper discusses the principles of cooperation neural network algorithms with evolutionary method, and support vector machines. In addition, a reference is made to other methods of artificial intelligence, which are used in finance prediction.

  16. NEURAL METHODS FOR THE FINANCIAL PREDICTION

    Directory of Open Access Journals (Sweden)

    Jerzy Balicki

    2016-06-01

    Full Text Available Artificial neural networks can be used to predict share investment on the stock market, assess the reliability of credit client or predicting banking crises. Moreover, this paper discusses the principles of cooperation neural network algorithms with evolutionary method, and support vector machines. In addition, a reference is made to other methods of artificial intelligence, which are used in finance prediction.

  17. Neural Network to Solve Concave Games

    OpenAIRE

    Liu, Zixin; Wang, Nengfa

    2014-01-01

    The issue on neural network method to solve concave games is concerned. Combined with variational inequality, Ky Fan inequality, and projection equation, concave games are transformed into a neural network model. On the basis of the Lyapunov stable theory, some stability results are also given. Finally, two classic games’ simulation results are given to illustrate the theoretical results.

  18. Neural Network Algorithm for Particle Loading

    International Nuclear Information System (INIS)

    Lewandowski, J.L.V.

    2003-01-01

    An artificial neural network algorithm for continuous minimization is developed and applied to the case of numerical particle loading. It is shown that higher-order moments of the probability distribution function can be efficiently renormalized using this technique. A general neural network for the renormalization of an arbitrary number of moments is given

  19. Neural constructivism or self-organization?

    NARCIS (Netherlands)

    van der Maas, H.L.J.; Molenaar, P.C.M.

    2000-01-01

    Comments on the article by S. R. Quartz et al (see record 1998-00749-001) which discussed the constructivist perspective of interaction between cognition and neural processes during development and consequences for theories of learning. Three arguments are given to show that neural constructivism

  20. Memory in Neural Networks and Glasses

    NARCIS (Netherlands)

    Heerema, M.

    2000-01-01

    The thesis tries and models a neural network in a way which, at essential points, is biologically realistic. In a biological context, the changes of the synapses of the neural network are most often described by what is called `Hebb's learning rule'. On careful analysis it is, in fact, nothing but a

  1. Windowed active sampling for reliable neural learning

    NARCIS (Netherlands)

    Barakova, E.I; Spaanenburg, L

    The composition of the example set has a major impact on the quality of neural learning. The popular approach is focused on extensive pre-processing to bridge the representation gap between process measurement and neural presentation. In contrast, windowed active sampling attempts to solve these

  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. Chronic heart failure

    OpenAIRE

    Hopper, Ingrid; Easton, Kellie

    2017-01-01

    1. The common symptoms and signs of chronic heart failure are dyspnoea, ankle swelling, raised jugular venous pressure and basal crepitations. Other conditions may be confused with chronic heart failure, including dependent oedema or oedema due to renal or hepatic disease. Shortness of breath may be due to respiratory disease or severe anaemia. Heart failure secondary to lung disease (cor pulmonale) should be distinguished from congestive cardiac failure. Heart failure may also present with l...

  4. A new perspective on behavioral inconsistency and neural noise in aging: Compensatory speeding of neural communication

    Directory of Open Access Journals (Sweden)

    S. Lee Hong

    2012-09-01

    Full Text Available This paper seeks to present a new perspective on the aging brain. Here, we make connections between two key phenomena of brain aging: 1 increased neural noise or random background activity; and 2 slowing of brain activity. Our perspective proposes the possibility that the slowing of neural processing due to decreasing nerve conduction velocities leads to a compensatory speeding of neuron firing rates. These increased firing rates lead to a broader distribution of power in the frequency spectrum of neural oscillations, which we propose, can just as easily be interpreted as neural noise. Compensatory speeding of neural activity, as we present, is constrained by the: A availability of metabolic energy sources; and B competition for frequency bandwidth needed for neural communication. We propose that these constraints lead to the eventual inability to compensate for age-related declines in neural function that are manifested clinically as deficits in cognition, affect, and motor behavior.

  5. Enhanced Brain Responses to Pain-Related Words in Chronic Back Pain Patients and Their Modulation by Current Pain

    OpenAIRE

    Ritter, Alexander; Franz, Marcel; Puta, Christian; Dietrich, Caroline; Miltner, Wolfgang H. R.; Weiss, Thomas

    2016-01-01

    Previous functional magnetic resonance imaging (fMRI) studies in healthy controls (HC) and pain-free migraine patients found activations to pain-related words in brain regions known to be activated while subjects experience pain. The aim of the present study was to identify neural activations induced by pain-related words in a sample of chronic back pain (CBP) patients experiencing current chronic pain compared to HC. In particular, we were interested in how current pain influences brain acti...

  6. 22nd Italian Workshop on Neural Nets

    CERN Document Server

    Bassis, Simone; Esposito, Anna; Morabito, Francesco

    2013-01-01

    This volume collects a selection of contributions which has been presented at the 22nd Italian Workshop on Neural Networks, the yearly meeting of the Italian Society for Neural Networks (SIREN). The conference was held in Italy, Vietri sul Mare (Salerno), during May 17-19, 2012. The annual meeting of SIREN is sponsored by International Neural Network Society (INNS), European Neural Network Society (ENNS) and IEEE Computational Intelligence Society (CIS). The book – as well as the workshop-  is organized in three main components, two special sessions and a group of regular sessions featuring different aspects and point of views of artificial neural networks and natural intelligence, also including applications of present compelling interest.

  7. Dynamic decomposition of spatiotemporal neural signals.

    Directory of Open Access Journals (Sweden)

    Luca Ambrogioni

    2017-05-01

    Full Text Available Neural signals are characterized by rich temporal and spatiotemporal dynamics that reflect the organization of cortical networks. Theoretical research has shown how neural networks can operate at different dynamic ranges that correspond to specific types of information processing. Here we present a data analysis framework that uses a linearized model of these dynamic states in order to decompose the measured neural signal into a series of components that capture both rhythmic and non-rhythmic neural activity. The method is based on stochastic differential equations and Gaussian process regression. Through computer simulations and analysis of magnetoencephalographic data, we demonstrate the efficacy of the method in identifying meaningful modulations of oscillatory signals corrupted by structured temporal and spatiotemporal noise. These results suggest that the method is particularly suitable for the analysis and interpretation of complex temporal and spatiotemporal neural signals.

  8. Signal Processing and Neural Network Simulator

    Science.gov (United States)

    Tebbe, Dennis L.; Billhartz, Thomas J.; Doner, John R.; Kraft, Timothy T.

    1995-04-01

    The signal processing and neural network simulator (SPANNS) is a digital signal processing simulator with the capability to invoke neural networks into signal processing chains. This is a generic tool which will greatly facilitate the design and simulation of systems with embedded neural networks. The SPANNS is based on the Signal Processing WorkSystemTM (SPWTM), a commercial-off-the-shelf signal processing simulator. SPW provides a block diagram approach to constructing signal processing simulations. Neural network paradigms implemented in the SPANNS include Backpropagation, Kohonen Feature Map, Outstar, Fully Recurrent, Adaptive Resonance Theory 1, 2, & 3, and Brain State in a Box. The SPANNS was developed by integrating SAIC's Industrial Strength Neural Networks (ISNN) Software into SPW.

  9. Neural networks with discontinuous/impact activations

    CERN Document Server

    Akhmet, Marat

    2014-01-01

    This book presents as its main subject new models in mathematical neuroscience. A wide range of neural networks models with discontinuities are discussed, including impulsive differential equations, differential equations with piecewise constant arguments, and models of mixed type. These models involve discontinuities, which are natural because huge velocities and short distances are usually observed in devices modeling the networks. A discussion of the models, appropriate for the proposed applications, is also provided. This book also: Explores questions related to the biological underpinning for models of neural networks\\ Considers neural networks modeling using differential equations with impulsive and piecewise constant argument discontinuities Provides all necessary mathematical basics for application to the theory of neural networks Neural Networks with Discontinuous/Impact Activations is an ideal book for researchers and professionals in the field of engineering mathematics that have an interest in app...

  10. International Conference on Artificial Neural Networks (ICANN)

    CERN Document Server

    Mladenov, Valeri; Kasabov, Nikola; Artificial Neural Networks : Methods and Applications in Bio-/Neuroinformatics

    2015-01-01

    The book reports on the latest theories on artificial neural networks, with a special emphasis on bio-neuroinformatics methods. It includes twenty-three papers selected from among the best contributions on bio-neuroinformatics-related issues, which were presented at the International Conference on Artificial Neural Networks, held in Sofia, Bulgaria, on September 10-13, 2013 (ICANN 2013). The book covers a broad range of topics concerning the theory and applications of artificial neural networks, including recurrent neural networks, super-Turing computation and reservoir computing, double-layer vector perceptrons, nonnegative matrix factorization, bio-inspired models of cell communities, Gestalt laws, embodied theory of language understanding, saccadic gaze shifts and memory formation, and new training algorithms for Deep Boltzmann Machines, as well as dynamic neural networks and kernel machines. It also reports on new approaches to reinforcement learning, optimal control of discrete time-delay systems, new al...

  11. Decentralized neural control application to robotics

    CERN Document Server

    Garcia-Hernandez, Ramon; Sanchez, Edgar N; Alanis, Alma y; Ruz-Hernandez, Jose A

    2017-01-01

    This book provides a decentralized approach for the identification and control of robotics systems. It also presents recent research in decentralized neural control and includes applications to robotics. Decentralized control is free from difficulties due to complexity in design, debugging, data gathering and storage requirements, making it preferable for interconnected systems. Furthermore, as opposed to the centralized approach, it can be implemented with parallel processors. This approach deals with four decentralized control schemes, which are able to identify the robot dynamics. The training of each neural network is performed on-line using an extended Kalman filter (EKF). The first indirect decentralized control scheme applies the discrete-time block control approach, to formulate a nonlinear sliding manifold. The second direct decentralized neural control scheme is based on the backstepping technique, approximated by a high order neural network. The third control scheme applies a decentralized neural i...

  12. Neural neworks in a management information systems

    Directory of Open Access Journals (Sweden)

    Jana Weinlichová

    2009-01-01

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

  13. Noradrenergic modulation of neural erotic stimulus perception.

    Science.gov (United States)

    Graf, Heiko; Wiegers, Maike; Metzger, Coraline Danielle; Walter, Martin; Grön, Georg; Abler, Birgit

    2017-09-01

    We recently investigated neuromodulatory effects of the noradrenergic agent reboxetine and the dopamine receptor affine amisulpride in healthy subjects on dynamic erotic stimulus processing. Whereas amisulpride left sexual functions and neural activations unimpaired, we observed detrimental activations under reboxetine within the caudate nucleus corresponding to motivational components of sexual behavior. However, broadly impaired subjective sexual functioning under reboxetine suggested effects on further neural components. We now investigated the same sample under these two agents with static erotic picture stimulation as alternative stimulus presentation mode to potentially observe further neural treatment effects of reboxetine. 19 healthy males were investigated under reboxetine, amisulpride and placebo for 7 days each within a double-blind cross-over design. During fMRI static erotic picture were presented with preceding anticipation periods. Subjective sexual functions were assessed by a self-reported questionnaire. Neural activations were attenuated within the caudate nucleus, putamen, ventral striatum, the pregenual and anterior midcingulate cortex and in the orbitofrontal cortex under reboxetine. Subjective diminished sexual arousal under reboxetine was correlated with attenuated neural reactivity within the posterior insula. Again, amisulpride left neural activations along with subjective sexual functioning unimpaired. Neither reboxetine nor amisulpride altered differential neural activations during anticipation of erotic stimuli. Our results verified detrimental effects of noradrenergic agents on neural motivational but also emotional and autonomic components of sexual behavior. Considering the overlap of neural network alterations with those evoked by serotonergic agents, our results suggest similar neuromodulatory effects of serotonergic and noradrenergic agents on common neural pathways relevant for sexual behavior. Copyright © 2017 Elsevier B.V. and

  14. Artificial control of muscle by endoneural multi electrode stimulation and sensing

    NARCIS (Netherlands)

    Rutten, Wim; Bouwman, R.L.M.

    1991-01-01

    Artificial electrical stimulation of motor nerves for muscle control can be made selective by using intrafascicular micro electrode arrays which contact many individual or small groups of nerve fibres. If at the same time te electrode arrays could record afferent information from the stimulated

  15. Nuclear electronic components of surface contamination monitor based on multi-electrode proportional counter

    International Nuclear Information System (INIS)

    Du Xiangyang; Zhang Yong; Han Shuping; Rao Xianming; Fang Jintu

    2001-01-01

    The nuclear electronic components applying in Portal Monitor and Hands and Feet Surface Contamination Monitor were based on modern integrated circuit are introduced. The detailed points in circuit design and manufacturing technique are analyzed

  16. Wavelet based analysis of multi-electrode EEG-signals in epilepsy

    Science.gov (United States)

    Hein, Daniel A.; Tetzlaff, Ronald

    2005-06-01

    For many epilepsy patients seizures cannot sufficiently be controlled by an antiepileptic pharmacatherapy. Furthermore, only in small number of cases a surgical treatment may be possible. The aim of this work is to contribute to the realization of an implantable seizure warning device. By using recordings of electroenzephalographical(EEG) signals obtained from the department of epileptology of the University of Bonn we studied a recently proposed algorithm for the detection of parameter changes in nonlinear systems. Firstly, after calculating the crosscorrelation function between the signals of two electrodes near the epileptic focus, a wavelet-analysis follows using a sliding window with the so called Mexican-Hat wavelet. Then the Shannon-Entropy of the wavelet-transformed data has been determined providing the information content on a time scale in subject to the dilation of the wavelet-transformation. It shows distinct changes at the seizure onset for all dilations and for all patients.

  17. Automatic monitoring of radial injection tracer tests using a novel multi-electrode resistivity system

    International Nuclear Information System (INIS)

    Ward, R.S.; Sen, M.A.; Williams, G.M.; Jackson, P.D.

    1990-01-01

    A radial injection tracer test has been carried out in an unconfined fluvial sand and gravel aquifer underlain by low permeability clay. Sodium chloride has been used as an electrolyte tracer and breakthrough has been monitored using a newly developed automatic resistivity system (RESCAN) incorporating six fully penetrating resistivity probes each having 80 electrodes spaced at 5 cm intervals along their length. Each electrode is individually addressable under computer control to either carry current or measure potential. Any four electrodes can be selected in the traditional Wenner configuration to measure formation resistivity. Rapid measurement of changes in resistivity allows a very detailed picture of tracer migration to be obtained. The resistivity probes were placed at 1 and 2 m radii from the central fully-screened tracer injection well along three limbs at 120 degrees. Resistivity measurements were compared with adjacent multi-level samplers. An 8 x 8 m grid of 140 surface electrodes centred on the central well was also installed. The resistivity profiles measured prior to tracer injection were used to infer lithology, particularly layering. Detailed breakthrough curves were obtained at 77 positions along each of the six probes and compared with adjacent multi-level sampler breakthrough curves. The results showed that the aquifer was extremely heterogeneous even on this small scale. Because the system operates automatically without the need to extract and analyse large numbers of water samples, it opens up the possibility of carrying out lots of small scale injection tests within a larger domain likely to be invaded by a tracer or pollution plume. Such detailed information for determining aquifer properties can provide the data set necessary for characterisation of the aquifer to predict dispersion parameters appropriate to the large scale. (Author) (6 refs., 9 figs., 2 tabs.)

  18. Microfluidic in-channel multi-electrode platform for neurotransmitter sensing

    Science.gov (United States)

    Kara, A.; Mathault, J.; Reitz, A.; Boisvert, M.; Tessier, F.; Greener, J.; Miled, A.

    2016-03-01

    In this project we present a microfluidic platform with in-channel micro-electrodes for in situ screening of bio/chemical samples through a lab-on-chip system. We used a novel method to incorporate electrochemical sensors array (16x20) connected to a PCB, which opens the way for imaging applications. A 200 μm height microfluidic channel was bonded to electrochemical sensors. The micro-channel contains 3 inlets used to introduce phosphate buffer saline (PBS), ferrocynide and neurotransmitters. The flow rate was controlled through automated micro-pumps. A multiplexer was used to scan electrodes and perform individual cyclic voltammograms by a custom potentiostat. The behavior of the system was linear in terms of variation of current versus concentration. It was used to detect the neurotransmitters serotonin, dopamine and glutamate.

  19. In situ detection of tree root distribution and biomass by multi-electrode resistivity imaging.

    Science.gov (United States)

    Amato, Mariana; Basso, Bruno; Celano, Giuseppe; Bitella, Giovanni; Morelli, Gianfranco; Rossi, Roberta

    2008-10-01

    Traditional methods for studying tree roots are destructive and labor intensive, but available nondestructive techniques are applicable only to small scale studies or are strongly limited by soil conditions and root size. Soil electrical resistivity measured by geoelectrical methods has the potential to detect belowground plant structures, but quantitative relationships of these measurements with root traits have not been assessed. We tested the ability of two-dimensional (2-D) DC resistivity tomography to detect the spatial variability of roots and to quantify their biomass in a tree stand. A high-resolution resistivity tomogram was generated along a 11.75 m transect under an Alnus glutinosa (L.) Gaertn. stand based on an alpha-Wenner configuration with 48 electrodes spaced 0.25 m apart. Data were processed by a 2-D finite-element inversion algorithm, and corrected for soil temperature. Data acquisition, inversion and imaging were completed in the field within 60 min. Root dry mass per unit soil volume (root mass density, RMD) was measured destructively on soil samples collected to a depth of 1.05 m. Soil sand, silt, clay and organic matter contents, electrical conductivity, water content and pH were measured on a subset of samples. The spatial pattern of soil resistivity closely matched the spatial distribution of RMD. Multiple linear regression showed that only RMD and soil water content were related to soil resistivity along the transect. Regression analysis of RMD against soil resistivity revealed a highly significant logistic relationship (n = 97), which was confirmed on a separate dataset (n = 67), showing that soil resistivity was quantitatively related to belowground tree root biomass. This relationship provides a basis for developing quick nondestructive methods for detecting root distribution and quantifying root biomass, as well as for optimizing sampling strategies for studying root-driven phenomena.

  20. Multi-electrode continuous flow microbial electrolysis cell for biogas production from acetate

    Energy Technology Data Exchange (ETDEWEB)

    Rader, Geoffrey K.; Logan, Bruce E. [Department of Civil and Environmental Engineering, Penn State University, University Park, PA 16802 (United States)

    2010-09-15

    Most microbial electrolysis cells (MECs) contain only a single set of electrodes. In order to examine the scalability of a multiple-electrode design, we constructed a 2.5 L MEC containing 8 separate electrode pairs made of graphite fiber brush anodes pre-acclimated for current generation using acetate, and 304 stainless steel mesh cathodes (64 m{sup 2}/m{sup 3}). Under continuous flow conditions and a one day hydraulic retention time, the maximum current was 181 mA (1.18 A/m{sup 2}, cathode surface area; 74 A/m{sup 3}) within three days of operation. The maximum hydrogen production (day 3) was 0.53 L/L-d, reaching an energy efficiency relative to electrical energy input of {eta}{sub E} = 144%. Current production remained relatively steady (days 3-18), but the gas composition dramatically shifted over time. By day 16, there was little H{sub 2} gas recovered and methane production increased from 0.049 L/L-d (day 3) to 0.118 L/L-d. When considering the energy value of both hydrogen and methane, efficiency relative to electrical input remained above 100% until near the end of the experiment (day 17) when only methane gas was being produced. Our results show that MECs can be scaled up primarily based on cathode surface area, but that hydrogen can be completely consumed in a continuous flow system unless methanogens can be completely eliminated from the system. (author)

  1. Domestic wastewater treatment using multi-electrode continuous flow MFCs with a separator electrode assembly design

    KAUST Repository

    Ahn, Yongtae

    2012-10-11

    Treatment of domestic wastewater using microbial fuel cells (MFCs) will require reactors with multiple electrodes, but this presents unique challenges under continuous flow conditions due to large changes in the chemical oxygen demand (COD) concentration within the reactor. Domestic wastewater treatment was examined using a single-chamber MFC (130 mL) with multiple graphite fiber brush anodes wired together and a single air cathode (cathode specific area of 27 m2/m3). In fed-batch operation, where the COD concentration was spatially uniform in the reactor but changed over time, the maximum current density was 148 ± 8 mA/m2 (1,000 Ω), the maximum power density was 120 mW/m2, and the overall COD removal was >90 %. However, in continuous flow operation (8 h hydraulic retention time, HRT), there was a 57 % change in the COD concentration across the reactor (influent versus effluent) and the current density was only 20 ± 13 mA/m2. Two approaches were used to increase performance under continuous flow conditions. First, the anodes were separately wired to the cathode, which increased the current density to 55 ± 15 mA/m2. Second, two MFCs were hydraulically connected in series (each with half the original HRT) to avoid large changes in COD among the anodes in the same reactor. The second approach improved current density to 73 ± 13 mA/m2. These results show that current generation from wastewaters in MFCs with multiple anodes, under continuous flow conditions, can be improved using multiple reactors in series, as this minimizes changes in COD in each reactor. © 2012 Springer-Verlag Berlin Heidelberg.

  2. Multiphase, multi-electrode Joule heat computations for glass melter and in situ vitrification simulations

    International Nuclear Information System (INIS)

    Lowery, P.S.; Lessor, D.L.

    1991-02-01

    Waste glass melter and in situ vitrification (ISV) processes represent the combination of electrical thermal, and fluid flow phenomena to produce a stable waste-from product. Computational modeling of the thermal and fluid flow aspects of these processes provides a useful tool for assessing the potential performance of proposed system designs. These computations can be performed at a fraction of the cost of experiment. Consequently, computational modeling of vitrification systems can also provide and economical means for assessing the suitability of a proposed process application. The computational model described in this paper employs finite difference representations of the basic continuum conservation laws governing the thermal, fluid flow, and electrical aspects of the vitrification process -- i.e., conservation of mass, momentum, energy, and electrical charge. The resulting code is a member of the TEMPEST family of codes developed at the Pacific Northwest Laboratory (operated by Battelle for the US Department of Energy). This paper provides an overview of the numerical approach employed in TEMPEST. In addition, results from several TEMPEST simulations of sample waste glass melter and ISV processes are provided to illustrate the insights to be gained from computational modeling of these processes. 3 refs., 13 figs

  3. Cross-interval histogram analysis of neuronal activity on multi-electrode arrays

    NARCIS (Netherlands)

    Castellone, P.; Rutten, Wim; Marani, Enrico

    2003-01-01

    Cross-neuron-interval histogram (CNIH) analysis has been performed in order to study correlated activity and connectivity between pairs of neurons in a spontaneously active developing cultured network of rat cortical cells. Thirty-eight histograms could be analyzed using two parameters, one for the

  4. MEGAS multi-electrode gas sensor system. Final report; MEGAS - Multi-Elektroden-Gassensorsystem. Abschlussbericht

    Energy Technology Data Exchange (ETDEWEB)

    Kelleter, J.

    2003-07-01

    In the context of the MEGAS project, GTE developed and and constructed an electronic system for controlling and data acquisition of sensors for laboratory and test applications. The system is based on microcontrollers and has a data bus connection. Measurements made in order to find out whether the concentrations of a binary gas mixture and combustion gases are detected separately were successful. A demonstration system was constructed. The MEGAS project showed that it is possible to separate two gases by a sensitive layer at constant sensor temperature. The sensor element is a promising technology. Further research is required on suppressing sensor poisoning by siloxanes, and on reduced sensitivity to interfering gases (e.g. ethanol in the case of combustion gases). (orig.)

  5. Multi-electrode gas sensor system - MEGAS. Final report; Multi-Elektroden-Gassensorsystem - MEGAS. Abschlussbericht

    Energy Technology Data Exchange (ETDEWEB)

    Heidtkamp, C.

    2002-07-01

    A tungsten/titanium - mixed-oxide based sensor for selective exhaust gas measurement of e.g. diesel engines (NO{sub x}, CO, hydrocarbons, NH{sub 3},..) is described. The special design of the used sensors should allow operation at high ambient temperature with the potential of quantitative determination of different exhaust gas components with only one sensor. Several batches of sensor prototypes are characterised according to sensitivity and stability. (orig.)

  6. Charge sharing in multi-electrode devices for deterministic doping studied by IBIC

    International Nuclear Information System (INIS)

    Jong, L.M.; Newnham, J.N.; Yang, C.; Van Donkelaar, J.A.; Hudson, F.E.; Dzurak, A.S.; Jamieson, D.N.

    2011-01-01

    Following a single ion strike in a semiconductor device the induced charge distribution changes rapidly with time and space. This phenomenon has important applications to the sensing of ionizing radiation with applications as diverse as deterministic doping in semiconductor devices to radiation dosimetry. We have developed a new method for the investigation of this phenomenon by using a nuclear microprobe and the technique of Ion Beam Induced Charge (IBIC) applied to a specially configured sub-100 μm scale silicon device fitted with two independent surface electrodes coupled to independent data acquisition systems. The separation between the electrodes is comparable to the range of the 2 MeV He ions used in our experiments. This system allows us to integrate the total charge induced in the device by summing the signals from the independent electrodes and to measure the sharing of charge between the electrodes as a function of the ion strike location as a nuclear microprobe beam is scanned over the sensitive region of the device. It was found that for a given ion strike location the charge sharing between the electrodes allowed the beam-strike location to be determined to higher precision than the probe resolution. This result has potential application to the development of a deterministic doping technique where counted ion implantation is used to fabricate devices that exploit the quantum mechanical attributes of the implanted ions.

  7. Multi-electrode continuous flow microbial electrolysis cell for biogas production from acetate

    KAUST Repository

    Rader, Geoffrey K.; Logan, Bruce E.

    2010-01-01

    Most microbial electrolysis cells (MECs) contain only a single set of electrodes. In order to examine the scalability of a multiple-electrode design, we constructed a 2.5 L MEC containing 8 separate electrode pairs made of graphite fiber brush anodes pre-acclimated for current generation using acetate, and 304 stainless steel mesh cathodes (64 m2/m3). Under continuous flow conditions and a one day hydraulic retention time, the maximum current was 181 mA (1.18 A/m2, cathode surface area; 74 A/m 3) within three days of operation. The maximum hydrogen production (day 3) was 0.53 L/L-d, reaching an energy efficiency relative to electrical energy input of ηE = 144%. Current production remained relatively steady (days 3-18), but the gas composition dramatically shifted over time. By day 16, there was little H2 gas recovered and methane production increased from 0.049 L/L-d (day 3) to 0.118 L/L-d. When considering the energy value of both hydrogen and methane, efficiency relative to electrical input remained above 100% until near the end of the experiment (day 17) when only methane gas was being produced. Our results show that MECs can be scaled up primarily based on cathode surface area, but that hydrogen can be completely consumed in a continuous flow system unless methanogens can be completely eliminated from the system. © 2010 Professor T. Nejat Veziroglu. Published by Elsevier Ltd. All rights reserved.

  8. Domestic wastewater treatment using multi-electrode continuous flow MFCs with a separator electrode assembly design

    KAUST Repository

    Ahn, Yongtae; Logan, Bruce E.

    2012-01-01

    Treatment of domestic wastewater using microbial fuel cells (MFCs) will require reactors with multiple electrodes, but this presents unique challenges under continuous flow conditions due to large changes in the chemical oxygen demand (COD

  9. Development of MATLAB software to control data acquisition from a multichannel systems multi-electrode array.

    Science.gov (United States)

    Messier, Erik

    2016-08-01

    A Multichannel Systems (MCS) microelectrode array data acquisition (DAQ) unit is used to collect multichannel electrograms (EGM) from a Langendorff perfused rabbit heart system to study sudden cardiac death (SCD). MCS provides software through which data being processed by the DAQ unit can be displayed and saved, but this software's combined utility with MATLAB is not very effective. MCSs software stores recorded EGM data in a MathCad (MCD) format, which is then converted to a text file format. These text files are very large, and it is therefore very time consuming to import the EGM data into MATLAB for real-time analysis. Therefore, customized MATLAB software was developed to control the acquisition of data from the MCS DAQ unit, and provide specific laboratory accommodations for this study of SCD. The developed DAQ unit control software will be able to accurately: provide real time display of EGM signals; record and save EGM signals in MATLAB in a desired format; and produce real time analysis of the EGM signals; all through an intuitive GUI.

  10. Built-in test of electrode degradation of multi-electrode array biosensors

    NARCIS (Netherlands)

    Liu, H.Y.; Dumas, N.; Richardson, A.; Heal, R.; Kerkhoff, Hans G.

    2006-01-01

    Micro-electrode array (MEA) is a widely used platform in biosensor systems, which provide a technology in communicating with micro chemical and biological world. This paper addresses hte topic of testing micro electrode degradation for MEAs, which is a common encountered damage during its

  11. Multi-electrode continuous flow microbial electrolysis cell for biogas production from acetate

    KAUST Repository

    Rader, Geoffrey K.

    2010-09-01

    Most microbial electrolysis cells (MECs) contain only a single set of electrodes. In order to examine the scalability of a multiple-electrode design, we constructed a 2.5 L MEC containing 8 separate electrode pairs made of graphite fiber brush anodes pre-acclimated for current generation using acetate, and 304 stainless steel mesh cathodes (64 m2/m3). Under continuous flow conditions and a one day hydraulic retention time, the maximum current was 181 mA (1.18 A/m2, cathode surface area; 74 A/m 3) within three days of operation. The maximum hydrogen production (day 3) was 0.53 L/L-d, reaching an energy efficiency relative to electrical energy input of ηE = 144%. Current production remained relatively steady (days 3-18), but the gas composition dramatically shifted over time. By day 16, there was little H2 gas recovered and methane production increased from 0.049 L/L-d (day 3) to 0.118 L/L-d. When considering the energy value of both hydrogen and methane, efficiency relative to electrical input remained above 100% until near the end of the experiment (day 17) when only methane gas was being produced. Our results show that MECs can be scaled up primarily based on cathode surface area, but that hydrogen can be completely consumed in a continuous flow system unless methanogens can be completely eliminated from the system. © 2010 Professor T. Nejat Veziroglu. Published by Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

    Avendano-Bolivar, Adrian Emmanuel

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

  13. Multi-scale, multi-modal analysis uncovers complex relationship at the brain tissue-implant neural interface: new emphasis on the biological interface

    Science.gov (United States)

    Michelson, Nicholas J.; Vazquez, Alberto L.; Eles, James R.; Salatino, Joseph W.; Purcell, Erin K.; Williams, Jordan J.; Cui, X. Tracy; Kozai, Takashi D. Y.

    2018-06-01

    Objective. Implantable neural electrode devices are important tools for neuroscience research and have an increasing range of clinical applications. However, the intricacies of the biological response after implantation, and their ultimate impact on recording performance, remain challenging to elucidate. Establishing a relationship between the neurobiology and chronic recording performance is confounded by technical challenges related to traditional electrophysiological, material, and histological limitations. This can greatly impact the interpretations of results pertaining to device performance and tissue health surrounding the implant. Approach. In this work, electrophysiological activity and immunohistological analysis are compared after controlling for motion artifacts, quiescent neuronal activity, and material failure of devices in order to better understand the relationship between histology and electrophysiological outcomes. Main results. Even after carefully accounting for these factors, the presence of viable neurons and lack of glial scarring does not convey single unit recording performance. Significance. To better understand the biological factors influencing neural activity, detailed cellular and molecular tissue responses were examined. Decreases in neural activity and blood oxygenation in the tissue surrounding the implant, shift in expression levels of vesicular transporter proteins and ion channels, axon and myelin injury, and interrupted blood flow in nearby capillaries can impact neural activity around implanted neural interfaces. Combined, these tissue changes highlight the need for more comprehensive, basic science research to elucidate the relationship between biology and chronic electrophysiology performance in order to advance neural technologies.

  14. Corticospinal signals recorded with MEAs can predict the volitional forearm forces in rats.

    Science.gov (United States)

    Guo, Yi; Mesut, Sahin; Foulds, Richard A; Adamovich, Sergei V

    2013-01-01

    We set out to investigate if volitional components in the descending tracts of the spinal cord white matter can be accessed with multi-electrode array (MEA) recording technique. Rats were trained to press a lever connected to a haptic device with force feedback to receive sugar pellets. A flexible-substrate multi-electrode array was chronically implanted into the dorsal column of the cervical spinal cord. Field potentials and multi-unit activities were recorded from the descending axons of the corticospinal tract while the rat performed a lever pressing task. Forelimb forces, recorded with the sensor attached to the lever, were reconstructed using the hand position data and the neural signals through multiple trials over three weeks. The regression coefficients found from the trial set were cross-validated on the other trials recorded on same day. Approximately 30 trials of at least 2 seconds were required for accurate model estimation. The maximum correlation coefficient between the actual and predicted force was 0.7 in the test set. Positional information and its interaction with neural signals improved the correlation coefficient by 0.1 to 0.15. These results suggest that the volitional information contained in the corticospinal tract can be extracted with multi-channel neural recordings made with parenchymal electrodes.

  15. EDITORIAL: Special issue on applied neurodynamics: from neural dynamics to neural engineering Special issue on applied neurodynamics: from neural dynamics to neural engineering

    Science.gov (United States)

    Chiel, Hillel J.; Thomas, Peter J.

    2011-12-01

    , the sun, earth and moon) proved to be far more difficult. In the late nineteenth century, Poincaré made significant progress on this problem, introducing a geometric method of reasoning about solutions to differential equations (Diacu and Holmes 1996). This work had a powerful impact on mathematicians and physicists, and also began to influence biology. In his 1925 book, based on his work starting in 1907, and that of others, Lotka used nonlinear differential equations and concepts from dynamical systems theory to analyze a wide variety of biological problems, including oscillations in the numbers of predators and prey (Lotka 1925). Although little was known in detail about the function of the nervous system, Lotka concluded his book with speculations about consciousness and the implications this might have for creating a mathematical formulation of biological systems. Much experimental work in the 1930s and 1940s focused on the biophysical mechanisms of excitability in neural tissue, and Rashevsky and others continued to apply tools and concepts from nonlinear dynamical systems theory as a means of providing a more general framework for understanding these results (Rashevsky 1960, Landahl and Podolsky 1949). The publication of Hodgkin and Huxley's classic quantitative model of the action potential in 1952 created a new impetus for these studies (Hodgkin and Huxley 1952). In 1955, FitzHugh published an important paper that summarized much of the earlier literature, and used concepts from phase plane analysis such as asymptotic stability, saddle points, separatrices and the role of noise to provide a deeper theoretical and conceptual understanding of threshold phenomena (Fitzhugh 1955, Izhikevich and FitzHugh 2006). The Fitzhugh-Nagumo equations constituted an important two-dimensional simplification of the four-dimensional Hodgkin and Huxley equations, and gave rise to an extensive literature of analysis. Many of the papers in this special issue build on tools

  16. Alcohol dependence as a chronic pain disorder

    Science.gov (United States)

    Egli, Mark; Koob, George F.; Edwards, Scott

    2013-01-01

    Dysregulation of pain neurocircuitry and neurochemistry has been increasingly recognized as playing a critical role in a diverse spectrum of diseases including migraine, fibromyalgia, depression, and PTSD. Evidence presented here supports the hypothesis that alcohol dependence is among the pathologies arising from aberrant neurobiological substrates of pain. In this review, we explore the possible influence of alcohol analgesia and hyperalgesia in promoting alcohol misuse and dependence. We examine evidence that neuroanatomical sites involved in the negative emotional states of alcohol dependence also play an important role in pain transmission and may be functionally altered under chronic pain conditions. We also consider possible genetic links between pain transmission and alcohol dependence. We propose an allostatic load model in which episodes of alcohol intoxication and withdrawal, traumatic stressors, and injury are each capable of dysregulating an overlapping set of neural substrates to engender sensory and affective pain states that are integral to alcohol dependence and comorbid conditions such as anxiety, depression, and chronic pain. PMID:22975446

  17. Hereditary chronic pancreatitis

    Directory of Open Access Journals (Sweden)

    Mössner Joachim

    2007-01-01

    Full Text Available Abstract Hereditary chronic pancreatitis (HCP is a very rare form of early onset chronic pancreatitis. With the exception of the young age at diagnosis and a slower progression, the clinical course, morphological features and laboratory findings of HCP do not differ from those of patients with alcoholic chronic pancreatitis. As well, diagnostic criteria and treatment of HCP resemble that of chronic pancreatitis of other causes. The clinical presentation is highly variable and includes chronic abdominal pain, impairment of endocrine and exocrine pancreatic function, nausea and vomiting, maldigestion, diabetes, pseudocysts, bile duct and duodenal obstruction, and rarely pancreatic cancer. Fortunately, most patients have a mild disease. Mutations in the PRSS1 gene, encoding cationic trypsinogen, play a causative role in chronic pancreatitis. It has been shown that the PRSS1 mutations increase autocatalytic conversion of trypsinogen to active trypsin, and thus probably cause premature, intrapancreatic trypsinogen activation disturbing the intrapancreatic balance of proteases and their inhibitors. Other genes, such as the anionic trypsinogen (PRSS2, the serine protease inhibitor, Kazal type 1 (SPINK1 and the cystic fibrosis transmembrane conductance regulator (CFTR have been found to be associated with chronic pancreatitis (idiopathic and hereditary as well. Genetic testing should only be performed in carefully selected patients by direct DNA sequencing and antenatal diagnosis should not be encouraged. Treatment focuses on enzyme and nutritional supplementation, pain management, pancreatic diabetes, and local organ complications, such as pseudocysts, bile duct or duodenal obstruction. The disease course and prognosis of patients with HCP is unpredictable. Pancreatic cancer risk is elevated. Therefore, HCP patients should strongly avoid environmental risk factors for pancreatic cancer.

  18. Neural Networks in Control Applications

    DEFF Research Database (Denmark)

    Sørensen, O.

    are examined. The models are separated into three groups representing input/output descriptions as well as state space descriptions: - Models, where all in- and outputs are measurable (static networks). - Models, where some inputs are non-measurable (recurrent networks). - Models, where some in- and some...... outputs are non-measurable (recurrent networks with incomplete state information). The three groups are ordered in increasing complexity, and for each group it is shown how to solve the problems concerning training and application of the specific model type. Of particular interest are the model types...... Kalmann filter) representing state space description. The potentials of neural networks for control of non-linear processes are also examined, focusing on three different groups of control concepts, all considered as generalizations of known linear control concepts to handle also non-linear processes...

  19. Primary neural leprosy: systematic review

    Directory of Open Access Journals (Sweden)

    Jose Antonio Garbino

    2013-06-01

    Full Text Available The authors proposed a systematic review on the current concepts of primary neural leprosy by consulting the following online databases: MEDLINE, Lilacs/SciELO, and Embase. Selected studies were classified based on the degree of recommendation and levels of scientific evidence according to the “Oxford Centre for Evidence-based Medicine”. The following aspects were reviewed: cutaneous clinical and laboratorial investigations, i.e. skin clinical exam, smears, and biopsy, and Mitsuda's reaction; neurological investigation (anamnesis, electromyography and nerve biopsy; serological investigation and molecular testing, i.e. serological testing for the detection of the phenolic glycolipid 1 (PGL-I and the polymerase chain reaction (PCR; and treatment (classification criteria for the definition of specific treatment, steroid treatment, and cure criteria.

  20. Neural Tube Defects and Pregnancy

    Directory of Open Access Journals (Sweden)

    Emine Çoşar

    2009-09-01

    Full Text Available OBJECTIVE: Neural tube defects are congenital malformations those mostly causing life-long morbidities. They are prevented by the periconseptional folic acid usage and prenatal diagnostic methods. MATERIALS-METHODS: Pregnants from Afyonkarahisar and neighbourhood cities applied to our hospital and determined NTD, were investigated. RESULTS: In our obstetrics clinic 1403 delivery were made and 43 of them had fetus with NTD. Among these fetuses 41.3% had meningomyelocel, 17.4% had meningocel, 21.7% had encephalocel, 8.7% had unencephali and 4.3% had iniencephali. CONCLUSION: Incidence of NTD is high in our region and geographic region, nutrition and other socioeconomic factors may be related to the high incidence. Education of the mother and periconceptional folic acid usage may reduce teh incidence of NTD.

  1. Collision avoidance using neural networks

    Science.gov (United States)

    Sugathan, Shilpa; Sowmya Shree, B. V.; Warrier, Mithila R.; Vidhyapathi, C. M.

    2017-11-01

    Now a days, accidents on roads are caused due to the negligence of drivers and pedestrians or due to unexpected obstacles that come into the vehicle’s path. In this paper, a model (robot) is developed to assist drivers for a smooth travel without accidents. It reacts to the real time obstacles on the four critical sides of the vehicle and takes necessary action. The sensor used for detecting the obstacle was an IR proximity sensor. A single layer perceptron neural network is used to train and test all possible combinations of sensors result by using Matlab (offline). A microcontroller (ARM Cortex-M3 LPC1768) is used to control the vehicle through the output data which is received from Matlab via serial communication. Hence, the vehicle becomes capable of reacting to any combination of real time obstacles.

  2. Neural networks: a biased overview

    International Nuclear Information System (INIS)

    Domany, E.

    1988-01-01

    An overview of recent activity in the field of neural networks is presented. The long-range aim of this research is to understand how the brain works. First some of the problems are stated and terminology defined; then an attempt is made to explain why physicists are drawn to the field, and their main potential contribution. In particular, in recent years some interesting models have been introduced by physicists. A small subset of these models is described, with particular emphasis on those that are analytically soluble. Finally a brief review of the history and recent developments of single- and multilayer perceptrons is given, bringing the situation up to date regarding the central immediate problem of the field: search for a learning algorithm that has an associated convergence theorem

  3. Unpredictable chronic mild stress differentially impairs social and contextual discrimination learning in two inbred mouse strains.

    Science.gov (United States)

    van Boxelaere, Michiel; Clements, Jason; Callaerts, Patrick; D'Hooge, Rudi; Callaerts-Vegh, Zsuzsanna

    2017-01-01

    Alterations in the social and cognitive domain are considered important indicators for increased disability in many stress-related disorders. Similar impairments have been observed in rodents chronically exposed to stress, mimicking potential endophenotypes of stress-related psychopathologies such as major depression disorder (MDD), anxiety, conduct disorder, and posttraumatic stress disorder (PTSD). Data from numerous studies suggest that deficient plasticity mechanisms in hippocampus (HC) and prefrontal cortex (PFC) might underlie these social and cognitive deficits. Specifically, stress-induced deficiencies in neural plasticity have been associated with a hypodopaminergic state and reduced neural plasticity persistence. Here we assessed the effects of unpredictable chronic mild stress (UCMS) on exploratory, social and cognitive behavior of females of two inbred mouse strains (C57BL/6J and DBA/2J) that differ in their dopaminergic profile. Exposure to chronic stress resulted in impaired circadian rhythmicity, sociability and social cognition in both inbred strains, but differentially affected activity patterns and contextual discrimination performance. These stress-induced behavioral impairments were accompanied by reduced expression levels of brain derived neurotrophic factor (BDNF) in the prefrontal cortex. The strain-specific cognitive impairment was coexistent with enhanced plasma corticosterone levels and reduced expression of genes related to dopamine signaling in hippocampus. These results underline the importance of assessing different strains with multiple test batteries to elucidate the neural and genetic basis of social and cognitive impairments related to chronic stress.

  4. Unpredictable chronic mild stress differentially impairs social and contextual discrimination learning in two inbred mouse strains.

    Directory of Open Access Journals (Sweden)

    Michiel van Boxelaere

    Full Text Available Alterations in the social and cognitive domain are considered important indicators for increased disability in many stress-related disorders. Similar impairments have been observed in rodents chronically exposed to stress, mimicking potential endophenotypes of stress-related psychopathologies such as major depression disorder (MDD, anxiety, conduct disorder, and posttraumatic stress disorder (PTSD. Data from numerous studies suggest that deficient plasticity mechanisms in hippocampus (HC and prefrontal cortex (PFC might underlie these social and cognitive deficits. Specifically, stress-induced deficiencies in neural plasticity have been associated with a hypodopaminergic state and reduced neural plasticity persistence. Here we assessed the effects of unpredictable chronic mild stress (UCMS on exploratory, social and cognitive behavior of females of two inbred mouse strains (C57BL/6J and DBA/2J that differ in their dopaminergic profile. Exposure to chronic stress resulted in impaired circadian rhythmicity, sociability and social cognition in both inbred strains, but differentially affected activity patterns and contextual discrimination performance. These stress-induced behavioral impairments were accompanied by reduced expression levels of brain derived neurotrophic factor (BDNF in the prefrontal cortex. The strain-specific cognitive impairment was coexistent with enhanced plasma corticosterone levels and reduced expression of genes related to dopamine signaling in hippocampus. These results underline the importance of assessing different strains with multiple test batteries to elucidate the neural and genetic basis of social and cognitive impairments related to chronic stress.

  5. Autoantibodies in chronic pancreatitis

    DEFF Research Database (Denmark)

    Rumessen, J J; Marner, B; Pedersen, N T

    1985-01-01

    In 60 consecutive patients clinically suspected of having chronic pancreatitis the serum concentration of the immunoglobulins (IgA, IgG, IgM), the IgG- and IgA-type non-organ-specific autoantibodies against nuclear material (ANA), smooth and striated muscle, mitochondria, basal membrane, and reti......In 60 consecutive patients clinically suspected of having chronic pancreatitis the serum concentration of the immunoglobulins (IgA, IgG, IgM), the IgG- and IgA-type non-organ-specific autoantibodies against nuclear material (ANA), smooth and striated muscle, mitochondria, basal membrane......, and reticulin, and the IgG- and IgA-type pancreas-specific antibodies against islet cells, acinus cells, and ductal cells (DA) were estimated blindly. In 23 of the patients chronic pancreatitis was verified, whereas chronic pancreatitis was rejected in 37 patients (control group). IgG and IgA were found...... in significantly higher concentrations in the patients with chronic pancreatitis than in the control group but within the normal range. ANA and DA occurred very frequently in both groups but with no statistical difference. Other autoantibodies only occurred sporadically. The findings of this study do not support...

  6. Chronic daily headaches

    Directory of Open Access Journals (Sweden)

    Fayyaz Ahmed

    2012-01-01

    Full Text Available Chronic Daily Headache is a descriptive term that includes disorders with headaches on more days than not and affects 4% of the general population. The condition has a debilitating effect on individuals and society through direct cost to healthcare and indirectly to the economy in general. To successfully manage chronic daily headache syndromes it is important to exclude secondary causes with comprehensive history and relevant investigations; identify risk factors that predict its development and recognise its sub-types to appropriately manage the condition. Chronic migraine, chronic tension-type headache, new daily persistent headache and medication overuse headache accounts for the vast majority of chronic daily headaches. The scope of this article is to review the primary headache disorders. Secondary headaches are not discussed except medication overuse headache that often accompanies primary headache disorders. The article critically reviews the literature on the current understanding of daily headache disorders focusing in particular on recent developments in the treatment of frequent headaches.

  7. Application of neural network to CT

    International Nuclear Information System (INIS)

    Ma, Xiao-Feng; Takeda, Tatsuoki

    1999-01-01

    This paper presents a new method for two-dimensional image reconstruction by using a multilayer neural network. Multilayer neural networks are extensively investigated and practically applied to solution of various problems such as inverse problems or time series prediction problems. From learning an input-output mapping from a set of examples, neural networks can be regarded as synthesizing an approximation of multidimensional function (that is, solving the problem of hypersurface reconstruction, including smoothing and interpolation). From this viewpoint, neural networks are well suited to the solution of CT image reconstruction. Though a conventionally used object function of a neural network is composed of a sum of squared errors of the output data, we can define an object function composed of a sum of residue of an integral equation. By employing an appropriate line integral for this integral equation, we can construct a neural network that can be used for CT. We applied this method to some model problems and obtained satisfactory results. As it is not necessary to discretized the integral equation using this reconstruction method, therefore it is application to the problem of complicated geometrical shapes is also feasible. Moreover, in neural networks, interpolation is performed quite smoothly, as a result, inverse mapping can be achieved smoothly even in case of including experimental and numerical errors, However, use of conventional back propagation technique for optimization leads to an expensive computation cost. To overcome this drawback, 2nd order optimization methods or parallel computing will be applied in future. (J.P.N.)

  8. Nonequilibrium landscape theory of neural networks.

    Science.gov (United States)

    Yan, Han; Zhao, Lei; Hu, Liang; Wang, Xidi; Wang, Erkang; Wang, Jin

    2013-11-05

    The brain map project aims to map out the neuron connections of the human brain. Even with all of the wirings mapped out, the global and physical understandings of the function and behavior are still challenging. Hopfield quantified the learning and memory process of symmetrically connected neural networks globally through equilibrium energy. The energy basins of attractions represent memories, and the memory retrieval dynamics is determined by the energy gradient. However, the realistic neural networks are asymmetrically connected, and oscillations cannot emerge from symmetric neural networks. Here, we developed a nonequilibrium landscape-flux theory for realistic asymmetrically connected neural networks. We uncovered the underlying potential landscape and the associated Lyapunov function for quantifying the global stability and function. We found the dynamics and oscillations in human brains responsible for cognitive processes and physiological rhythm regulations are determined not only by the landscape gradient but also by the flux. We found that the flux is closely related to the degrees of the asymmetric connections in neural networks and is the origin of the neural oscillations. The neural oscillation landscape shows a closed-ring attractor topology. The landscape gradient attracts the network down to the ring. The flux is responsible for coherent oscillations on the ring. We suggest the flux may provide the driving force for associations among memories. We applied our theory to rapid-eye movement sleep cycle. We identified the key regulation factors for function through global sensitivity analysis of landscape topography against wirings, which are in good agreements with experiments.

  9. Nonequilibrium landscape theory of neural networks

    Science.gov (United States)

    Yan, Han; Zhao, Lei; Hu, Liang; Wang, Xidi; Wang, Erkang; Wang, Jin

    2013-01-01

    The brain map project aims to map out the neuron connections of the human brain. Even with all of the wirings mapped out, the global and physical understandings of the function and behavior are still challenging. Hopfield quantified the learning and memory process of symmetrically connected neural networks globally through equilibrium energy. The energy basins of attractions represent memories, and the memory retrieval dynamics is determined by the energy gradient. However, the realistic neural networks are asymmetrically connected, and oscillations cannot emerge from symmetric neural networks. Here, we developed a nonequilibrium landscape–flux theory for realistic asymmetrically connected neural networks. We uncovered the underlying potential landscape and the associated Lyapunov function for quantifying the global stability and function. We found the dynamics and oscillations in human brains responsible for cognitive processes and physiological rhythm regulations are determined not only by the landscape gradient but also by the flux. We found that the flux is closely related to the degrees of the asymmetric connections in neural networks and is the origin of the neural oscillations. The neural oscillation landscape shows a closed-ring attractor topology. The landscape gradient attracts the network down to the ring. The flux is responsible for coherent oscillations on the ring. We suggest the flux may provide the driving force for associations among memories. We applied our theory to rapid-eye movement sleep cycle. We identified the key regulation factors for function through global sensitivity analysis of landscape topography against wirings, which are in good agreements with experiments. PMID:24145451

  10. Adolescent girls' neural response to reward mediates the relation between childhood financial disadvantage and depression.

    Science.gov (United States)

    Romens, Sarah E; Casement, Melynda D; McAloon, Rose; Keenan, Kate; Hipwell, Alison E; Guyer, Amanda E; Forbes, Erika E

    2015-11-01

    Children who experience socioeconomic disadvantage are at heightened risk for developing depression; however, little is known about neurobiological mechanisms underlying this association. Low socioeconomic status (SES) during childhood may confer risk for depression through its stress-related effects on the neural circuitry associated with processing monetary rewards. In a prospective study, we examined the relationships among the number of years of household receipt of public assistance from age 5-16 years, neural activation during monetary reward anticipation and receipt at age 16, and depression symptoms at age 16 in 123 girls. Number of years of household receipt of public assistance was positively associated with heightened response in the medial prefrontal cortex during reward anticipation, and this heightened neural response mediated the relationship between socioeconomic disadvantage and current depression symptoms, controlling for past depression. Chronic exposure to socioeconomic disadvantage in childhood may alter neural circuitry involved in reward anticipation in adolescence, which in turn may confer risk for depression. © 2015 Association for Child and Adolescent Mental Health.

  11. Modeling Cerebrovascular Pathophysiology in Amyloid-β Metabolism using Neural-Crest-Derived Smooth Muscle Cells

    Directory of Open Access Journals (Sweden)

    Christine Cheung

    2014-10-01

    Full Text Available Summary: There is growing recognition of cerebrovascular contributions to neurodegenerative diseases. In the walls of cerebral arteries, amyloid-beta (Aβ accumulation is evident in a majority of aged people and patients with cerebral amyloid angiopathy. Here, we leverage human pluripotent stem cells to generate vascular smooth muscle cells (SMCs from neural crest progenitors, recapitulating brain-vasculature-specific attributes of Aβ metabolism. We confirm that the lipoprotein receptor, LRP1, functions in our neural-crest-derived SMCs to mediate Aβ uptake and intracellular lysosomal degradation. Hypoxia significantly compromises the contribution of SMCs to Aβ clearance by suppressing LRP1 expression. This enabled us to develop an assay of Aβ uptake by using the neural crest-derived SMCs with hypoxia as a stress paradigm. We then tested several vascular protective compounds in a high-throughput format, demonstrating the value of stem-cell-based phenotypic screening for novel therapeutics and drug repurposing, aimed at alleviating amyloid burden. : The contribution of blood vessel pathologies to neurodegenerative disorders is relatively neglected, partly due to inadequate human tissues for research. By using human stem cells, Cheung et al. establish a method of generating vascular smooth muscle cells (SMCs from neural crest progenitors, the primary precursors that give rise to brain blood vessels. These stem-cell-derived SMCs display defective amyloid processing under chronic hypoxia, a phenomenon well documented in the cerebral vasculatures of aged people and patients with Alzheimer’s disease.

  12. Permutation parity machines for neural synchronization

    International Nuclear Information System (INIS)

    Reyes, O M; Kopitzke, I; Zimmermann, K-H

    2009-01-01

    Synchronization of neural networks has been studied in recent years as an alternative to cryptographic applications such as the realization of symmetric key exchange protocols. This paper presents a first view of the so-called permutation parity machine, an artificial neural network proposed as a binary variant of the tree parity machine. The dynamics of the synchronization process by mutual learning between permutation parity machines is analytically studied and the results are compared with those of tree parity machines. It will turn out that for neural synchronization, permutation parity machines form a viable alternative to tree parity machines

  13. Enhancing neural-network performance via assortativity

    International Nuclear Information System (INIS)

    Franciscis, Sebastiano de; Johnson, Samuel; Torres, Joaquin J.

    2011-01-01

    The performance of attractor neural networks has been shown to depend crucially on the heterogeneity of the underlying topology. We take this analysis a step further by examining the effect of degree-degree correlations - assortativity - on neural-network behavior. We make use of a method recently put forward for studying correlated networks and dynamics thereon, both analytically and computationally, which is independent of how the topology may have evolved. We show how the robustness to noise is greatly enhanced in assortative (positively correlated) neural networks, especially if it is the hub neurons that store the information.

  14. Genetic algorithm for neural networks optimization

    Science.gov (United States)

    Setyawati, Bina R.; Creese, Robert C.; Sahirman, Sidharta

    2004-11-01

    This paper examines the forecasting performance of multi-layer feed forward neural networks in modeling a particular foreign exchange rates, i.e. Japanese Yen/US Dollar. The effects of two learning methods, Back Propagation and Genetic Algorithm, in which the neural network topology and other parameters fixed, were investigated. The early results indicate that the application of this hybrid system seems to be well suited for the forecasting of foreign exchange rates. The Neural Networks and Genetic Algorithm were programmed using MATLAB«.

  15. Stock market index prediction using neural networks

    Science.gov (United States)

    Komo, Darmadi; Chang, Chein-I.; Ko, Hanseok

    1994-03-01

    A neural network approach to stock market index prediction is presented. Actual data of the Wall Street Journal's Dow Jones Industrial Index has been used for a benchmark in our experiments where Radial Basis Function based neural networks have been designed to model these indices over the period from January 1988 to Dec 1992. A notable success has been achieved with the proposed model producing over 90% prediction accuracies observed based on monthly Dow Jones Industrial Index predictions. The model has also captured both moderate and heavy index fluctuations. The experiments conducted in this study demonstrated that the Radial Basis Function neural network represents an excellent candidate to predict stock market index.

  16. Mass reconstruction with a neural network

    International Nuclear Information System (INIS)

    Loennblad, L.; Peterson, C.; Roegnvaldsson, T.

    1992-01-01

    A feed-forward neural network method is developed for reconstructing the invariant mass of hadronic jets appearing in a calorimeter. The approach is illustrated in W→qanti q, where W-bosons are produced in panti p reactions at SPS collider energies. The neural network method yields results that are superior to conventional methods. This neural network application differs from the classification ones in the sense that an analog number (the mass) is computed by the network, rather than a binary decision being made. As a by-product our application clearly demonstrates the need for using 'intelligent' variables in instances when the amount of training instances is limited. (orig.)

  17. Neural network recognition of mammographic lesions

    International Nuclear Information System (INIS)

    Oldham, W.J.B.; Downes, P.T.; Hunter, V.

    1987-01-01

    A method for recognition of mammographic lesions through the use of neural networks is presented. Neural networks have exhibited the ability to learn the shape andinternal structure of patterns. Digitized mammograms containing circumscribed and stelate lesions were used to train a feedfoward synchronous neural network that self-organizes to stable attractor states. Encoding of data for submission to the network was accomplished by performing a fractal analysis of the digitized image. This results in scale invariant representation of the lesions. Results are discussed

  18. Estimation of Conditional Quantile using Neural Networks

    DEFF Research Database (Denmark)

    Kulczycki, P.; Schiøler, Henrik

    1999-01-01

    The problem of estimating conditional quantiles using neural networks is investigated here. A basic structure is developed using the methodology of kernel estimation, and a theory guaranteeing con-sistency on a mild set of assumptions is provided. The constructed structure constitutes a basis...... for the design of a variety of different neural networks, some of which are considered in detail. The task of estimating conditional quantiles is related to Bayes point estimation whereby a broad range of applications within engineering, economics and management can be suggested. Numerical results illustrating...... the capabilities of the elaborated neural network are also given....

  19. Convolutional Neural Network for Image Recognition

    CERN Document Server

    Seifnashri, Sahand

    2015-01-01

    The aim of this project is to use machine learning techniques especially Convolutional Neural Networks for image processing. These techniques can be used for Quark-Gluon discrimination using calorimeters data, but unfortunately I didn’t manage to get the calorimeters data and I just used the Jet data fromminiaodsim(ak4 chs). The Jet data was not good enough for Convolutional Neural Network which is designed for ’image’ recognition. This report is made of twomain part, part one is mainly about implementing Convolutional Neural Network on unphysical data such as MNIST digits and CIFAR-10 dataset and part 2 is about the Jet data.

  20. Applications of neural network to numerical analyses

    International Nuclear Information System (INIS)

    Takeda, Tatsuoki; Fukuhara, Makoto; Ma, Xiao-Feng; Liaqat, Ali

    1999-01-01

    Applications of a multi-layer neural network to numerical analyses are described. We are mainly concerned with the computed tomography and the solution of differential equations. In both cases as the objective functions for the training process of the neural network we employed residuals of the integral equation or the differential equations. This is different from the conventional neural network training where sum of the squared errors of the output values is adopted as the objective function. For model problems both the methods gave satisfactory results and the methods are considered promising for some kind of problems. (author)

  1. A neural network approach to burst detection.

    Science.gov (United States)

    Mounce, S R; Day, A J; Wood, A S; Khan, A; Widdop, P D; Machell, J

    2002-01-01

    This paper describes how hydraulic and water quality data from a distribution network may be used to provide a more efficient leakage management capability for the water industry. The research presented concerns the application of artificial neural networks to the issue of detection and location of leakage in treated water distribution systems. An architecture for an Artificial Neural Network (ANN) based system is outlined. The neural network uses time series data produced by sensors to directly construct an empirical model for predication and classification of leaks. Results are presented using data from an experimental site in Yorkshire Water's Keighley distribution system.

  2. Multistability in bidirectional associative memory neural networks

    International Nuclear Information System (INIS)

    Huang Gan; Cao Jinde

    2008-01-01

    In this Letter, the multistability issue is studied for Bidirectional Associative Memory (BAM) neural networks. Based on the existence and stability analysis of the neural networks with or without delay, it is found that the 2n-dimensional networks can have 3 n equilibria and 2 n equilibria of them are locally exponentially stable, where each layer of the BAM network has n neurons. Furthermore, the results has been extended to (n+m)-dimensional BAM neural networks, where there are n and m neurons on the two layers respectively. Finally, two numerical examples are presented to illustrate the validity of our results

  3. Multistability in bidirectional associative memory neural networks

    Science.gov (United States)

    Huang, Gan; Cao, Jinde

    2008-04-01

    In this Letter, the multistability issue is studied for Bidirectional Associative Memory (BAM) neural networks. Based on the existence and stability analysis of the neural networks with or without delay, it is found that the 2 n-dimensional networks can have 3 equilibria and 2 equilibria of them are locally exponentially stable, where each layer of the BAM network has n neurons. Furthermore, the results has been extended to (n+m)-dimensional BAM neural networks, where there are n and m neurons on the two layers respectively. Finally, two numerical examples are presented to illustrate the validity of our results.

  4. Chronic pelvic floor dysfunction.

    Science.gov (United States)

    Hartmann, Dee; Sarton, Julie

    2014-10-01

    The successful treatment of women with vestibulodynia and its associated chronic pelvic floor dysfunctions requires interventions that address a broad field of possible pain contributors. Pelvic floor muscle hypertonicity was implicated in the mid-1990s as a trigger of major chronic vulvar pain. Painful bladder syndrome, irritable bowel syndrome, fibromyalgia, and temporomandibular jaw disorder are known common comorbidities that can cause a host of associated muscular, visceral, bony, and fascial dysfunctions. It appears that normalizing all of those disorders plays a pivotal role in reducing complaints of chronic vulvar pain and sexual dysfunction. Though the studies have yet to prove a specific protocol, physical therapists trained in pelvic dysfunction are reporting success with restoring tissue normalcy and reducing vulvar and sexual pain. A review of pelvic anatomy and common findings are presented along with suggested physical therapy management. Copyright © 2014 Elsevier Ltd. All rights reserved.

  5. Omalizumab for chronic urticaria

    DEFF Research Database (Denmark)

    Ivyanskiy, Ilya; Sand, Carsten; Thomsen, Simon Francis

    2012-01-01

    Omalizumab is a recombinant humanized monoclonal antibody that blocks the high-affinity Fc receptor of IgE. Omalizumab has been approved for the treatment of moderate to severe asthma; however, there is currently more and more data showing promising results in the management also of chronic...... urticaria. We present a case series of 19 patients with chronic urticaria treated in a university department with omalizumab and give an overview of the existing literature comprising an additional 59 cases as well as a total of 139 patients enrolled in two randomized controlled trials comparing omalizumab...... with placebo. The collective evidence points to omalizumab as a safe and effective treatment option for patients with chronic urticaria who do not sufficiently respond to standard therapy as recommended by existing guidelines....

  6. Chronic hypersensitivity pneumonitis

    Directory of Open Access Journals (Sweden)

    Pereira CA

    2016-09-01

    Full Text Available Carlos AC Pereira,1 Andréa Gimenez,2 Lilian Kuranishi,2 Karin Storrer 2 1Interstitial Lung Diseases Program, 2Pulmonology Postgraduate, Federal University of São Paulo, São Paulo, Brazil Abstract: Hypersensitivity pneumonitis (HSP is a common interstitial lung disease resulting from inhalation of a large variety of antigens by susceptible individuals. The disease is best classified as acute and chronic. Chronic HSP can be fibrosing or not. Fibrotic HSP has a large differential diagnosis and has a worse prognosis. The most common etiologies for HSP are reviewed. Diagnostic criteria are proposed for both chronic forms based on exposure, lung auscultation, lung function tests, HRCT findings, bronchoalveolar lavage, and biopsies. Treatment options are limited, but lung transplantation results in greater survival in comparison to idiopathic pulmonary fibrosis. Randomized trials with new antifibrotic agents are necessary. Keywords: interstitial lung diseases, extrinsic allergic alveolitis, diffuse lung disease, lung immune response, HRCT, farmers lung

  7. An Introduction to Neural Networks for Hearing Aid Noise Recognition.

    Science.gov (United States)

    Kim, Jun W.; Tyler, Richard S.

    1995-01-01

    This article introduces the use of multilayered artificial neural networks in hearing aid noise recognition. It reviews basic principles of neural networks, and offers an example of an application in which a neural network is used to identify the presence or absence of noise in speech. The ability of neural networks to "learn" the…

  8. Neural correlates and neural computations in posterior parietal cortex during perceptual decision-making

    Directory of Open Access Journals (Sweden)

    Alexander eHuk

    2012-10-01

    Full Text Available A recent line of work has found remarkable success in relating perceptual decision-making and the spiking activity in the macaque lateral intraparietal area (LIP. In this review, we focus on questions about the neural computations in LIP that are not answered by demonstrations of neural correlates of psychological processes. We highlight three areas of limitations in our current understanding of the precise neural computations that might underlie neural correlates of decisions: (1 empirical questions not yet answered by existing data; (2 implementation issues related to how neural circuits could actually implement the mechanisms suggested by both physiology and psychology; and (3 ecological constraints related to the use of well-controlled laboratory tasks and whether they provide an accurate window on sensorimotor computation. These issues motivate the adoption of a more general encoding-decoding framework that will be fruitful for more detailed contemplation of how neural computations in LIP relate to the formation of perceptual decisions.

  9. Chronic periodontitis, inflammatory cytokines, and interrelationship with other chronic diseases.

    Science.gov (United States)

    Cardoso, Elsa Maria; Reis, Cátia; Manzanares-Céspedes, Maria Cristina

    2018-01-01

    Periodontal diseases, such as chronic periodontitis, share common inflammatory risk factors with other systemic and chronic inflammatory disorders. Mucosal tissues, such as oral epithelia, are exposed to environmental stressors, such as tobacco and oral bacteria, that might be involved in promoting a systemic inflammatory state. Conversely, chronic disorders can also affect oral health. This review will summarize recent evidence for the interrelationship between chronic periodontitis and other prevalent chronic diseases such as cardiovascular diseases, diabetes, cancer and chronic respiratory diseases. The association with pregnancy is also included due to possible obstetric complications. We will focus on inflammatory cytokines such as TNF-alpha, IL-1, and IL-6, because they have been shown to be increased in patients with chronic periodontitis, in patients with chronic systemic diseases, and in patients with both chronic periodontitis and other chronic diseases. Therefore, an imbalance towards a proinflammatory immune response could underline a bidirectional link between chronic periodontitis and other chronic diseases. Finally, we highlight that a close coordination between dental and other health professionals could promote oral health and prevent or ameliorate other chronic diseases.

  10. Fatigue sensation induced by the sounds associated with mental fatigue and its related neural activities: revealed by magnetoencephalography

    OpenAIRE

    Ishii, Akira; Tanaka, Masaaki; Iwamae, Masayoshi; Kim, Chongsoo; Yamano, Emi; Watanabe, Yasuyoshi

    2013-01-01

    Background It has been proposed that an inappropriately conditioned fatigue sensation could be one cause of chronic fatigue. Although classical conditioning of the fatigue sensation has been reported in rats, there have been no reports in humans. Our aim was to examine whether classical conditioning of the mental fatigue sensation can take place in humans and to clarify the neural mechanisms of fatigue sensation using magnetoencephalography (MEG). Methods Ten and 9 healthy volunteers particip...

  11. Enhanced biocompatibility of neural probes by integrating microstructures and delivering anti-inflammatory agents via microfluidic channels

    Science.gov (United States)

    Liu, Bin; Kim, Eric; Meggo, Anika; Gandhi, Sachin; Luo, Hao; Kallakuri, Srinivas; Xu, Yong; Zhang, Jinsheng

    2017-04-01

    Objective. Biocompatibility is a major issue for chronic neural implants, involving inflammatory and wound healing responses of neurons and glial cells. To enhance biocompatibility, we developed silicon-parylene hybrid neural probes with open architecture electrodes, microfluidic channels and a reservoir for drug delivery to suppress tissue responses. Approach. We chronically implanted our neural probes in the rat auditory cortex and investigated (1) whether open architecture electrode reduces inflammatory reaction by measuring glial responses; and (2) whether delivery of antibiotic minocycline reduces inflammatory and tissue reaction. Four weeks after implantation, immunostaining for glial fibrillary acid protein (astrocyte marker) and ionizing calcium-binding adaptor molecule 1 (macrophages/microglia cell marker) were conducted to identify immunoreactive astrocyte and microglial cells, and to determine the extent of astrocytes and microglial cell reaction/activation. A comparison was made between using traditional solid-surface electrodes and newly-designed electrodes with open architecture, as well as between deliveries of minocycline and artificial cerebral-spinal fluid diffused through microfluidic channels. Main results. The new probes with integrated micro-structures induced minimal tissue reaction compared to traditional electrodes at 4 weeks after implantation. Microcycline delivered through integrated microfluidic channels reduced tissue response as indicated by decreased microglial reaction around the neural probes implanted. Significance. The new design will help enhance the long-term stability of the implantable devices.

  12. Children with chronic continence problems: the challenges for families.

    Science.gov (United States)

    Erickson, David V; Ray, Lynne D

    2004-01-01

    For families who are raising children with myelomeningocele, bowel and bladder incontinence presents unique challenges for everyday life. The Parenting and Childhood Chronicity model is used to describe the work of raising a child with a chronic condition in 6 areas, including medical care, adapted parenting, dealing with the systems, caring for siblings, maintaining relationships, and personal coping (keeping yourself going). This article provides an overview of the physiologic and developmental challenges inherent in this neural tube defect and illustrates the work that is involved in the child's care and the challenges of maintaining a balance in family life. Clinical implications are discussed, including the setting of appropriate expectations, providing parents with accurate information, ensuring that a focus on continence is not at the expense of other important aspects of the child's functioning, and supporting parents in their interaction with the school system. The medical team, consisting of nursing, urology, nephrology, and psychology working together, can be a strong support for families.

  13. Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis

    Directory of Open Access Journals (Sweden)

    Chernoded Andrey

    2017-01-01

    Full Text Available Most of the modern analyses in high energy physics use signal-versus-background classification techniques of machine learning methods and neural networks in particular. Deep learning neural network is the most promising modern technique to separate signal and background and now days can be widely and successfully implemented as a part of physical analysis. In this article we compare Deep learning and Bayesian neural networks application as a classifiers in an instance of top quark analysis.

  14. Chondroitin sulfate effects on neural stem cell differentiation.

    Science.gov (United States)

    Canning, David R; Brelsford, Natalie R; Lovett, Neil W

    2016-01-01

    We have investigated the role chondroitin sulfate has on cell interactions during neural plate formation in the early chick embryo. Using tissue culture isolates from the prospective neural plate, we have measured neural gene expression profiles associated with neural stem cell differentiation. Removal of chondroitin sulfate from stage 4 neural plate tissue leads to altered associations of N-cadherin-positive neural progenitors and causes changes in the normal sequence of neural marker gene expression. Absence of chondroitin sulfate in the neural plate leads to reduced Sox2 expression and is accompanied by an increase in the expression of anterior markers of neural regionalization. Results obtained in this study suggest that the presence of chondroitin sulfate in the anterior chick embryo is instrumental in maintaining cells in the neural precursor state.

  15. 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

  16. Neural processing of auditory signals and modular neural control for sound tropism of walking machines

    DEFF Research Database (Denmark)

    Manoonpong, Poramate; Pasemann, Frank; Fischer, Joern

    2005-01-01

    and a neural preprocessing system together with a modular neural controller are used to generate a sound tropism of a four-legged walking machine. The neural preprocessing network is acting as a low-pass filter and it is followed by a network which discerns between signals coming from the left or the right....... The parameters of these networks are optimized by an evolutionary algorithm. In addition, a simple modular neural controller then generates the desired different walking patterns such that the machine walks straight, then turns towards a switched-on sound source, and then stops near to it....

  17. Chronic Obstructive Pulmonary Disease (COPD) Includes: Chronic Bronchitis and Emphysema

    Science.gov (United States)

    ... Submit Button NCHS Home Chronic Obstructive Pulmonary Disease (COPD) Includes: Chronic Bronchitis and Emphysema Recommend on Facebook ... Percent of visits to office-based physicians with COPD indicated on the medical record: 3.2% Source: ...

  18. Local Dynamics in Trained Recurrent Neural Networks.

    Science.gov (United States)

    Rivkind, Alexander; Barak, Omri

    2017-06-23

    Learning a task induces connectivity changes in neural circuits, thereby changing their dynamics. To elucidate task-related neural dynamics, we study trained recurrent neural networks. We develop a mean field theory for reservoir computing networks trained to have multiple fixed point attractors. Our main result is that the dynamics of the network's output in the vicinity of attractors is governed by a low-order linear ordinary differential equation. The stability of the resulting equation can be assessed, predicting training success or failure. As a consequence, networks of rectified linear units and of sigmoidal nonlinearities are shown to have diametrically different properties when it comes to learning attractors. Furthermore, a characteristic time constant, which remains finite at the edge of chaos, offers an explanation of the network's output robustness in the presence of variability of the internal neural dynamics. Finally, the proposed theory predicts state-dependent frequency selectivity in the network response.

  19. Neural Networks in Mobile Robot Motion

    Directory of Open Access Journals (Sweden)

    Danica Janglová

    2004-03-01

    Full Text Available This paper deals with a path planning and intelligent control of an autonomous robot which should move safely in partially structured environment. This environment may involve any number of obstacles of arbitrary shape and size; some of them are allowed to move. We describe our approach to solving the motion-planning problem in mobile robot control using neural networks-based technique. Our method of the construction of a collision-free path for moving robot among obstacles is based on two neural networks. The first neural network is used to determine the “free” space using ultrasound range finder data. The second neural network “finds” a safe direction for the next robot section of the path in the workspace while avoiding the nearest obstacles. Simulation examples of generated path with proposed techniques will be presented.

  20. water demand prediction using artificial neural network

    African Journals Online (AJOL)

    user

    2017-01-01

    Jan 1, 2017 ... Interface for activation and deactivation of valves. •. Interface demand ... process could be done and monitored at the computer terminal as expected of a .... [15] Arbib, M. A.The Handbook of Brain Theory and Neural. Networks.

  1. Machine Learning Topological Invariants with Neural Networks

    Science.gov (United States)

    Zhang, Pengfei; Shen, Huitao; Zhai, Hui

    2018-02-01

    In this Letter we supervisedly train neural networks to distinguish different topological phases in the context of topological band insulators. After training with Hamiltonians of one-dimensional insulators with chiral symmetry, the neural network can predict their topological winding numbers with nearly 100% accuracy, even for Hamiltonians with larger winding numbers that are not included in the training data. These results show a remarkable success that the neural network can capture the global and nonlinear topological features of quantum phases from local inputs. By opening up the neural network, we confirm that the network does learn the discrete version of the winding number formula. We also make a couple of remarks regarding the role of the symmetry and the opposite effect of regularization techniques when applying machine learning to physical systems.

  2. Hopfield neural network in HEP track reconstruction

    International Nuclear Information System (INIS)

    Muresan, R.; Pentia, M.

    1997-01-01

    In experimental particle physics, pattern recognition problems, specifically for neural network methods, occur frequently in track finding or feature extraction. Track finding is a combinatorial optimization problem. Given a set of points in Euclidean space, one tries the reconstruction of particle trajectories, subject to smoothness constraints.The basic ingredients in a neural network are the N binary neurons and the synaptic strengths connecting them. In our case the neurons are the segments connecting all possible point pairs.The dynamics of the neural network is given by a local updating rule wich evaluates for each neuron the sign of the 'upstream activity'. An updating rule in the form of sigmoid function is given. The synaptic strengths are defined in terms of angle between the segments and the lengths of the segments implied in the track reconstruction. An algorithm based on Hopfield neural network has been developed and tested on the track coordinates measured by silicon microstrip tracking system

  3. Additive Feed Forward Control with Neural Networks

    DEFF Research Database (Denmark)

    Sørensen, O.

    1999-01-01

    This paper demonstrates a method to control a non-linear, multivariable, noisy process using trained neural networks. The basis for the method is a trained neural network controller acting as the inverse process model. A training method for obtaining such an inverse process model is applied....... A suitable 'shaped' (low-pass filtered) reference is used to overcome problems with excessive control action when using a controller acting as the inverse process model. The control concept is Additive Feed Forward Control, where the trained neural network controller, acting as the inverse process model......, is placed in a supplementary pure feed-forward path to an existing feedback controller. This concept benefits from the fact, that an existing, traditional designed, feedback controller can be retained without any modifications, and after training the connection of the neural network feed-forward controller...

  4. Hardware Acceleration of Adaptive Neural Algorithms.

    Energy Technology Data Exchange (ETDEWEB)

    James, Conrad D. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-11-01

    As tradit ional numerical computing has faced challenges, researchers have turned towards alternative computing approaches to reduce power - per - computation metrics and improve algorithm performance. Here, we describe an approach towards non - conventional computing that strengthens the connection between machine learning and neuroscience concepts. The Hardware Acceleration of Adaptive Neural Algorithms (HAANA) project ha s develop ed neural machine learning algorithms and hardware for applications in image processing and cybersecurity. While machine learning methods are effective at extracting relevant features from many types of data, the effectiveness of these algorithms degrades when subjected to real - world conditions. Our team has generated novel neural - inspired approa ches to improve the resiliency and adaptability of machine learning algorithms. In addition, we have also designed and fabricated hardware architectures and microelectronic devices specifically tuned towards the training and inference operations of neural - inspired algorithms. Finally, our multi - scale simulation framework allows us to assess the impact of microelectronic device properties on algorithm performance.

  5. Local Dynamics in Trained Recurrent Neural Networks

    Science.gov (United States)

    Rivkind, Alexander; Barak, Omri

    2017-06-01

    Learning a task induces connectivity changes in neural circuits, thereby changing their dynamics. To elucidate task-related neural dynamics, we study trained recurrent neural networks. We develop a mean field theory for reservoir computing networks trained to have multiple fixed point attractors. Our main result is that the dynamics of the network's output in the vicinity of attractors is governed by a low-order linear ordinary differential equation. The stability of the resulting equation can be assessed, predicting training success or failure. As a consequence, networks of rectified linear units and of sigmoidal nonlinearities are shown to have diametrically different properties when it comes to learning attractors. Furthermore, a characteristic time constant, which remains finite at the edge of chaos, offers an explanation of the network's output robustness in the presence of variability of the internal neural dynamics. Finally, the proposed theory predicts state-dependent frequency selectivity in the network response.

  6. Experimental Demonstrations of Optical Neural Computers

    OpenAIRE

    Hsu, Ken; Brady, David; Psaltis, Demetri

    1988-01-01

    We describe two experiments in optical neural computing. In the first a closed optical feedback loop is used to implement auto-associative image recall. In the second a perceptron-like learning algorithm is implemented with photorefractive holography.

  7. PREDIKSI FOREX MENGGUNAKAN MODEL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    R. Hadapiningradja Kusumodestoni

    2015-11-01

    Full Text Available ABSTRAK Prediksi adalah salah satu teknik yang paling penting dalam menjalankan bisnis forex. Keputusan dalam memprediksi adalah sangatlah penting, karena dengan prediksi dapat membantu mengetahui nilai forex di waktu tertentu kedepan sehingga dapat mengurangi resiko kerugian. Tujuan dari penelitian ini dimaksudkan memprediksi bisnis fores menggunakan model neural network dengan data time series per 1 menit untuk mengetahui nilai akurasi prediksi sehingga dapat mengurangi resiko dalam menjalankan bisnis forex. Metode penelitian pada penelitian ini meliputi metode pengumpulan data kemudian dilanjutkan ke metode training, learning, testing menggunakan neural network. Setelah di evaluasi hasil penelitian ini menunjukan bahwa penerapan algoritma Neural Network mampu untuk memprediksi forex dengan tingkat akurasi prediksi 0.431 +/- 0.096 sehingga dengan prediksi ini dapat membantu mengurangi resiko dalam menjalankan bisnis forex. Kata kunci: prediksi, forex, neural network.

  8. NEURAL NETWORKS FOR STOCK MARKET OPTION PRICING

    Directory of Open Access Journals (Sweden)

    Sergey A. Sannikov

    2017-03-01

    Full Text Available Introduction: The use of neural networks for non-linear models helps to understand where linear model drawbacks, coused by their specification, reveal themselves. This paper attempts to find this out. The objective of research is to determine the meaning of “option prices calculation using neural networks”. Materials and Methods: We use two kinds of variables: endogenous (variables included in the model of neural network and variables affecting on the model (permanent disturbance. Results: All data are divided into 3 sets: learning, affirming and testing. All selected variables are normalised from 0 to 1. Extreme values of income were shortcut. Discussion and Conclusions: Using the 33-14-1 neural network with direct links we obtained two sets of forecasts. Optimal criteria of strategies in stock markets’ option pricing were developed.

  9. Hardware implementation of stochastic spiking neural networks.

    Science.gov (United States)

    Rosselló, Josep L; Canals, Vincent; Morro, Antoni; Oliver, Antoni

    2012-08-01

    Spiking Neural Networks, the last generation of Artificial Neural Networks, are characterized by its bio-inspired nature and by a higher computational capacity with respect to other neural models. In real biological neurons, stochastic processes represent an important mechanism of neural behavior and are responsible of its special arithmetic capabilities. In this work we present a simple hardware implementation of spiking neurons that considers this probabilistic nature. The advantage of the proposed implementation is that it is fully digital and therefore can be massively implemented in Field Programmable Gate Arrays. The high computational capabilities of the proposed model are demonstrated by the study of both feed-forward and recurrent networks that are able to implement high-speed signal filtering and to solve complex systems of linear equations.

  10. Neural adaptations to electrical stimulation strength training

    NARCIS (Netherlands)

    Hortobagyi, Tibor; Maffiuletti, Nicola A.

    2011-01-01

    This review provides evidence for the hypothesis that electrostimulation strength training (EST) increases the force of a maximal voluntary contraction (MVC) through neural adaptations in healthy skeletal muscle. Although electrical stimulation and voluntary effort activate muscle differently, there

  11. Optimal neural computations require analog processors

    Energy Technology Data Exchange (ETDEWEB)

    Beiu, V.

    1998-12-31

    This paper discusses some of the limitations of hardware implementations of neural networks. The authors start by presenting neural structures and their biological inspirations, while mentioning the simplifications leading to artificial neural networks. Further, the focus will be on hardware imposed constraints. They will present recent results for three different alternatives of parallel implementations of neural networks: digital circuits, threshold gate circuits, and analog circuits. The area and the delay will be related to the neurons` fan-in and to the precision of their synaptic weights. The main conclusion is that hardware-efficient solutions require analog computations, and suggests the following two alternatives: (i) cope with the limitations imposed by silicon, by speeding up the computation of the elementary silicon neurons; (2) investigate solutions which would allow the use of the third dimension (e.g. using optical interconnections).

  12. Artificial neural networks for plasma spectroscopy analysis

    International Nuclear Information System (INIS)

    Morgan, W.L.; Larsen, J.T.; Goldstein, W.H.

    1992-01-01

    Artificial neural networks have been applied to a variety of signal processing and image recognition problems. Of the several common neural models the feed-forward, back-propagation network is well suited for the analysis of scientific laboratory data, which can be viewed as a pattern recognition problem. The authors present a discussion of the basic neural network concepts and illustrate its potential for analysis of experiments by applying it to the spectra of laser produced plasmas in order to obtain estimates of electron temperatures and densities. Although these are high temperature and density plasmas, the neural network technique may be of interest in the analysis of the low temperature and density plasmas characteristic of experiments and devices in gaseous electronics

  13. Artificial neural networks a practical course

    CERN Document Server

    da Silva, Ivan Nunes; Andrade Flauzino, Rogerio; Liboni, Luisa Helena Bartocci; dos Reis Alves, Silas Franco

    2017-01-01

    This book provides comprehensive coverage of neural networks, their evolution, their structure, the problems they can solve, and their applications. The first half of the book looks at theoretical investigations on artificial neural networks and addresses the key architectures that are capable of implementation in various application scenarios. The second half is designed specifically for the production of solutions using artificial neural networks to solve practical problems arising from different areas of knowledge. It also describes the various implementation details that were taken into account to achieve the reported results. These aspects contribute to the maturation and improvement of experimental techniques to specify the neural network architecture that is most appropriate for a particular application scope. The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals.

  14. Control of autonomous robot using neural networks

    Science.gov (United States)

    Barton, Adam; Volna, Eva

    2017-07-01

    The aim of the article is to design a method of control of an autonomous robot using artificial neural networks. The introductory part describes control issues from the perspective of autonomous robot navigation and the current mobile robots controlled by neural networks. The core of the article is the design of the controlling neural network, and generation and filtration of the training set using ART1 (Adaptive Resonance Theory). The outcome of the practical part is an assembled Lego Mindstorms EV3 robot solving the problem of avoiding obstacles in space. To verify models of an autonomous robot behavior, a set of experiments was created as well as evaluation criteria. The speed of each motor was adjusted by the controlling neural network with respect to the situation in which the robot was found.

  15. Neural activation in stress-related exhaustion

    DEFF Research Database (Denmark)

    Gavelin, Hanna Malmberg; Neely, Anna Stigsdotter; Andersson, Micael

    2017-01-01

    The primary purpose of this study was to investigate the association between burnout and neural activation during working memory processing in patients with stress-related exhaustion. Additionally, we investigated the neural effects of cognitive training as part of stress rehabilitation. Fifty...... association between burnout level and working memory performance was found, however, our findings indicate that frontostriatal neural responses related to working memory were modulated by burnout severity. We suggest that patients with high levels of burnout need to recruit additional cognitive resources...... to uphold task performance. Following cognitive training, increased neural activation was observed during 3-back in working memory-related regions, including the striatum, however, low sample size limits any firm conclusions....

  16. Front Propagation in Stochastic Neural Fields

    KAUST Repository

    Bressloff, Paul C.; Webber, Matthew A.

    2012-01-01

    We analyze the effects of extrinsic multiplicative noise on front propagation in a scalar neural field with excitatory connections. Using a separation of time scales, we represent the fluctuating front in terms of a diffusive-like displacement

  17. Diagnosis method utilizing neural networks

    International Nuclear Information System (INIS)

    Watanabe, K.; Tamayama, K.

    1990-01-01

    Studies have been made on the technique of neural networks, which will be used to identify a cause of a small anomalous state in the reactor coolant system of the ATR (Advance Thermal Reactor). Three phases of analyses were carried out in this study. First, simulation for 100 seconds was made to determine how the plant parameters respond after the occurence of a transient decrease in reactivity, flow rate and temperature of feed water and increase in the steam flow rate and steam pressure, which would produce a decrease of water level in a steam drum of the ATR. Next, the simulation data was analysed utilizing an autoregressive model. From this analysis, a total of 36 coherency functions up to 0.5 Hz in each transient were computed among nine important and detectable plant parameters: neutron flux, flow rate of coolant, steam or feed water, water level in the steam drum, pressure and opening area of control valve in a steam pipe, feed water temperature and electrical power. Last, learning of neural networks composed of 96 input, 4-9 hidden and 5 output layer units was done by use of the generalized delta rule, namely a back-propagation algorithm. These convergent computations were continued as far as the difference between the desired outputs, 1 for direct cause or 0 for four other ones and actual outputs reached less than 10%. (1) Coherency functions were not governed by decreasing rate of reactivity in the range of 0.41x10 -2 dollar/s to 1.62x10 -2 dollar /s or by decreasing depth of the feed water temperature in the range of 3 deg C to 10 deg C or by a change of 10% or less in the three other causes. Change in coherency functions only depended on the type of cause. (2) The direct cause from the other four ones could be discriminated with 0.94+-0.01 of output level. A maximum of 0.06 output height was found among the other four causes. (3) Calculation load which is represented as products of learning times and numbers of the hidden units did not depend on the

  18. Parameter extraction with neural networks

    Science.gov (United States)

    Cazzanti, Luca; Khan, Mumit; Cerrina, Franco

    1998-06-01

    In semiconductor processing, the modeling of the process is becoming more and more important. While the ultimate goal is that of developing a set of tools for designing a complete process (Technology CAD), it is also necessary to have modules to simulate the various technologies and, in particular, to optimize specific steps. This need is particularly acute in lithography, where the continuous decrease in CD forces the technologies to operate near their limits. In the development of a 'model' for a physical process, we face several levels of challenges. First, it is necessary to develop a 'physical model,' i.e. a rational description of the process itself on the basis of know physical laws. Second, we need an 'algorithmic model' to represent in a virtual environment the behavior of the 'physical model.' After a 'complete' model has been developed and verified, it becomes possible to do performance analysis. In many cases the input parameters are poorly known or not accessible directly to experiment. It would be extremely useful to obtain the values of these 'hidden' parameters from experimental results by comparing model to data. This is particularly severe, because the complexity and costs associated with semiconductor processing make a simple 'trial-and-error' approach infeasible and cost- inefficient. Even when computer models of the process already exists, obtaining data through simulations may be time consuming. Neural networks (NN) are powerful computational tools to predict the behavior of a system from an existing data set. They are able to adaptively 'learn' input/output mappings and to act as universal function approximators. In this paper we use artificial neural networks to build a mapping from the input parameters of the process to output parameters which are indicative of the performance of the process. Once the NN has been 'trained,' it is also possible to observe the process 'in reverse,' and to extract the values of the inputs which yield outputs

  19. Neural Elements for Predictive Coding

    Directory of Open Access Journals (Sweden)

    Stewart SHIPP

    2016-11-01

    Full Text Available Predictive coding theories of sensory brain function interpret the hierarchical construction of the cerebral cortex as a Bayesian, generative model capable of predicting the sensory data consistent with any given percept. Predictions are fed backwards in the hierarchy and reciprocated by prediction error in the forward direction, acting to modify the representation of the outside world at increasing levels of abstraction, and so to optimize the nature of perception over a series of iterations. This accounts for many ‘illusory’ instances of perception where what is seen (heard, etc is unduly influenced by what is expected, based on past experience. This simple conception, the hierarchical exchange of prediction and prediction error, confronts a rich cortical microcircuitry that is yet to be fully documented. This article presents the view that, in the current state of theory and practice, it is profitable to begin a two-way exchange: that predictive coding theory can support an understanding of cortical microcircuit function, and prompt particular aspects of future investigation, whilst existing knowledge of microcircuitry can, in return, influence theoretical development. As an example, a neural inference arising from the earliest formulations of predictive coding is that the source populations of forwards and backwards pathways should be completely separate, given their functional distinction; this aspect of circuitry – that neurons with extrinsically bifurcating axons do not project in both directions – has only recently been confirmed. Here, the computational architecture prescribed by a generalized (free-energy formulation of predictive coding is combined with the classic ‘canonical microcircuit’ and the laminar architecture of hierarchical extrinsic connectivity to produce a template schematic, that is further examined in the light of (a updates in the microcircuitry of primate visual cortex, and (b rapid technical advances made

  20. Neural Elements for Predictive Coding.

    Science.gov (United States)

    Shipp, Stewart

    2016-01-01

    Predictive coding theories of sensory brain function interpret the hierarchical construction of the cerebral cortex as a Bayesian, generative model capable of predicting the sensory data consistent with any given percept. Predictions are fed backward in the hierarchy and reciprocated by prediction error in the forward direction, acting to modify the representation of the outside world at increasing levels of abstraction, and so to optimize the nature of perception over a series of iterations. This accounts for many 'illusory' instances of perception where what is seen (heard, etc.) is unduly influenced by what is expected, based on past experience. This simple conception, the hierarchical exchange of prediction and prediction error, confronts a rich cortical microcircuitry that is yet to be fully documented. This article presents the view that, in the current state of theory and practice, it is profitable to begin a two-way exchange: that predictive coding theory can support an understanding of cortical microcircuit function, and prompt particular aspects of future investigation, whilst existing knowledge of microcircuitry can, in return, influence theoretical development. As an example, a neural inference arising from the earliest formulations of predictive coding is that the source populations of forward and backward pathways should be completely separate, given their functional distinction; this aspect of circuitry - that neurons with extrinsically bifurcating axons do not project in both directions - has only recently been confirmed. Here, the computational architecture prescribed by a generalized (free-energy) formulation of predictive coding is combined with the classic 'canonical microcircuit' and the laminar architecture of hierarchical extrinsic connectivity to produce a template schematic, that is further examined in the light of (a) updates in the microcircuitry of primate visual cortex, and (b) rapid technical advances made possible by transgenic neural