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Sample records for early interfaced neural

  1. 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

  2. Flexible neural interfaces with integrated stiffening shank

    Energy Technology Data Exchange (ETDEWEB)

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

    2017-10-17

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

  3. Flexible neural interfaces with integrated stiffening shank

    Science.gov (United States)

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

    2016-07-26

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

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

    Directory of Open Access Journals (Sweden)

    Bahr Andreas

    2016-09-01

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

  5. 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.

  6. 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.

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

    Science.gov (United States)

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

    2014-03-26

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

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

    Science.gov (United States)

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

    2015-01-01

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

  9. 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.

  10. Time to address the problems at the neural interface

    Science.gov (United States)

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

    2014-04-01

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

  11. Incorporating an optical waveguide into a neural interface

    Energy Technology Data Exchange (ETDEWEB)

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

    2016-11-08

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

  12. Neural growth into a microchannel network: towards a regenerative neural interface

    NARCIS (Netherlands)

    Wieringa, P.A.; Wiertz, Remy; le Feber, Jakob; Rutten, Wim

    2009-01-01

    We propose and validated a design for a highly selective 'endcap' regenerative neural interface towards a neuroprosthesis. In vitro studies using rat cortical neurons determine if a branching microchannel structure can counter fasciculated growth and cause neurites to separte from one another,

  13. 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

  14. 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

  15. ORGANIC ELECTRODE COATINGS FOR NEXT-GENERATION NEURAL INTERFACES

    Directory of Open Access Journals (Sweden)

    Ulises A Aregueta-Robles

    2014-05-01

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

  16. Fractal Interfaces for Stimulating and Recording Neural Implants

    Science.gov (United States)

    Watterson, William James

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

  17. Orbiter Interface Unit and Early Communication System

    Science.gov (United States)

    Cobbs, Ronald M.; Cooke, Michael P.; Cox, Gary L.; Ellenberger, Richard; Fink, Patrick W.; Haynes, Dena S.; Hyams, Buddy; Ling, Robert Y.; Neighbors, Helen M.; Phan, Chau T.; hide

    2004-01-01

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

  18. Modality-Specific Axonal Regeneration: Towards selective regenerative neural interfaces

    Directory of Open Access Journals (Sweden)

    Parisa eLotfi

    2011-10-01

    Full Text Available Regenerative peripheral nerve interfaces have been proposed as viable alternatives for the natural control of robotic prosthetic devices. However, sensory and motor axons at the neural interface are of mixed submodality types, which difficult the specific recording from motor axons and the eliciting of precise sensory modalities through selective stimulation. Here we evaluated the possibility of using type-specific neurotrophins to preferentially entice the regeneration of defined axonal populations from transected peripheral nerves into separate compartments. Segregation of mixed sensory fibers from dorsal root ganglion neurons was evaluated in vitro by compartmentalized diffusion delivery of nerve growth factor (NGF and neurotrophin-3 (NT-3, to preferentially entice the growth of TrkA+ nociceptive and TrkC+ proprioceptive subsets of sensory neurons, respectively. The average axon length in the NGF channel increased 2.5 fold compared to that in saline or NT-3, whereas the number of branches increased 3 fold in the NT-3 channels. These results were confirmed using a 3-D Y-shaped in vitro assay showing that the arm containing NGF was able to entice a 5-fold increase in axonal length of unbranched fibers. To address if such segregation can be enticed in vivo, a Y-shaped tubing was used to allow regeneration of the transected adult rat sciatic nerve into separate compartments filled with either NFG or NT-3. A significant increase in the number of CGRP+ pain fibers were attracted towards the sural nerve, while N-52+ large diameter axons were observed in the tibial and NT-3 compartments. This study demonstrates the guided enrichment of sensory axons in specific regenerative chambers, and supports the notion that neurotrophic factors can be used to segregate sensory and perhaps motor axons in separate peripheral interfaces.

  19. Characterization of Early Cortical Neural Network ...

    Science.gov (United States)

    We examined the development of neural network activity using microelectrode array (MEA) recordings made in multi-well MEA plates (mwMEAs) over the first 12 days in vitro (DIV). In primary cortical cultures made from postnatal rats, action potential spiking activity was essentially absent on DIV 2 and developed rapidly between DIV 5 and 12. Spiking activity was primarily sporadic and unorganized at early DIV, and became progressively more organized with time in culture, with bursting parameters, synchrony and network bursting increasing between DIV 5 and 12. We selected 12 features to describe network activity and principal components analysis using these features demonstrated a general segregation of data by age at both the well and plate levels. Using a combination of random forest classifiers and Support Vector Machines, we demonstrated that 4 features (CV of within burst ISI, CV of IBI, network spike rate and burst rate) were sufficient to predict the age (either DIV 5, 7, 9 or 12) of each well recording with >65% accuracy. When restricting the classification problem to a binary decision, we found that classification improved dramatically, e.g. 95% accuracy for discriminating DIV 5 vs DIV 12 wells. Further, we present a novel resampling approach to determine the number of wells that might be needed for conducting comparisons of different treatments using mwMEA plates. Overall, these results demonstrate that network development on mwMEA plates is similar to

  20. 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

  1. Early perception and structural identity: neural implementation

    Science.gov (United States)

    Ligomenides, Panos A.

    1992-03-01

    It is suggested that there exists a minimal set of rules for the perceptual composition of the unending variety of spatio-temporal patterns in our perceptual world. Driven by perceptual discernment of "sudden change" and "unexpectedness", these rules specify conditions (such as co-linearity and virtual continuation) for perceptual grouping and for recursive compositions of perceptual "modalities" and "signatures". Beginning with a smallset of primitive perceptual elements, selected contextually at some relevant level of abstraction, perceptual compositions can graduate to an unlimited variety of spatiotemporal signatures, scenes and activities. Local discernible elements, often perceptually ambiguous by themselves, may be integrated into spatiotemporal compositions, which generate unambiguous perceptual separations between "figure" and "ground". The definition of computational algorithms for the effective instantiation of the rules of perceptual grouping remains a principal problem. In this paper we present our approach for solving the problem of perceptual recognition within the confines of one-D variational profiles. More specifically, concerning "early" (pre-attentive) recognition, we define the "structural identity of a k-norm, k ∈ K,"--SkID--as a tool for discerning and locating the instantiation of spatiotemporal objects or events. The SkID profile also serves a s a reference coordinate framework for the "perceptual focusing of attention" and the eventual assessment of resemblance. Neural network implementations of pre-attentive and attentive recognition are also discussed briefly. Our principles are exemplified by application to one-D perceptual profiles, which allows simplicity of definitions and of the rules of perceptual composition.

  2. Modification of surface/neuron interfaces for neural cell-type specific responses: a review

    International Nuclear Information System (INIS)

    Chen, Cen; Kong, Xiangdong; Lee, In-Seop

    2016-01-01

    Surface/neuron interfaces have played an important role in neural repair including neural prostheses and tissue engineered scaffolds. This comprehensive literature review covers recent studies on the modification of surface/neuron interfaces. These interfaces are identified in cases both where the surfaces of substrates or scaffolds were in direct contact with cells and where the surfaces were modified to facilitate cell adhesion and controlling cell-type specific responses. Different sources of cells for neural repair are described, such as pheochromocytoma neuronal-like cell, neural stem cell (NSC), embryonic stem cell (ESC), mesenchymal stem cell (MSC) and induced pluripotent stem cell (iPS). Commonly modified methods are discussed including patterned surfaces at micro- or nano-scale, surface modification with conducting coatings, and functionalized surfaces with immobilized bioactive molecules. These approaches to control cell-type specific responses have enormous potential implications in neural repair. (paper)

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

    Science.gov (United States)

    Weiland, James D.

    2011-07-01

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

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

    International Nuclear Information System (INIS)

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

    2012-01-01

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

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

    Science.gov (United States)

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

    2017-08-01

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

  6. Inversion of Density Interfaces Using the Pseudo-Backpropagation Neural Network Method

    Science.gov (United States)

    Chen, Xiaohong; Du, Yukun; Liu, Zhan; Zhao, Wenju; Chen, Xiaocheng

    2018-05-01

    This paper presents a new pseudo-backpropagation (BP) neural network method that can invert multi-density interfaces at one time. The new method is based on the conventional forward modeling and inverse modeling theories in addition to conventional pseudo-BP neural network arithmetic. A 3D inversion model for gravity anomalies of multi-density interfaces using the pseudo-BP neural network method is constructed after analyzing the structure and function of the artificial neural network. The corresponding iterative inverse formula of the space field is presented at the same time. Based on trials of gravity anomalies and density noise, the influence of the two kinds of noise on the inverse result is discussed and the scale of noise requested for the stability of the arithmetic is analyzed. The effects of the initial model on the reduction of the ambiguity of the result and improvement of the precision of inversion are discussed. The correctness and validity of the method were verified by the 3D model of the three interfaces. 3D inversion was performed on the observed gravity anomaly data of the Okinawa trough using the program presented herein. The Tertiary basement and Moho depth were obtained from the inversion results, which also testify the adaptability of the method. This study has made a useful attempt for the inversion of gravity density interfaces.

  7. Boron-doped nanocrystalline diamond electrodes for neural interfaces: in vivo biocompatibility evaluation

    Czech Academy of Sciences Publication Activity Database

    Alcaide, M.; Taylor, Andrew; Fjorback, M.; Zachar, V.; Pennisi, C.P.

    2016-01-01

    Roč. 10, Mar (2016), 1-9, č. článku 87. ISSN 1662-453X Institutional support: RVO:68378271 Keywords : nanocrystalline diamond * neuroprosthetic interfaces * neural electrodes * boron-doped diamond * titanium nitride * foreign body reaction Subject RIV: BM - Solid Matter Physics ; Magnetism Impact factor: 3.566, year: 2016

  8. Power Conditioning and Stimulation for Wireless Neural Interface ICs

    OpenAIRE

    Biederman, William

    2014-01-01

    Brain machine interfaces have the potential to revolutionize our understanding of the brain, restore motor function, and improve the quality of life to patients with neurological con- ditions. In recent human trials, control of robotic prostheses has been demonstrated using micro-electrode arrays, which provide high spatio-temporal resolution and an electrical feed- back path to the brain. However, after implantation, scar tissue degrades the recording signal-to-noise ratio and limits the use...

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

    Directory of Open Access Journals (Sweden)

    Yujuan Zhao

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

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

    Science.gov (United States)

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

    2017-08-01

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

  11. A Low Noise Amplifier for Neural Spike Recording Interfaces

    Directory of Open Access Journals (Sweden)

    Jesus Ruiz-Amaya

    2015-09-01

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

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

    Science.gov (United States)

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

    2011-02-01

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

  13. Implantable neurotechnologies: bidirectional neural interfaces--applications and VLSI circuit implementations.

    Science.gov (United States)

    Greenwald, Elliot; Masters, Matthew R; Thakor, Nitish V

    2016-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Miriam eZacksenhouse

    2015-05-01

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

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

    Science.gov (United States)

    Benyamini, Miri; Zacksenhouse, Miriam

    2015-01-01

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

  16. Neural changes underlying early stages of L2 vocabulary acquisition.

    Science.gov (United States)

    Pu, He; Holcomb, Phillip J; Midgley, Katherine J

    2016-11-01

    Research has shown neural changes following second language (L2) acquisition after weeks or months of instruction. But are such changes detectable even earlier than previously shown? The present study examines the electrophysiological changes underlying the earliest stages of second language vocabulary acquisition by recording event-related potentials (ERPs) within the first week of learning. Adult native English speakers with no previous Spanish experience completed less than four hours of Spanish vocabulary training, with pre- and post-training ERPs recorded to a backward translation task. Results indicate that beginning L2 learners show rapid neural changes following learning, manifested in changes to the N400 - an ERP component sensitive to lexicosemantic processing and degree of L2 proficiency. Specifically, learners in early stages of L2 acquisition show growth in N400 amplitude to L2 words following learning as well as a backward translation N400 priming effect that was absent pre-training. These results were shown within days of minimal L2 training, suggesting that the neural changes captured during adult second language acquisition are more rapid than previously shown. Such findings are consistent with models of early stages of bilingualism in adult learners of L2 ( e.g. Kroll and Stewart's RHM) and reinforce the use of ERP measures to assess L2 learning.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2017-01-24

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

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

    Science.gov (United States)

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

    2014-12-01

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

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

    Science.gov (United States)

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

    2014-12-01

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

  20. Flexible microelectrode array for interfacing with the surface of neural ganglia

    Science.gov (United States)

    Sperry, Zachariah J.; Na, Kyounghwan; Parizi, Saman S.; Chiel, Hillel J.; Seymour, John; Yoon, Euisik; Bruns, Tim M.

    2018-06-01

    Objective. The dorsal root ganglia (DRG) are promising nerve structures for sensory neural interfaces because they provide centralized access to primary afferent cell bodies and spinal reflex circuitry. In order to harness this potential, new electrode technologies are needed which take advantage of the unique properties of DRG, specifically the high density of neural cell bodies at the dorsal surface. Here we report initial in vivo results from the development of a flexible non-penetrating polyimide electrode array interfacing with the surface of ganglia. Approach. Multiple layouts of a 64-channel iridium electrode (420 µm2) array were tested, with pitch as small as 25 µm. The buccal ganglia of invertebrate sea slug Aplysia californica were used to develop handling and recording techniques with ganglionic surface electrode arrays (GSEAs). We also demonstrated the GSEA’s capability to record single- and multi-unit activity from feline lumbosacral DRG related to a variety of sensory inputs, including cutaneous brushing, joint flexion, and bladder pressure. Main results. We recorded action potentials from a variety of Aplysia neurons activated by nerve stimulation, and units were observed firing simultaneously on closely spaced electrode sites. We also recorded single- and multi-unit activity associated with sensory inputs from feline DRG. We utilized spatial oversampling of action potentials on closely-spaced electrode sites to estimate the location of neural sources at between 25 µm and 107 µm below the DRG surface. We also used the high spatial sampling to demonstrate a possible spatial sensory map of one feline’s DRG. We obtained activation of sensory fibers with low-amplitude stimulation through individual or groups of GSEA electrode sites. Significance. Overall, the GSEA has been shown to provide a variety of information types from ganglia neurons and to have significant potential as a tool for neural mapping and interfacing.

  1. Models of neural networks IV early vision and attention

    CERN Document Server

    Cowan, Jack; Domany, Eytan

    2002-01-01

    Close this book for a moment and look around you. You scan the scene by directing your attention, and gaze, at certain specific objects. Despite the background, you discern them. The process is partially intentional and partially preattentive. How all this can be done is described in the fourth volume of Models of Neural Networks devoted to Early Vision and Atten­ tion that you are holding in your hands. Early vision comprises the first stages of visual information processing. It is as such a scientific challenge whose clarification calls for a penetrating review. Here you see the result. The Heraeus Foundation (Hanau) is to be thanked for its support during the initial phase of this project. John Hertz, who has extensive experience in both computational and ex­ perimental neuroscience, provides in "Neurons, Networks, and Cognition" to neural modeling. John Van Opstal explains in a theoretical introduction "The Gaze Control System" how the eye's gaze control is performed and presents a novel theoretical des...

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

    Science.gov (United States)

    Schendel, Amelia Ann

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

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

    Science.gov (United States)

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

    2017-12-21

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

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

    Directory of Open Access Journals (Sweden)

    Ahnsei Shon

    2017-12-01

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

  5. Poly(3,4-ethylene dioxythiophene (PEDOT as a micro-neural interface material for electrostimulation

    Directory of Open Access Journals (Sweden)

    Seth J Wilks

    2009-06-01

    Full Text Available Chronic microstimulation-based devices are being investigated to treat conditions such as blindness, deafness, pain, paralysis and epilepsy. Small area electrodes are desired to achieve high selectivity. However, a major trade-off with electrode miniaturization is an increase in impedance and charge density requirements. Thus, the development of novel materials with lower interfacial impedance and enhanced charge storage capacity is essential for the development of micro-neural interface-based neuroprostheses. In this report, we study the use of conducting polymer poly(3,4-ethylene dioxythiophene (PEDOT as a neural interface material for microstimulation of small area iridium electrodes on silicon-substrate arrays. Characterized by electrochemical impedance spectroscopy, electrodeposition of PEDOT results in lower interfacial impedance at physiologically-relevant frequencies, with the 1kHz impedance magnitude being 23.3 ± 0.7 kΩ compared to 113.6 ± 3.5 kΩ for iridium oxide (IrOx on 177μm2 sites. Further, PEDOT exhibits enhanced charge storage capacity at 75.6 ± 5.4 mC/cm2 compared to 28.8 ± 0.3 mC/cm2 for IrOx, characterized by cyclic voltammetry (50 mV/s. These improvements at the electrode interface were corroborated by observation of the voltage excursions that result from constant current pulsing. The PEDOT coatings provide both a lower amplitude voltage and a more ohmic representation of the applied current compared to IrOx. During repetitive pulsing, PEDOT-coated electrodes show stable performance and little change in electrical properties, even at relatively high current densities which cause IrOx instability. These findings support the potential of PEDOT coatings as a micro-neural interface material for electrostimulation.

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

    Science.gov (United States)

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

    2010-06-01

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

  7. Programmable neural processing on a smartdust for brain-computer interfaces.

    Science.gov (United States)

    Yuwen Sun; Shimeng Huang; Oresko, Joseph J; Cheng, Allen C

    2010-10-01

    Brain-computer interfaces (BCIs) offer tremendous promise for improving the quality of life for disabled individuals. BCIs use spike sorting to identify the source of each neural firing. To date, spike sorting has been performed by either using off-chip analysis, which requires a wired connection penetrating the skull to a bulky external power/processing unit, or via custom application-specific integrated circuits that lack the programmability to perform different algorithms and upgrades. In this research, we propose and test the feasibility of performing on-chip, real-time spike sorting on a programmable smartdust, including feature extraction, classification, compression, and wireless transmission. A detailed power/performance tradeoff analysis using DVFS is presented. Our experimental results show that the execution time and power density meet the requirements to perform real-time spike sorting and wireless transmission on a single neural channel.

  8. CMOS On-Chip Optoelectronic Neural Interface Device with Integrated Light Source for Optogenetics

    International Nuclear Information System (INIS)

    Sawadsaringkarn, Y; Kimura, H; Maezawa, Y; Nakajima, A; Kobayashi, T; Sasagawa, K; Noda, T; Tokuda, T; Ohta, J

    2012-01-01

    A novel optoelectronic neural interface device is proposed for target applications in optogenetics for neural science. The device consists of a light emitting diode (LED) array implemented on a CMOS image sensor for on-chip local light stimulation. In this study, we designed a suitable CMOS image sensor equipped with on-chip electrodes to drive the LEDs, and developed a device structure and packaging process for LED integration. The prototype device produced an illumination intensity of approximately 1 mW with a driving current of 2.0 mA, which is expected to be sufficient to activate channelrhodopsin (ChR2). We also demonstrated the functions of light stimulation and on-chip imaging using a brain slice from a mouse as a target sample.

  9. An ovine model of cerebral catheter venography for implantation of an endovascular neural interface.

    Science.gov (United States)

    Oxley, Thomas James; Opie, Nicholas Lachlan; Rind, Gil Simon; Liyanage, Kishan; John, Sam Emmanuel; Ronayne, Stephen; McDonald, Alan James; Dornom, Anthony; Lovell, Timothy John Haynes; Mitchell, Peter John; Bennett, Iwan; Bauquier, Sebastien; Warne, Leon Norris; Steward, Chris; Grayden, David Bruce; Desmond, Patricia; Davis, Stephen M; O'Brien, Terence John; May, Clive N

    2018-04-01

    OBJECTIVE Neural interface technology may enable the development of novel therapies to treat neurological conditions, including motor prostheses for spinal cord injury. Intracranial neural interfaces currently require a craniotomy to achieve implantation and may result in chronic tissue inflammation. Novel approaches are required that achieve less invasive implantation methods while maintaining high spatial resolution. An endovascular stent electrode array avoids direct brain trauma and is able to record electrocorticography in local cortical tissue from within the venous vasculature. The motor area in sheep runs in a parasagittal plane immediately adjacent to the superior sagittal sinus (SSS). The authors aimed to develop a sheep model of cerebral venography that would enable validation of an endovascular neural interface. METHODS Cerebral catheter venography was performed in 39 consecutive sheep. Contrast-enhanced MRI of the brain was performed on 13 animals. Multiple telescoping coaxial catheter systems were assessed to determine the largest wide-bore delivery catheter that could be delivered into the anterior SSS. Measurements of SSS diameter and distance from the motor area were taken. The location of the motor area was determined in relation to lateral and superior projections of digital subtraction venography images and confirmed on MRI. RESULTS The venous pathway from the common jugular vein (7.4 mm) to the anterior SSS (1.2 mm) was technically challenging to selectively catheterize. The SSS coursed immediately adjacent to the motor cortex (SSS. Attempted access with 5-Fr and 6-Fr delivery catheters was associated with longer procedure times and higher complication rates. A 4-Fr catheter (internal lumen diameter 1.1 mm) was successful in accessing the SSS in 100% of cases with no associated complications. Complications included procedure-related venous dissection in two major areas: the torcular herophili, and the anterior formation of the SSS. The

  10. Function of FEZF1 during early neural differentiation of human embryonic stem cells.

    Science.gov (United States)

    Liu, Xin; Su, Pei; Lu, Lisha; Feng, Zicen; Wang, Hongtao; Zhou, Jiaxi

    2018-01-01

    The understanding of the mechanism underlying human neural development has been hampered due to lack of a cellular system and complicated ethical issues. Human embryonic stem cells (hESCs) provide an invaluable model for dissecting human development because of unlimited self-renewal and the capacity to differentiate into nearly all cell types in the human body. In this study, using a chemical defined neural induction protocol and molecular profiling, we identified Fez family zinc finger 1 (FEZF1) as a potential regulator of early human neural development. FEZF1 is rapidly up-regulated during neural differentiation in hESCs and expressed before PAX6, a well-established marker of early human neural induction. We generated FEZF1-knockout H1 hESC lines using CRISPR-CAS9 technology and found that depletion of FEZF1 abrogates neural differentiation of hESCs. Moreover, loss of FEZF1 impairs the pluripotency exit of hESCs during neural specification, which partially explains the neural induction defect caused by FEZF1 deletion. However, enforced expression of FEZF1 itself fails to drive neural differentiation in hESCs, suggesting that FEZF1 is necessary but not sufficient for neural differentiation from hESCs. Taken together, our findings identify one of the earliest regulators expressed upon neural induction and provide insight into early neural development in human.

  11. 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.

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

    Science.gov (United States)

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

    2015-10-01

    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.

  13. Neural Basis of Brain Dysfunction Produced by Early Sleep Problems

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    Jun Kohyama

    2016-01-01

    Full Text Available There is a wealth of evidence that disrupted sleep and circadian rhythms, which are common in modern society even during the early stages of life, have unfavorable effects on brain function. Altered brain function can cause problem behaviors later in life, such as truancy from or dropping out of school, quitting employment, and committing suicide. In this review, we discuss findings from several large cohort studies together with recent results of a cohort study using the marshmallow test, which was first introduced in the 1960s. This test assessed the ability of four-year-olds to delay gratification and showed how this ability correlated with success later in life. The role of the serotonergic system in sleep and how this role changes with age are also discussed. The serotonergic system is involved in reward processing and interactions with the dorsal striatum, ventral striatum, and the prefrontal cortex are thought to comprise the neural basis for behavioral patterns that are affected by the quantity, quality, and timing of sleep early in life.

  14. Neural Basis of Brain Dysfunction Produced by Early Sleep Problems.

    Science.gov (United States)

    Kohyama, Jun

    2016-01-29

    There is a wealth of evidence that disrupted sleep and circadian rhythms, which are common in modern society even during the early stages of life, have unfavorable effects on brain function. Altered brain function can cause problem behaviors later in life, such as truancy from or dropping out of school, quitting employment, and committing suicide. In this review, we discuss findings from several large cohort studies together with recent results of a cohort study using the marshmallow test, which was first introduced in the 1960s. This test assessed the ability of four-year-olds to delay gratification and showed how this ability correlated with success later in life. The role of the serotonergic system in sleep and how this role changes with age are also discussed. The serotonergic system is involved in reward processing and interactions with the dorsal striatum, ventral striatum, and the prefrontal cortex are thought to comprise the neural basis for behavioral patterns that are affected by the quantity, quality, and timing of sleep early in life.

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

    Energy Technology Data Exchange (ETDEWEB)

    Hébert, Clément, E-mail: clement.hebert@cea.fr [Institut Néel, CNRS et Université Joseph Fourier, BP 166, F-38042 Grenoble Cedex 9 (France); Warnking, Jan; Depaulis, Antoine [INSERM, U836, Grenoble Institut des Neurosciences, Grenoble (France); Garçon, Laurie Amandine [Institut Néel, CNRS et Université Joseph Fourier, BP 166, F-38042 Grenoble Cedex 9 (France); CEA/INAC/SPrAM/CREAB, 17 rue des Martyrs, 38054 Grenoble Cedex 9 (France); Mermoux, Michel [Université Grenoble Alpes, LEPMI, F-38000 Grenoble (France); CNRS, LEPMI, F-38000 Grenoble (France); Eon, David [Institut Néel, CNRS et Université Joseph Fourier, BP 166, F-38042 Grenoble Cedex 9 (France); Mailley, Pascal [CEA-LETI-DTBS Minatec, 17 rue des Martyres, 38054 Grenoble (France); Omnès, Franck [Institut Néel, CNRS et Université Joseph Fourier, BP 166, F-38042 Grenoble Cedex 9 (France)

    2015-01-01

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

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

    Science.gov (United States)

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

    2014-02-01

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

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

    Science.gov (United States)

    Pecevski, Dejan; Kappel, David; Jonke, Zeno

    2014-01-01

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

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

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    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.

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

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    Huixia Zhao

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

  20. Fabrication and Microassembly of a mm-Sized Floating Probe for a Distributed Wireless Neural Interface

    Directory of Open Access Journals (Sweden)

    Pyungwoo Yeon

    2016-09-01

    Full Text Available A new class of wireless neural interfaces is under development in the form of tens to hundreds of mm-sized untethered implants, distributed across the target brain region(s. Unlike traditional interfaces that are tethered to a centralized control unit and suffer from micromotions that may damage the surrounding neural tissue, the new free-floating wireless implantable neural recording (FF-WINeR probes will be stand-alone, directly communicating with an external interrogator. Towards development of the FF-WINeR, in this paper we describe the micromachining, microassembly, and hermetic packaging of 1-mm3 passive probes, each of which consists of a thinned micromachined silicon die with a centered Ø(diameter 130 μm through-hole, an Ø81 μm sharpened tungsten electrode, a 7-turn gold wire-wound coil wrapped around the die, two 0201 surface mount capacitors on the die, and parylene-C/Polydimethylsiloxane (PDMS coating. The fabricated passive probe is tested under a 3-coil inductive link to evaluate power transfer efficiency (PTE and power delivered to a load (PDL for feasibility assessment. The minimum PTE/PDL at 137 MHz were 0.76%/240 μW and 0.6%/191 μW in the air and lamb head medium, respectively, with coil separation of 2.8 cm and 9 kΩ receiver (Rx loading. Six hermetically sealed probes went through wireless hermeticity testing, using a 2-coil inductive link under accelerated lifetime testing condition of 85 °C, 1 atm, and 100%RH. The mean-time-to-failure (MTTF of the probes at 37 °C is extrapolated to be 28.7 years, which is over their lifetime.

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

    Science.gov (United States)

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

    2014-01-01

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

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

    Science.gov (United States)

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

    2014-01-01

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

  3. Emotional sounds modulate early neural processing of emotional pictures

    Directory of Open Access Journals (Sweden)

    Antje B M Gerdes

    2013-10-01

    Full Text Available In our natural environment, emotional information is conveyed by converging visual and auditory information; multimodal integration is of utmost importance. In the laboratory, however, emotion researchers have mostly focused on the examination of unimodal stimuli. Few existing studies on multimodal emotion processing have focused on human communication such as the integration of facial and vocal expressions. Extending the concept of multimodality, the current study examines how the neural processing of emotional pictures is influenced by simultaneously presented sounds. Twenty pleasant, unpleasant, and neutral pictures of complex scenes were presented to 22 healthy participants. On the critical trials these pictures were paired with pleasant, unpleasant and neutral sounds. Sound presentation started 500 ms before picture onset and each stimulus presentation lasted for 2s. EEG was recorded from 64 channels and ERP analyses focused on the picture onset. In addition, valence, and arousal ratings were obtained. Previous findings for the neural processing of emotional pictures were replicated. Specifically, unpleasant compared to neutral pictures were associated with an increased parietal P200 and a more pronounced centroparietal late positive potential (LPP, independent of the accompanying sound valence. For audiovisual stimulation, increased parietal P100 and P200 were found in response to all pictures which were accompanied by unpleasant or pleasant sounds compared to pictures with neutral sounds. Most importantly, incongruent audiovisual pairs of unpleasant pictures and pleasant sounds enhanced parietal P100 and P200 compared to pairings with congruent sounds. Taken together, the present findings indicate that emotional sounds modulate early stages of visual processing and, therefore, provide an avenue by which multimodal experience may enhance perception.

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

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    Sun-Il Chang

    2018-01-01

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

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

    Science.gov (United States)

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

    2018-01-17

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

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

    Directory of Open Access Journals (Sweden)

    Yoko Saito

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

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

    Science.gov (United States)

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

    2012-01-01

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

  8. SpineCreator: a Graphical User Interface for the Creation of Layered Neural Models.

    Science.gov (United States)

    Cope, A J; Richmond, P; James, S S; Gurney, K; Allerton, D J

    2017-01-01

    There is a growing requirement in computational neuroscience for tools that permit collaborative model building, model sharing, combining existing models into a larger system (multi-scale model integration), and are able to simulate models using a variety of simulation engines and hardware platforms. Layered XML model specification formats solve many of these problems, however they are difficult to write and visualise without tools. Here we describe a new graphical software tool, SpineCreator, which facilitates the creation and visualisation of layered models of point spiking neurons or rate coded neurons without requiring the need for programming. We demonstrate the tool through the reproduction and visualisation of published models and show simulation results using code generation interfaced directly into SpineCreator. As a unique application for the graphical creation of neural networks, SpineCreator represents an important step forward for neuronal modelling.

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

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    A. Novellino

    2007-01-01

    Full Text Available One of the key properties of intelligent behaviors is the capability to learn and adapt to changing environmental conditions. These features are the result of the continuous and intense interaction of the brain with the external world, mediated by the body. For this reason x201C;embodiment” represents an innovative and very suitable experimental paradigm when studying the neural processes underlying learning new behaviors and adapting to unpredicted situations. To this purpose, we developed a novel bidirectional neural interface. We interconnected in vitro neurons, extracted from rat embryos and plated on a microelectrode array (MEA, to external devices, thus allowing real-time closed-loop interaction. The novelty of this experimental approach entails the necessity to explore different computational schemes and experimental hypotheses. In this paper, we present an open, scalable architecture, which allows fast prototyping of different modules and where coding and decoding schemes and different experimental configurations can be tested. This hybrid system can be used for studying the computational properties and information coding in biological neuronal networks with far-reaching implications for the future development of advanced neuroprostheses.

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

    Science.gov (United States)

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

    2007-01-01

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

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

    Science.gov (United States)

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

    2015-07-01

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

  12. Neural control of finger movement via intracortical brain-machine interface

    Science.gov (United States)

    Irwin, Z. T.; Schroeder, K. E.; Vu, P. P.; Bullard, A. J.; Tat, D. M.; Nu, C. S.; Vaskov, A.; Nason, S. R.; Thompson, D. E.; Bentley, J. N.; Patil, P. G.; Chestek, C. A.

    2017-12-01

    Objective. Intracortical brain-machine interfaces (BMIs) are a promising source of prosthesis control signals for individuals with severe motor disabilities. Previous BMI studies have primarily focused on predicting and controlling whole-arm movements; precise control of hand kinematics, however, has not been fully demonstrated. Here, we investigate the continuous decoding of precise finger movements in rhesus macaques. Approach. In order to elicit precise and repeatable finger movements, we have developed a novel behavioral task paradigm which requires the subject to acquire virtual fingertip position targets. In the physical control condition, four rhesus macaques performed this task by moving all four fingers together in order to acquire a single target. This movement was equivalent to controlling the aperture of a power grasp. During this task performance, we recorded neural spikes from intracortical electrode arrays in primary motor cortex. Main results. Using a standard Kalman filter, we could reconstruct continuous finger movement offline with an average correlation of ρ  =  0.78 between actual and predicted position across four rhesus macaques. For two of the monkeys, this movement prediction was performed in real-time to enable direct brain control of the virtual hand. Compared to physical control, neural control performance was slightly degraded; however, the monkeys were still able to successfully perform the task with an average target acquisition rate of 83.1%. The monkeys’ ability to arbitrarily specify fingertip position was also quantified using an information throughput metric. During brain control task performance, the monkeys achieved an average 1.01 bits s-1 throughput, similar to that achieved in previous studies which decoded upper-arm movements to control computer cursors using a standard Kalman filter. Significance. This is, to our knowledge, the first demonstration of brain control of finger-level fine motor skills. We believe

  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. Implications of the dependence of neuronal activity on neural network states for the design of brain-machine interfaces

    Directory of Open Access Journals (Sweden)

    Stefano ePanzeri

    2016-04-01

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

  15. Mitochondrial metabolism in early neural fate and its relevance for neuronal disease modeling.

    Science.gov (United States)

    Lorenz, Carmen; Prigione, Alessandro

    2017-12-01

    Modulation of energy metabolism is emerging as a key aspect associated with cell fate transition. The establishment of a correct metabolic program is particularly relevant for neural cells given their high bioenergetic requirements. Accordingly, diseases of the nervous system commonly involve mitochondrial impairment. Recent studies in animals and in neural derivatives of human pluripotent stem cells (PSCs) highlighted the importance of mitochondrial metabolism for neural fate decisions in health and disease. The mitochondria-based metabolic program of early neurogenesis suggests that PSC-derived neural stem cells (NSCs) may be used for modeling neurological disorders. Understanding how metabolic programming is orchestrated during neural commitment may provide important information for the development of therapies against conditions affecting neural functions, including aging and mitochondrial disorders. Copyright © 2017. Published by Elsevier Ltd.

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

    Science.gov (United States)

    Kuo, Chin-chen

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

  17. Neural stem cell-encoded temporal patterning delineates an early window of malignant susceptibility in Drosophila.

    Science.gov (United States)

    Narbonne-Reveau, Karine; Lanet, Elodie; Dillard, Caroline; Foppolo, Sophie; Chen, Ching-Huan; Parrinello, Hugues; Rialle, Stéphanie; Sokol, Nicholas S; Maurange, Cédric

    2016-06-14

    Pediatric neural tumors are often initiated during early development and can undergo very rapid transformation. However, the molecular basis of this early malignant susceptibility remains unknown. During Drosophila development, neural stem cells (NSCs) divide asymmetrically and generate intermediate progenitors that rapidly differentiate in neurons. Upon gene inactivation, these progeny can dedifferentiate and generate malignant tumors. Here, we find that intermediate progenitors are prone to malignancy only when born during an early window of development while expressing the transcription factor Chinmo, and the mRNA-binding proteins Imp/IGF2BP and Lin-28. These genes compose an oncogenic module that is coopted upon dedifferentiation of early-born intermediate progenitors to drive unlimited tumor growth. In late larvae, temporal transcription factor progression in NSCs silences the module, thereby limiting mitotic potential and terminating the window of malignant susceptibility. Thus, this study identifies the gene regulatory network that confers malignant potential to neural tumors with early developmental origins.

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

    Science.gov (United States)

    Secundo, Lavi

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

  19. Ensemble of Neural Network Conditional Random Fields for Self-Paced Brain Computer Interfaces

    Directory of Open Access Journals (Sweden)

    Hossein Bashashati

    2017-07-01

    Full Text Available Classification of EEG signals in self-paced Brain Computer Interfaces (BCI is an extremely challenging task. The main difficulty stems from the fact that start time of a control task is not defined. Therefore it is imperative to exploit the characteristics of the EEG data to the extent possible. In sensory motor self-paced BCIs, while performing the mental task, the user’s brain goes through several well-defined internal state changes. Applying appropriate classifiers that can capture these state changes and exploit the temporal correlation in EEG data can enhance the performance of the BCI. In this paper, we propose an ensemble learning approach for self-paced BCIs. We use Bayesian optimization to train several different classifiers on different parts of the BCI hyper- parameter space. We call each of these classifiers Neural Network Conditional Random Field (NNCRF. NNCRF is a combination of a neural network and conditional random field (CRF. As in the standard CRF, NNCRF is able to model the correlation between adjacent EEG samples. However, NNCRF can also model the nonlinear dependencies between the input and the output, which makes it more powerful than the standard CRF. We compare the performance of our algorithm to those of three popular sequence labeling algorithms (Hidden Markov Models, Hidden Markov Support Vector Machines and CRF, and to two classical classifiers (Logistic Regression and Support Vector Machines. The classifiers are compared for the two cases: when the ensemble learning approach is not used and when it is. The data used in our studies are those from the BCI competition IV and the SM2 dataset. We show that our algorithm is considerably superior to the other approaches in terms of the Area Under the Curve (AUC of the BCI system.

  20. 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.

  1. 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.

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

    Directory of Open Access Journals (Sweden)

    Markus A Wenzel

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

  3. 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

  4. 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.

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

    Science.gov (United States)

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

    2015-01-01

    Neural interfacing still requires highly stable and biocompatible materials, in particular for in vivo applications. Indeed, most of the currently used materials are degraded and/or encapsulated by the proximal tissue leading to a loss of efficiency. Here, we considered boron doped diamond microelectrodes to address this issue and we evaluated the performances of a diamond microelectrode array. We described the microfabrication process of the device and discuss its functionalities. We characterized its electrochemical performances by cyclic voltammetry and impedance spectroscopy in saline buffer and observed the typical diamond electrode electrochemical properties, wide potential window and low background current, allowing efficient electrochemical detection. The charge storage capacitance and the modulus of the electrochemical impedance were found to remain in the same range as platinum electrodes used for standard commercial devices. Finally we observed a reduced Magnetic Resonance Imaging artifact when the device was implanted on a rat cortex, suggesting that boron doped-diamond is a very promising electrode material allowing functional imaging. Copyright © 2014 Elsevier B.V. All rights reserved.

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

    Science.gov (United States)

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

    2016-06-01

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

  7. Commentary: Elucidating the Neural Correlates of Early Childhood Memory

    Science.gov (United States)

    Mullally, Sinead L.

    2015-01-01

    Both episodic memory and the key neural structure believed to support it, namely the hippocampus, are believed to undergo protracted periods of postnatal developmental. Critically however, the hippocampus is comprised of distinct subfields and circuits, and these circuits appear to mature at different rates (Lavenex and Banta Lavenex, 2013).…

  8. Usage of Probabilistic and General Regression Neural Network for Early Detection and Prevention of Oral Cancer

    OpenAIRE

    Sharma, Neha; Om, Hari

    2015-01-01

    In India, the oral cancers are usually presented in advanced stage of malignancy. It is critical to ascertain the diagnosis in order to initiate most advantageous treatment of the suspicious lesions. The main hurdle in appropriate treatment and control of oral cancer is identification and risk assessment of early disease in the community in a cost-effective fashion. The objective of this research is to design a data mining model using probabilistic neural network and general regression neural...

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

    Science.gov (United States)

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

    2011-08-01

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

  10. Modeling the electrode-neuron interface of cochlear implants: effects of neural survival, electrode placement, and the partial tripolar configuration.

    Science.gov (United States)

    Goldwyn, Joshua H; Bierer, Steven M; Bierer, Julie Arenberg

    2010-09-01

    The partial tripolar electrode configuration is a relatively novel stimulation strategy that can generate more spatially focused electric fields than the commonly used monopolar configuration. Focused stimulation strategies should improve spectral resolution in cochlear implant users, but may also be more sensitive to local irregularities in the electrode-neuron interface. In this study, we develop a practical computer model of cochlear implant stimulation that can simulate neural activation in a simplified cochlear geometry and we relate the resulting patterns of neural activity to basic psychophysical measures. We examine how two types of local irregularities in the electrode-neuron interface, variations in spiral ganglion nerve density and electrode position within the scala tympani, affect the simulated neural activation patterns and how these patterns change with electrode configuration. The model shows that higher partial tripolar fractions activate more spatially restricted populations of neurons at all current levels and require higher current levels to excite a given number of neurons. We find that threshold levels are more sensitive at high partial tripolar fractions to both types of irregularities, but these effects are not independent. In particular, at close electrode-neuron distances, activation is typically more spatially localized which leads to a greater influence of neural dead regions. Copyright (c) 2010 Elsevier B.V. All rights reserved.

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

    Science.gov (United States)

    Bottino, Emanuele; Martinoia, Sergio; Valle, Maurizio

    2005-06-01

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

  12. In vitro verification of a 3-D regenerative neural interface design: examination of neurite growth and electrical properties within a bifurcating microchannel structure

    NARCIS (Netherlands)

    Wieringa, P.A.; Wiertz, Remy; de Weerd, Eddy L; Rutten, Wim

    2010-01-01

    Toward the development of neuroprosthesis, we propose a 3-D regenerative neural interface design for connecting with the peripheral nervous system. This approach relies on bifurcating microstructures to achieve defasciculated ingrowth patterns and, consequently, high selectivity. In vitro studies

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

    Science.gov (United States)

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

    2015-05-01

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

  14. Augmenting intracortical brain-machine interface with neurally driven error detectors

    Science.gov (United States)

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

    2017-12-01

    Objective. Making mistakes is inevitable, but identifying them allows us to correct or adapt our behavior to improve future performance. Current brain-machine interfaces (BMIs) make errors that need to be explicitly corrected by the user, thereby consuming time and thus hindering performance. We hypothesized that neural correlates of the user perceiving the mistake could be used by the BMI to automatically correct errors. However, it was unknown whether intracortical outcome error signals were present in the premotor and primary motor cortices, brain regions successfully used for intracortical BMIs. Approach. We report here for the first time a putative outcome error signal in spiking activity within these cortices when rhesus macaques performed an intracortical BMI computer cursor task. Main results. We decoded BMI trial outcomes shortly after and even before a trial ended with 96% and 84% accuracy, respectively. This led us to develop and implement in real-time a first-of-its-kind intracortical BMI error ‘detect-and-act’ system that attempts to automatically ‘undo’ or ‘prevent’ mistakes. The detect-and-act system works independently and in parallel to a kinematic BMI decoder. In a challenging task that resulted in substantial errors, this approach improved the performance of a BMI employing two variants of the ubiquitous Kalman velocity filter, including a state-of-the-art decoder (ReFIT-KF). Significance. Detecting errors in real-time from the same brain regions that are commonly used to control BMIs should improve the clinical viability of BMIs aimed at restoring motor function to people with paralysis.

  15. Early-onset Alzheimer's Disease Phenotypes: Neuropsychology and Neural Networks

    Science.gov (United States)

    2017-05-11

    Alzheimer Disease, Early Onset; Alzheimer Disease; Alzheimer Disease, Late Onset; Dementia, Alzheimer Type; Logopenic Progressive Aphasia; Primary Progressive Aphasia; Visuospatial/Perceptual Abilities; Posterior Cortical Atrophy; Executive Dysfunction; Corticobasal Degeneration; Ideomotor Apraxia

  16. Neural mirroring and social interaction: Motor system involvement during action observation relates to early peer cooperation.

    Science.gov (United States)

    Endedijk, H M; Meyer, M; Bekkering, H; Cillessen, A H N; Hunnius, S

    2017-04-01

    Whether we hand over objects to someone, play a team sport, or make music together, social interaction often involves interpersonal action coordination, both during instances of cooperation and entrainment. Neural mirroring is thought to play a crucial role in processing other's actions and is therefore considered important for social interaction. Still, to date, it is unknown whether interindividual differences in neural mirroring play a role in interpersonal coordination during different instances of social interaction. A relation between neural mirroring and interpersonal coordination has particularly relevant implications for early childhood, since successful early interaction with peers is predictive of a more favorable social development. We examined the relation between neural mirroring and children's interpersonal coordination during peer interaction using EEG and longitudinal behavioral data. Results showed that 4-year-old children with higher levels of motor system involvement during action observation (as indicated by lower beta-power) were more successful in early peer cooperation. This is the first evidence for a relation between motor system involvement during action observation and interpersonal coordination during other instances of social interaction. The findings suggest that interindividual differences in neural mirroring are related to interpersonal coordination and thus successful social interaction. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  17. Early Model of Traffic Sign Reminder Based on Neural Network

    Directory of Open Access Journals (Sweden)

    Budi Rahmani

    2012-12-01

    Full Text Available Recognizing the traffic signs installed on the streets is one of the requirements of driving on the road. Laxity in driving may result in traffic accident. This paper describes a real-time reminder model, by utilizing a camera that can be installed in a car to capture image of traffic signs, and is processed and later to inform the driver. The extracting feature harnessing the morphological elements (strel is used in this paper. Artificial Neural Networks is used to train the system and to produce a final decision. The result shows that the accuracy in detecting and recognizing the ten types of traffic signs in real-time is 80%.

  18. Inductive differentiation of two neural lineages reconstituted in a microculture system from Xenopus early gastrula cells.

    Science.gov (United States)

    Mitani, S; Okamoto, H

    1991-05-01

    Neural induction of ectoderm cells has been reconstituted and examined in a microculture system derived from dissociated early gastrula cells of Xenopus laevis. We have used monoclonal antibodies as specific markers to monitor cellular differentiation from three distinct ectoderm lineages in culture (N1 for CNS neurons from neural tube, Me1 for melanophores from neural crest and E3 for skin epidermal cells from epidermal lineages). CNS neurons and melanophores differentiate when deep layer cells of the ventral ectoderm (VE, prospective epidermis region; 150 cells/culture) and an appropriate region of the marginal zone (MZ, prospective mesoderm region; 5-150 cells/culture) are co-cultured, but not in cultures of either cell type on their own; VE cells cultured alone yield epidermal cells as we have previously reported. The extent of inductive neural differentiation in the co-culture system strongly depends on the origin and number of MZ cells initially added to culture wells. The potency to induce CNS neurons is highest for dorsal MZ cells and sharply decreases as more ventrally located cells are used. The same dorsoventral distribution of potency is seen in the ability of MZ cells to inhibit epidermal differentiation. In contrast, the ability of MZ cells to induce melanophores shows the reverse polarity, ventral to dorsal. These data indicate that separate developmental mechanisms are used for the induction of neural tube and neural crest lineages. Co-differentiation of CNS neurons or melanophores with epidermal cells can be obtained in a single well of co-cultures of VE cells (150) and a wide range of numbers of MZ cells (5 to 100). Further, reproducible differentiation of both neural lineages requires intimate association between cells from the two gastrula regions; virtually no differentiation is obtained when cells from the VE and MZ are separated in a culture well. These results indicate that the inducing signals from MZ cells for both neural tube and neural

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

    Science.gov (United States)

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

    2017-08-01

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

  20. Early capillary flux homogenization in response to neural activation.

    Science.gov (United States)

    Lee, Jonghwan; Wu, Weicheng; Boas, David A

    2016-02-01

    This Brief Communication reports early homogenization of capillary network flow during somatosensory activation in the rat cerebral cortex. We used optical coherence tomography and statistical intensity variation analysis for tracing changes in the red blood cell flux over hundreds of capillaries nearly at the same time with 1-s resolution. We observed that while the mean capillary flux exhibited a typical increase during activation, the standard deviation of the capillary flux exhibited an early decrease that happened before the mean flux increase. This network-level data is consistent with the theoretical hypothesis that capillary flow homogenizes during activation to improve oxygen delivery. © The Author(s) 2015.

  1. 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

  2. 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.

  3. A Nestin-cre transgenic mouse is insufficient for recombination in early embryonic neural progenitors

    Directory of Open Access Journals (Sweden)

    Huixuan Liang

    2012-09-01

    Nestin-cre transgenic mice have been widely used to direct recombination to neural stem cells (NSCs and intermediate neural progenitor cells (NPCs. Here we report that a readily utilized, and the only commercially available, Nestin-cre line is insufficient for directing recombination in early embryonic NSCs and NPCs. Analysis of recombination efficiency in multiple cre-dependent reporters and a genetic mosaic line revealed consistent temporal and spatial patterns of recombination in NSCs and NPCs. For comparison we utilized a knock-in Emx1cre line and found robust recombination in NSCs and NPCs in ventricular and subventricular zones of the cerebral cortices as early as embryonic day 12.5. In addition we found that the rate of Nestin-cre driven recombination only reaches sufficiently high levels in NSCs and NPCs during late embryonic and early postnatal periods. These findings are important when commercially available cre lines are considered for directing recombination to embryonic NSCs and NPCs.

  4. EARLY DIAGNOSIS OF SKIN CANCER USING ARTIFICIAL NEURAL NETWORKS

    OpenAIRE

    Birajdar Yogesh; Rengaprabhu P

    2017-01-01

    The proposed work is to present an approach to easily detect the skin cancer and classify into benign and malignant classes differentiating with the wounds. The skin cancer occurs for many people in some regions of the countries like Australia & New Zealand where the sunlight is difficult to reach during winters. Thus the deficiency of Vitamin D causes skin cancer for the people dwelling in such regions. Self-assessment is being encouraged in such cities to detect the skin cancers in early st...

  5. Transport and metabolism at blood-brain interfaces and in neural cells: relevance to bilirubin-induced encephalopathy

    Directory of Open Access Journals (Sweden)

    Silvia eGazzin

    2012-05-01

    Full Text Available Bilirubin, the end-product of heme catabolism, circulates in non pathological plasma mostly as a protein-bound species. When bilirubin concentration builds up, the free fraction of the molecule increases. Unbound bilirubin then diffuses across blood-brain interfaces into the brain, where it accumulates and exerts neurotoxic effects. In this classical view of bilirubin neurotoxicity, blood-brain interfaces act merely as structural barriers impeding the penetration of the pigment-bound carrier protein, and neural cells are considered as passive targets of its toxicity. Yet, the role of blood-brain interfaces in the occurrence of bilirubin encephalopathy appears more complex than being simple barriers to the diffusion of bilirubin, and neural cells such as astrocytes and neurons can play an active role in controlling the balance between the neuroprotective and neurotoxic effects of bilirubin. This article reviews the emerging in vivo and in vitro data showing that transport and metabolic detoxification mechanisms at the blood-brain and blood-CSF barriers may modulate bilirubin flux across both cellular interfaces, and that these protective functions can be affected in chronic hyperbilirubinemia. Then the in vivo and in vitro arguments in favor of the physiological antioxidant function of intracerebral bilirubin are presented, as well as with the potential role of transporters such as ABCC-1 and metabolizing enzymes such as cytochromes P-450 in setting the cerebral cell- and structure-specific toxicity of bilirubin following hyperbilirubinemia. The relevance of these data to the pathophysiology of bilirubin-induced neurological diseases is discussed.

  6. 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.

  7. Neural associations of the early retinotopic cortex with the lateral occipital complex during visual perception.

    Directory of Open Access Journals (Sweden)

    Delong Zhang

    Full Text Available Previous studies have demonstrated that the early retinotopic cortex (ERC, i.e., V1/V2/V3 is highly associated with the lateral occipital complex (LOC during visual perception. However, it remains largely unclear how to evaluate their associations in quantitative way. The present study tried to apply a multivariate pattern analysis (MVPA to quantify the neural activity in ERC and its association with that of the LOC when participants saw visual images. To this end, we assessed whether low-level visual features (Gabor features could predict the neural activity in the ERC and LOC according to a voxel-based encoding model (VBEM, and then quantified the association of the neural activity between these regions by using an analogical VBEM. We found that the Gabor features remarkably predicted the activity of the ERC (e.g., the predicted accuracy was 52.5% for a participant instead of that of the LOC (4.2%. Moreover, the MVPA approach can also be used to establish corresponding relationships between the activity patterns in the LOC and those in the ERC (64.2%. In particular, we found that the integration of the Gabor features and LOC visual information could dramatically improve the 'prediction' of ERC activity (88.3%. Overall, the present study provides new evidences for the possibility of quantifying the association of the neural activity between the regions of ERC and LOC. This approach will help to provide further insights into the neural substrates of the visual processing.

  8. Ultra low-power integrated circuit design for wireless neural interfaces

    CERN Document Server

    Holleman, Jeremy; Otis, Brian

    2014-01-01

    Presenting results from real prototype systems, this volume provides an overview of ultra low-power integrated circuits and systems for neural signal processing and wireless communication. Topics include analog, radio, and signal processing theory and design for ultra low-power circuits.

  9. Girls’ challenging social experiences in early adolescence predict neural response to rewards and depressive symptoms

    Directory of Open Access Journals (Sweden)

    Melynda D. Casement

    2014-04-01

    Full Text Available Developmental models of psychopathology posit that exposure to social stressors may confer risk for depression in adolescent girls by disrupting neural reward circuitry. The current study tested this hypothesis by examining the relationship between early adolescent social stressors and later neural reward processing and depressive symptoms. Participants were 120 girls from an ongoing longitudinal study of precursors to depression across adolescent development. Low parental warmth, peer victimization, and depressive symptoms were assessed when the girls were 11 and 12 years old, and participants completed a monetary reward guessing fMRI task and assessment of depressive symptoms at age 16. Results indicate that low parental warmth was associated with increased response to potential rewards in the medial prefrontal cortex (mPFC, striatum, and amygdala, whereas peer victimization was associated with decreased response to potential rewards in the mPFC. Furthermore, concurrent depressive symptoms were associated with increased reward anticipation response in mPFC and striatal regions that were also associated with early adolescent psychosocial stressors, with mPFC and striatal response mediating the association between social stressors and depressive symptoms. These findings are consistent with developmental models that emphasize the adverse impact of early psychosocial stressors on neural reward processing and risk for depression in adolescence.

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

    Science.gov (United States)

    Gatica-Rojas, Valeska; Méndez-Rebolledo, Guillermo

    2014-01-01

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

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

    Science.gov (United States)

    Gatica-Rojas, Valeska; Méndez-Rebolledo, Guillermo

    2014-04-15

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

  12. Dynamic methylation and expression of Oct4 in early neural stem cells.

    Science.gov (United States)

    Lee, Shih-Han; Jeyapalan, Jennie N; Appleby, Vanessa; Mohamed Noor, Dzul Azri; Sottile, Virginie; Scotting, Paul J

    2010-09-01

    Neural stem cells are a multipotent population of tissue-specific stem cells with a broad but limited differentiation potential. However, recent studies have shown that over-expression of the pluripotency gene, Oct4, alone is sufficient to initiate a process by which these can form 'induced pluripotent stem cells' (iPS cells) with the same broad potential as embryonic stem cells. This led us to examine the expression of Oct4 in endogenous neural stem cells, as data regarding its expression in neural stem cells in vivo are contradictory and incomplete. In this study we have therefore analysed the expression of Oct4 and other genes associated with pluripotency throughout development of the mouse CNS and in neural stem cells grown in vitro. We find that Oct4 is still expressed in the CNS by E8.5, but that this expression declines rapidly until it is undetectable by E15.5. This decline is coincident with the gradual methylation of the Oct4 promoter and proximal enhancer. Immunostaining suggests that the Oct4 protein is predominantly cytoplasmic in location. We also found that neural stem cells from all ages expressed the pluripotency associated genes, Sox2, c-Myc, Klf4 and Nanog. These data provide an explanation for the varying behaviour of cells from the early neuroepithelium at different stages of development. The expression of these genes also provides an indication of why Oct4 alone is sufficient to induce iPS formation in neural stem cells at later stages.

  13. The response of early neural genes to FGF signaling or inhibition of BMP indicate the absence of a conserved neural induction module

    Directory of Open Access Journals (Sweden)

    Rogers Crystal D

    2011-12-01

    Full Text Available Abstract Background The molecular mechanism that initiates the formation of the vertebrate central nervous system has long been debated. Studies in Xenopus and mouse demonstrate that inhibition of BMP signaling is sufficient to induce neural tissue in explants or ES cells respectively, whereas studies in chick argue that instructive FGF signaling is also required for the expression of neural genes. Although additional signals may be involved in neural induction and patterning, here we focus on the roles of BMP inhibition and FGF8a. Results To address the question of necessity and sufficiency of BMP inhibition and FGF signaling, we compared the temporal expression of the five earliest genes expressed in the neuroectoderm and determined their requirements for induction at the onset of neural plate formation in Xenopus. Our results demonstrate that the onset and peak of expression of the genes vary and that they have different regulatory requirements and are therefore unlikely to share a conserved neural induction regulatory module. Even though all require inhibition of BMP for expression, some also require FGF signaling; expression of the early-onset pan-neural genes sox2 and foxd5α requires FGF signaling while other early genes, sox3, geminin and zicr1 are induced by BMP inhibition alone. Conclusions We demonstrate that BMP inhibition and FGF signaling induce neural genes independently of each other. Together our data indicate that although the spatiotemporal expression patterns of early neural genes are similar, the mechanisms involved in their expression are distinct and there are different signaling requirements for the expression of each gene.

  14. Learning representations for the early detection of sepsis with deep neural networks.

    Science.gov (United States)

    Kam, Hye Jin; Kim, Ha Young

    2017-10-01

    Sepsis is one of the leading causes of death in intensive care unit patients. Early detection of sepsis is vital because mortality increases as the sepsis stage worsens. This study aimed to develop detection models for the early stage of sepsis using deep learning methodologies, and to compare the feasibility and performance of the new deep learning methodology with those of the regression method with conventional temporal feature extraction. Study group selection adhered to the InSight model. The results of the deep learning-based models and the InSight model were compared. With deep feedforward networks, the area under the ROC curve (AUC) of the models were 0.887 and 0.915 for the InSight and the new feature sets, respectively. For the model with the combined feature set, the AUC was the same as that of the basic feature set (0.915). For the long short-term memory model, only the basic feature set was applied and the AUC improved to 0.929 compared with the existing 0.887 of the InSight model. The contributions of this paper can be summarized in three ways: (i) improved performance without feature extraction using domain knowledge, (ii) verification of feature extraction capability of deep neural networks through comparison with reference features, and (iii) improved performance with feedforward neural networks using long short-term memory, a neural network architecture that can learn sequential patterns. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

    Ereifej, Evon S.

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

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

    Science.gov (United States)

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

    2013-06-01

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

  17. Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image.

    Science.gov (United States)

    Xu, Kele; Feng, Dawei; Mi, Haibo

    2017-11-23

    The automatic detection of diabetic retinopathy is of vital importance, as it is the main cause of irreversible vision loss in the working-age population in the developed world. The early detection of diabetic retinopathy occurrence can be very helpful for clinical treatment; although several different feature extraction approaches have been proposed, the classification task for retinal images is still tedious even for those trained clinicians. Recently, deep convolutional neural networks have manifested superior performance in image classification compared to previous handcrafted feature-based image classification methods. Thus, in this paper, we explored the use of deep convolutional neural network methodology for the automatic classification of diabetic retinopathy using color fundus image, and obtained an accuracy of 94.5% on our dataset, outperforming the results obtained by using classical approaches.

  18. Behavioral and neural plasticity caused by early social experiences: the case of the honeybee

    Directory of Open Access Journals (Sweden)

    Andrés eArenas

    2013-08-01

    Full Text Available Cognitive experiences during the early stages of life play an important role in shaping future behavior. Behavioral and neural long-term changes after early sensory and associative experiences have been recently reported in the honeybee. This invertebrate is an excellent model for assessing the role of precocious experiences on later behavior due to its extraordinarily tuned division of labor based on age polyethism. These studies are mainly focused on the role and importance of experiences occurred during the first days of the adult lifespan, their impact on foraging decisions and their contribution to coordinate food gathering. Odor-rewarded experiences during the first days of honeybee adulthood alter the responsiveness to sucrose, making young hive bees more sensitive to assess gustatory features about the nectar brought back to the hive and affecting the dynamic of the food transfers and the propagation of food-related information within the colony as well. Early olfactory experiences lead to stable and long-term associative memories that can be successfully recalled after many days, even at foraging ages. Also they improve memorizing of new associative learning events later in life. The establishment of early memories promotes stable reorganization of the olfactory circuits inducing structural and functional changes in the antennal lobe. Early rewarded experiences have relevant consequences at the social level too, biasing dance and trophallaxis partner choice and affecting recruitment. Here, we revised recent results in bees´ physiology, behavior and sociobiology to depict how the early experiences affect their cognition abilities and neural-related circuits.

  19. Effect of socioeconomic status disparity on child language and neural outcome: how early is early?

    Science.gov (United States)

    Hurt, Hallam; Betancourt, Laura M

    2016-01-01

    It is not news that poverty adversely affects child outcome. The literature is replete with reports of deleterious effects on developmental outcome, cognitive function, and school performance in children and youth. Causative factors include poor nutrition, exposure to toxins, inadequate parenting, lack of cognitive stimulation, unstable social support, genetics, and toxic environments. Less is known regarding how early in life adverse effects may be detected. This review proposes to elucidate "how early is early" through discussion of seminal articles related to the effect of socioeconomic status on language outcome and a discussion of the emerging literature on effects of socioeconomic status disparity on brain structure in very young children. Given the young ages at which such outcomes are detected, the critical need for early targeted interventions for our youngest is underscored. Further, the fiscal reasonableness of initiating quality interventions supports these initiatives. As early life adversity produces lasting and deleterious effects on developmental outcome and brain structure, increased focus on programs and policies directed to reducing the impact of socioeconomic disparities is essential.

  20. Usage of Probabilistic and General Regression Neural Network for Early Detection and Prevention of Oral Cancer

    Directory of Open Access Journals (Sweden)

    Neha Sharma

    2015-01-01

    Full Text Available In India, the oral cancers are usually presented in advanced stage of malignancy. It is critical to ascertain the diagnosis in order to initiate most advantageous treatment of the suspicious lesions. The main hurdle in appropriate treatment and control of oral cancer is identification and risk assessment of early disease in the community in a cost-effective fashion. The objective of this research is to design a data mining model using probabilistic neural network and general regression neural network (PNN/GRNN for early detection and prevention of oral malignancy. The model is built using the oral cancer database which has 35 attributes and 1025 records. All the attributes pertaining to clinical symptoms and history are considered to classify malignant and non-malignant cases. Subsequently, the model attempts to predict particular type of cancer, its stage and extent with the help of attributes pertaining to symptoms, gross examination and investigations. Also, the model envisages anticipating the survivability of a patient on the basis of treatment and follow-up details. Finally, the performance of the PNN/GRNN model is compared with that of other classification models. The classification accuracy of PNN/GRNN model is 80% and hence is better for early detection and prevention of the oral cancer.

  1. Usage of Probabilistic and General Regression Neural Network for Early Detection and Prevention of Oral Cancer.

    Science.gov (United States)

    Sharma, Neha; Om, Hari

    2015-01-01

    In India, the oral cancers are usually presented in advanced stage of malignancy. It is critical to ascertain the diagnosis in order to initiate most advantageous treatment of the suspicious lesions. The main hurdle in appropriate treatment and control of oral cancer is identification and risk assessment of early disease in the community in a cost-effective fashion. The objective of this research is to design a data mining model using probabilistic neural network and general regression neural network (PNN/GRNN) for early detection and prevention of oral malignancy. The model is built using the oral cancer database which has 35 attributes and 1025 records. All the attributes pertaining to clinical symptoms and history are considered to classify malignant and non-malignant cases. Subsequently, the model attempts to predict particular type of cancer, its stage and extent with the help of attributes pertaining to symptoms, gross examination and investigations. Also, the model envisages anticipating the survivability of a patient on the basis of treatment and follow-up details. Finally, the performance of the PNN/GRNN model is compared with that of other classification models. The classification accuracy of PNN/GRNN model is 80% and hence is better for early detection and prevention of the oral cancer.

  2. Artificial Neural Network-Based Early-Age Concrete Strength Monitoring Using Dynamic Response Signals.

    Science.gov (United States)

    Kim, Junkyeong; Lee, Chaggil; Park, Seunghee

    2017-06-07

    Concrete is one of the most common materials used to construct a variety of civil infrastructures. However, since concrete might be susceptible to brittle fracture, it is essential to confirm the strength of concrete at the early-age stage of the curing process to prevent unexpected collapse. To address this issue, this study proposes a novel method to estimate the early-age strength of concrete, by integrating an artificial neural network algorithm with a dynamic response measurement of the concrete material. The dynamic response signals of the concrete, including both electromechanical impedances and guided ultrasonic waves, are obtained from an embedded piezoelectric sensor module. The cross-correlation coefficient of the electromechanical impedance signals and the amplitude of the guided ultrasonic wave signals are selected to quantify the variation in dynamic responses according to the strength of the concrete. Furthermore, an artificial neural network algorithm is used to verify a relationship between the variation in dynamic response signals and concrete strength. The results of an experimental study confirm that the proposed approach can be effectively applied to estimate the strength of concrete material from the early-age stage of the curing process.

  3. Implementation of Hierarchical Task Analysis for User Interface Design in Drawing Application for Early Childhood Education

    Directory of Open Access Journals (Sweden)

    Mira Kania Sabariah

    2016-05-01

    Full Text Available Draw learning in early childhood is an important lesson and full of stimulation of the process of growth and development of children which could help to train the fine motor skills. We have had a lot of applications that can be used to perform learning, including interactive learning applications. Referring to the observations that have been conducted showed that the experiences given by the applications that exist today are very diverse and have not been able to represent the model of learning and characteristics of early childhood (4-6 years. Based on the results, Hierarchical Task Analysis method generated a list of tasks that must be done in designing an user interface that represents the user experience in draw learning. Then by using the Heuristic Evaluation method the usability of the model has fulfilled a very good level of understanding and also it can be enhanced and produce a better model.

  4. Early life stress accelerates behavioral and neural maturation of the hippocampus in male mice.

    Science.gov (United States)

    Bath, K; Manzano-Nieves, G; Goodwill, H

    2016-06-01

    Early life stress (ELS) increases the risk for later cognitive and emotional dysfunction. ELS is known to truncate neural development through effects on suppressing cell birth, increasing cell death, and altering neuronal morphology, effects that have been associated with behavioral profiles indicative of precocious maturation. However, how earlier silencing of growth drives accelerated behavioral maturation has remained puzzling. Here, we test the novel hypothesis that, ELS drives a switch from growth to maturation to accelerate neural and behavioral development. To test this, we used a mouse model of ELS, fragmented maternal care, and a cross-sectional dense sampling approach focusing on hippocampus and measured effects of ELS on the ontogeny of behavioral development and biomarkers of neural maturation. Consistent with previous work, ELS was associated with an earlier developmental decline in expression of markers of cell proliferation (Ki-67) and differentiation (doublecortin). However, ELS also led to a precocious arrival of Parvalbumin-positive cells, led to an earlier switch in NMDA receptor subunit expression (marker of synaptic maturity), and was associated with an earlier rise in myelin basic protein expression (key component of the myelin sheath). In addition, in a contextual fear-conditioning task, ELS accelerated the timed developmental suppression of contextual fear. Together, these data provide support for the hypothesis that ELS serves to switch neurodevelopment from processes of growth to maturation and promotes accelerated development of some forms of emotional learning. Copyright © 2016 Elsevier Inc. All rights reserved.

  5. Imidacloprid Exposure Suppresses Neural Crest Cells Generation during Early Chick Embryo Development.

    Science.gov (United States)

    Wang, Chao-Jie; Wang, Guang; Wang, Xiao-Yu; Liu, Meng; Chuai, Manli; Lee, Kenneth Ka Ho; He, Xiao-Song; Lu, Da-Xiang; Yang, Xuesong

    2016-06-15

    Imidacloprid is a neonicotinoid pesticide that is widely used in the control pests found on crops and fleas on pets. However, it is still unclear whether imidacloprid exposure could affect early embryo development-despite some studies having been conducted on the gametes. In this study, we demonstrated that imidacloprid exposure could lead to abnormal craniofacial osteogenesis in the developing chick embryo. Cranial neural crest cells (NCCs) are the progenitor cells of the chick cranial skull. We found that the imidacloprid exposure retards the development of gastrulating chick embryos. HNK-1, PAX7, and Ap-2α immunohistological stainings indicated that cranial NCCs generation was inhibited after imidacloprid exposure. Double immunofluorescent staining (Ap-2α and PHIS3 or PAX7 and c-Caspase3) revealed that imidacloprid exposure inhibited both NCC proliferation and apoptosis. In addition, it inhibited NCCs production by repressing Msx1 and BMP4 expression in the developing neural tube and by altering expression of EMT-related adhesion molecules (Cad6B, E-Cadherin, and N-cadherin) in the developing neural crests. We also determined that imidacloprid exposure suppressed cranial NCCs migration and their ability to differentiate. In sum, we have provided experimental evidence that imidacloprid exposure during embryogenesis disrupts NCCs development, which in turn causes defective cranial bone development.

  6. Young Adults with Autism Spectrum Disorder Show Early Atypical Neural Activity during Emotional Face Processing

    Directory of Open Access Journals (Sweden)

    Rachel C. Leung

    2018-02-01

    Full Text Available Social cognition is impaired in autism spectrum disorder (ASD. The ability to perceive and interpret affect is integral to successful social functioning and has an extended developmental course. However, the neural mechanisms underlying emotional face processing in ASD are unclear. Using magnetoencephalography (MEG, the present study explored neural activation during implicit emotional face processing in young adults with and without ASD. Twenty-six young adults with ASD and 26 healthy controls were recruited. Participants indicated the location of a scrambled pattern (target that was presented alongside a happy or angry face. Emotion-related activation sources for each emotion were estimated using the Empirical Bayes Beamformer (pcorr ≤ 0.001 in Statistical Parametric Mapping 12 (SPM12. Emotional faces elicited elevated fusiform, amygdala and anterior insula and reduced anterior cingulate cortex (ACC activity in adults with ASD relative to controls. Within group comparisons revealed that angry vs. happy faces elicited distinct neural activity in typically developing adults; there was no distinction in young adults with ASD. Our data suggest difficulties in affect processing in ASD reflect atypical recruitment of traditional emotional processing areas. These early differences may contribute to difficulties in deriving social reward from faces, ascribing salience to faces, and an immature threat processing system, which collectively could result in deficits in emotional face processing.

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

    Science.gov (United States)

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

    2014-01-01

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

  8. Abnormal neural precursor cell regulation in the early postnatal Fragile X mouse hippocampus.

    Science.gov (United States)

    Sourial, Mary; Doering, Laurie C

    2017-07-01

    The regulation of neural precursor cells (NPCs) is indispensable for a properly functioning brain. Abnormalities in NPC proliferation, differentiation, survival, or integration have been linked to various neurological diseases including Fragile X syndrome. Yet, no studies have examined NPCs from the early postnatal Fragile X mouse hippocampus despite the importance of this developmental time point, which marks the highest expression level of FMRP, the protein missing in Fragile X, in the rodent hippocampus and is when hippocampal NPCs have migrated to the dentate gyrus (DG) to give rise to lifelong neurogenesis. In this study, we examined NPCs from the early postnatal hippocampus and DG of Fragile X mice (Fmr1-KO). Immunocytochemistry on neurospheres showed increased Nestin expression and decreased Ki67 expression, which collectively indicated aberrant NPC biology. Intriguingly, flow cytometric analysis of the expression of the antigens CD15, CD24, CD133, GLAST, and PSA-NCAM showed a decreased proportion of neural stem cells (GLAST + CD15 + CD133 + ) and an increased proportion of neuroblasts (PSA-NCAM + CD15 + ) in the DG of P7 Fmr1-KO mice. This was mirrored by lower expression levels of Nestin and the mitotic marker phospho-histone H3 in vivo in the P9 hippocampus, as well as a decreased proportion of cells in the G 2 /M phases of the P7 DG. Thus, the absence of FMRP leads to fewer actively cycling NPCs, coinciding with a decrease in neural stem cells and an increase in neuroblasts. Together, these results show the importance of FMRP in the developing hippocampal formation and suggest abnormalities in cell cycle regulation in Fragile X. Crown Copyright © 2017. Published by Elsevier B.V. All rights reserved.

  9. Temperament and Parenting Styles in Early Childhood Differentially Influence Neural Response to Peer Evaluation in Adolescence

    Science.gov (United States)

    Guyer, Amanda E.; Jarcho, Johanna M.; Pérez-Edgar, Koraly; Degnan, Kathryn A.; Pine, Daniel S.; Fox, Nathan A.; Nelson, Eric E.

    2015-01-01

    Behavioral inhibition (BI) is a temperament characterized by social reticence and withdrawal from unfamiliar or novel contexts and conveys risk for social anxiety disorder. Developmental outcomes associated with this temperament can be influenced by children’s caregiving context. The convergence of a child’s temperamental disposition and rearing environment is ultimately expressed at both the behavioral and neural levels in emotional and cognitive response patterns to social challenges. The present study used functional neuroimaging to assess the moderating effects of different parenting styles on neural response to peer rejection in two groups of adolescents characterized by their early childhood temperament (Mage = 17.89 years, N= 39, 17 males, 22 females; 18 with BI; 21 without BI). The moderating effects of authoritarian and authoritative parenting styles were examined in three brain regions linked with social anxiety: ventrolateral prefrontal cortex (vlPFC), striatum, and amygdala. In youth characterized with BI in childhood, but not in those without BI, diminished responses to peer rejection in vlPFC were associated with higher levels of authoritarian parenting. In contrast, all youth showed decreased caudate response to peer rejection at higher levels of authoritative parenting. These findings indicate that BI in early life relates to greater neurobiological sensitivity to variance in parenting styles, particularly harsh parenting, in late adolescence. These results are discussed in relation to biopsychosocial models of development. PMID:25588884

  10. Temperament and Parenting Styles in Early Childhood Differentially Influence Neural Response to Peer Evaluation in Adolescence.

    Science.gov (United States)

    Guyer, Amanda E; Jarcho, Johanna M; Pérez-Edgar, Koraly; Degnan, Kathryn A; Pine, Daniel S; Fox, Nathan A; Nelson, Eric E

    2015-07-01

    Behavioral inhibition (BI) is a temperament characterized by social reticence and withdrawal from unfamiliar or novel contexts and conveys risk for social anxiety disorder. Developmental outcomes associated with this temperament can be influenced by children's caregiving context. The convergence of a child's temperamental disposition and rearing environment is ultimately expressed at both the behavioral and neural levels in emotional and cognitive response patterns to social challenges. The present study used functional neuroimaging to assess the moderating effects of different parenting styles on neural response to peer rejection in two groups of adolescents characterized by their early childhood temperament (M(age) = 17.89 years, N = 39, 17 males, 22 females; 18 with BI; 21 without BI). The moderating effects of authoritarian and authoritative parenting styles were examined in three brain regions linked with social anxiety: ventrolateral prefrontal cortex (vlPFC), striatum, and amygdala. In youth characterized with BI in childhood, but not in those without BI, diminished responses to peer rejection in vlPFC were associated with higher levels of authoritarian parenting. In contrast, all youth showed decreased caudate response to peer rejection at higher levels of authoritative parenting. These findings indicate that BI in early life relates to greater neurobiological sensitivity to variance in parenting styles, particularly harsh parenting, in late adolescence. These results are discussed in relation to biopsychosocial models of development.

  11. Early Parenting Moderates the Association between Parental Depression and Neural Reactivity to Rewards and Losses in Offspring

    OpenAIRE

    Kujawa, Autumn; Proudfit, Greg H.; Laptook, Rebecca; Klein, Daniel N.

    2014-01-01

    Children of parents with depression exhibit neural abnormalities in reward processing. Examining contributions of parenting could provide insight into the development of these abnormalities and to the etiology of depression. We evaluated whether early parenting moderates the effects of parental depression on a neural measure of reward and loss processing in mid-late childhood. Parenting was assessed when children were preschoolers. At age nine, children completed an event-related potential as...

  12. The Pleiotropic MET Receptor Network: Circuit Development and the Neural-Medical Interface of Autism.

    Science.gov (United States)

    Eagleson, Kathie L; Xie, Zhihui; Levitt, Pat

    2017-03-01

    People with autism spectrum disorder and other neurodevelopmental disorders (NDDs) are behaviorally and medically heterogeneous. The combination of polygenicity and gene pleiotropy-the influence of one gene on distinct phenotypes-raises questions of how specific genes and their protein products interact to contribute to NDDs. A preponderance of evidence supports developmental and pathophysiological roles for the MET receptor tyrosine kinase, a multifunctional receptor that mediates distinct biological responses depending upon cell context. MET influences neuron architecture and synapse maturation in the forebrain and regulates homeostasis in gastrointestinal and immune systems, both commonly disrupted in NDDs. Peak expression of synapse-enriched MET is conserved across rodent and primate forebrain, yet regional differences in primate neocortex are pronounced, with enrichment in circuits that participate in social information processing. A functional risk allele in the MET promoter, enriched in subgroups of children with autism spectrum disorder, reduces transcription and disrupts socially relevant neural circuits structurally and functionally. In mice, circuit-specific deletion of Met causes distinct atypical behaviors. MET activation increases dendritic complexity and nascent synapse number, but synapse maturation requires reductions in MET. MET mediates its specific biological effects through different intracellular signaling pathways and has a complex protein interactome that is enriched in autism spectrum disorder and other NDD candidates. The interactome is coregulated in developing human neocortex. We suggest that a gene as pleiotropic and highly regulated as MET, together with its interactome, is biologically relevant in normal and pathophysiological contexts, affecting central and peripheral phenotypes that contribute to NDD risk and clinical symptoms. Copyright © 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

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

    Science.gov (United States)

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

    2014-10-08

    Touch perception on the fingers and hand is essential for fine motor control, contributes to our sense of self, allows for effective communication, and aids in our fundamental perception of the world. Despite increasingly sophisticated mechatronics, prosthetic devices still do not directly convey sensation back to their wearers. We show that implanted peripheral nerve interfaces in two human subjects with upper limb amputation provided stable, natural touch sensation in their hands for more than 1 year. Electrical stimulation using implanted peripheral nerve cuff electrodes that did not penetrate the nerve produced touch perceptions at many locations on the phantom hand with repeatable, stable responses in the two subjects for 16 and 24 months. Patterned stimulation intensity produced a sensation that the subjects described as natural and without "tingling," or paresthesia. Different patterns produced different types of sensory perception at the same location on the phantom hand. The two subjects reported tactile perceptions they described as natural tapping, constant pressure, light moving touch, and vibration. Changing average stimulation intensity controlled the size of the percept area; changing stimulation frequency controlled sensation strength. Artificial touch sensation improved the subjects' ability to control grasping strength of the prosthesis and enabled them to better manipulate delicate objects. Thus, electrical stimulation through peripheral nerve electrodes produced long-term sensory restoration after limb loss. Copyright © 2014, American Association for the Advancement of Science.

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

    Directory of Open Access Journals (Sweden)

    Cipriani Christian

    2011-09-01

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

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

    Science.gov (United States)

    Micera, Silvestro; Rossini, Paolo M; Rigosa, Jacopo; Citi, Luca; Carpaneto, Jacopo; Raspopovic, Stanisa; Tombini, Mario; Cipriani, Christian; Assenza, Giovanni; Carrozza, Maria C; Hoffmann, Klaus-Peter; Yoshida, Ken; Navarro, Xavier; Dario, Paolo

    2011-09-05

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

  16. Early warning of illegal development for protected areas by integrating cellular automata with neural networks.

    Science.gov (United States)

    Li, Xia; Lao, Chunhua; Liu, Yilun; Liu, Xiaoping; Chen, Yimin; Li, Shaoying; Ai, Bing; He, Zijian

    2013-11-30

    Ecological security has become a major issue under fast urbanization in China. As the first two cities in this country, Shenzhen and Dongguan issued the ordinance of Eco-designated Line of Control (ELC) to "wire" ecologically important areas for strict protection in 2005 and 2009 respectively. Early warning systems (EWS) are a useful tool for assisting the implementation ELC. In this study, a multi-model approach is proposed for the early warning of illegal development by integrating cellular automata (CA) and artificial neural networks (ANN). The objective is to prevent the ecological risks or catastrophe caused by such development at an early stage. The integrated model is calibrated by using the empirical information from both remote sensing and handheld GPS (global positioning systems). The MAR indicator which is the ratio of missing alarms to all the warnings is proposed for better assessment of the model performance. It is found that the fast urban development has caused significant threats to natural-area protection in the study area. The integration of CA, ANN and GPS provides a powerful tool for describing and predicting illegal development which is in highly non-linear and fragmented forms. The comparison shows that this multi-model approach has much better performances than the single-model approach for the early warning. Compared with the single models of CA and ANN, this integrated multi-model can improve the value of MAR by 65.48% and 5.17% respectively. Copyright © 2013 Elsevier Ltd. All rights reserved.

  17. Early Yield Prediction Using Image Analysis of Apple Fruit and Tree Canopy Features with Neural Networks

    Directory of Open Access Journals (Sweden)

    Hong Cheng

    2017-01-01

    Full Text Available (1 Background: Since early yield prediction is relevant for resource requirements of harvesting and marketing in the whole fruit industry, this paper presents a new approach of using image analysis and tree canopy features to predict early yield with artificial neural networks (ANN; (2 Methods: Two back propagation neural network (BPNN models were developed for the early period after natural fruit drop in June and the ripening period, respectively. Within the same periods, images of apple cv. “Gala” trees were captured from an orchard near Bonn, Germany. Two sample sets were developed to train and test models; each set included 150 samples from the 2009 and 2010 growing season. For each sample (each canopy image, pixels were segmented into fruit, foliage, and background using image segmentation. The four features extracted from the data set for the canopy were: total cross-sectional area of fruits, fruit number, total cross-section area of small fruits, and cross-sectional area of foliage, and were used as inputs. With the actual weighted yield per tree as a target, BPNN was employed to learn their mutual relationship as a prerequisite to develop the prediction; (3 Results: For the developed BPNN model of the early period after June drop, correlation coefficients (R2 between the estimated and the actual weighted yield, mean forecast error (MFE, mean absolute percentage error (MAPE, and root mean square error (RMSE were 0.81, −0.05, 10.7%, 2.34 kg/tree, respectively. For the model of the ripening period, these measures were 0.83, −0.03, 8.9%, 2.3 kg/tree, respectively. In 2011, the two previously developed models were used to predict apple yield. The RMSE and R2 values between the estimated and harvested apple yield were 2.6 kg/tree and 0.62 for the early period (small, green fruit and improved near harvest (red, large fruit to 2.5 kg/tree and 0.75 for a tree with ca. 18 kg yield per tree. For further method verification, the cv.

  18. 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

  19. Neural Correlates of User-initiated Motor Success and Failure - A Brain-Computer Interface Perspective.

    Science.gov (United States)

    Yazmir, Boris; Reiner, Miriam

    2018-05-15

    Any motor action is, by nature, potentially accompanied by human errors. In order to facilitate development of error-tailored Brain-Computer Interface (BCI) correction systems, we focused on internal, human-initiated errors, and investigated EEG correlates of user outcome successes and errors during a continuous 3D virtual tennis game against a computer player. We used a multisensory, 3D, highly immersive environment. Missing and repelling the tennis ball were considered, as 'error' (miss) and 'success' (repel). Unlike most previous studies, where the environment "encouraged" the participant to perform a mistake, here errors happened naturally, resulting from motor-perceptual-cognitive processes of incorrect estimation of the ball kinematics, and can be regarded as user internal, self-initiated errors. Results show distinct and well-defined Event-Related Potentials (ERPs), embedded in the ongoing EEG, that differ across conditions by waveforms, scalp signal distribution maps, source estimation results (sLORETA) and time-frequency patterns, establishing a series of typical features that allow valid discrimination between user internal outcome success and error. The significant delay in latency between positive peaks of error- and success-related ERPs, suggests a cross-talk between top-down and bottom-up processing, represented by an outcome recognition process, in the context of the game world. Success-related ERPs had a central scalp distribution, while error-related ERPs were centro-parietal. The unique characteristics and sharp differences between EEG correlates of error/success provide the crucial components for an improved BCI system. The features of the EEG waveform can be used to detect user action outcome, to be fed into the BCI correction system. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

  20. Early stages of interface reactions between AlN and Ti thin films

    CERN Document Server

    Pinkas, M; Froumin, N; Pelleg, J; Dariel, M P

    2002-01-01

    The early stages of interface reactions between AlN and Ti thin films were investigated using x-ray diffractions, Auger electron spectroscopy, cross section transmission electron microscopy (XTEM), and high resolution XTEM. The AlN/Ti bilayers were deposited on a molybdenum substrate using reactive and nonreactive magnetron sputtering techniques. After deposition, the bilayers were heat treated for 1-10 h at 600 deg. C in a nitrogen atmosphere. Decomposition of the AlN layer took place at the AlN/Ti interface and its products, Al and N, reacted with Ti to produce a AlN/Al sub 3 Ti/Ti sub 2 N/Ti sub 3 Al/alpha-(Ti, Al)ss phase sequence. This phase sequence is not consistent with the Ti-Al-N phase diagram and is believed to be the outcome of the particular conditions that prevail in the thin film and correspond to a particular set of kinetic parameters. A model that explains the development of the phase sequence and predicts its evolution after prolonged heat treatments is put forward. The applicability of such...

  1. Restriction of neural precursor ability to respond to Nurr1 by early regional specification.

    Directory of Open Access Journals (Sweden)

    Chiara Soldati

    Full Text Available During neural development, spatially regulated expression of specific transcription factors is crucial for central nervous system (CNS regionalization, generation of neural precursors (NPs and subsequent differentiation of specific cell types within defined regions. A critical role in dopaminergic differentiation in the midbrain (MB has been assigned to the transcription factor Nurr1. Nurr1 controls the expression of key genes involved in dopamine (DA neurotransmission, e.g. tyrosine hydroxylase (TH and the DA transporter (DAT, and promotes the dopaminergic phenotype in embryonic stem cells. We investigated whether cells derived from different areas of the mouse CNS could be directed to differentiate into dopaminergic neurons in vitro by forced expression of the transcription factor Nurr1. We show that Nurr1 overexpression can promote dopaminergic cell fate specification only in NPs obtained from E13.5 ganglionic eminence (GE and MB, but not in NPs isolated from E13.5 cortex (CTX and spinal cord (SC or from the adult subventricular zone (SVZ. Confirming previous studies, we also show that Nurr1 overexpression can increase the generation of TH-positive neurons in mouse embryonic stem cells. These data show that Nurr1 ability to induce a dopaminergic phenotype becomes restricted during CNS development and is critically dependent on the region of NPs derivation. Our results suggest that the plasticity of NPs and their ability to activate a dopaminergic differentiation program in response to Nurr1 is regulated during early stages of neurogenesis, possibly through mechanisms controlling CNS regionalization.

  2. Shaping Early Reorganization of Neural Networks Promotes Motor Function after Stroke

    Science.gov (United States)

    Volz, L. J.; Rehme, A. K.; Michely, J.; Nettekoven, C.; Eickhoff, S. B.; Fink, G. R.; Grefkes, C.

    2016-01-01

    Neural plasticity is a major factor driving cortical reorganization after stroke. We here tested whether repetitively enhancing motor cortex plasticity by means of intermittent theta-burst stimulation (iTBS) prior to physiotherapy might promote recovery of function early after stroke. Functional magnetic resonance imaging (fMRI) was used to elucidate underlying neural mechanisms. Twenty-six hospitalized, first-ever stroke patients (time since stroke: 1–16 days) with hand motor deficits were enrolled in a sham-controlled design and pseudo-randomized into 2 groups. iTBS was administered prior to physiotherapy on 5 consecutive days either over ipsilesional primary motor cortex (M1-stimulation group) or parieto-occipital vertex (control-stimulation group). Hand motor function, cortical excitability, and resting-state fMRI were assessed 1 day prior to the first stimulation and 1 day after the last stimulation. Recovery of grip strength was significantly stronger in the M1-stimulation compared to the control-stimulation group. Higher levels of motor network connectivity were associated with better motor outcome. Consistently, control-stimulated patients featured a decrease in intra- and interhemispheric connectivity of the motor network, which was absent in the M1-stimulation group. Hence, adding iTBS to prime physiotherapy in recovering stroke patients seems to interfere with motor network degradation, possibly reflecting alleviation of post-stroke diaschisis. PMID:26980614

  3. Effects of task demands on the early neural processing of fearful and happy facial expressions.

    Science.gov (United States)

    Itier, Roxane J; Neath-Tavares, Karly N

    2017-05-15

    Task demands shape how we process environmental stimuli but their impact on the early neural processing of facial expressions remains unclear. In a within-subject design, ERPs were recorded to the same fearful, happy and neutral facial expressions presented during a gender discrimination, an explicit emotion discrimination and an oddball detection tasks, the most studied tasks in the field. Using an eye tracker, fixation on the face nose was enforced using a gaze-contingent presentation. Task demands modulated amplitudes from 200 to 350ms at occipito-temporal sites spanning the EPN component. Amplitudes were more negative for fearful than neutral expressions starting on N170 from 150 to 350ms, with a temporo-occipital distribution, whereas no clear effect of happy expressions was seen. Task and emotion effects never interacted in any time window or for the ERP components analyzed (P1, N170, EPN). Thus, whether emotion is explicitly discriminated or irrelevant for the task at hand, neural correlates of fearful and happy facial expressions seem immune to these task demands during the first 350ms of visual processing. Copyright © 2017 Elsevier B.V. All rights reserved.

  4. Deep Recurrent Neural Networks for seizure detection and early seizure detection systems

    Energy Technology Data Exchange (ETDEWEB)

    Talathi, S. S. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2017-06-05

    Epilepsy is common neurological diseases, affecting about 0.6-0.8 % of world population. Epileptic patients suffer from chronic unprovoked seizures, which can result in broad spectrum of debilitating medical and social consequences. Since seizures, in general, occur infrequently and are unpredictable, automated seizure detection systems are recommended to screen for seizures during long-term electroencephalogram (EEG) recordings. In addition, systems for early seizure detection can lead to the development of new types of intervention systems that are designed to control or shorten the duration of seizure events. In this article, we investigate the utility of recurrent neural networks (RNNs) in designing seizure detection and early seizure detection systems. We propose a deep learning framework via the use of Gated Recurrent Unit (GRU) RNNs for seizure detection. We use publicly available data in order to evaluate our method and demonstrate very promising evaluation results with overall accuracy close to 100 %. We also systematically investigate the application of our method for early seizure warning systems. Our method can detect about 98% of seizure events within the first 5 seconds of the overall epileptic seizure duration.

  5. Early Parenting Moderates the Association between Parental Depression and Neural Reactivity to Rewards and Losses in Offspring.

    Science.gov (United States)

    Kujawa, Autumn; Proudfit, Greg H; Laptook, Rebecca; Klein, Daniel N

    2015-07-01

    Children of parents with depression exhibit neural abnormalities in reward processing. Examining contributions of parenting could provide insight into the development of these abnormalities and to the etiology of depression. We evaluated whether early parenting moderates the effects of parental depression on a neural measure of reward and loss processing in mid-late childhood. Parenting was assessed when children were preschoolers. At age nine, children completed an event-related potential assessment and the feedback negativity (FN) was measured following rewards and losses ( N =344). Maternal authoritative parenting moderated the effect of maternal depression; among offspring of mothers with histories of depression, low authoritative parenting predicted a blunted FN. Observed maternal positive parenting interacted with paternal depression in a comparable manner, indicating that maternal parenting may buffer the effects of paternal depression. Early parenting may be important in shaping the neural systems involved in reward processing among children at high risk for depression.

  6. Implications of a neural network model of early sensori-motor development for the field of developmental neurology

    NARCIS (Netherlands)

    van Heijst, JJ; Touwen, BCL; Vos, JE

    This paper reports on a neural network model for early sensori-motor development and on the possible implications of this research for our understanding and, eventually, treatment of motor disorders like cerebral palsy. We recapitulate the results we published in detail in a series of papers [1-4].

  7. Pricing and hedging derivative securities with neural networks: Bayesian regularization, early stopping, and bagging.

    Science.gov (United States)

    Gençay, R; Qi, M

    2001-01-01

    We study the effectiveness of cross validation, Bayesian regularization, early stopping, and bagging to mitigate overfitting and improving generalization for pricing and hedging derivative securities with daily S&P 500 index daily call options from January 1988 to December 1993. Our results indicate that Bayesian regularization can generate significantly smaller pricing and delta-hedging errors than the baseline neural-network (NN) model and the Black-Scholes model for some years. While early stopping does not affect the pricing errors, it significantly reduces the hedging error (HE) in four of the six years we investigated. Although computationally most demanding, bagging seems to provide the most accurate pricing and delta hedging. Furthermore, the standard deviation of the MSPE of bagging is far less than that of the baseline model in all six years, and the standard deviation of the average HE of bagging is far less than that of the baseline model in five out of six years. We conclude that they be used at least in cases when no appropriate hints are available.

  8. User interface prototype for geospatial early warning systems - a tsunami showcase

    Science.gov (United States)

    Hammitzsch, M.; Lendholt, M.; Esbrí, M. Á.

    2012-03-01

    The command and control unit's graphical user interface (GUI) is a central part of early warning systems (EWS) for man-made and natural hazards. The GUI combines and concentrates the relevant information of the system and offers it to human operators. It has to support operators successfully performing their tasks in complex workflows. Most notably in critical situations when operators make important decisions in a limited amount of time, the command and control unit's GUI has to work reliably and stably, providing the relevant information and functionality with the required quality and in time. The design of the GUI application is essential in the development of any EWS to manage hazards effectively. The design and development of such GUI is performed repeatedly for each EWS by various software architects and developers. Implementations differ based on their application in different domains. But similarities designing and equal approaches implementing GUIs of EWS are not quite harmonized enough with related activities and do not exploit possible synergy effects. Thus, the GUI's implementation of an EWS for tsunamis is successively introduced, providing a generic approach to be applied in each EWS for man-made and natural hazards.

  9. User interface prototype for geospatial early warning systems – a tsunami showcase

    Directory of Open Access Journals (Sweden)

    M. Hammitzsch

    2012-03-01

    Full Text Available The command and control unit's graphical user interface (GUI is a central part of early warning systems (EWS for man-made and natural hazards. The GUI combines and concentrates the relevant information of the system and offers it to human operators. It has to support operators successfully performing their tasks in complex workflows. Most notably in critical situations when operators make important decisions in a limited amount of time, the command and control unit's GUI has to work reliably and stably, providing the relevant information and functionality with the required quality and in time.

    The design of the GUI application is essential in the development of any EWS to manage hazards effectively. The design and development of such GUI is performed repeatedly for each EWS by various software architects and developers. Implementations differ based on their application in different domains. But similarities designing and equal approaches implementing GUIs of EWS are not quite harmonized enough with related activities and do not exploit possible synergy effects. Thus, the GUI's implementation of an EWS for tsunamis is successively introduced, providing a generic approach to be applied in each EWS for man-made and natural hazards.

  10. EDITORIAL: Deep brain stimulation, deontology and duty: the moral obligation of non-abandonment at the neural interface Deep brain stimulation, deontology and duty: the moral obligation of non-abandonment at the neural interface

    Science.gov (United States)

    Fins, Joseph J.; MD; FACP

    2009-10-01

    intrusions on their bodies and their selves. Previously, I suggested that stimulation parameters for the treatment of neuropsychiatric disorders might be manipulated by patients one day. I envisioned a degree of patient discretion, within a pre-set safe range determined by physicians, much like patient-controlled analgesia (PCA) pumps give patients control over the dosing of opioid analgesia [3]. I am glad that such an advance is evolving as a means to preserve batteries in the treatment of motor disorders [16]. I would encourage the neural engineers to embrace the ethical mandate to develop additional platforms that might enhance patient self-determination and foster a greater degree of functional independence. While the neuromodulation community has every reason to celebrate its accomplishments, it would be better served by appreciating that the insertion of a device into the human brain comes with, if not the penumbra of sacrilege, a moral obligation to step out of the shadows and remain clearly available to patients and families over the long haul. Although neuromodulation has liberated many patients from the shackles of disease, we need to appreciate that the hardware that has made this possible can remain tethering. The challenge for the next generation of innovators is to minimize these burdens at this neural interface. By reducing barriers to care that exist in an unprepared health care system and developing more user-friendly technology, the neuromodulation community can expand its reach and broaden the relief provided by these neuro-palliative interventions [17]. Acknowledgements and Disclosures Dr Fins is the recipient of an Investigator Award in Health Policy Research (Minds Apart: Severe Brain Injury and Health Policy) from The Robert Wood Johnson Foundation. He also gratefully acknowledges grant support from the Buster Foundation (Neuroethics and Disorders of Consciousness). He is an unfunded co-investigator of a study of deep brain stimulation in the minimally

  11. An investigation on effects of amputee's physiological parameters on maximum pressure developed at the prosthetic socket interface using artificial neural network.

    Science.gov (United States)

    Nayak, Chitresh; Singh, Amit; Chaudhary, Himanshu; Unune, Deepak Rajendra

    2017-10-23

    Technological advances in prosthetics have attracted the curiosity of researchers in monitoring design and developments of the sockets to sustain maximum pressure without any soft tissue damage, skin breakdown, and painful sores. Numerous studies have been reported in the area of pressure measurement at the limb/socket interface, though, the relation between amputee's physiological parameters and the pressure developed at the limb/socket interface is still not studied. Therefore, the purpose of this work is to investigate the effects of patient-specific physiological parameters viz. height, weight, and stump length on the pressure development at the transtibial prosthetic limb/socket interface. Initially, the pressure values at the limb/socket interface were clinically measured during stance and walking conditions for different patients using strain gauges placed at critical locations of the stump. The measured maximum pressure data related to patient's physiological parameters was used to develop an artificial neural network (ANN) model. The effects of physiological parameters on the pressure development at the limb/socket interface were examined using the ANN model. The analyzed results indicated that the weight and stump length significantly affects the maximum pressure values. The outcomes of this work could be an important platform for the design and development of patient-specific prosthetic socket which can endure the maximum pressure conditions at stance and ambulation conditions.

  12. Neural correlates of early-closure garden-path processing: Effects of prosody and plausibility.

    Science.gov (United States)

    den Ouden, Dirk-Bart; Dickey, Michael Walsh; Anderson, Catherine; Christianson, Kiel

    2016-01-01

    Functional magnetic resonance imaging (fMRI) was used to investigate neural correlates of early-closure garden-path sentence processing and use of extrasyntactic information to resolve temporary syntactic ambiguities. Sixteen participants performed an auditory picture verification task on sentences presented with natural versus flat intonation. Stimuli included sentences in which the garden-path interpretation was plausible, implausible because of a late pragmatic cue, or implausible because of a semantic mismatch between an optionally transitive verb and the following noun. Natural sentence intonation was correlated with left-hemisphere temporal activation, but also with activation that suggests the allocation of more resources to interpretation when natural prosody is provided. Garden-path processing was associated with upregulation in bilateral inferior parietal and right-hemisphere dorsolateral prefrontal and inferior frontal cortex, while differences between the strength and type of plausibility cues were also reflected in activation patterns. Region of interest (ROI) analyses in regions associated with complex syntactic processing are consistent with a role for posterior temporal cortex supporting access to verb argument structure. Furthermore, ROI analyses within left-hemisphere inferior frontal gyrus suggest a division of labour, with the anterior-ventral part primarily involved in syntactic-semantic mismatch detection, the central part supporting structural reanalysis, and the posterior-dorsal part showing a general structural complexity effect.

  13. The Effect of Early Visual Deprivation on the Neural Bases of Auditory Processing.

    Science.gov (United States)

    Guerreiro, Maria J S; Putzar, Lisa; Röder, Brigitte

    2016-02-03

    Transient congenital visual deprivation affects visual and multisensory processing. In contrast, the extent to which it affects auditory processing has not been investigated systematically. Research in permanently blind individuals has revealed brain reorganization during auditory processing, involving both intramodal and crossmodal plasticity. The present study investigated the effect of transient congenital visual deprivation on the neural bases of auditory processing in humans. Cataract-reversal individuals and normally sighted controls performed a speech-in-noise task while undergoing functional magnetic resonance imaging. Although there were no behavioral group differences, groups differed in auditory cortical responses: in the normally sighted group, auditory cortex activation increased with increasing noise level, whereas in the cataract-reversal group, no activation difference was observed across noise levels. An auditory activation of visual cortex was not observed at the group level in cataract-reversal individuals. The present data suggest prevailing auditory processing advantages after transient congenital visual deprivation, even many years after sight restoration. The present study demonstrates that people whose sight was restored after a transient period of congenital blindness show more efficient cortical processing of auditory stimuli (here speech), similarly to what has been observed in congenitally permanently blind individuals. These results underscore the importance of early sensory experience in permanently shaping brain function. Copyright © 2016 the authors 0270-6474/16/361620-11$15.00/0.

  14. Musicians' Enhanced Neural Differentiation of Speech Sounds Arises Early in Life: Developmental Evidence from Ages 3 to 30

    Science.gov (United States)

    Strait, Dana L.; O'Connell, Samantha; Parbery-Clark, Alexandra; Kraus, Nina

    2014-01-01

    The perception and neural representation of acoustically similar speech sounds underlie language development. Music training hones the perception of minute acoustic differences that distinguish sounds; this training may generalize to speech processing given that adult musicians have enhanced neural differentiation of similar speech syllables compared with nonmusicians. Here, we asked whether this neural advantage in musicians is present early in life by assessing musically trained and untrained children as young as age 3. We assessed auditory brainstem responses to the speech syllables /ba/ and /ga/ as well as auditory and visual cognitive abilities in musicians and nonmusicians across 3 developmental time-points: preschoolers, school-aged children, and adults. Cross-phase analyses objectively measured the degree to which subcortical responses differed to these speech syllables in musicians and nonmusicians for each age group. Results reveal that musicians exhibit enhanced neural differentiation of stop consonants early in life and with as little as a few years of training. Furthermore, the extent of subcortical stop consonant distinction correlates with auditory-specific cognitive abilities (i.e., auditory working memory and attention). Results are interpreted according to a corticofugal framework for auditory learning in which subcortical processing enhancements are engendered by strengthened cognitive control over auditory function in musicians. PMID:23599166

  15. A novel neural prosthesis providing long-term electrocorticography recording and cortical stimulation for epilepsy and brain-computer interface.

    Science.gov (United States)

    Romanelli, Pantaleo; Piangerelli, Marco; Ratel, David; Gaude, Christophe; Costecalde, Thomas; Puttilli, Cosimo; Picciafuoco, Mauro; Benabid, Alim; Torres, Napoleon

    2018-05-11

    OBJECTIVE Wireless technology is a novel tool for the transmission of cortical signals. Wireless electrocorticography (ECoG) aims to improve the safety and diagnostic gain of procedures requiring invasive localization of seizure foci and also to provide long-term recording of brain activity for brain-computer interfaces (BCIs). However, no wireless devices aimed at these clinical applications are currently available. The authors present the application of a fully implantable and externally rechargeable neural prosthesis providing wireless ECoG recording and direct cortical stimulation (DCS). Prolonged wireless ECoG monitoring was tested in nonhuman primates by using a custom-made device (the ECoG implantable wireless 16-electrode [ECOGIW-16E] device) containing a 16-contact subdural grid. This is a preliminary step toward large-scale, long-term wireless ECoG recording in humans. METHODS The authors implanted the ECOGIW-16E device over the left sensorimotor cortex of a nonhuman primate ( Macaca fascicularis), recording ECoG signals over a time span of 6 months. Daily electrode impedances were measured, aiming to maintain the impedance values below a threshold of 100 KΩ. Brain mapping was obtained through wireless cortical stimulation at fixed intervals (1, 3, and 6 months). After 6 months, the device was removed. The authors analyzed cortical tissues by using conventional histological and immunohistological investigation to assess whether there was evidence of damage after the long-term implantation of the grid. RESULTS The implant was well tolerated; no neurological or behavioral consequences were reported in the monkey, which resumed his normal activities within a few hours of the procedure. The signal quality of wireless ECoG remained excellent over the 6-month observation period. Impedance values remained well below the threshold value; the average impedance per contact remains approximately 40 KΩ. Wireless cortical stimulation induced movements of the upper

  16. Lifetime assessment of atomic-layer-deposited Al2O3-Parylene C bilayer coating for neural interfaces using accelerated age testing and electrochemical characterization.

    Science.gov (United States)

    Minnikanti, Saugandhika; Diao, Guoqing; Pancrazio, Joseph J; Xie, Xianzong; Rieth, Loren; Solzbacher, Florian; Peixoto, Nathalia

    2014-02-01

    The lifetime and stability of insulation are critical features for the reliable operation of an implantable neural interface device. A critical factor for an implanted insulation's performance is its barrier properties that limit access of biological fluids to the underlying device or metal electrode. Parylene C is a material that has been used in FDA-approved implantable devices. Considered a biocompatible polymer with barrier properties, it has been used as a substrate, insulation or an encapsulation for neural implant technology. Recently, it has been suggested that a bilayer coating of Parylene C on top of atomic-layer-deposited Al2O3 would provide enhanced barrier properties. Here we report a comprehensive study to examine the mean time to failure of Parylene C and Al2O3-Parylene C coated devices using accelerated lifetime testing. Samples were tested at 60°C for up to 3 months while performing electrochemical measurements to characterize the integrity of the insulation. The mean time to failure for Al2O3-Parylene C was 4.6 times longer than Parylene C coated samples. In addition, based on modeling of the data using electrical circuit equivalents, we show here that there are two main modes of failure. Our results suggest that failure of the insulating layer is due to pore formation or blistering as well as thinning of the coating over time. The enhanced barrier properties of the bilayer Al2O3-Parylene C over Parylene C makes it a promising candidate as an encapsulating neural interface. Copyright © 2013 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

  17. Maximally resolved anharmonic OH vibrational spectrum of the water/ZnO(101 \\xAF 0) interface from a high-dimensional neural network potential

    Science.gov (United States)

    Quaranta, Vanessa; Hellström, Matti; Behler, Jörg; Kullgren, Jolla; Mitev, Pavlin D.; Hermansson, Kersti

    2018-06-01

    Unraveling the atomistic details of solid/liquid interfaces, e.g., by means of vibrational spectroscopy, is of vital importance in numerous applications, from electrochemistry to heterogeneous catalysis. Water-oxide interfaces represent a formidable challenge because a large variety of molecular and dissociated water species are present at the surface. Here, we present a comprehensive theoretical analysis of the anharmonic OH stretching vibrations at the water/ZnO(101 ¯ 0) interface as a prototypical case. Molecular dynamics simulations employing a reactive high-dimensional neural network potential based on density functional theory calculations have been used to sample the interfacial structures. In the second step, one-dimensional potential energy curves have been generated for a large number of configurations to solve the nuclear Schrödinger equation. We find that (i) the ZnO surface gives rise to OH frequency shifts up to a distance of about 4 Å from the surface; (ii) the spectrum contains a number of overlapping signals arising from different chemical species, with the frequencies decreasing in the order ν(adsorbed hydroxide) > ν(non-adsorbed water) > ν(surface hydroxide) > ν(adsorbed water); (iii) stretching frequencies are strongly influenced by the hydrogen bond pattern of these interfacial species. Finally, we have been able to identify substantial correlations between the stretching frequencies and hydrogen bond lengths for all species.

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

    Science.gov (United States)

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

    2017-04-01

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

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

    Science.gov (United States)

    Chang, Sun-Il; Yoon, Euisik

    2009-01-01

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

  20. Neural Dynamics of Multiple Object Processing in Mild Cognitive Impairment and Alzheimer's Disease: Future Early Diagnostic Biomarkers?

    Science.gov (United States)

    Bagattini, Chiara; Mazza, Veronica; Panizza, Laura; Ferrari, Clarissa; Bonomini, Cristina; Brignani, Debora

    2017-01-01

    The aim of this study was to investigate the behavioral and electrophysiological dynamics of multiple object processing (MOP) in mild cognitive impairment (MCI) and Alzheimer's disease (AD), and to test whether its neural signatures may represent reliable diagnostic biomarkers. Behavioral performance and event-related potentials [N2pc and contralateral delay activity (CDA)] were measured in AD, MCI, and healthy controls during a MOP task, which consisted in enumerating a variable number of targets presented among distractors. AD patients showed an overall decline in accuracy for both small and large target quantities, whereas in MCI patients, only enumeration of large quantities was impaired. N2pc, a neural marker of attentive individuation, was spared in both AD and MCI patients. In contrast, CDA, which indexes visual short term memory abilities, was altered in both groups of patients, with a non-linear pattern of amplitude modulation along the continuum of the disease: a reduction in AD and an increase in MCI. These results indicate that AD pathology shows a progressive decline in MOP, which is associated to the decay of visual short-term memory mechanisms. Crucially, CDA may be considered as a useful neural signature both to distinguish between healthy and pathological aging and to characterize the different stages along the AD continuum, possibly becoming a reliable candidate for an early diagnostic biomarker of AD pathology.

  1. Musical Training during Early Childhood Enhances the Neural Encoding of Speech in Noise

    Science.gov (United States)

    Strait, Dana L.; Parbery-Clark, Alexandra; Hittner, Emily; Kraus, Nina

    2012-01-01

    For children, learning often occurs in the presence of background noise. As such, there is growing desire to improve a child's access to a target signal in noise. Given adult musicians' perceptual and neural speech-in-noise enhancements, we asked whether similar effects are present in musically-trained children. We assessed the perception and…

  2. Neural Indices of Semantic Processing in Early Childhood Distinguish Eventual Stuttering Persistence and Recovery

    Science.gov (United States)

    Kreidler, Kathryn; Wray, Amanda Hampton; Usler, Evan; Weber, Christine

    2017-01-01

    Purpose: Maturation of neural processes for language may lag in some children who stutter (CWS), and event-related potentials (ERPs) distinguish CWS who have recovered from those who have persisted. The current study explores whether ERPs indexing semantic processing may distinguish children who will eventually persist in stuttering…

  3. Characterization of Early Cortical Neural Network Development in Multiwell Microelectrode Array Plates

    Science.gov (United States)

    We examined the development of neural network activity using microelectrode array (MEA) recordings made in multi-well MEA plates (mwMEAs) over the first 12 days in vitro (DIV). In primary cortical cultures made from postnatal rats, action potential spiking activity was essentiall...

  4. Temperament and Parenting Styles in Early Childhood Differentially Influence Neural Response to Peer Evaluation in Adolescence

    OpenAIRE

    Guyer, Amanda E.; Jarcho, Johanna M.; Pérez-Edgar, Koraly; Degnan, Kathryn A.; Pine, Daniel S.; Fox, Nathan A.; Nelson, Eric E.

    2015-01-01

    Behavioral inhibition (BI) is a temperament characterized by social reticence and withdrawal from unfamiliar or novel contexts and conveys risk for social anxiety disorder. Developmental outcomes associated with this temperament can be influenced by children’s caregiving context. The convergence of a child’s temperamental disposition and rearing environment is ultimately expressed at both the behavioral and neural levels in emotional and cognitive response patterns to social challenges. The p...

  5. Large-scale generation of human iPSC-derived neural stem cells/early neural progenitor cells and their neuronal differentiation.

    Science.gov (United States)

    D'Aiuto, Leonardo; Zhi, Yun; Kumar Das, Dhanjit; Wilcox, Madeleine R; Johnson, Jon W; McClain, Lora; MacDonald, Matthew L; Di Maio, Roberto; Schurdak, Mark E; Piazza, Paolo; Viggiano, Luigi; Sweet, Robert; Kinchington, Paul R; Bhattacharjee, Ayantika G; Yolken, Robert; Nimgaonka, Vishwajit L; Nimgaonkar, Vishwajit L

    2014-01-01

    Induced pluripotent stem cell (iPSC)-based technologies offer an unprecedented opportunity to perform high-throughput screening of novel drugs for neurological and neurodegenerative diseases. Such screenings require a robust and scalable method for generating large numbers of mature, differentiated neuronal cells. Currently available methods based on differentiation of embryoid bodies (EBs) or directed differentiation of adherent culture systems are either expensive or are not scalable. We developed a protocol for large-scale generation of neuronal stem cells (NSCs)/early neural progenitor cells (eNPCs) and their differentiation into neurons. Our scalable protocol allows robust and cost-effective generation of NSCs/eNPCs from iPSCs. Following culture in neurobasal medium supplemented with B27 and BDNF, NSCs/eNPCs differentiate predominantly into vesicular glutamate transporter 1 (VGLUT1) positive neurons. Targeted mass spectrometry analysis demonstrates that iPSC-derived neurons express ligand-gated channels and other synaptic proteins and whole-cell patch-clamp experiments indicate that these channels are functional. The robust and cost-effective differentiation protocol described here for large-scale generation of NSCs/eNPCs and their differentiation into neurons paves the way for automated high-throughput screening of drugs for neurological and neurodegenerative diseases.

  6. Segmentation of retinal blood vessels using artificial neural networks for early detection of diabetic retinopathy

    Science.gov (United States)

    Mann, Kulwinder S.; Kaur, Sukhpreet

    2017-06-01

    There are various eye diseases in the patients suffering from the diabetes which includes Diabetic Retinopathy, Glaucoma, Hypertension etc. These all are the most common sight threatening eye diseases due to the changes in the blood vessel structure. The proposed method using supervised methods concluded that the segmentation of the retinal blood vessels can be performed accurately using neural networks training. It uses features which include Gray level features; Moment Invariant based features, Gabor filtering, Intensity feature, Vesselness feature for feature vector computation. Then the feature vector is calculated using only the prominent features.

  7. Interfaces, syntactic movement, and neural activation: A new perspective on the implementation of language in the brain

    DEFF Research Database (Denmark)

    Christensen, Ken Ramshøj

    2008-01-01

    Studies of language deficits as well as neuroimaging studies indicate that syntactic processing of displaced constituents is implemented in the brain as a distributed cortical network of modules. The data from the present fMRI study on two types of syntactic movement in Danish offers further...... support for such a distributed syntactic network. These results, together with the results from a number of other fMRI studies in the literature, form the basis for the Domain Hypothesis according to which differential activation in the subcomponents of the cortical network reflects computation...... of different syntactic domains—the interface levels between syntax, semantics, and pragmatics. The activation patters result from the interaction between movement and target domain, not (non-) canonicity or working memory per se. Specifically, movement to the CP-domain activates areas including Broca's area...

  8. Musical training during early childhood enhances the neural encoding of speech in noise.

    Science.gov (United States)

    Strait, Dana L; Parbery-Clark, Alexandra; Hittner, Emily; Kraus, Nina

    2012-12-01

    For children, learning often occurs in the presence of background noise. As such, there is growing desire to improve a child's access to a target signal in noise. Given adult musicians' perceptual and neural speech-in-noise enhancements, we asked whether similar effects are present in musically-trained children. We assessed the perception and subcortical processing of speech in noise and related cognitive abilities in musician and nonmusician children that were matched for a variety of overarching factors. Outcomes reveal that musicians' advantages for processing speech in noise are present during pivotal developmental years. Supported by correlations between auditory working memory and attention and auditory brainstem response properties, we propose that musicians' perceptual and neural enhancements are driven in a top-down manner by strengthened cognitive abilities with training. Our results may be considered by professionals involved in the remediation of language-based learning deficits, which are often characterized by poor speech perception in noise. Copyright © 2012 Elsevier Inc. All rights reserved.

  9. The neural coding of creative idea generation across adolescence and early adulthood

    Directory of Open Access Journals (Sweden)

    Sietske eKleibeuker

    2013-12-01

    Full Text Available Creativity is considered key to human prosperity, yet the neurocognitive principles underlying creative performance, and their development, are still poorly understood. To fill this void, we examined the neural correlates of divergent thinking in adults (25-30 yrs and adolescents (15-17 yrs. Participants generated alternative uses (AU or ordinary characteristics (OC for common objects while brain activity was assessed using fMRI. Adults outperformed adolescents on the number of solutions for AU and OC trials. Contrasting neural activity for AU with OC trials revealed increased recruitment of left angular gyrus, left supramarginal gyrus, and bilateral middle temporal gyrus in both adults and adolescents. When only trials with multiple alternative uses were included in the analysis, participants showed additional left inferior frontal gyrus (IFG/middle frontal gyrus (MFG activation for AU compared to OC trials. Correspondingly, individual difference analyses showed a positive correlation between activations for AU relative to OC trials in left IFG/MFG and divergent thinking performance and activations were more pronounced in adults than in adolescents. Taken together, the results of this study demonstrated that creative idea generation involves recruitment of mainly left lateralized parietal and temporal brain regions. Generating multiple creative ideas, a hallmark of divergent thinking, shows additional lateral PFC activation that is not yet optimized in adolescence.

  10. Amygdala reactivity to sad faces in preschool children: An early neural marker of persistent negative affect

    Directory of Open Access Journals (Sweden)

    Michael S. Gaffrey

    2016-02-01

    Conclusions: The current findings provide preliminary evidence for amygdala activity as a potential biomarker of persistent negative affect during early childhood and suggest future work examining the origins and long-term implications of this relationship is necessary.

  11. Detection of anti-streptococcal, antienolase, and anti-neural antibodies in subjects with early-onset psychiatric disorders.

    Science.gov (United States)

    Nicolini, Humberto; López, Yaumara; Genis-Mendoza, Alma D; Manrique, Viana; Lopez-Canovas, Lilia; Niubo, Esperanza; Hernández, Lázaro; Bobes, María A; Riverón, Ana M; López-Casamichana, Mavil; Flores, Julio; Lanzagorta, Nuria; De la Fuente-Sandoval, Camilo; Santana, Daniel

    2015-01-01

    Infection with group A Streptococcus (StrepA) can cause post-infectious sequelae, including a spectrum of childhood-onset obsessive-compulsive (OCD) and tic disorders with autoimmune origin (PANDAS, Pediatric Autoimmune Neuropsychiatric Disorders Associated with Streptococcal Infections). Until now, no single immunological test has been designed that unequivocally diagnoses these disorders. In this study, we assessed the detection of serum antibodies against human brain enolase (AE), neural tissue (AN) and Streptococcus (AS) as a laboratory tool for the diagnosis of early-onset psychiatric disorders. Serum antibodies against human brain enolase, total brain proteins, and total proteins from StrepA were detected by ELISA in 37 patients with a presumptive diagnosis of PANDAS and in 12 healthy subjects from Mexico and Cuba. The antibody titers against human brain enolase (AE) and Streptococcal proteins (AS) were higher in patients than in control subjects (t-student, tAE=-2.17, P=0.035; tAS=-2.68, P=0.01, n=12 and 37/group, df=47, significance level 0.05), while the neural antibody titers did not differ between the two groups (P(t)=0.05). The number of subjects (titers> meancontrol + CI95) with simultaneous seropositivity to all three antibodies was higher in the patient group (51.4%) than in the control group (8.3%) group (X2=5.27, P=0.022, df=1, n=49). The simultaneous detection of all three of these antibodies could provide valuable information for the etiologic diagnosis of individuals with early-onset obsessive-compulsive disorders associated with streptococcal infection and, consequently, for prescribing suitable therapy.

  12. Early detection of incipient faults in power plants using accelerated neural network learning

    International Nuclear Information System (INIS)

    Parlos, A.G.; Jayakumar, M.; Atiya, A.

    1992-01-01

    An important aspect of power plant automation is the development of computer systems able to detect and isolate incipient (slowly developing) faults at the earliest possible stages of their occurrence. In this paper, the development and testing of such a fault detection scheme is presented based on recognition of sensor signatures during various failure modes. An accelerated learning algorithm, namely adaptive backpropagation (ABP), has been developed that allows the training of a multilayer perceptron (MLP) network to a high degree of accuracy, with an order of magnitude improvement in convergence speed. An artificial neural network (ANN) has been successfully trained using the ABP algorithm, and it has been extensively tested with simulated data to detect and classify incipient faults of various types and severity and in the presence of varying sensor noise levels

  13. Using recurrent neural network models for early detection of heart failure onset.

    Science.gov (United States)

    Choi, Edward; Schuetz, Andy; Stewart, Walter F; Sun, Jimeng

    2017-03-01

    We explored whether use of deep learning to model temporal relations among events in electronic health records (EHRs) would improve model performance in predicting initial diagnosis of heart failure (HF) compared to conventional methods that ignore temporality. Data were from a health system's EHR on 3884 incident HF cases and 28 903 controls, identified as primary care patients, between May 16, 2000, and May 23, 2013. Recurrent neural network (RNN) models using gated recurrent units (GRUs) were adapted to detect relations among time-stamped events (eg, disease diagnosis, medication orders, procedure orders, etc.) with a 12- to 18-month observation window of cases and controls. Model performance metrics were compared to regularized logistic regression, neural network, support vector machine, and K-nearest neighbor classifier approaches. Using a 12-month observation window, the area under the curve (AUC) for the RNN model was 0.777, compared to AUCs for logistic regression (0.747), multilayer perceptron (MLP) with 1 hidden layer (0.765), support vector machine (SVM) (0.743), and K-nearest neighbor (KNN) (0.730). When using an 18-month observation window, the AUC for the RNN model increased to 0.883 and was significantly higher than the 0.834 AUC for the best of the baseline methods (MLP). Deep learning models adapted to leverage temporal relations appear to improve performance of models for detection of incident heart failure with a short observation window of 12-18 months. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association.

  14. Evolutionary conserved neural signature of early life stress affects animal social competence.

    Science.gov (United States)

    Nyman, Cecilia; Fischer, Stefan; Aubin-Horth, Nadia; Taborsky, Barbara

    2018-01-31

    In vertebrates, the early social environment can persistently influence behaviour and social competence later in life. However, the molecular mechanisms underlying variation in animal social competence are largely unknown. In rats, high-quality maternal care causes an upregulation of hippocampal glucocorticoid receptors ( gr ) and reduces offspring stress responsiveness. This identifies gr regulation as a candidate mechanism for maintaining variation in animal social competence. We tested this hypothesis in a highly social cichlid fish, Neolamprologus pulcher , reared with or without caring parents. We find that the molecular pathway translating early social experience into later-life alterations of the stress axis is homologous across vertebrates: fish reared with parents expressed the glucocorticoid receptor gr1 more in the telencephalon. Furthermore, expression levels of the transcription factor egr-1 (early growth response 1) were associated with gr1 expression in the telencephalon and hypothalamus. When blocking glucocorticoid receptors (GR) with an antagonist, mifepristone (RU486), parent-reared individuals showed more socially appropriate, submissive behaviour when intruding on a larger conspecific's territory. Remarkably, mifepristone-treated fish were less attacked by territory owners and had a higher likelihood of territory takeover. Our results indicate that early social-environment effects on stress axis programming are mediated by an evolutionary conserved molecular pathway, which is causally involved in environmentally induced variation of animal social competence. © 2018 The Author(s).

  15. The neural substrates of semantic memory deficits in early Alzheimer's disease: Clues from semantic priming effects and FDG-PET

    International Nuclear Information System (INIS)

    Giffard, B.; Laisney, M.; Mezenge, F.; De la Sayette, V.; Eustache, F.; Desgranges, B.

    2008-01-01

    The neural substrates responsible for semantic dysfunction during the early stages of AD have yet to be clearly identified. After a brief overview of the literature on normal and pathological semantic memory, we describe a new approach, designed to provide fresh insights into semantic deficits in AD. We mapped the correlations between resting-state brain glucose utilisation measured by FDG-PET and semantic priming scores in a group of 17 AD patients. The priming task, which yields a particularly pure measurement of semantic memory, was composed of related pairs of words sharing an attribute relationship (e.g. tiger-stripe). The priming scores correlated positively with the metabolism of the superior temporal areas on both sides, especially the right side, and this correlation was shown to be specific to the semantic priming effect.This pattern of results is discussed in the light of recent theoretical models of semantic memory, and suggests that a dysfunction of the right superior temporal cortex may contribute to early semantic deficits, characterised by the loss of specific features of concepts in AD. (authors)

  16. The effect of early visual deprivation on the neural bases of multisensory processing.

    Science.gov (United States)

    Guerreiro, Maria J S; Putzar, Lisa; Röder, Brigitte

    2015-06-01

    Developmental vision is deemed to be necessary for the maturation of multisensory cortical circuits. Thus far, this has only been investigated in animal studies, which have shown that congenital visual deprivation markedly reduces the capability of neurons to integrate cross-modal inputs. The present study investigated the effect of transient congenital visual deprivation on the neural mechanisms of multisensory processing in humans. We used functional magnetic resonance imaging to compare responses of visual and auditory cortical areas to visual, auditory and audio-visual stimulation in cataract-reversal patients and normally sighted controls. The results showed that cataract-reversal patients, unlike normally sighted controls, did not exhibit multisensory integration in auditory areas. Furthermore, cataract-reversal patients, but not normally sighted controls, exhibited lower visual cortical processing within visual cortex during audio-visual stimulation than during visual stimulation. These results indicate that congenital visual deprivation affects the capability of cortical areas to integrate cross-modal inputs in humans, possibly because visual processing is suppressed during cross-modal stimulation. Arguably, the lack of vision in the first months after birth may result in a reorganization of visual cortex, including the suppression of noisy visual input from the deprived retina in order to reduce interference during auditory processing. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  17. Neural correlates associated with superior tactile symmetry perception in the early blind

    OpenAIRE

    Bauer, Corinna; Yazzolino, Lindsay; Hirsch, Gabriella; Cattaneo, Zaira; Vecchi, Tomaso; Merabet, Lotfi B.

    2014-01-01

    Symmetry is an organizational principle that is ubiquitous throughout the visual world. However, this property can also be detected through non-visual modalities such as touch. The role of prior visual experience on detecting tactile patterns containing symmetry remains unclear. We compared the behavioral performance of early blind and sighted (blindfolded) controls on a tactile symmetry detection task. The tactile patterns used were similar in design and complexity as in previous visual perc...

  18. Effects of Early-Life Stress on Actions, Habits, and the Neural Systems Supporting Instrumental Behavior

    OpenAIRE

    Patterson, Tara

    2017-01-01

    Factors contributing to the formation of habits, defined as the stimulus-response associations that form the basis of much human and animal behavior, are not well understood, and although habits are believed to underlie many negative health behaviors such as addictions, the extent to which findings from animal research on habits apply in the human is largely unknown. In Study 1 (Chapter 2), we conducted two experiments on appetitive habit formation in adults with a history of early-life stres...

  19. Lithium prevents long-term neural and behavioral pathology induced by early alcohol exposure.

    Science.gov (United States)

    Sadrian, B; Subbanna, S; Wilson, D A; Basavarajappa, B S; Saito, M

    2012-03-29

    Fetal alcohol exposure can cause developmental defects in offspring known as fetal alcohol spectrum disorder (FASD). FASD symptoms range from obvious facial deformities to changes in neuroanatomy and neurophysiology that disrupt normal brain function and behavior. Ethanol exposure at postnatal day 7 in C57BL/6 mice induces neuronal cell death and long-lasting neurobehavioral dysfunction. Previous work has demonstrated that early ethanol exposure impairs spatial memory task performance into adulthood and perturbs local and interregional brain circuit integrity in the olfacto-hippocampal pathway. Here we pursue these findings to examine whether lithium prevents anatomical, neurophysiological, and behavioral pathologies that result from early ethanol exposure. Lithium has neuroprotective properties that have been shown to prevent ethanol-induced apoptosis. Here we show that mice co-treated with lithium on the same day as ethanol exposure exhibit dramatically reduced acute neurodegeneration in the hippocampus and retain hippocampal-dependent spatial memory as adults. Lithium co-treatment also blocked ethanol-induced disruption in synaptic plasticity in slice recordings of hippocampal CA1 in the adult mouse brain. Moreover, long-lasting dysfunctions caused by ethanol in olfacto-hippocampal networks, including sensory-evoked oscillations and resting state coherence, were prevented in mice co-treated with lithium. Together, these results provide behavioral and physiological evidence that lithium is capable of preventing or reducing immediate and long-term deleterious consequences of early ethanol exposure on brain function. Copyright © 2012 IBRO. Published by Elsevier Ltd. All rights reserved.

  20. Specific and spatial labeling of P0-Cre versus Wnt1-Cre in cranial neural crest in early mouse embryos.

    Science.gov (United States)

    Chen, Guiqian; Ishan, Mohamed; Yang, Jingwen; Kishigami, Satoshi; Fukuda, Tomokazu; Scott, Greg; Ray, Manas K; Sun, Chenming; Chen, Shi-You; Komatsu, Yoshihiro; Mishina, Yuji; Liu, Hong-Xiang

    2017-06-01

    P0-Cre and Wnt1-Cre mouse lines have been widely used in combination with loxP-flanked mice to label and genetically modify neural crest (NC) cells and their derivatives. Wnt1-Cre has been regarded as the gold standard and there have been concerns about the specificity of P0-Cre because it is not clear about the timing and spatial distribution of the P0-Cre transgene in labeling NC cells at early embryonic stages. We re-visited P0-Cre and Wnt1-Cre models in the labeling of NC cells in early mouse embryos with a focus on cranial NC. We found that R26-lacZ Cre reporter responded to Cre activity more reliably than CAAG-lacZ Cre reporter during early embryogenesis. Cre immunosignals in P0-Cre and reporter (lacZ and RFP) activity in P0-Cre/R26-lacZ and P0-Cre/R26-RFP embryos was detected in the cranial NC and notochord regions in E8.0-9.5 (4-19 somites) embryos. P0-Cre transgene expression was observed in migrating NC cells and was more extensive in the forebrain and hindbrain but not apparent in the midbrain. Differences in the Cre distribution patterns of P0-Cre and Wnt1-Cre were profound in the midbrain and hindbrain regions, that is, extensive in the midbrain of Wnt1-Cre and in the hindbrain of P0-Cre embryos. The difference between P0-Cre and Wnt1-Cre in labeling cranial NC may provide a better explanation of the differential distributions of their NC derivatives and of the phenotypes caused by Cre-driven genetic modifications. © 2017 Wiley Periodicals, Inc.

  1. Neural Mechanisms of Early-Life Social Stress as a Developmental Risk Factor for Severe Psychiatric Disorders.

    Science.gov (United States)

    Reinwald, Jonathan Rochus; Becker, Robert; Mallien, Anne Stephanie; Falfan-Melgoza, Claudia; Sack, Markus; Clemm von Hohenberg, Christian; Braun, Urs; Cosa Linan, Alejandro; Gass, Natalia; Vasilescu, Andrei-Nicolae; Tollens, Fabian; Lebhardt, Philipp; Pfeiffer, Natascha; Inta, Dragos; Meyer-Lindenberg, Andreas; Gass, Peter; Sartorius, Alexander; Weber-Fahr, Wolfgang

    2017-12-28

    To explore the domain-general risk factor of early-life social stress in mental illness, rearing rodents in persistent postweaning social isolation has been established as a widely used animal model with translational relevance for neurodevelopmental psychiatric disorders such as schizophrenia. Although changes in resting-state brain connectivity are a transdiagnostic key finding in neurodevelopmental diseases, a characterization of imaging correlates elicited by early-life social stress is lacking. We performed resting-state functional magnetic resonance imaging of postweaning social isolation rats (N = 23) 9 weeks after isolation. Addressing well-established transdiagnostic connectivity changes of psychiatric disorders, we focused on altered frontal and posterior connectivity using a seed-based approach. Then, we examined changes in regional network architecture and global topology using graph theoretical analysis. Seed-based analyses demonstrated reduced functional connectivity in frontal brain regions and increased functional connectivity in posterior brain regions of postweaning social isolation rats. Graph analyses revealed a shift of the regional architecture, characterized by loss of dominance of frontal regions and emergence of nonfrontal regions, correlating to our behavioral results, and a reduced modularity in isolation-reared rats. Our result of functional connectivity alterations in the frontal brain supports previous investigations postulating social neural circuits, including prefrontal brain regions, as key pathways for risk for mental disorders arising through social stressors. We extend this knowledge by demonstrating more widespread changes of brain network organization elicited by early-life social stress, namely a shift of hubness and dysmodularity. Our results highly resemble core alterations in neurodevelopmental psychiatric disorders such as schizophrenia, autism, and attention-deficit/hyperactivity disorder in humans. Copyright © 2017

  2. Motor-related brain activity during action observation: a neural substrate for electrocorticographic brain-computer interfaces after spinal cord injury

    Directory of Open Access Journals (Sweden)

    Jennifer L Collinger

    2014-02-01

    Full Text Available After spinal cord injury (SCI, motor commands from the brain are unable to reach peripheral nerves and muscles below the level of the lesion. Action observation, in which a person observes someone else performing an action, has been used to augment traditional rehabilitation paradigms. Similarly, action observation can be used to derive the relationship between brain activity and movement kinematics for a motor-based brain-computer interface (BCI even when the user cannot generate overt movements. BCIs use brain signals to control external devices to replace functions that have been lost due to SCI or other motor impairment. Previous studies have reported congruent motor cortical activity during observed and overt movements using magnetoencephalography (MEG and functional magnetic resonance imaging (fMRI. Recent single-unit studies using intracortical microelectrodes also demonstrated that a large number of motor cortical neurons had similar firing rate patterns between overt and observed movements. Given the increasing interest in electrocorticography (ECoG-based BCIs, our goal was to identify whether action observation-related cortical activity could be recorded using ECoG during grasping tasks. Specifically, we aimed to identify congruent neural activity during observed and executed movements in both the sensorimotor rhythm (10-40 Hz and the high-gamma band (65-115 Hz which contains significant movement-related information. We observed significant motor-related high-gamma band activity during action observation in both able-bodied individuals and one participant with a complete C4 SCI. Furthermore, in able-bodied participants, both the low and high frequency bands demonstrated congruent activity between action execution and observation. Our results suggest that action observation could be an effective and critical procedure for deriving the mapping from ECoG signals to intended movement for an ECoG-based BCI system for individuals with

  3. Developing a Graphical User Interface to Automate the Estimation and Prediction of Risk Values for Flood Protective Structures using Artificial Neural Network

    Science.gov (United States)

    Hasan, M.; Helal, A.; Gabr, M.

    2014-12-01

    In this project, we focus on providing a computer-automated platform for a better assessment of the potential failures and retrofit measures of flood-protecting earth structures, e.g., dams and levees. Such structures play an important role during extreme flooding events as well as during normal operating conditions. Furthermore, they are part of other civil infrastructures such as water storage and hydropower generation. Hence, there is a clear need for accurate evaluation of stability and functionality levels during their service lifetime so that the rehabilitation and maintenance costs are effectively guided. Among condition assessment approaches based on the factor of safety, the limit states (LS) approach utilizes numerical modeling to quantify the probability of potential failures. The parameters for LS numerical modeling include i) geometry and side slopes of the embankment, ii) loading conditions in terms of rate of rising and duration of high water levels in the reservoir, and iii) cycles of rising and falling water levels simulating the effect of consecutive storms throughout the service life of the structure. Sample data regarding the correlations of these parameters are available through previous research studies. We have unified these criteria and extended the risk assessment in term of loss of life through the implementation of a graphical user interface to automate input parameters that divides data into training and testing sets, and then feeds them into Artificial Neural Network (ANN) tool through MATLAB programming. The ANN modeling allows us to predict risk values of flood protective structures based on user feedback quickly and easily. In future, we expect to fine-tune the software by adding extensive data on variations of parameters.

  4. Methodology for Developing Hydrological Models Based on an Artificial Neural Network to Establish an Early Warning System in Small Catchments

    Directory of Open Access Journals (Sweden)

    Ivana Sušanj

    2016-01-01

    Full Text Available In some situations, there is no possibility of hazard mitigation, especially if the hazard is induced by water. Thus, it is important to prevent consequences via an early warning system (EWS to announce the possible occurrence of a hazard. The aim and objective of this paper are to investigate the possibility of implementing an EWS in a small-scale catchment and to develop a methodology for developing a hydrological prediction model based on an artificial neural network (ANN as an essential part of the EWS. The methodology is implemented in the case study of the Slani Potok catchment, which is historically recognized as a hazard-prone area, by establishing continuous monitoring of meteorological and hydrological parameters to collect data for the training, validation, and evaluation of the prediction capabilities of the ANN model. The model is validated and evaluated by visual and common calculation approaches and a new evaluation for the assessment. This new evaluation is proposed based on the separation of the observed data into classes based on the mean data value and the percentages of classes above or below the mean data value as well as on the performance of the mean absolute error.

  5. Neural correlates of an early attentional capture by positive distractor words.

    Science.gov (United States)

    Hinojosa, José A; Mercado, Francisco; Albert, Jacobo; Barjola, Paloma; Peláez, Irene; Villalba-García, Cristina; Carretié, Luis

    2015-01-01

    Exogenous or automatic attention to emotional distractors has been observed for emotional scenes and faces. In the language domain, however, automatic attention capture by emotional words has been scarcely investigated. In the current event-related potentials study we explored distractor effects elicited by positive, negative and neutral words in a concurrent but distinct target distractor paradigm. Specifically, participants performed a digit categorization task in which task-irrelevant words were flanked by numbers. The results of both temporo-spatial principal component and source location analyses revealed the existence of early distractor effects that were specifically triggered by positive words. At the scalp level, task-irrelevant positive compared to neutral and negative words elicited larger amplitudes in an anterior negative component that peaked around 120 ms. Also, at the voxel level, positive distractor words increased activity in orbitofrontal regions compared to negative words. These results suggest that positive distractor words quickly and automatically capture attentional resources diverting them from the task where attention was voluntarily directed.

  6. Neural correlates of an early attentional capture by positive distractor words

    Directory of Open Access Journals (Sweden)

    José Antonio Hinojosa

    2015-01-01

    Full Text Available Exogenous or automatic attention to emotional distractors has been observed for emotional scenes and faces. In the language domain, however, automatic attention capture by emotional words has been scarcely investigated. In the current event-related potentials study we explored distractor effects elicited by positive, negative and neutral words in a concurrent but distinct target distractor paradigm. Specifically, participants performed a digit categorization task in which task-irrelevant words were flanked by numbers. The results of both temporo-spatial principal component and source location analyses revealed the existence of early distractor effects that were specifically triggered by positive words. At the scalp level, task-irrelevant positive compared to neutral and negative words elicited larger amplitudes in an anterior negative component that peaked around 120 ms. Also, at the voxel level, positive distractor words increased activity in orbitofrontal regions compared to negative words. These results suggest that positive distractor words quickly and automatically capture attentional resources diverting them from the task where attention was voluntarily directed.

  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. Attentional states influence early neural responses associated with motivational processes: local vs. global attentional scope and N1 amplitude to appetitive stimuli.

    Science.gov (United States)

    Gable, Philip A; Harmon-Jones, Eddie

    2011-05-01

    Positive affects vary in the degree with which they are associated with approach motivation, the drive to approach an object or a goal. High approach-motivated positive affects cause a narrowing of attention, whereas low approach-motivated positive affects causes a broadening of attention. The current study was designed to extend this work by examining whether the relationship between motivation and attentional bias was bi-directional. Specifically, the experiment investigated whether a manipulated local attentional scope would cause greater approach motivational processing than a global attentional scope as measured by neural processes as early as 100 ms. As compared to a global attentional scope, a local attentional scope caused greater neural processing associated with approach motivation as measured by the N1 to appetitive pictures. Copyright © 2011 Elsevier B.V. All rights reserved.

  9. Changes in neural circuitry associated with depression at pre-clinical, pre-motor and early motor phases of Parkinson's disease.

    Science.gov (United States)

    Borgonovo, Janina; Allende-Castro, Camilo; Laliena, Almudena; Guerrero, Néstor; Silva, Hernán; Concha, Miguel L

    2017-02-01

    Although Parkinson's Disease (PD) is mostly considered a motor disorder, it can present at early stages as a non-motor pathology. Among the non-motor clinical manifestations, depression shows a high prevalence and can be one of the first clinical signs to appear, even a decade before the onset of motor symptoms. Here, we review the evidence of early dysfunction in neural circuitry associated with depression in the context of PD, focusing on pre-clinical, pre-motor and early motor phases of the disease. In the pre-clinical phase, structural and functional changes in the substantia nigra, basal ganglia and limbic structures are already observed. Some of these changes are linked to motor compensation mechanisms while others correspond to pathological processes common to PD and depression and thus could underlie the appearance of depressive symptoms during the pre-motor phase. Studies of the early motor phase (less than five years post diagnosis) reveal an association between the extent of damage in different monoaminergic systems and the appearance of emotional disorders. We propose that the limbic loop of the basal ganglia and the lateral habenula play key roles in the early genesis of depression in PD. Alterations in the neural circuitry linked with emotional control might be sensitive markers of the ongoing neurodegenerative process and thus may serve to facilitate an early diagnosis of this disease. To take advantage of this, we need to improve the clinical criteria and develop biomarkers to identify depression, which could be used to determine individuals at risk to develop PD. Copyright © 2016 Elsevier Ltd. All rights reserved.

  10. 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.

  11. 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.

  12. Galectin-1 is expressed in early-type neural progenitor cells and down-regulates neurogenesis in the adult hippocampus

    Directory of Open Access Journals (Sweden)

    Imaizumi Yoichi

    2011-01-01

    Full Text Available Abstract Background In the adult mammalian brain, neural stem cells (NSCs proliferate in the dentate gyrus (DG of the hippocampus and generate new neurons throughout life. A multimodal protein, Galectin-1, is expressed in neural progenitor cells (NPCs and implicated in the proliferation of the NPCs in the DG. However, little is known about its detailed expression profile in the NPCs and functions in adult neurogenesis in the DG. Results Our immunohistochemical and morphological analysis showed that Galectin-1 was expressed in the type 1 and 2a cells, which are putative NSCs, in the subgranular zone (SGZ of the adult mouse DG. To study Galectin-1's function in adult hippocampal neurogenesis, we made galectin-1 knock-out mice on the C57BL6 background and characterized the effects on neurogenesis. In the SGZ of the galectin-1 knock-out mice, increased numbers of type 1 cells, DCX-positive immature progenitors, and NeuN-positive newborn neurons were observed. Using triple-labeling immunohistochemistry and morphological analyses, we found that the proliferation of the type-1 cells was increased in the SGZ of the galectin-1 knock-out mice, and we propose that this proliferation is the mechanism for the net increase in the adult neurogenesis in these knock-out mice DG. Conclusions Galectin-1 is expressed in the neural stem cells and down-regulates neurogenesis in the adult hippocampus.

  13. Establishment of turbidity forecasting model and early-warning system for source water turbidity management using back-propagation artificial neural network algorithm and probability analysis.

    Science.gov (United States)

    Yang, Tsung-Ming; Fan, Shu-Kai; Fan, Chihhao; Hsu, Nien-Sheng

    2014-08-01

    The purpose of this study is to establish a turbidity forecasting model as well as an early-warning system for turbidity management using rainfall records as the input variables. The Taipei Water Source Domain was employed as the study area, and ANOVA analysis showed that the accumulative rainfall records of 1-day Ping-lin, 2-day Ping-lin, 2-day Fei-tsui, 2-day Shi-san-gu, 2-day Tai-pin and 2-day Tong-hou were the six most significant parameters for downstream turbidity development. The artificial neural network model was developed and proven capable of predicting the turbidity concentration in the investigated catchment downstream area. The observed and model-calculated turbidity data were applied to developing the turbidity early-warning system. Using a previously determined turbidity as the threshold, the rainfall criterion, above which the downstream turbidity would possibly exceed this respective threshold turbidity, for the investigated rain gauge stations was determined. An exemplary illustration demonstrated the effectiveness of the proposed turbidity early-warning system as a precautionary alarm of possible significant increase of downstream turbidity. This study is the first report of the establishment of the turbidity early-warning system. Hopefully, this system can be applied to source water turbidity forecasting during storm events and provide a useful reference for subsequent adjustment of drinking water treatment operation.

  14. Convolutional neural network for high-accuracy functional near-infrared spectroscopy in a brain-computer interface: three-class classification of rest, right-, and left-hand motor execution.

    Science.gov (United States)

    Trakoolwilaiwan, Thanawin; Behboodi, Bahareh; Lee, Jaeseok; Kim, Kyungsoo; Choi, Ji-Woong

    2018-01-01

    The aim of this work is to develop an effective brain-computer interface (BCI) method based on functional near-infrared spectroscopy (fNIRS). In order to improve the performance of the BCI system in terms of accuracy, the ability to discriminate features from input signals and proper classification are desired. Previous studies have mainly extracted features from the signal manually, but proper features need to be selected carefully. To avoid performance degradation caused by manual feature selection, we applied convolutional neural networks (CNNs) as the automatic feature extractor and classifier for fNIRS-based BCI. In this study, the hemodynamic responses evoked by performing rest, right-, and left-hand motor execution tasks were measured on eight healthy subjects to compare performances. Our CNN-based method provided improvements in classification accuracy over conventional methods employing the most commonly used features of mean, peak, slope, variance, kurtosis, and skewness, classified by support vector machine (SVM) and artificial neural network (ANN). Specifically, up to 6.49% and 3.33% improvement in classification accuracy was achieved by CNN compared with SVM and ANN, respectively.

  15. Identification of Noncanonical Wnt Receptors Required for Wnt-3a-Induced Early Differentiation of Human Neural Stem Cells.

    Science.gov (United States)

    Bengoa-Vergniory, Nora; Gorroño-Etxebarria, Irantzu; López-Sánchez, Inmaculada; Marra, Michele; Di Chiaro, Pierluigi; Kypta, Robert

    2017-10-01

    Wnt proteins preferentially activate either β-catenin-dependent or β-catenin-independent signals, but the activity of a particular Wnt also depends on cellular context and receptor availability. We previously reported that Wnt-3a induces neural differentiation of human embryonic stem cell-derived neural stem cells (NSCs) in a β-catenin-independent manner by activating a signal involving JNK and the AP-1 family member ATF-2. Here, we report the results of a gene silencing approach to identify the Wnt receptors that mediate this response to Wnt-3a. Silencing of ROR2 increased neuronal differentiation, as measured by expression of the genes DCX, NEUROD1, and NGN1, suggesting ROR2 signals normally prevent differentiation. Silencing of the other Wnt receptors singly did not affect Wnt-3a-induced neuronal differentiation. However, pairwise silencing of ROR1 and FZD4 or FZD5 and of LRP6 and FZD4 or FZD5 inhibited neuronal differentiation, as detected by reductions in the expression of neuronal genes and immunocytochemical detection of DCX, NEUROD1 and DCX. Ectopic expression of these receptors in HEK 293 cells increased ATF2-dependent transcription. In addition, ROR1 coimmunoprecipitated with FZD4 and LRP6 in transfected HEK 293 cells and colocalized with FZD4 and with LRP6 at the cell surface of transfected L cells. Wnt-3a did not appear to affect these interactions but did alter the interactions between LRP6 and FZD4/5. Together, these observations highlight roles for ROR1, LRP6, FZD4, and FZD5 in neural stem cell differentiation and provide support for a model in which dynamic interactions among these receptors mediate Wnt-3a activation of ATF2 signaling.

  16. First Time Rapid and Accurate Detection of Massive Number of Metal Absorption Lines in the Early Universe Using Deep Neural Network

    Science.gov (United States)

    Zhao, Yinan; Ge, Jian; Yuan, Xiaoyong; Li, Xiaolin; Zhao, Tiffany; Wang, Cindy

    2018-01-01

    Metal absorption line systems in the distant quasar spectra have been used as one of the most powerful tools to probe gas content in the early Universe. The MgII λλ 2796, 2803 doublet is one of the most popular metal absorption lines and has been used to trace gas and global star formation at redshifts between ~0.5 to 2.5. In the past, machine learning algorithms have been used to detect absorption lines systems in the large sky survey, such as Principle Component Analysis, Gaussian Process and decision tree, but the overall detection process is not only complicated, but also time consuming. It usually takes a few months to go through the entire quasar spectral dataset from each of the Sloan Digital Sky Survey (SDSS) data release. In this work, we applied the deep neural network, or “ deep learning” algorithms, in the most recently SDSS DR14 quasar spectra and were able to randomly search 20000 quasar spectra and detect 2887 strong Mg II absorption features in just 9 seconds. Our detection algorithms were verified with previously released DR12 and DR7 data and published Mg II catalog and the detection accuracy is 90%. This is the first time that deep neural network has demonstrated its promising power in both speed and accuracy in replacing tedious, repetitive human work in searching for narrow absorption patterns in a big dataset. We will present our detection algorithms and also statistical results of the newly detected Mg II absorption lines.

  17. Beneficial Effects of Highly Palatable Food on the Behavioral and Neural Adversities induced by Early Life Stress Experience in Female Rats.

    Science.gov (United States)

    Kim, Jin Young; Lee, Jong-Ho; Kim, Doyun; Kim, Soung-Min; Koo, JaeHyung; Jahng, Jeong Won

    2015-01-01

    This study examined the effects of highly palatable food during adolescence on the psycho-emotional and neural disturbances caused by early life stress experience in female rats. Female Sprague-Dawley pups were separated from dam for 3 h daily during the first two weeks of birth (MS) or left undisturbed (NH). Half of MS females received free access to chocolate cookies in addition to ad libitum chow from postnatal day 28. Pups were subjected to the behavioral tests during young adulthood. The plasma corticosterone response to acute stress, ΔFosB and brain-derived neurotrophic factor (BDNF) levels in the brain regions were analyzed. Total caloric intake and body weight gain during the whole experimental period did not differ among the experimental groups. Cookie access during adolescence and youth improved anxiety-/depression-like behaviors by MS experience. ΔFosB expression was decreased, but BDNF was increased in the nucleus accumbens of MS females, and ΔFosB expression was normalized and BDNF was further increased following cookie access. Corticosterone response to acute stress was blunted by MS experience and cookie access did not improve it. Results suggest that cookie access during adolescence improves the psycho-emotional disturbances of MS females, and ΔFosB and/or BDNF expression in the nucleus accumbens may play a role in its underlying neural mechanisms.

  18. Early-Stage Investigators and Institutional Interface: Importance of Organization in the Mentoring Culture of Today's Universities.

    Science.gov (United States)

    Manson, Spero M

    2016-09-01

    Mentors have an active role in teaching mentees to scan their academic environments for the resources to advance their research careers, to assess the gaps between what's available and needed to succeed, and to develop strategies to fill these gaps. Yet achieving instrumentality is a necessary, but insufficient condition by which to accomplish the desired endpoints. Mentors and mentees must recognize that the organizations to which they belong are cultural in nature: characterized by vision, values, norms, systems, symbols, language, assumptions, beliefs, and habits. Understanding the collective behaviors and assumptions of peers and leaders in terms of the shared perceptions, thoughts, and feelings of organizational membership is essential to success. Institutions, in turn, must examine the extent to which they offer action possibilities: opportunities that promote the developmental trajectories of early stage investigators-in-training. Lack of awareness of the possible dissonance of this reality adversely affects many young faculty members.

  19. Early 20th-century research at the interfaces of genetics, development, and evolution: reflections on progress and dead ends.

    Science.gov (United States)

    Deichmann, Ute

    2011-09-01

    Three early 20th-century attempts at unifying separate areas of biology, in particular development, genetics, physiology, and evolution, are compared in regard to their success and fruitfulness for further research: Jacques Loeb's reductionist project of unifying approaches by physico-chemical explanations; Richard Goldschmidt's anti-reductionist attempts to unify by integration; and Sewall Wright's combination of reductionist research and vision of hierarchical genetic systems. Loeb's program, demanding that all aspects of biology, including evolution, be studied by the methods of the experimental sciences, proved highly successful and indispensible for higher level investigations, even though evolutionary change and properties of biological systems up to now cannot be fully explained on the molecular level alone. Goldschmidt has been appraised as pioneer of physiological and developmental genetics and of a new evolutionary synthesis which transcended neo-Darwinism. However, this study concludes that his anti-reductionist attempts to integrate genetics, development and evolution have to be regarded as failures or dead ends. His grand speculations were based on the one hand on concepts and experimental systems that were too vague in order to stimulate further research, and on the other on experiments which in their core parts turned out not to be reproducible. In contrast, Sewall Wright, apart from being one of the architects of the neo-Darwinian synthesis of the 1930s, opened up new paths of testable quantitative developmental genetic investigations. He placed his research within a framework of logical reasoning, which resulted in the farsighted speculation that examinations of biological systems should be related to the regulation of hierarchical genetic subsystems, possibly providing a mechanism for development and evolution. I argue that his suggestion of basing the study of systems on clearly defined properties of the components has proved superior to

  20. The Cognitive and Neural Expression of Semantic Memory Impairment in Mild Cognitive Impairment and Early Alzheimer's Disease

    Science.gov (United States)

    Joubert, Sven; Brambati, Simona M.; Ansado, Jennyfer; Barbeau, Emmanuel J.; Felician, Olivier; Didic, Mira; Lacombe, Jacinthe; Goldstein, Rachel; Chayer, Celine; Kergoat, Marie-Jeanne

    2010-01-01

    Semantic deficits in Alzheimer's disease have been widely documented, but little is known about the integrity of semantic memory in the prodromal stage of the illness. The aims of the present study were to: (i) investigate naming abilities and semantic memory in amnestic mild cognitive impairment (aMCI), early Alzheimer's disease (AD) compared to…

  1. The neural substrates of semantic memory deficits in early Alzheimer's disease: Clues from semantic priming effects and FDG-PET

    Energy Technology Data Exchange (ETDEWEB)

    Giffard, B.; Laisney, M.; Mezenge, F.; De la Sayette, V.; Eustache, F.; Desgranges, B. [Univ Caen Basse Normandie, INSERM, U923, Unite Rech, EPHE, Lab Neuropsychol, CHU Cote Nacre, GIP Cyceron, F-14033 Caen (France)

    2008-07-01

    The neural substrates responsible for semantic dysfunction during the early stages of AD have yet to be clearly identified. After a brief overview of the literature on normal and pathological semantic memory, we describe a new approach, designed to provide fresh insights into semantic deficits in AD. We mapped the correlations between resting-state brain glucose utilisation measured by FDG-PET and semantic priming scores in a group of 17 AD patients. The priming task, which yields a particularly pure measurement of semantic memory, was composed of related pairs of words sharing an attribute relationship (e.g. tiger-stripe). The priming scores correlated positively with the metabolism of the superior temporal areas on both sides, especially the right side, and this correlation was shown to be specific to the semantic priming effect.This pattern of results is discussed in the light of recent theoretical models of semantic memory, and suggests that a dysfunction of the right superior temporal cortex may contribute to early semantic deficits, characterised by the loss of specific features of concepts in AD. (authors)

  2. Kinetic Interface

    DEFF Research Database (Denmark)

    2009-01-01

    A kinetic interface for orientation detection in a video training system is disclosed. The interface includes a balance platform instrumented with inertial motion sensors. The interface engages a participant's sense of balance in training exercises.......A kinetic interface for orientation detection in a video training system is disclosed. The interface includes a balance platform instrumented with inertial motion sensors. The interface engages a participant's sense of balance in training exercises....

  3. 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%.

  4. Higher serum cholesterol is associated with intensified age-related neural network decoupling and cognitive decline in early- to mid-life.

    Science.gov (United States)

    Spielberg, Jeffrey M; Sadeh, Naomi; Leritz, Elizabeth C; McGlinchey, Regina E; Milberg, William P; Hayes, Jasmeet P; Salat, David H

    2017-06-01

    Mounting evidence indicates that serum cholesterol and other risk factors for cardiovascular disease intensify normative trajectories of age-related cognitive decline. However, the neural mechanisms by which this occurs remain largely unknown. To understand the impact of cholesterol on brain networks, we applied graph theory to resting-state fMRI in a large sample of early- to mid-life Veterans (N = 206, Mean age  = 32). A network emerged (centered on the banks of the superior temporal sulcus) that evidenced age-related decoupling (i.e., decreased network connectivity with age), but only in participants with clinically-elevated total cholesterol (≥180 mg/dL). Crucially, decoupling in this network corresponded to greater day-to-day disability and mediated age-related declines in psychomotor speed. Finally, examination of network organization revealed a pattern of age-related dedifferentiation for the banks of the superior temporal sulcus, again present only with higher cholesterol. More specifically, age was related to decreasing within-module communication (indexed by Within-Module Degree Z-Score) and increasing between-module communication (indexed by Participation Coefficient), but only in participants with clinically-elevated cholesterol. Follow-up analyses indicated that all findings were driven by low-density lipoprotein (LDL) levels, rather than high-density lipoprotein (HDL) or triglycerides, which is interesting as LDL levels have been linked to increased risk for cardiovascular disease, whereas HDL levels appear inversely related to such disease. These findings provide novel insight into the deleterious effects of cholesterol on brain health and suggest that cholesterol accelerates the impact of age on neural trajectories by disrupting connectivity in circuits implicated in integrative processes and behavioral control. Hum Brain Mapp 38:3249-3261, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  5. Multiscale deep neural network based analysis of FDG-PET images for the early diagnosis of Alzheimer's disease.

    Science.gov (United States)

    Lu, Donghuan; Popuri, Karteek; Ding, Gavin Weiguang; Balachandar, Rakesh; Beg, Mirza Faisal

    2018-05-01

    Alzheimer's disease (AD) is one of the most common neurodegenerative diseases with a commonly seen prodromal mild cognitive impairment (MCI) phase where memory loss is the main complaint progressively worsening with behavior issues and poor self-care. However, not all individuals clinically diagnosed with MCI progress to AD. A fraction of subjects with MCI either progress to non-AD dementia or remain stable at the MCI stage without progressing to dementia. Although a curative treatment of AD is currently unavailable, it is extremely important to correctly identify the individuals in the MCI phase that will go on to develop AD so that they may benefit from a curative treatment when one becomes available in the near future. At the same time, it would be highly desirable to also correctly identify those in the MCI phase that do not have AD pathology so they may be spared from unnecessary pharmocologic interventions that, at best, may provide them no benefit, and at worse, could further harm them with adverse side-effects. Additionally, it may be easier and simpler to identify the cause of the cognitive impairment in these non-AD cases, and hence proper identification of prodromal AD will be of benefit to these individuals as well. Fluorodeoxy glucose positron emission tomography (FDG-PET) captures the metabolic activity of the brain, and this imaging modality has been reported to identify changes related to AD prior to the onset of structural changes. Prior work on designing classifier using FDG-PET imaging has been promising. Since deep-learning has recently emerged as a powerful tool to mine features and use them for accurate labeling of the group membership of given images, we propose a novel deep-learning framework using FDG-PET metabolism imaging to identify subjects at the MCI stage with presymptomatic AD and discriminate them from other subjects with MCI (non-AD / non-progressive). Our multiscale deep neural network obtained 82.51% accuracy of classification

  6. 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.

  7. Can early-life growth disruptions predict longevity? Testing the association between vertebral neural canal (VNC) size and age-at-death.

    Science.gov (United States)

    Amoroso, Alexandra; Garcia, Susana J

    2018-04-04

    This study tests the association of vertebral neural canal (VNC) size and age-at-death in a Portuguese skeletal collection from the 19 th -20th century. If the plasticity and constraint model best explains this association, VNC size would be negatively related to mortality risk. If the predictive adaptive response (PAR) model is a better fit, no association can be inferred between VNC size and age-at-death. Ninety individuals were used in this study. The anteroposterior and transverse diameters of all vertebrae were measured. A Cox regression analysis was performed by sex to assess the effect of VNC size on age-at-death, after adjusting for the effects of year of birth and cause of death. Several measurements of VNC diameters have a statistically significant effect on age-at-death, but when the covariates were considered, this association became non-significant. The PAR model seems the best fit to explain the relation between VNC and age-at-death. Individuals who went through stressful events early in life were prepared to face a stressful environment later in life, allowing them to cope with adversity without affecting longevity. However, developmental plasticity may be buffered by maternal capital accumulated over several generations, and health hazards encountered throughout life can contribute to health outcomes and longevity. Copyright © 2018 Elsevier Inc. All rights reserved.

  8. Interface Consistency

    DEFF Research Database (Denmark)

    Staunstrup, Jørgen

    1998-01-01

    This paper proposes that Interface Consistency is an important issue for the development of modular designs. Byproviding a precise specification of component interfaces it becomes possible to check that separately developedcomponents use a common interface in a coherent matter thus avoiding a very...... significant source of design errors. Awide range of interface specifications are possible, the simplest form is a syntactical check of parameter types.However, today it is possible to do more sophisticated forms involving semantic checks....

  9. HTRA3 expression in non-pregnant rhesus monkey ovary and endometrium, and at the maternal-fetal interface during early pregnancy

    Directory of Open Access Journals (Sweden)

    Findlay Jock K

    2008-06-01

    Full Text Available Abstract Background HTRA3 is a recently identified member of the mammalian serine protease family HTRA (high temperature requirement factor A. In both the rodent and the human HTRA3 is transcribed into two mRNA species (long and short through alternative splicing. We have previously shown that HTRA3 is expressed in the mature rat ovary and may be involved in folliculogenesis and luteinisation. HTRA3 is also upregulated during mouse and human placental development. The current study investigated whether HTRA3 is also localised in the primate ovary (rhesus monkey n = 7. In addition, we examined the non-pregnant rhesus monkey endometrium (n = 4 and maternal-fetal interface during early pregnancy (n = 5 to further investigate expression of HTRA3 in primate endometrium and placentation. Methods HTRA3 mRNA levels in several rhesus monkey tissues was determined by semiquantitative RT-PCR. Protein expression and localisation of HTRA3 was determined by immunohistochemistry. Results Long and short forms of HTRA3 mRNA were detected in the ovary, aorta, bladder, small intestine, skeletal muscle, heart and uterus but not the liver nor the kidney. HTRA3 protein was immunolocalised to the oocyte of all follicular stages in the rhesus monkey ovary. Protein expression in mural and cumulus granulosa cells of late secondary follicles increased significantly compared to granulosa cells of primordial, primary and secondary follicles. Mural and cumulus granulosa cells of antral follicles also showed a significant increase in expression. Staining intensity was higher in the granulosa-lutein cells compared to the theca-lutein cells of corpora lutea (n = 3. In the non-pregnant monkey endometrium, HTRA3 was detected in the glandular epithelium. The basalis endometrial glands showed higher staining intensity than functionalis endometrial glands. During early pregnancy, strong staining for HTRA3 protein was seen in both maternal decidual cells and glands. Conclusion We

  10. Interface models

    DEFF Research Database (Denmark)

    Ravn, Anders P.; Staunstrup, Jørgen

    1994-01-01

    This paper proposes a model for specifying interfaces between concurrently executing modules of a computing system. The model does not prescribe a particular type of communication protocol and is aimed at describing interfaces between both software and hardware modules or a combination of the two....... The model describes both functional and timing properties of an interface...

  11. Radiation-induced apoptosis of neural precursors cell cultures: early modulation of the response mediated by reactive oxygen and nitrogen species (ROS/RNS)

    Energy Technology Data Exchange (ETDEWEB)

    Gisone, P.; Dubner, D.; Robello, E.; Michelin, S.; Perez, M. R.

    2004-07-01

    Apoptosis, the typical mode of radiation-induced cell death in developing Central Nervous System (CNS), is closely related with the oxidative status. Enhanced radiation-induced generation of ROS/RNS has been observed after exposures to low radiation doses leading to cellular amplification of signal transduction and further molecular and cellular radiation-responses. Moreover Nitric oxide (NO) and hydroxyl radical are implicated in dopaminergic neurotoxicity in different parading. This study is an attempt to address the participation of radiation-induced free radicals production, the contribution of endogenous NO generation, and the excitonic pathway, in the radiation-induced apoptosis of neural cortical precursors. Cortical cells obtained from at 17 gestational day (gd) were irradiated with doses from 0,2 Gy to 2 Gy at a dose-rate of 0.3 Gy/m. A significant decrease of Luminol-dependent Chemiluminescence was evident 30 m after irradiation reaching basal levels at 120 m follow for a tendency to increasing values Incubations with Superoxide Dismatuse (SOD) decreased significantly the chemiluminescence in irradiated samples NO content estimated by measuring the stable products NO{sub 2} and NO{sub 3} released to the culture medium in the same period, has shown a time-dependent accumulation from 1 h post-irradiation. the apoptosis, determined 24 h post-irradiation by flow cytometry, morphology and DNA fragmentation revealed a dose-effect relationship with significant differences from 0.4 Gy. The samples pre-treated with 10 mM of N-acetyl cysteine (NAC) a precursor of intracellular GSH synthesis, shown a significant decrease of the apoptosis. Apoptosis was significantly increased in irradiated cells after inhibition of nitric oxide synthase (NOS) byL-NAME. We conclude that ROS/RNS play a pivotal role in the early signaling pathways leading to a radiation-induced cell death. (Author) 40 refs.

  12. Radiation-induced apoptosis of neural precursors cell cultures: early modulation of the response mediated by reactive oxygen and nitrogen species (ROS/RNS)

    International Nuclear Information System (INIS)

    Gisone, P.; Dubner, D.; Robello, E.; Michelin, S.; Perez, M. R.

    2004-01-01

    Apoptosis, the typical mode of radiation-induced cell death in developing Central Nervous System (CNS), is closely related with the oxidative status. Enhanced radiation-induced generation of ROS/RNS has been observed after exposures to low radiation doses leading to cellular amplification of signal transduction and further molecular and cellular radiation-responses. Moreover Nitric oxide (NO) and hydroxyl radical are implicated in dopaminergic neurotoxicity in different parading. This study is an attempt to address the participation of radiation-induced free radicals production, the contribution of endogenous NO generation, and the excitonic pathway, in the radiation-induced apoptosis of neural cortical precursors. Cortical cells obtained from at 17 gestational day (gd) were irradiated with doses from 0,2 Gy to 2 Gy at a dose-rate of 0.3 Gy/m. A significant decrease of Luminol-dependent Chemiluminescence was evident 30 m after irradiation reaching basal levels at 120 m follow for a tendency to increasing values Incubations with Superoxide Dismatuse (SOD) decreased significantly the chemiluminescence in irradiated samples NO content estimated by measuring the stable products NO 2 and NO 3 released to the culture medium in the same period, has shown a time-dependent accumulation from 1 h post-irradiation. the apoptosis, determined 24 h post-irradiation by flow cytometry, morphology and DNA fragmentation revealed a dose-effect relationship with significant differences from 0.4 Gy. The samples pre-treated with 10 mM of N-acetyl cysteine (NAC) a precursor of intracellular GSH synthesis, shown a significant decrease of the apoptosis. Apoptosis was significantly increased in irradiated cells after inhibition of nitric oxide synthase (NOS) byL-NAME. We conclude that ROS/RNS play a pivotal role in the early signaling pathways leading to a radiation-induced cell death. (Author) 40 refs

  13. Conditional deletion of AP-2β in mouse cranial neural crest results in anterior segment dysgenesis and early-onset glaucoma

    Directory of Open Access Journals (Sweden)

    Vanessa B. Martino

    2016-08-01

    Full Text Available Anterior segment dysgenesis (ASD encompasses a group of developmental disorders in which a closed angle phenotype in the anterior chamber of the eye can occur and 50% of patients develop glaucoma. Many ASDs are thought to involve an inappropriate patterning and migration of the periocular mesenchyme (POM, which is derived from cranial neural crest cells (NCCs and mesoderm. Although, the mechanism of this disruption is not well understood, a number of transcriptional regulatory molecules have previously been implicated in ASDs. Here, we investigate the function of the transcription factor AP-2β, encoded by Tfap2b, which is expressed in NCCs and their derivatives. Wnt1-Cre-mediated conditional deletion of Tfap2b in NCCs resulted in post-natal ocular defects typified by opacity. Histological data revealed that the conditional AP-2β NCC knockout (KO mutants exhibited dysgenesis of multiple structures in the anterior segment of the eye including defects in the corneal endothelium, corneal stroma, ciliary body and disruption in the iridocorneal angle with adherence of the iris to the cornea. We further show that this phenotype leads to a significant increase in intraocular pressure and a subsequent loss of retinal ganglion cells and optic nerve degeneration, features indicative of glaucoma. Overall, our findings demonstrate that AP-2β is required in the POM for normal development of the anterior segment of the eye and that the AP-2β NCC KO mice might serve as a new and exciting model of ASD and glaucoma that is fully penetrant and with early post-natal onset.

  14. 199. Disrupted Integration in Early Psychosis: A Preliminary Exploration of the Relationship Between Neural Synchronization and Higher Order Cognition in a First-Episode Psychosis Sample.

    Science.gov (United States)

    Leonhardt, Bethany; Vohs, Jennifer; Lysaker, Paul; Bartolomeo, Lisa; O’Donnell, Brian; Breier, Alan

    2017-01-01

    Abstract Background: Disruptions in the ability to integrate information into complex ideas needed to make sense of and recover from psychiatric challenges are considered a core source of dysfunction in schizophrenia spectrum disorders (SSD). These disruptions are believed to take place at the level of basic brain functioning through neural synchrony and neurocognitive functioning in which information is encountered, encoded and available for memory and at the level of higher order cognition in which ideas are formed and reflected upon. In this study, we sought to explore the link of difficulties in integration at the level of basic brain functioning with integration at the level of self-reflectivity and insight in first episode patients. The role of disrupted integration has particular importance in early phases of illness, as it may impact the likelihood that an individual is able to move toward recovery. As more work is done in early intervention in SSD, it is pivotal that underlying factors that impact ability to recover are investigated. Methods: To assess the ability to integrate information at the level of basic brain function we used electroencephalography (EEG) collected using an Auditory Steady State Response (ASSR) and the Brief Assessment of Cognition in Schizophrenia (BACS). To assess integration at the level of conscious reflection we used the Metacognition Assessment Scale Abbreviated and insight we used the Scale to Assess Awareness of Mental Disorders (SUMD). Participants were 14 adults with first episode psychosis. Results: Pearson correlations were calculated to assess the relationship of EEG power across a range of frequency bands and neurocognition with MAS-A total scores and SUMD insight score. These revealed that the MAS-A total score was significantly negatively correlated with gamma activity, and was positively correlated with BACS total score. SUMD insight was significantly positively correlated with gamma activity, and negatively

  15. Keratocyte apoptosis and not myofibroblast differentiation mark the graft/host interface at early time-points post-DSAEK in a cat model.

    Directory of Open Access Journals (Sweden)

    Adam J Weis

    Full Text Available To evaluate myofibroblast differentiation as an etiology of haze at the graft-host interface in a cat model of Descemet's Stripping Automated Endothelial Keratoplasty (DSAEK.DSAEK was performed on 10 eyes of 5 adult domestic short-hair cats. In vivo corneal imaging with slit lamp, confocal, and optical coherence tomography (OCT were performed twice weekly. Cats were sacrificed and corneas harvested 4 hours, and 2, 4, 6, and 9 days post-DSAEK. Corneal sections were stained with the TUNEL method and immunohistochemistry was performed for α-smooth muscle actin (α-SMA and fibronectin with DAPI counterstain.At all in vivo imaging time-points, corneal OCT revealed an increase in backscatter of light and confocal imaging revealed an acellular zone at the graft-host interface. At all post-mortem time-points, immunohistochemistry revealed a complete absence of α-SMA staining at the graft-host interface. At 4 hours, extracellular fibronectin staining was identified along the graft-host interface and both fibronectin and TUNEL assay were positive within adjacent cells extending into the host stroma. By day 2, fibronectin and TUNEL staining diminished and a distinct acellular zone was present in the region of previously TUNEL-positive cells.OCT imaging consistently showed increased reflectivity at the graft-host interface in cat corneas in the days post-DSAEK. This was not associated with myofibroblast differentiation at the graft-host interface, but rather with apoptosis and the development of a subsequent acellular zone. The roles of extracellular matrix changes and keratocyte cell death and repopulation should be investigated further as potential contributors to the interface optical changes.

  16. Organic interfaces

    NARCIS (Netherlands)

    Poelman, W.A.; Tempelman, E.

    2014-01-01

    This paper deals with the consequences for product designers resulting from the replacement of traditional interfaces by responsive materials. Part 1 presents a theoretical framework regarding a new paradigm for man-machine interfacing. Part 2 provides an analysis of the opportunities offered by new

  17. Interface Realisms

    DEFF Research Database (Denmark)

    Pold, Søren

    2005-01-01

    This article argues for seeing the interface as an important representational and aesthetic form with implications for postmodern culture and digital aesthetics. The interface emphasizes realism due in part to the desire for transparency in Human-Computer Interaction (HCI) and partly...

  18. 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

  19. Early Career. Harnessing nanotechnology for fusion plasma-material interface research in an in-situ particle-surface interaction facility

    Energy Technology Data Exchange (ETDEWEB)

    Allain, Jean Paul [Univ. of Illinois, Champaign, IL (United States)

    2014-08-08

    This project consisted of fundamental and applied research of advanced in-situ particle-beam interactions with surfaces/interfaces to discover novel materials able to tolerate intense conditions at the plasma-material interface (PMI) in future fusion burning plasma devices. The project established a novel facility that is capable of not only characterizing new fusion nanomaterials but, more importantly probing and manipulating materials at the nanoscale while performing subsequent single-effect in-situ testing of their performance under simulated environments in fusion PMI.

  20. Microprocessor interfacing

    CERN Document Server

    Vears, R E

    2014-01-01

    Microprocessor Interfacing provides the coverage of the Business and Technician Education Council level NIII unit in Microprocessor Interfacing (syllabus U86/335). Composed of seven chapters, the book explains the foundation in microprocessor interfacing techniques in hardware and software that can be used for problem identification and solving. The book focuses on the 6502, Z80, and 6800/02 microprocessor families. The technique starts with signal conditioning, filtering, and cleaning before the signal can be processed. The signal conversion, from analog to digital or vice versa, is expl

  1. Interface Anywhere

    Data.gov (United States)

    National Aeronautics and Space Administration — Current paradigms for crew interfaces to the systems that require control are constrained by decades old technologies which require the crew to be physically near an...

  2. Implantable neurotechnologies: a review of integrated circuit neural amplifiers.

    Science.gov (United States)

    Ng, Kian Ann; Greenwald, Elliot; Xu, Yong Ping; Thakor, Nitish V

    2016-01-01

    Neural signal recording is critical in modern day neuroscience research and emerging neural prosthesis programs. Neural recording requires the use of precise, low-noise amplifier systems to acquire and condition the weak neural signals that are transduced through electrode interfaces. Neural amplifiers and amplifier-based systems are available commercially or can be designed in-house and fabricated using integrated circuit (IC) technologies, resulting in very large-scale integration or application-specific integrated circuit solutions. IC-based neural amplifiers are now used to acquire untethered/portable neural recordings, as they meet the requirements of a miniaturized form factor, light weight and low power consumption. Furthermore, such miniaturized and low-power IC neural amplifiers are now being used in emerging implantable neural prosthesis technologies. This review focuses on neural amplifier-based devices and is presented in two interrelated parts. First, neural signal recording is reviewed, and practical challenges are highlighted. Current amplifier designs with increased functionality and performance and without penalties in chip size and power are featured. Second, applications of IC-based neural amplifiers in basic science experiments (e.g., cortical studies using animal models), neural prostheses (e.g., brain/nerve machine interfaces) and treatment of neuronal diseases (e.g., DBS for treatment of epilepsy) are highlighted. The review concludes with future outlooks of this technology and important challenges with regard to neural signal amplification.

  3. SimulRad: a Java interface for a Monte-Carlo simulation code to visualize in 3D the early stages of water radiolysis

    International Nuclear Information System (INIS)

    Plante, Ianik L.; Filali-Mouhim, Abdelali; Jay-Gerin, Jean-Paul

    2005-01-01

    Using a Fortran step-by-step Monte-Carlo simulation code of liquid water radiolysis and the Java programming language, we have developed a Java interface software, called SimulRad. This interface enables a user, in a three-dimensional environment, to either visualize the spatial distribution of all reactive species present in the track of an ionizing particle at a chosen simulation time, or present an animation of the chemical development of the particle track over a chosen time interval (between ∼10 -12 and 10 -6 s). It also allows one to select a particular radiation-induced cluster of species to view, in fine detail, the chemical reactions that occur between these species

  4. Functional Strength Training and Movement Performance Therapy for Upper Limb Recovery Early Poststroke—Efficacy, Neural Correlates, Predictive Markers, and Cost-Effectiveness: FAST-INdiCATE Trial

    Directory of Open Access Journals (Sweden)

    Susan M. Hunter

    2018-01-01

    Full Text Available BackgroundVariation in physiological deficits underlying upper limb paresis after stroke could influence how people recover and to which physical therapy they best respond.ObjectivesTo determine whether functional strength training (FST improves upper limb recovery more than movement performance therapy (MPT. To identify: (a neural correlates of response and (b whether pre-intervention neural characteristics predict response.DesignExplanatory investigations within a randomised, controlled, observer-blind, and multicentre trial. Randomisation was computer-generated and concealed by an independent facility until baseline measures were completed. Primary time point was outcome, after the 6-week intervention phase. Follow-up was at 6 months after stroke.ParticipantsWith some voluntary muscle contraction in the paretic upper limb, not full dexterity, when recruited up to 60 days after an anterior cerebral circulation territory stroke.InterventionsConventional physical therapy (CPT plus either MPT or FST for up to 90 min-a-day, 5 days-a-week for 6 weeks. FST was “hands-off” progressive resistive exercise cemented into functional task training. MPT was “hands-on” sensory/facilitation techniques for smooth and accurate movement.OutcomesThe primary efficacy measure was the Action Research Arm Test (ARAT. Neural measures: fractional anisotropy (FA corpus callosum midline; asymmetry of corticospinal tracts FA; and resting motor threshold (RMT of motor-evoked potentials.AnalysisCovariance models tested ARAT change from baseline. At outcome: correlation coefficients assessed relationship between change in ARAT and neural measures; an interaction term assessed whether baseline neural characteristics predicted response.Results288 Participants had: mean age of 72.2 (SD 12.5 years and mean ARAT 25.5 (18.2. For 240 participants with ARAT at baseline and outcome the mean change was 9.70 (11.72 for FST + CPT and 7.90 (9.18 for MPT

  5. Designing Interfaces

    CERN Document Server

    Tidwell, Jenifer

    2010-01-01

    Despite all of the UI toolkits available today, it's still not easy to design good application interfaces. This bestselling book is one of the few reliable sources to help you navigate through the maze of design options. By capturing UI best practices and reusable ideas as design patterns, Designing Interfaces provides solutions to common design problems that you can tailor to the situation at hand. This updated edition includes patterns for mobile apps and social media, as well as web applications and desktop software. Each pattern contains full-color examples and practical design advice th

  6. Knowledge synthesis with maps of neural connectivity.

    Science.gov (United States)

    Tallis, Marcelo; Thompson, Richard; Russ, Thomas A; Burns, Gully A P C

    2011-01-01

    This paper describes software for neuroanatomical knowledge synthesis based on neural connectivity data. This software supports a mature methodology developed since the early 1990s. Over this time, the Swanson laboratory at USC has generated an account of the neural connectivity of the sub-structures of the hypothalamus, amygdala, septum, hippocampus, and bed nucleus of the stria terminalis. This is based on neuroanatomical data maps drawn into a standard brain atlas by experts. In earlier work, we presented an application for visualizing and comparing anatomical macro connections using the Swanson third edition atlas as a framework for accurate registration. Here we describe major improvements to the NeuARt application based on the incorporation of a knowledge representation of experimental design. We also present improvements in the interface and features of the data mapping components within a unified web-application. As a step toward developing an accurate sub-regional account of neural connectivity, we provide navigational access between the data maps and a semantic representation of area-to-area connections that they support. We do so based on an approach called "Knowledge Engineering from Experimental Design" (KEfED) model that is based on experimental variables. We have extended the underlying KEfED representation of tract-tracing experiments by incorporating the definition of a neuronanatomical data map as a measurement variable in the study design. This paper describes the software design of a web-application that allows anatomical data sets to be described within a standard experimental context and thus indexed by non-spatial experimental design features.

  7. Knowledge synthesis with maps of neural connectivity

    Directory of Open Access Journals (Sweden)

    Marcelo eTallis

    2011-11-01

    Full Text Available This paper describes software for neuroanatomical knowledge synthesis based on high-quality neural connectivity data. This software supports a mature neuroanatomical methodology developed since the early 1990s. Over this time, the Swanson laboratory at USC has generated an account of the neural connectivity of the sub-structures of the hypothalamus, amygdala, septum, hippocampus and bed nucleus of the stria terminalis. This is based on neuroanatomical data maps drawn into a standard brain atlas by experts. In earlier work, we presented an application for visualizing and comparing anatomical macroconnections using the Swanson 3rd edition atlas as a framework for accurate registration. Here we describe major improvements to the NeuARt application based on the incorporation of a knowledge representation of experimental design. We also present improvements in the interface and features of the neuroanatomical data mapping components within a unified web-application. As a step towards developing an accurate sub-regional account of neural connectivity, we provide navigational access between the neuroanatomical data maps and a semantic representation of area-to-area connections that they support. We do so based on an approach called ’Knowledge Engineering from Experimental Design’ (KEfED model that is based on experimental variables. We have extended the underlying KEfED representation of tract-tracing experiments by incorporating the definition of a neuronanatomical data map as a measurement variable in the study design. This paper describes the software design of a web application that allows anatomical data sets to be described within a standard experimental context and thus incorporated with non-spatial data sets.

  8. Is MSAFP still a useful test for detecting open neural tube defects and ventral wall defects in the era of first-trimester and early second-trimester fetal anatomical ultrasounds?

    Science.gov (United States)

    Roman, Ashley S; Gupta, Simi; Fox, Nathan S; Saltzman, Daniel; Klauser, Chad K; Rebarber, Andrei

    2015-01-01

    To evaluate whether maternal serum α-fetoprotein (MSAFP) improves the detection rate for open neural tube defects (ONTDs) and ventral wall defects (VWD) in patients undergoing first-trimester and early second-trimester fetal anatomical survey. A cohort of women undergoing screening between 2005 and 2012 was identified. All patients were offered an ultrasound at between 11 weeks and 13 weeks and 6 days of gestational age for nuchal translucency/fetal anatomy followed by an early second-trimester ultrasound at between 15 weeks and 17 weeks and 6 days of gestational age for fetal anatomy and MSAFP screening. All cases of ONTD and VWD were identified via query of billing and reporting software. Sensitivity and specificity for detection of ONTD/VWD were calculated, and groups were compared using the Fisher exact test, with p met the criteria for inclusion. Overall, 15 cases of ONTD and 17 cases of VWD were identified; 100% of cases were diagnosed by ultrasound prior to 18 weeks' gestation; none were diagnosed via MSAFP screening (p < 0.001). First-trimester and early second-trimester ultrasound had 100% sensitivity and 100% specificity for diagnosing ONTD/VWD. Ultrasound for fetal anatomy during the first and early second trimester detected 100% of ONTD/VWD in our population. MSAFP is not useful as a screening tool for ONTD and VWD in the setting of this ultrasound screening protocol. © 2014 S. Karger AG, Basel.

  9. Portable non-invasive brain-computer interface: challenges and opportunities of optical modalities

    Science.gov (United States)

    Scholl, Clara A.; Hendrickson, Scott M.; Swett, Bruce A.; Fitch, Michael J.; Walter, Erich C.; McLoughlin, Michael P.; Chevillet, Mark A.; Blodgett, David W.; Hwang, Grace M.

    2017-05-01

    The development of portable non-invasive brain computer interface technologies with higher spatio-temporal resolution has been motivated by the tremendous success seen with implanted devices. This talk will discuss efforts to overcome several major obstacles to viability including approaches that promise to improve spatial and temporal resolution. Optical approaches in particular will be highlighted and the potential benefits of both Blood-Oxygen Level Dependent (BOLD) and Fast Optical Signal (FOS) will be discussed. Early-stage research into the correlations between neural activity and FOS will be explored.

  10. Interface unit

    NARCIS (Netherlands)

    Keyson, D.V.; Freudenthal, A.; De Hoogh, M.P.A.; Dekoven, E.A.M.

    2001-01-01

    The invention relates to an interface unit comprising at least a display unit for communication with a user, which is designed for being coupled with a control unit for at least one or more parameters in a living or working environment, such as the temperature setting in a house, which control unit

  11. Interface superconductivity

    Energy Technology Data Exchange (ETDEWEB)

    Gariglio, S., E-mail: stefano.gariglio@unige.ch [DQMP, Université de Genève, 24 Quai E.-Ansermet, CH-1211 Genève (Switzerland); Gabay, M. [Laboratoire de Physique des Solides, Bat 510, Université Paris-Sud 11, Centre d’Orsay, 91405 Orsay Cedex (France); Mannhart, J. [Max Planck Institute for Solid State Research, 70569 Stuttgart (Germany); Triscone, J.-M. [DQMP, Université de Genève, 24 Quai E.-Ansermet, CH-1211 Genève (Switzerland)

    2015-07-15

    Highlights: • We discuss interfacial superconductivity, a field boosted by the discovery of the superconducting interface between LaAlO. • This system allows the electric field control and the on/off switching of the superconducting state. • We compare superconductivity at the interface and in bulk doped SrTiO. • We discuss the role of the interfacially induced Rashba type spin–orbit. • We briefly discuss superconductivity in cuprates, in electrical double layer transistor field effect experiments. • Recent observations of a high T{sub c} in a monolayer of FeSe deposited on SrTiO{sub 3} are presented. - Abstract: Low dimensional superconducting systems have been the subject of numerous studies for many years. In this article, we focus our attention on interfacial superconductivity, a field that has been boosted by the discovery of superconductivity at the interface between the two band insulators LaAlO{sub 3} and SrTiO{sub 3}. We explore the properties of this amazing system that allows the electric field control and on/off switching of superconductivity. We discuss the similarities and differences between bulk doped SrTiO{sub 3} and the interface system and the possible role of the interfacially induced Rashba type spin–orbit. We also, more briefly, discuss interface superconductivity in cuprates, in electrical double layer transistor field effect experiments, and the recent observation of a high T{sub c} in a monolayer of FeSe deposited on SrTiO{sub 3}.

  12. 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.

  13. 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.

  14. A neural cell adhesion molecule-derived fibroblast growth factor receptor agonist, the FGL-peptide, promotes early postnatal sensorimotor development and enhances social memory retention

    DEFF Research Database (Denmark)

    Secher, Thomas; Novitskaia, V; Berezin, Vladimir

    2006-01-01

    of coordination skills. In adult animals s.c. administration of FGL resulted in a prolonged retention of social memory. We found that FGL rapidly penetrated into the blood and cerebrospinal fluid after both intranasal and s.c. administration and remained detectable in the fluids for up to 5 hours.......The neural cell adhesion molecule (NCAM) belongs to the immunoglobulin (Ig) superfamily and is composed extracellularly of five Ig-like and two fibronectin type III (F3) modules. It plays a pivotal role in neuronal development and synaptic plasticity. NCAM signals via a direct interaction...

  15. 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.

  16. Interface learning

    DEFF Research Database (Denmark)

    Thorhauge, Sally

    2014-01-01

    "Interface learning - New goals for museum and upper secondary school collaboration" investigates and analyzes the learning that takes place when museums and upper secondary schools in Denmark work together in local partnerships to develop and carry out school-related, museum-based coursework...... for students. The research focuses on the learning that the students experience in the interface of the two learning environments: The formal learning environment of the upper secondary school and the informal learning environment of the museum. Focus is also on the learning that the teachers and museum...... professionals experience as a result of their collaboration. The dissertation demonstrates how a given partnership’s collaboration affects the students’ learning experiences when they are doing the coursework. The dissertation presents findings that museum-school partnerships can use in order to develop...

  17. ESTIMATION OF THE DEVELOPMENT STANDARD OF NEURAL TUBE IN EMBRYOS FROM TRANSYLVANIAN NAKED NECK AND PLYMOUTH ROCK HEN BREEDS, DURING EARLY EMBRYOGENESIS

    Directory of Open Access Journals (Sweden)

    D. DRONCA

    2007-05-01

    Full Text Available In Romania, the Transylvanian Naked Neck hen breed is considered to be an“endangered” population, reason for which we consider that a special attentionshould have been given until now. Plymouth Rock breed was imported for the firsttime to Romania from the Studler Company, France in 1969. This paper is aimingto perform a profound analysis of the development patterns of the neural tube inthe two breeds, by measurements carried out at 30, 40, 50, and 60 hours ofincubation. Observations show that the closure of the neural canal and itstransformation into a tube follows an undulatory pattern, of which positive andnegative curls are diametrically opposed in the two breeds, while the developmentspeed during the whole studied period have a relative similar value between thetwo breeds. We estimate that the two breeds have a good combinative capacity,which recommend the utilization of these genetic materials to obtain hybrids forproducing “peasant-type” chicken meat, very well-appreciated by the Europeansbetween the two World Wars.

  18. 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.

  19. Emerging trends in neuro engineering and neural computation

    CERN Document Server

    Lee, Kendall; Garmestani, Hamid; Lim, Chee

    2017-01-01

    This book focuses on neuro-engineering and neural computing, a multi-disciplinary field of research attracting considerable attention from engineers, neuroscientists, microbiologists and material scientists. It explores a range of topics concerning the design and development of innovative neural and brain interfacing technologies, as well as novel information acquisition and processing algorithms to make sense of the acquired data. The book also highlights emerging trends and advances regarding the applications of neuro-engineering in real-world scenarios, such as neural prostheses, diagnosis of neural degenerative diseases, deep brain stimulation, biosensors, real neural network-inspired artificial neural networks (ANNs) and the predictive modeling of information flows in neuronal networks. The book is broadly divided into three main sections including: current trends in technological developments, neural computation techniques to make sense of the neural behavioral data, and application of these technologie...

  20. Falls Risk Prediction for Older Inpatients in Acute Care Medical Wards: Is There an Interest to Combine an Early Nurse Assessment and the Artificial Neural Network Analysis?

    Science.gov (United States)

    Beauchet, O; Noublanche, F; Simon, R; Sekhon, H; Chabot, J; Levinoff, E J; Kabeshova, A; Launay, C P

    2018-01-01

    Identification of the risk of falls is important among older inpatients. This study aims to examine performance criteria (i.e.; sensitivity, specificity, positive predictive value, negative predictive value and accuracy) for fall prediction resulting from a nurse assessment and an artificial neural networks (ANNs) analysis in older inpatients hospitalized in acute care medical wards. A total of 848 older inpatients (mean age, 83.0±7.2 years; 41.8% female) admitted to acute care medical wards in Angers University hospital (France) were included in this study using an observational prospective cohort design. Within 24 hours after admission of older inpatients, nurses performed a bedside clinical assessment. Participants were separated into non-fallers and fallers (i.e.; ≥1 fall during hospitalization stay). The analysis was conducted using three feed forward ANNs (multilayer perceptron [MLP], averaged neural network, and neuroevolution of augmenting topologies [NEAT]). Seventy-three (8.6%) participants fell at least once during their hospital stay. ANNs showed a high specificity, regardless of which ANN was used, and the highest value reported was with MLP (99.8%). In contrast, sensitivity was lower, with values ranging between 98.4 to 14.8%. MLP had the highest accuracy (99.7). Performance criteria for fall prediction resulting from a bedside nursing assessment and an ANNs analysis was associated with a high specificity but a low sensitivity, suggesting that this combined approach should be used more as a diagnostic test than a screening test when considering older inpatients in acute care medical ward.

  1. Soft Interfaces

    International Nuclear Information System (INIS)

    Strzalkowski, Ireneusz

    1997-01-01

    This book presents an extended form of the 1994 Dirac Memorial Lecture delivered by Pierre Gilles de Gennes at Cambridge University. The main task of the presentation is to show the beauty and richness of structural forms and phenomena which are observed at soft interfaces between two media. They are much more complex than forms and phenomena existing in each phase separately. Problems are discussed including both traditional, classical techniques, such as the contact angle in static and dynamic partial wetting, as well as the latest research methodology, like 'environmental' scanning electron microscopes. The book is not a systematic lecture on phenomena but it can be considered as a compact set of essays on topics which particularly fascinate the author. The continuum theory widely used in the book is based on a deep molecular approach. The author is particularly interested in a broad-minded rheology of liquid systems at interfaces with specific emphasis on polymer melts. To study this, the author has developed a special methodology called anemometry near walls. The second main topic presented in the book is the problem of adhesion. Molecular processes, energy transformations and electrostatic interaction are included in an interesting discussion of the many aspects of the principles of adhesion. The third topic concerns welding between two polymer surfaces, such as A/A and A/B interfaces. Of great worth is the presentation of various unsolved, open problems. The kind of topics and brevity of description indicate that this book is intended for a well prepared reader. However, for any reader it will present an interesting picture of how many mysterious processes are acting in the surrounding world and how these phenomena are perceived by a Nobel Laureate, who won that prize mainly for his investigations in this field. (book review)

  2. Interface Screenings

    DEFF Research Database (Denmark)

    Thomsen, Bodil Marie Stavning

    2015-01-01

    In Wim Wenders' film Until the End of the World (1991), three different diagrams for the visual integration of bodies are presented: 1) GPS tracking and mapping in a landscape, 2) video recordings layered with the memory perception of these recordings, and 3) data-created images from dreams...... and memories. From a transvisual perspective, the question is whether or not these (by now realized) diagrammatic modes involving the body in ubiquitous global media can be analysed in terms of the affects and events created in concrete interfaces. The examples used are filmic as felt sensations...

  3. Invasive Intraneural Interfaces: Foreign Body Reaction Issues

    Science.gov (United States)

    Lotti, Fiorenza; Ranieri, Federico; Vadalà, Gianluca; Zollo, Loredana; Di Pino, Giovanni

    2017-01-01

    Intraneural interfaces are stimulation/registration devices designed to couple the peripheral nervous system (PNS) with the environment. Over the last years, their use has increased in a wide range of applications, such as the control of a new generation of neural-interfaced prostheses. At present, the success of this technology is limited by an electrical impedance increase, due to an inflammatory response called foreign body reaction (FBR), which leads to the formation of a fibrotic tissue around the interface, eventually causing an inefficient transduction of the electrical signal. Based on recent developments in biomaterials and inflammatory/fibrotic pathologies, we explore and select the biological solutions that might be adopted in the neural interfaces FBR context: modifications of the interface surface, such as organic and synthetic coatings; the use of specific drugs or molecular biology tools to target the microenvironment around the interface; the development of bio-engineered-scaffold to reduce immune response and promote interface-tissue integration. By linking what we believe are the major crucial steps of the FBR process with related solutions, we point out the main issues that future research has to focus on: biocompatibility without losing signal conduction properties, good reproducible in vitro/in vivo models, drugs exhaustion and undesired side effects. The underlined pros and cons of proposed solutions show clearly the importance of a better understanding of all the molecular and cellular pathways involved and the need of a multi-target action based on a bio-engineered combination approach. PMID:28932181

  4. 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.

  5. Activity patterns of cultured neural networks on micro electrode arrays

    NARCIS (Netherlands)

    Rutten, Wim; van Pelt, J.

    2001-01-01

    A hybrid neuro-electronic interface is a cell-cultured micro electrode array, acting as a neural information transducer for stimulation and/or recording of neural activity in the brain or the spinal cord (ventral motor region or dorsal sensory region). It consists of an array of micro electrodes on

  6. Neural Parallel Engine: A toolbox for massively parallel neural signal processing.

    Science.gov (United States)

    Tam, Wing-Kin; Yang, Zhi

    2018-05-01

    Large-scale neural recordings provide detailed information on neuronal activities and can help elicit the underlying neural mechanisms of the brain. However, the computational burden is also formidable when we try to process the huge data stream generated by such recordings. In this study, we report the development of Neural Parallel Engine (NPE), a toolbox for massively parallel neural signal processing on graphical processing units (GPUs). It offers a selection of the most commonly used routines in neural signal processing such as spike detection and spike sorting, including advanced algorithms such as exponential-component-power-component (EC-PC) spike detection and binary pursuit spike sorting. We also propose a new method for detecting peaks in parallel through a parallel compact operation. Our toolbox is able to offer a 5× to 110× speedup compared with its CPU counterparts depending on the algorithms. A user-friendly MATLAB interface is provided to allow easy integration of the toolbox into existing workflows. Previous efforts on GPU neural signal processing only focus on a few rudimentary algorithms, are not well-optimized and often do not provide a user-friendly programming interface to fit into existing workflows. There is a strong need for a comprehensive toolbox for massively parallel neural signal processing. A new toolbox for massively parallel neural signal processing has been created. It can offer significant speedup in processing signals from large-scale recordings up to thousands of channels. Copyright © 2018 Elsevier B.V. All rights reserved.

  7. Museets interface

    DEFF Research Database (Denmark)

    Pold, Søren

    2007-01-01

    Søren Pold gør sig overvejelser med udgangspunkt i museumsprojekterne Kongedragter.dk og Stigombord.dk. Han argumenterer for, at udviklingen af internettets interfaces skaber nye måder at se, forstå og interagere med kulturen på. Brugerne får nye medievaner og perceptionsmønstre, der må medtænkes i...... tilrettelæggelsen af den fremtidige formidling. Samtidig får museets genstande en ny status som flygtige ikoner i det digitale rum, og alt i alt inviterer det til, at museerne kan forholde sig mere åbent og eksperimenterende til egen praksis og rolle som kulturinstitution....

  8. A Visible and Passive Millimeter Wave Image Fusion Algorithm Based on Pulse-Coupled Neural Network in Tetrolet Domain for Early Risk Warning

    Directory of Open Access Journals (Sweden)

    Yuanjiang Li

    2018-01-01

    Full Text Available An algorithm based on pulse-coupled neural network (PCNN constructed in the Tetrolet transform domain is proposed for the fusion of the visible and passive millimeter wave images in order to effectively identify concealed targets. The Tetrolet transform is applied to build the framework of the multiscale decomposition due to its high sparse degree. Meanwhile, a Laplacian pyramid is used to decompose the low-pass band of the Tetrolet transform for improving the approximation performance. In addition, the maximum criterion based on regional average gradient is applied to fuse the top layers along with selecting the maximum absolute values of the other layers. Furthermore, an improved PCNN model is employed to enhance the contour feature of the hidden targets and obtain the fusion results of the high-pass band based on the firing time. Finally, the inverse transform of Tetrolet is exploited to obtain the fused results. Some objective evaluation indexes, such as information entropy, mutual information, and QAB/F, are adopted for evaluating the quality of the fused images. The experimental results show that the proposed algorithm is superior to other image fusion algorithms.

  9. 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.

  10. Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS

    Directory of Open Access Journals (Sweden)

    Christopher Bergmeir

    2012-01-01

    Full Text Available Neural networks are important standard machine learning procedures for classification and regression. We describe the R package RSNNS that provides a convenient interface to the popular Stuttgart Neural Network Simulator SNNS. The main features are (a encapsulation of the relevant SNNS parts in a C++ class, for sequential and parallel usage of different networks, (b accessibility of all of the SNNSalgorithmic functionality from R using a low-level interface, and (c a high-level interface for convenient, R-style usage of many standard neural network procedures. The package also includes functions for visualization and analysis of the models and the training procedures, as well as functions for data input/output from/to the original SNNSfile formats.

  11. A NOS1 variant implicated in cognitive performance influences evoked neural responses during a high density EEG study of early visual perception.

    LENUS (Irish Health Repository)

    O'Donoghue, Therese

    2012-05-01

    The nitric oxide synthasase-1 gene (NOS1) has been implicated in mental disorders including schizophrenia and variation in cognition. The NOS1 variant rs6490121 identified in a genome wide association study of schizophrenia has recently been associated with variation in general intelligence and working memory in both patients and healthy participants. Whether this variant is also associated with variation in early sensory processing remains unclear.

  12. 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

  13. Interfaces habladas

    Directory of Open Access Journals (Sweden)

    María Teresa Soto Sanfiel

    2012-04-01

    Full Text Available Este artículo describe y piensa al fenómeno de las Interfaces habladas (IH desde variados puntos de vista y niveles de análisis. El texto se ha concebido con los objetivos específicos de: 1.- procurar una visión panorámica de aspectos de la producción y consumo comunicativo de las IH; 2.- ofrecer recomendaciones para su creación y uso eficaz, y 3.- llamar la atención sobre su proliferación e inspirar su estudio desde la comunicación. A pesar de la creciente presencia de las IF en nues-tras vidas cotidianas, hay ausencia de textos que las caractericen y analicen por sus aspectos comunicativos. El trabajo es pertinente porque el fenómeno significa un cambio respecto a estadios comunica-tivos precedentes con consecuencias en las concepciones intelectuales y emocionales de los usuarios. La proliferación de IH nos abre a nue-vas realidades comunicativas: hablamos con máquinas.

  14. 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

  15. A NOS1 variant implicated in cognitive performance influences evoked neural responses during a high density EEG study of early visual perception.

    Science.gov (United States)

    O'Donoghue, Therese; Morris, Derek W; Fahey, Ciara; Da Costa, Andreia; Foxe, John J; Hoerold, Doreen; Tropea, Daniela; Gill, Michael; Corvin, Aiden; Donohoe, Gary

    2012-05-01

    The nitric oxide synthasase-1 gene (NOS1) has been implicated in mental disorders including schizophrenia and variation in cognition. The NOS1 variant rs6490121 identified in a genome wide association study of schizophrenia has recently been associated with variation in general intelligence and working memory in both patients and healthy participants. Whether this variant is also associated with variation in early sensory processing remains unclear. We investigated differences in the P1 visual evoked potential in a high density EEG study of 54 healthy participants. Given both NOS1's association with cognition and recent evidence that cognitive performance and P1 response are correlated, we investigated whether NOS1's effect on P1 response was independent of its effects on cognition using CANTAB's spatial working memory (SWM) task. We found that carriers of the previously identified risk "G" allele showed significantly lower P1 responses than non-carriers. We also found that while P1 response and SWM performance were correlated, NOS1 continued to explain a significant proportion of variation in P1 response even when its effects on cognition were accounted for. The schizophrenia implicated NOS1 variants rs6490121 influences visual sensory processing as measured by the P1 response, either as part of the gene's pleiotropic effects on multiple aspects of brain function, or because of a primary influence on sensory processing that mediates the effects already seen in higher cognitive processes. Copyright © 2011 Wiley-Liss, Inc.

  16. 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

  17. A Brain–Spinal Interface Alleviating Gait Deficits after Spinal Cord Injury in Primates

    Science.gov (United States)

    Capogrosso, Marco; Milekovic, Tomislav; Borton, David; Wagner, Fabien; Moraud, Eduardo Martin; Mignardot, Jean-Baptiste; Buse, Nicolas; Gandar, Jerome; Barraud, Quentin; Xing, David; Rey, Elodie; Duis, Simone; Jianzhong, Yang; Ko, Wai Kin D.; Li, Qin; Detemple, Peter; Denison, Tim; Micera, Silvestro; Bezard, Erwan; Bloch, Jocelyne; Courtine, Grégoire

    2016-01-01

    Spinal cord injury disrupts the communication between the brain and the spinal circuits that orchestrate movement. To bypass the lesion, brain–computer interfaces1–3 have directly linked cortical activity to electrical stimulation of muscles, which have restored grasping abilities after hand paralysis1,4. Theoretically, this strategy could also restore control over leg muscle activity for walking5. However, replicating the complex sequence of individual muscle activation patterns underlying natural and adaptive locomotor movements poses formidable conceptual and technological challenges6,7. Recently, we showed in rats that epidural electrical stimulation of the lumbar spinal cord can reproduce the natural activation of synergistic muscle groups producing locomotion8–10. Here, we interfaced leg motor cortex activity with epidural electrical stimulation protocols to establish a brain–spinal interface that alleviated gait deficits after a spinal cord injury in nonhuman primates. Rhesus monkeys were implanted with an intracortical microelectrode array into the leg area of motor cortex; and a spinal cord stimulation system composed of a spatially selective epidural implant and a pulse generator with real-time triggering capabilities. We designed and implemented wireless control systems that linked online neural decoding of extension and flexion motor states with stimulation protocols promoting these movements. These systems allowed the monkeys to behave freely without any restrictions or constraining tethered electronics. After validation of the brain–spinal interface in intact monkeys, we performed a unilateral corticospinal tract lesion at the thoracic level. As early as six days post-injury and without prior training of the monkeys, the brain–spinal interface restored weight-bearing locomotion of the paralyzed leg on a treadmill and overground. The implantable components integrated in the brain–spinal interface have all been approved for investigational

  18. 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«.

  19. Dlx proteins position the neural plate border and determine adjacent cell fates.

    Science.gov (United States)

    Woda, Juliana M; Pastagia, Julie; Mercola, Mark; Artinger, Kristin Bruk

    2003-01-01

    The lateral border of the neural plate is a major source of signals that induce primary neurons, neural crest cells and cranial placodes as well as provide patterning cues to mesodermal structures such as somites and heart. Whereas secreted BMP, FGF and Wnt proteins influence the differentiation of neural and non-neural ectoderm, we show here that members of the Dlx family of transcription factors position the border between neural and non-neural ectoderm and are required for the specification of adjacent cell fates. Inhibition of endogenous Dlx activity in Xenopus embryos with an EnR-Dlx homeodomain fusion protein expands the neural plate into non-neural ectoderm tissue whereas ectopic activation of Dlx target genes inhibits neural plate differentiation. Importantly, the stereotypic pattern of border cell fates in the adjacent ectoderm is re-established only under conditions where the expanded neural plate abuts Dlx-positive non-neural ectoderm. Experiments in which presumptive neural plate was grafted to ventral ectoderm reiterate induction of neural crest and placodal lineages and also demonstrate that Dlx activity is required in non-neural ectoderm for the production of signals needed for induction of these cells. We propose that Dlx proteins regulate intercellular signaling across the interface between neural and non-neural ectoderm that is critical for inducing and patterning adjacent cell fates.

  20. 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.

  1. Auditory interfaces: The human perceiver

    Science.gov (United States)

    Colburn, H. Steven

    1991-01-01

    A brief introduction to the basic auditory abilities of the human perceiver with particular attention toward issues that may be important for the design of auditory interfaces is presented. The importance of appropriate auditory inputs to observers with normal hearing is probably related to the role of hearing as an omnidirectional, early warning system and to its role as the primary vehicle for communication of strong personal feelings.

  2. Interface Simulation Distances

    Directory of Open Access Journals (Sweden)

    Pavol Černý

    2012-10-01

    Full Text Available The classical (boolean notion of refinement for behavioral interfaces of system components is the alternating refinement preorder. In this paper, we define a distance for interfaces, called interface simulation distance. It makes the alternating refinement preorder quantitative by, intuitively, tolerating errors (while counting them in the alternating simulation game. We show that the interface simulation distance satisfies the triangle inequality, that the distance between two interfaces does not increase under parallel composition with a third interface, and that the distance between two interfaces can be bounded from above and below by distances between abstractions of the two interfaces. We illustrate the framework, and the properties of the distances under composition of interfaces, with two case studies.

  3. Recent Advances in Neural Recording Microsystems

    Directory of Open Access Journals (Sweden)

    Benoit Gosselin

    2011-04-01

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

  4. Neural prostheses in clinical applications--trends from precision mechanics towards biomedical microsystems in neurological rehabilitation.

    Science.gov (United States)

    Stieglitz, T; Schuettler, M; Koch, K P

    2004-04-01

    Neural prostheses partially restore body functions by technical nerve excitation after trauma or neurological diseases. External devices and implants have been developed since the early 1960s for many applications. Several systems have reached nowadays clinical practice: Cochlea implants help the deaf to hear, micturition is induced by bladder stimulators in paralyzed persons and deep brain stimulation helps patients with Parkinson's disease to participate in daily life again. So far, clinical neural prostheses are fabricated with means of precision mechanics. Since microsystem technology opens the opportunity to design and develop complex systems with a high number of electrodes to interface with the nervous systems, the opportunity for selective stimulation and complex implant scenarios seems to be feasible in the near future. The potentials and limitations with regard to biomedical microdevices are introduced and discussed in this paper. Target specifications are derived from existing implants and are discussed on selected applications that has been investigated in experimental research: a micromachined implant to interface a nerve stump with a sieve electrode, cuff electrodes with integrated electronics, and an epiretinal vision prosthesis.

  5. Neural crest cells: from developmental biology to clinical interventions.

    Science.gov (United States)

    Noisa, Parinya; Raivio, Taneli

    2014-09-01

    Neural crest cells are multipotent cells, which are specified in embryonic ectoderm in the border of neural plate and epiderm during early development by interconnection of extrinsic stimuli and intrinsic factors. Neural crest cells are capable of differentiating into various somatic cell types, including melanocytes, craniofacial cartilage and bone, smooth muscle, and peripheral nervous cells, which supports their promise for cell therapy. In this work, we provide a comprehensive review of wide aspects of neural crest cells from their developmental biology to applicability in medical research. We provide a simplified model of neural crest cell development and highlight the key external stimuli and intrinsic regulators that determine the neural crest cell fate. Defects of neural crest cell development leading to several human disorders are also mentioned, with the emphasis of using human induced pluripotent stem cells to model neurocristopathic syndromes. © 2014 Wiley Periodicals, Inc.

  6. Optimized Neural Network for Fault Diagnosis and Classification

    International Nuclear Information System (INIS)

    Elaraby, S.M.

    2005-01-01

    This paper presents a developed and implemented toolbox for optimizing neural network structure of fault diagnosis and classification. Evolutionary algorithm based on hierarchical genetic algorithm structure is used for optimization. The simplest feed-forward neural network architecture is selected. Developed toolbox has friendly user interface. Multiple solutions are generated. The performance and applicability of the proposed toolbox is verified with benchmark data patterns and accident diagnosis of Egyptian Second research reactor (ETRR-2)

  7. Ontogeny of avian thermoregulation from a neural point of view

    NARCIS (Netherlands)

    Baarendse, P.J.J.; Debonne, M.; Decuypere, M.P.; Kemp, B.; Brand, van den H.

    2007-01-01

    The ontogeny of thermoregulation differs among (avian) species, but in all species both neural and endocrinological processes are involved. In this review the neural processes in ontogeny of thermoregulation during the prenatal and early postnatal phase are discussed. Only in a few avian species

  8. Brain–muscle interface

    Indian Academy of Sciences (India)

    2011-05-16

    May 16, 2011 ... Clipboard: Brain–muscle interface: The next-generation BMI. Radhika Rajan Neeraj Jain ... Keywords. Assistive devices; brain–machine interface; motor cortex; paralysis; spinal cord injury ... Journal of Biosciences | News ...

  9. 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.

  10. 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 ...

  11. Garbage collector interface

    OpenAIRE

    Ive, Anders; Blomdell, Anders; Ekman, Torbjörn; Henriksson, Roger; Nilsson, Anders; Nilsson, Klas; Robertz, Sven

    2002-01-01

    The purpose of the presented garbage collector interface is to provide a universal interface for many different implementations of garbage collectors. This is to simplify the integration and exchange of garbage collectors, but also to support incremental, non-conservative, and thread safe implementations. Due to the complexity of the interface, it is aimed at code generators and preprocessors. Experiences from ongoing implementations indicate that the garbage collector interface successfully ...

  12. Microcomputer interfacing and applications

    CERN Document Server

    Mustafa, M A

    1990-01-01

    This is the applications guide to interfacing microcomputers. It offers practical non-mathematical solutions to interfacing problems in many applications including data acquisition and control. Emphasis is given to the definition of the objectives of the interface, then comparing possible solutions and producing the best interface for every situation. Dr Mustafa A Mustafa is a senior designer of control equipment and has written many technical articles and papers on the subject of computers and their application to control engineering.

  13. 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.

  14. Improving Neural Recording Technology at the Nanoscale

    Science.gov (United States)

    Ferguson, John Eric

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

  15. Interface magnons. Magnetic superstructure

    International Nuclear Information System (INIS)

    Djafari-Rouhani, B.; Dobrzynski, L.

    1975-01-01

    The localized magnons at an interface between two Heisenberg ferromagnets are studied with a simple model. The effect of the coupling at the interface on the existence condition for the localized modes, the dispersion laws and the possible occurrence of magnetic superstructures due to soft modes are investigated. Finally a comparison is made with the similar results obtained for interface phonons [fr

  16. Water at Interfaces

    DEFF Research Database (Denmark)

    Björneholm, Olle; Hansen, Martin Hangaard; Hodgson, Andrew

    2016-01-01

    The interfaces of neat water and aqueous solutions play a prominent role in many technological processes and in the environment. Examples of aqueous interfaces are ultrathin water films that cover most hydrophilic surfaces under ambient relative humidities, the liquid/solid interface which drives...

  17. User Interface History

    DEFF Research Database (Denmark)

    Jørgensen, Anker Helms; Myers, Brad A

    2008-01-01

    User Interfaces have been around as long as computers have existed, even well before the field of Human-Computer Interaction was established. Over the years, some papers on the history of Human-Computer Interaction and User Interfaces have appeared, primarily focusing on the graphical interface e...

  18. Graphical Interfaces for Simulation.

    Science.gov (United States)

    Hollan, J. D.; And Others

    This document presents a discussion of the development of a set of software tools to assist in the construction of interfaces to simulations and real-time systems. Presuppositions to the approach to interface design that was used are surveyed, the tools are described, and the conclusions drawn from these experiences in graphical interface design…

  19. 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...

  20. Neural decoding of collective wisdom with multi-brain computing.

    Science.gov (United States)

    Eckstein, Miguel P; Das, Koel; Pham, Binh T; Peterson, Matthew F; Abbey, Craig K; Sy, Jocelyn L; Giesbrecht, Barry

    2012-01-02

    Group decisions and even aggregation of multiple opinions lead to greater decision accuracy, a phenomenon known as collective wisdom. Little is known about the neural basis of collective wisdom and whether its benefits arise in late decision stages or in early sensory coding. Here, we use electroencephalography and multi-brain computing with twenty humans making perceptual decisions to show that combining neural activity across brains increases decision accuracy paralleling the improvements shown by aggregating the observers' opinions. Although the largest gains result from an optimal linear combination of neural decision variables across brains, a simpler neural majority decision rule, ubiquitous in human behavior, results in substantial benefits. In contrast, an extreme neural response rule, akin to a group following the most extreme opinion, results in the least improvement with group size. Analyses controlling for number of electrodes and time-points while increasing number of brains demonstrate unique benefits arising from integrating neural activity across different brains. The benefits of multi-brain integration are present in neural activity as early as 200 ms after stimulus presentation in lateral occipital sites and no additional benefits arise in decision related neural activity. Sensory-related neural activity can predict collective choices reached by aggregating individual opinions, voting results, and decision confidence as accurately as neural activity related to decision components. Estimation of the potential for the collective to execute fast decisions by combining information across numerous brains, a strategy prevalent in many animals, shows large time-savings. Together, the findings suggest that for perceptual decisions the neural activity supporting collective wisdom and decisions arises in early sensory stages and that many properties of collective cognition are explainable by the neural coding of information across multiple brains. Finally

  1. The Brain Computer Interface Future: Time for a Strategy

    Science.gov (United States)

    2013-02-14

    neural processing software developer Mind Technologies, Geger Technologies in Austria and the Sony Corporation in Japan. The WTEC report in 2007...managing photos, video, web surfing, and music , and although in their infancy, researchers have used a web browser interface to control Google...society as a whole. The prospect of BCI entertainment , neuroprostheses, online neuroresearching and marketing, and cognitive performance enhancement

  2. Quantization of interface currents

    Energy Technology Data Exchange (ETDEWEB)

    Kotani, Motoko [AIMR, Tohoku University, Sendai (Japan); Schulz-Baldes, Hermann [Department Mathematik, Universität Erlangen-Nürnberg, Erlangen (Germany); Villegas-Blas, Carlos [Instituto de Matematicas, Cuernavaca, UNAM, Cuernavaca (Mexico)

    2014-12-15

    At the interface of two two-dimensional quantum systems, there may exist interface currents similar to edge currents in quantum Hall systems. It is proved that these interface currents are macroscopically quantized by an integer that is given by the difference of the Chern numbers of the two systems. It is also argued that at the interface between two time-reversal invariant systems with half-integer spin, one of which is trivial and the other non-trivial, there are dissipationless spin-polarized interface currents.

  3. Water at Interfaces.

    Science.gov (United States)

    Björneholm, Olle; Hansen, Martin H; Hodgson, Andrew; Liu, Li-Min; Limmer, David T; Michaelides, Angelos; Pedevilla, Philipp; Rossmeisl, Jan; Shen, Huaze; Tocci, Gabriele; Tyrode, Eric; Walz, Marie-Madeleine; Werner, Josephina; Bluhm, Hendrik

    2016-07-13

    The interfaces of neat water and aqueous solutions play a prominent role in many technological processes and in the environment. Examples of aqueous interfaces are ultrathin water films that cover most hydrophilic surfaces under ambient relative humidities, the liquid/solid interface which drives many electrochemical reactions, and the liquid/vapor interface, which governs the uptake and release of trace gases by the oceans and cloud droplets. In this article we review some of the recent experimental and theoretical advances in our knowledge of the properties of aqueous interfaces and discuss open questions and gaps in our understanding.

  4. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm

    OpenAIRE

    Hochberg, Leigh R.; Bacher, Daniel; Jarosiewicz, Beata; Masse, Nicolas Y.; Simeral, John D.; Vogel, Joern; Haddadin, Sami; Liu, Jie; Cash, Sydney S.; van der Smagt, Patrick; Donoghue, John P.

    2012-01-01

    Paralysis following spinal cord injury, brainstemstroke, amyotrophic lateral sclerosis and other disorders can disconnect the brain from the body, eliminating the ability to perform volitional movements. A neural interface system could restore mobility and independence for people with paralysis by translating neuronal activity directly into control signals for assistive devices. We have previously shown that people with long-standing tetraplegia can use a neural interface ...

  5. Drosophila olfactory memory: single genes to complex neural circuits.

    Science.gov (United States)

    Keene, Alex C; Waddell, Scott

    2007-05-01

    A central goal of neuroscience is to understand how neural circuits encode memory and guide behaviour. Studying simple, genetically tractable organisms, such as Drosophila melanogaster, can illuminate principles of neural circuit organization and function. Early genetic dissection of D. melanogaster olfactory memory focused on individual genes and molecules. These molecular tags subsequently revealed key neural circuits for memory. Recent advances in genetic technology have allowed us to manipulate and observe activity in these circuits, and even individual neurons, in live animals. The studies have transformed D. melanogaster from a useful organism for gene discovery to an ideal model to understand neural circuit function in memory.

  6. Shape-changing interfaces:

    DEFF Research Database (Denmark)

    Rasmussen, Majken Kirkegård; Pedersen, Esben Warming; Petersen, Marianne Graves

    2015-01-01

    Shape change is increasingly used in physical user interfaces, both as input and output. Yet, the progress made and the key research questions for shape-changing interfaces are rarely analyzed systematically. We review a sample of existing work on shape-changing interfaces to address these shortc......Shape change is increasingly used in physical user interfaces, both as input and output. Yet, the progress made and the key research questions for shape-changing interfaces are rarely analyzed systematically. We review a sample of existing work on shape-changing interfaces to address...... these shortcomings. We identify eight types of shape that are transformed in various ways to serve both functional and hedonic design purposes. Interaction with shape-changing interfaces is simple and rarely merges input and output. Three questions are discussed based on the review: (a) which design purposes may...

  7. 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.

  8. Kernel Temporal Differences for Neural Decoding

    Science.gov (United States)

    Bae, Jihye; Sanchez Giraldo, Luis G.; Pohlmeyer, Eric A.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.

    2015-01-01

    We study the feasibility and capability of the kernel temporal difference (KTD)(λ) algorithm for neural decoding. KTD(λ) is an online, kernel-based learning algorithm, which has been introduced to estimate value functions in reinforcement learning. This algorithm combines kernel-based representations with the temporal difference approach to learning. One of our key observations is that by using strictly positive definite kernels, algorithm's convergence can be guaranteed for policy evaluation. The algorithm's nonlinear functional approximation capabilities are shown in both simulations of policy evaluation and neural decoding problems (policy improvement). KTD can handle high-dimensional neural states containing spatial-temporal information at a reasonable computational complexity allowing real-time applications. When the algorithm seeks a proper mapping between a monkey's neural states and desired positions of a computer cursor or a robot arm, in both open-loop and closed-loop experiments, it can effectively learn the neural state to action mapping. Finally, a visualization of the coadaptation process between the decoder and the subject shows the algorithm's capabilities in reinforcement learning brain machine interfaces. PMID:25866504

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

    Directory of Open Access Journals (Sweden)

    Wilfredo Blanco

    2017-09-01

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

  10. Biomechatronics in medical rehabilitation biomodelling, interface, and control

    CERN Document Server

    Xie, Shane (S Q )

    2017-01-01

    This book focuses on the key technologies in developing biomechatronic systems for medical rehabilitation purposes. It includes a detailed analysis of biosignal processing, biomechanics modelling, neural and muscular interfaces, artificial actuators, robot-assisted training, clinical setup/implementation and rehabilitation robot control. Encompassing highly multidisciplinary themes in the engineering and medical fields, it presents researchers’ insights into the emerging technologies and developments that are being utilized in biomechatronics for medical purposes. Presenting a detailed analysis of five key areas in rehabilitation robotics: (i) biosignal processing; (ii) biomechanics modelling; (iii) neural and muscular interfaces; (iv) artificial actuators and devices; and (v) the use of neurological and muscular interfaces in rehabilitation robots control, the book describes the design of biomechatronic systems, the methods and control systems used and the implementation and testing in order to show how th...

  11. Flexible Regenerative Nanoelectronics for Advanced Peripheral Neural Interfaces

    Science.gov (United States)

    2017-10-01

    photolithography on a nickel metal release layer deposited on a silicon substrate (900  nm SiO2 , n-type 0.005  V·cm, University Wafer). SU-8...electrode from (b). (e) Mesh electrode released from glass wafer, suspended in water. Subtask 1.2. Validation of mesh electrode properties. With the

  12. 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)

  13. Structural Analysis of Three-dimensional Human Neural Tissue derived from Induced Pluripotent Stem Cells

    DEFF Research Database (Denmark)

    Terrence Brooks, Patrick; Rasmussen, Mikkel Aabech; Hyttel, Poul

    2016-01-01

    Objective: The present study aimed at establishing a method for production of a three-dimensional (3D) human neural tissue derived from induced pluripotent stem cells (iPSCs) and analyzing the outcome by a combination of tissue ultrastructure and expression of neural markers. Methods: A two......-step cell culture procedure was implemented by subjecting human iPSCs to a 3D scaffoldbased neural differentiation protocol. First, neural fate-inducing small molecules were used to create a neuroepithelial monolayer. Second, the monolayer was trypsinized into single cells and seeded into a porous...... polystyrene scaffold and further cultured to produce a 3D neural tissue. The neural tissue was characterized by a combination of immunohistochemistry and transmission electron microscopy (TEM). Results: iPSCs developed into a 3D neural tissue expressing markers for neural progenitor cells, early neural...

  14. A Cognitive Neural Architecture Able to Learn and Communicate through Natural Language.

    Directory of Open Access Journals (Sweden)

    Bruno Golosio

    Full Text Available Communicative interactions involve a kind of procedural knowledge that is used by the human brain for processing verbal and nonverbal inputs and for language production. Although considerable work has been done on modeling human language abilities, it has been difficult to bring them together to a comprehensive tabula rasa system compatible with current knowledge of how verbal information is processed in the brain. This work presents a cognitive system, entirely based on a large-scale neural architecture, which was developed to shed light on the procedural knowledge involved in language elaboration. The main component of this system is the central executive, which is a supervising system that coordinates the other components of the working memory. In our model, the central executive is a neural network that takes as input the neural activation states of the short-term memory and yields as output mental actions, which control the flow of information among the working memory components through neural gating mechanisms. The proposed system is capable of learning to communicate through natural language starting from tabula rasa, without any a priori knowledge of the structure of phrases, meaning of words, role of the different classes of words, only by interacting with a human through a text-based interface, using an open-ended incremental learning process. It is able to learn nouns, verbs, adjectives, pronouns and other word classes, and to use them in expressive language. The model was validated on a corpus of 1587 input sentences, based on literature on early language assessment, at the level of about 4-years old child, and produced 521 output sentences, expressing a broad range of language processing functionalities.

  15. Diffusion between evolving interfaces

    International Nuclear Information System (INIS)

    Juntunen, Janne; Merikoski, Juha

    2010-01-01

    Diffusion in an evolving environment is studied by continuous-time Monte Carlo simulations. Diffusion is modeled by continuous-time random walkers on a lattice, in a dynamic environment provided by bubbles between two one-dimensional interfaces driven symmetrically towards each other. For one-dimensional random walkers constrained by the interfaces, the bubble size distribution dominates diffusion. For two-dimensional random walkers, it is also controlled by the topography and dynamics of the interfaces. The results of the one-dimensional case are recovered in the limit where the interfaces are strongly driven. Even with simple hard-core repulsion between the interfaces and the particles, diffusion is found to depend strongly on the details of the dynamical rules of particles close to the interfaces.

  16. User interface support

    Science.gov (United States)

    Lewis, Clayton; Wilde, Nick

    1989-01-01

    Space construction will require heavy investment in the development of a wide variety of user interfaces for the computer-based tools that will be involved at every stage of construction operations. Using today's technology, user interface development is very expensive for two reasons: (1) specialized and scarce programming skills are required to implement the necessary graphical representations and complex control regimes for high-quality interfaces; (2) iteration on prototypes is required to meet user and task requirements, since these are difficult to anticipate with current (and foreseeable) design knowledge. We are attacking this problem by building a user interface development tool based on extensions to the spreadsheet model of computation. The tool provides high-level support for graphical user interfaces and permits dynamic modification of interfaces, without requiring conventional programming concepts and skills.

  17. Complex Interfaces Under Change

    DEFF Research Database (Denmark)

    Rosbjerg, Dan

    The hydrosphere is dynamic across the major compartments of the Earth system: the atmosphere, the oceans and seas, the land surface water, and the groundwater within the strata below the two last compartments. The global geography of the hydrosphere essentially depends on thermodynamic and mechan...... these interfaces and interfaced compartments and processes. Climate, sea-level, oceanographic currents and hydrological processes are all affected, while anthropogenic changes are often intense in the geographic settings corresponding to such interfaces....... and mechanical processes that develop within this structure. Water-related processes at the interfaces between the compartments are complex, depending both on the interface itself, and on the characteristics of the interfaced compartments. Various aspects of global change directly or indirectly impact...

  18. Refinement by interface instantiation

    DEFF Research Database (Denmark)

    Hallerstede, Stefan; Hoang, Thai Son

    2012-01-01

    be easily refined. Our first contribution hence is a proposal for a new construct called interface that encapsulates the external variables, along with a mechanism for interface instantiation. Using the new construct and mechanism, external variables can be refined consistently. Our second contribution...... is an approach for verifying the correctness of Event-B extensions using the supporting Rodin tool. We illustrate our approach by proving the correctness of interface instantiation....

  19. Universal computer interfaces

    CERN Document Server

    Dheere, RFBM

    1988-01-01

    Presents a survey of the latest developments in the field of the universal computer interface, resulting from a study of the world patent literature. Illustrating the state of the art today, the book ranges from basic interface structure, through parameters and common characteristics, to the most important industrial bus realizations. Recent technical enhancements are also included, with special emphasis devoted to the universal interface adapter circuit. Comprehensively indexed.

  20. Man machine interface based on speech recognition

    International Nuclear Information System (INIS)

    Jorge, Carlos A.F.; Aghina, Mauricio A.C.; Mol, Antonio C.A.; Pereira, Claudio M.N.A.

    2007-01-01

    This work reports the development of a Man Machine Interface based on speech recognition. The system must recognize spoken commands, and execute the desired tasks, without manual interventions of operators. The range of applications goes from the execution of commands in an industrial plant's control room, to navigation and interaction in virtual environments. Results are reported for isolated word recognition, the isolated words corresponding to the spoken commands. For the pre-processing stage, relevant parameters are extracted from the speech signals, using the cepstral analysis technique, that are used for isolated word recognition, and corresponds to the inputs of an artificial neural network, that performs recognition tasks. (author)

  1. Electromagnetic Interface Testing Facility

    Data.gov (United States)

    Federal Laboratory Consortium — The Electromagnetic Interface Testing facilitysupports such testing asEmissions, Field Strength, Mode Stirring, EMP Pulser, 4 Probe Monitoring/Leveling System, and...

  2. Automatic Speech Recognition from Neural Signals: A Focused Review

    Directory of Open Access Journals (Sweden)

    Christian Herff

    2016-09-01

    Full Text Available Speech interfaces have become widely accepted and are nowadays integrated in various real-life applications and devices. They have become a part of our daily life. However, speech interfaces presume the ability to produce intelligible speech, which might be impossible due to either loud environments, bothering bystanders or incapabilities to produce speech (i.e.~patients suffering from locked-in syndrome. For these reasons it would be highly desirable to not speak but to simply envision oneself to say words or sentences. Interfaces based on imagined speech would enable fast and natural communication without the need for audible speech and would give a voice to otherwise mute people.This focused review analyzes the potential of different brain imaging techniques to recognize speech from neural signals by applying Automatic Speech Recognition technology. We argue that modalities based on metabolic processes, such as functional Near Infrared Spectroscopy and functional Magnetic Resonance Imaging, are less suited for Automatic Speech Recognition from neural signals due to low temporal resolution but are very useful for the investigation of the underlying neural mechanisms involved in speech processes. In contrast, electrophysiologic activity is fast enough to capture speech processes and is therefor better suited for ASR. Our experimental results indicate the potential of these signals for speech recognition from neural data with a focus on invasively measured brain activity (electrocorticography. As a first example of Automatic Speech Recognition techniques used from neural signals, we discuss the emph{Brain-to-text} system.

  3. 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).

  4. Solar wind stream interfaces

    International Nuclear Information System (INIS)

    Gosling, J.T.; Asbridge, J.R.; Bame, S.J.; Feldman, W.C.

    1978-01-01

    Measurements aboard Imp 6, 7, and 8 reveal that approximately one third of all high-speed solar wind streams observed at 1 AU contain a sharp boundary (of thickness less than approx.4 x 10 4 km) near their leading edge, called a stream interface, which separates plasma of distinctly different properties and origins. Identified as discontinuities across which the density drops abruptly, the proton temperature increases abruptly, and the speed rises, stream interfaces are remarkably similar in character from one stream to the next. A superposed epoch analysis of plasma data has been performed for 23 discontinuous stream interfaces observed during the interval March 1971 through August 1974. Among the results of this analysis are the following: (1) a stream interface separates what was originally thick (i.e., dense) slow gas from what was originally thin (i.e., rare) fast gas; (2) the interface is the site of a discontinuous shear in the solar wind flow in a frame of reference corotating with the sun; (3) stream interfaces occur at speeds less than 450 km s - 1 and close to or at the maximum of the pressure ridge at the leading edges of high-speed streams; (4) a discontinuous rise by approx.40% in electron temperature occurs at the interface; and (5) discontinuous changes (usually rises) in alpha particle abundance and flow speed relative to the protons occur at the interface. Stream interfaces do not generally recur on successive solar rotations, even though the streams in which they are embedded often do. At distances beyond several astronomical units, stream interfaces should be bounded by forward-reverse shock pairs; three of four reverse shocks observed at 1 AU during 1971--1974 were preceded within approx.1 day by stream interfaces. Our observations suggest that many streams close to the sun are bounded on all sides by large radial velocity shears separating rapidly expanding plasma from more slowly expanding plasma

  5. 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...

  6. 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...

  7. Visual Prosthesis: Interfacing Stimulating Electrodes with Retinal Neurons to Restore Vision

    Directory of Open Access Journals (Sweden)

    Alejandro Barriga-Rivera

    2017-11-01

    Full Text Available The bypassing of degenerated photoreceptors using retinal neurostimulators is helping the blind to recover functional vision. Researchers are investigating new ways to improve visual percepts elicited by these means as the vision produced by these early devices remain rudimentary. However, several factors are hampering the progression of bionic technologies: the charge injection limits of metallic electrodes, the mechanical mismatch between excitable tissue and the stimulating elements, neural and electric crosstalk, the physical size of the implanted devices, and the inability to selectively activate different types of retinal neurons. Electrochemical and mechanical limitations are being addressed by the application of electromaterials such as conducting polymers, carbon nanotubes and nanocrystalline diamonds, among other biomaterials, to electrical neuromodulation. In addition, the use of synthetic hydrogels and cell-laden biomaterials is promising better interfaces, as it opens a door to establishing synaptic connections between the electrode material and the excitable cells. Finally, new electrostimulation approaches relying on the use of high-frequency stimulation and field overlapping techniques are being developed to better replicate the neural code of the retina. All these elements combined will bring bionic vision beyond its present state and into the realm of a viable, mainstream therapy for vision loss.

  8. Interface, a dispersed architecture

    NARCIS (Netherlands)

    Vissers, C.A.

    1976-01-01

    Past and current specification techniques use timing diagrams and written text to describe the phenomenology of an interface. This paper treats an interface as the architecture of a number of processes, which are dispersed over the related system parts and the message path. This approach yields a

  9. Icinga Monitoring System Interface

    CERN Document Server

    Neculae, Alina Georgiana

    2014-01-01

    The aim of this project is to develop a web interface that would be used by the Icinga monitoring system to manage the CMS online cluster, in the experimental site. The interface would allow users to visualize the information in a compressed and intuitive way, as well as modify the information of each individual object and edit the relationships between classes.

  10. Verden som interface

    DEFF Research Database (Denmark)

    2007-01-01

    Oversættelse af Peter Weibels tekst "The World as Interface" i Passepartout # 27. Interfacekulturens æstetik. Udgivelsesdato: 28.04.07......Oversættelse af Peter Weibels tekst "The World as Interface" i Passepartout # 27. Interfacekulturens æstetik. Udgivelsesdato: 28.04.07...

  11. Ecological Interface Design

    DEFF Research Database (Denmark)

    Vicente, Kim J.; Rasmussen, Jens

    1992-01-01

    A theoretical framework for designing interfaces for complex human-machine systems is proposed. The framework, called ecological interface design (EID), is based on the skills, rules, knowledge taxonomy of cognitive control. The basic goal of EID is twofold: first, not to force processing...

  12. Engineering Musculoskeletal Tissue Interfaces

    Directory of Open Access Journals (Sweden)

    Ece Bayrak

    2018-04-01

    Full Text Available Tissue engineering aims to bring together biomaterials, cells, and signaling molecules within properly designed microenvironments in order to create viable treatment options for the lost or malfunctioning tissues. Design and production of scaffolds and cell-laden grafts that mimic the complex structural and functional features of tissues are among the most important elements of tissue engineering strategy. Although all tissues have their own complex structure, an even more complex case in terms of engineering a proper carrier material is encountered at the tissue interfaces, where two distinct tissues come together. The interfaces in the body can be examined in four categories; cartilage-bone and ligament-bone interfaces at the knee and the spine, tendon-bone interfaces at the shoulder and the feet, and muscle-tendon interface at the skeletal system. These interfaces are seen mainly at the soft-to-hard tissue transitions and they are especially susceptible to injury and tear due to the biomechanical inconsistency between these tissues where high strain fields are present. Therefore, engineering the musculoskeletal tissue interfaces remain a challenge. This review focuses on recent advancements in strategies for musculoskeletal interface engineering using different biomaterial-based platforms and surface modification techniques.

  13. Adaptive user interfaces

    CERN Document Server

    1990-01-01

    This book describes techniques for designing and building adaptive user interfaces developed in the large AID project undertaken by the contributors.Key Features* Describes one of the few large-scale adaptive interface projects in the world* Outlines the principles of adaptivity in human-computer interaction

  14. Interface colloidal robotic manipulator

    Science.gov (United States)

    Aronson, Igor; Snezhko, Oleksiy

    2015-08-04

    A magnetic colloidal system confined at the interface between two immiscible liquids and energized by an alternating magnetic field dynamically self-assembles into localized asters and arrays of asters. The colloidal system exhibits locomotion and shape change. By controlling a small external magnetic field applied parallel to the interface, structures can capture, transport, and position target particles.

  15. 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

  16. Brain machine interfaces combining microelectrode arrays with nanostructured optical biochemical sensors

    Science.gov (United States)

    Hajj-Hassan, Mohamad; Gonzalez, Timothy; Ghafer-Zadeh, Ebrahim; Chodavarapu, Vamsy; Musallam, Sam; Andrews, Mark

    2009-02-01

    Neural microelectrodes are an important component of neural prosthetic systems which assist paralyzed patients by allowing them to operate computers or robots using their neural activity. These microelectrodes are also used in clinical settings to localize the locus of seizure initiation in epilepsy or to stimulate sub-cortical structures in patients with Parkinson's disease. In neural prosthetic systems, implanted microelectrodes record the electrical potential generated by specific thoughts and relay the signals to algorithms trained to interpret these thoughts. In this paper, we describe novel elongated multi-site neural electrodes that can record electrical signals and specific neural biomarkers and that can reach depths greater than 8mm in the sulcus of non-human primates (monkeys). We hypothesize that additional signals recorded by the multimodal probes will increase the information yield when compared to standard probes that record just electropotentials. We describe integration of optical biochemical sensors with neural microelectrodes. The sensors are made using sol-gel derived xerogel thin films that encapsulate specific biomarker responsive luminophores in their nanostructured pores. The desired neural biomarkers are O2, pH, K+, and Na+ ions. As a prototype, we demonstrate direct-write patterning to create oxygen-responsive xerogel waveguide structures on the neural microelectrodes. The recording of neural biomarkers along with electrical activity could help the development of intelligent and more userfriendly neural prosthesis/brain machine interfaces as well as aid in providing answers to complex brain diseases and disorders.

  17. Prototyping of user interfaces for mobile applications

    CERN Document Server

    Bähr, Benjamin

    2017-01-01

    This book investigates processes for the prototyping of user interfaces for mobile apps, and describes the development of new concepts and tools that can improve the prototype driven app development in the early stages. It presents the development and evaluation of a new requirements catalogue for prototyping mobile app tools that identifies the most important criteria such tools should meet at different prototype-development stages. This catalogue is not just a good point of orientation for designing new prototyping approaches, but also provides a set of metrics for a comparing the performance of alternative prototyping tools. In addition, the book discusses the development of Blended Prototyping, a new approach for prototyping user interfaces for mobile applications in the early and middle development stages, and presents the results of an evaluation of its performance, showing that it provides a tool for teamwork-oriented, creative prototyping of mobile apps in the early design stages.

  18. Modelling collective cell migration of neural crest.

    Science.gov (United States)

    Szabó, András; Mayor, Roberto

    2016-10-01

    Collective cell migration has emerged in the recent decade as an important phenomenon in cell and developmental biology and can be defined as the coordinated and cooperative movement of groups of cells. Most studies concentrate on tightly connected epithelial tissues, even though collective migration does not require a constant physical contact. Movement of mesenchymal cells is more independent, making their emergent collective behaviour less intuitive and therefore lending importance to computational modelling. Here we focus on such modelling efforts that aim to understand the collective migration of neural crest cells, a mesenchymal embryonic population that migrates large distances as a group during early vertebrate development. By comparing different models of neural crest migration, we emphasize the similarity and complementary nature of these approaches and suggest a future direction for the field. The principles derived from neural crest modelling could aid understanding the collective migration of other mesenchymal cell types. Copyright © 2016 Elsevier Ltd. All rights reserved.

  19. ANNarchy: a code generation approach to neural simulations on parallel hardware

    Science.gov (United States)

    Vitay, Julien; Dinkelbach, Helge Ü.; Hamker, Fred H.

    2015-01-01

    Many modern neural simulators focus on the simulation of networks of spiking neurons on parallel hardware. Another important framework in computational neuroscience, rate-coded neural networks, is mostly difficult or impossible to implement using these simulators. We present here the ANNarchy (Artificial Neural Networks architect) neural simulator, which allows to easily define and simulate rate-coded and spiking networks, as well as combinations of both. The interface in Python has been designed to be close to the PyNN interface, while the definition of neuron and synapse models can be specified using an equation-oriented mathematical description similar to the Brian neural simulator. This information is used to generate C++ code that will efficiently perform the simulation on the chosen parallel hardware (multi-core system or graphical processing unit). Several numerical methods are available to transform ordinary differential equations into an efficient C++code. We compare the parallel performance of the simulator to existing solutions. PMID:26283957

  20. 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…

  1. 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...

  2. The Shape of Things to Come: The Military Benefits of the Brain-Computer Interface in 2040

    Science.gov (United States)

    2015-04-01

    capability. Bidirectional interfaces with the ability to influence specific neural groups will not only revolutionize health-care, but transform how the...both read and stimulate neural activity. Unidirectional BCIs are useful; however, it is the bidirectional device that opens the potential for the...connected intra-cranially distributed networks that communicate with thousands of ‘ neural reading & stimulating’ devices that could be safely inserted

  3. Pax7 lineage contributions to the mammalian neural crest.

    Directory of Open Access Journals (Sweden)

    Barbara Murdoch

    Full Text Available Neural crest cells are vertebrate-specific multipotent cells that contribute to a variety of tissues including the peripheral nervous system, melanocytes, and craniofacial bones and cartilage. Abnormal development of the neural crest is associated with several human maladies including cleft/lip palate, aggressive cancers such as melanoma and neuroblastoma, and rare syndromes, like Waardenburg syndrome, a complex disorder involving hearing loss and pigment defects. We previously identified the transcription factor Pax7 as an early marker, and required component for neural crest development in chick embryos. In mammals, Pax7 is also thought to play a role in neural crest development, yet the precise contribution of Pax7 progenitors to the neural crest lineage has not been determined.Here we use Cre/loxP technology in double transgenic mice to fate map the Pax7 lineage in neural crest derivates. We find that Pax7 descendants contribute to multiple tissues including the cranial, cardiac and trunk neural crest, which in the cranial cartilage form a distinct regional pattern. The Pax7 lineage, like the Pax3 lineage, is additionally detected in some non-neural crest tissues, including a subset of the epithelial cells in specific organs.These results demonstrate a previously unappreciated widespread distribution of Pax7 descendants within and beyond the neural crest. They shed light regarding the regionally distinct phenotypes observed in Pax3 and Pax7 mutants, and provide a unique perspective into the potential roles of Pax7 during disease and development.

  4. Neural engineering from advanced biomaterials to 3D fabrication techniques

    CERN Document Server

    Kaplan, David

    2016-01-01

    This book covers the principles of advanced 3D fabrication techniques, stem cells and biomaterials for neural engineering. Renowned contributors cover topics such as neural tissue regeneration, peripheral and central nervous system repair, brain-machine interfaces and in vitro nervous system modeling. Within these areas, focus remains on exciting and emerging technologies such as highly developed neuroprostheses and the communication channels between the brain and prostheses, enabling technologies that are beneficial for development of therapeutic interventions, advanced fabrication techniques such as 3D bioprinting, photolithography, microfluidics, and subtractive fabrication, and the engineering of implantable neural grafts. There is a strong focus on stem cells and 3D bioprinting technologies throughout the book, including working with embryonic, fetal, neonatal, and adult stem cells and a variety of sophisticated 3D bioprinting methods for neural engineering applications. There is also a strong focus on b...

  5. The Java Legacy Interface

    DEFF Research Database (Denmark)

    Korsholm, Stephan

    2007-01-01

    The Java Legacy Interface is designed to use Java for encapsulating native legacy code on small embedded platforms. We discuss why existing technologies for encapsulating legacy code (JNI) is not sufficient for an important range of small embedded platforms, and we show how the Java Legacy...... Interface offers this previously missing functionality. We describe an implementation of the Java Legacy Interface for a particular virtual machine, and how we have used this virtual machine to integrate Java with an existing, commercial, soft real-time, C/C++ legacy platform....

  6. Operator interface for vehicles

    Science.gov (United States)

    Bissontz, Jay E

    2015-03-10

    A control interface for drivetrain braking provided by a regenerative brake and a non-regenerative brake is implemented using a combination of switches and graphic interface elements. The control interface comprises a control system for allocating drivetrain braking effort between the regenerative brake and the non-regenerative brake, a first operator actuated control for enabling operation of the drivetrain braking, and a second operator actuated control for selecting a target braking effort for drivetrain braking. A graphic display displays to an operator the selected target braking effort and can be used to further display actual braking effort achieved by drivetrain braking.

  7. The interface effect

    CERN Document Server

    Galloway, Alexander R

    2013-01-01

    Interfaces are back, or perhaps they never left. The familiar Socratic conceit from the Phaedrus, of communication as the process of writing directly on the soul of the other, has returned to center stage in today's discussions of culture and media. Indeed Western thought has long construed media as a grand choice between two kinds of interfaces. Following the optimistic path, media seamlessly interface self and other in a transparent and immediate connection. But, following the pessimistic path, media are the obstacles to direct communion, disintegrating self and other into misunderstanding

  8. The computer graphics interface

    CERN Document Server

    Steinbrugge Chauveau, Karla; Niles Reed, Theodore; Shepherd, B

    2014-01-01

    The Computer Graphics Interface provides a concise discussion of computer graphics interface (CGI) standards. The title is comprised of seven chapters that cover the concepts of the CGI standard. Figures and examples are also included. The first chapter provides a general overview of CGI; this chapter covers graphics standards, functional specifications, and syntactic interfaces. Next, the book discusses the basic concepts of CGI, such as inquiry, profiles, and registration. The third chapter covers the CGI concepts and functions, while the fourth chapter deals with the concept of graphic obje

  9. Project Interface Requirements Process Including Shuttle Lessons Learned

    Science.gov (United States)

    Bauch, Garland T.

    2010-01-01

    Most failures occur at interfaces between organizations and hardware. Processing interface requirements at the start of a project life cycle will reduce the likelihood of costly interface changes/failures later. This can be done by adding Interface Control Documents (ICDs) to the Project top level drawing tree, providing technical direction to the Projects for interface requirements, and by funding the interface requirements function directly from the Project Manager's office. The interface requirements function within the Project Systems Engineering and Integration (SE&I) Office would work in-line with the project element design engineers early in the life cycle to enhance communications and negotiate technical issues between the elements. This function would work as the technical arm of the Project Manager to help ensure that the Project cost, schedule, and risk objectives can be met during the Life Cycle. Some ICD Lessons Learned during the Space Shuttle Program (SSP) Life Cycle will include the use of hardware interface photos in the ICD, progressive life cycle design certification by analysis, test, & operations experience, assigning interface design engineers to Element Interface (EI) and Project technical panels, and linking interface design drawings with project build drawings

  10. Rodent Zic Genes in Neural Network Wiring.

    Science.gov (United States)

    Herrera, Eloísa

    2018-01-01

    The formation of the nervous system is a multistep process that yields a mature brain. Failure in any of the steps of this process may cause brain malfunction. In the early stages of embryonic development, neural progenitors quickly proliferate and then, at a specific moment, differentiate into neurons or glia. Once they become postmitotic neurons, they migrate to their final destinations and begin to extend their axons to connect with other neurons, sometimes located in quite distant regions, to establish different neural circuits. During the last decade, it has become evident that Zic genes, in addition to playing important roles in early development (e.g., gastrulation and neural tube closure), are involved in different processes of late brain development, such as neuronal migration, axon guidance, and refinement of axon terminals. ZIC proteins are therefore essential for the proper wiring and connectivity of the brain. In this chapter, we review our current knowledge of the role of Zic genes in the late stages of neural circuit formation.

  11. A wirelessly powered microspectrometer for neural probe-pin device

    Science.gov (United States)

    Choi, Sang H.; Kim, Min H.; Song, Kyo D.; Yoon, Hargsoon; Lee, Uhn

    2015-12-01

    Treatment of neurological anomalies, whether done invasively or not, places stringent demands on device functionality and size. We have developed a micro-spectrometer for use as an implantable neural probe to monitor neuro-chemistry in synapses. The micro-spectrometer, based on a NASA-invented miniature Fresnel grating, is capable of differentiating the emission spectra from various brain tissues. The micro-spectrometer meets the size requirements, and is able to probe the neuro-chemistry and suppression voltage typically associated with a neural anomaly. This neural probe-pin device (PPD) is equipped with wireless power technology (WPT) to enable operation in a continuous manner without requiring an implanted battery. The implanted neural PPD, together with a neural electronics interface and WPT, enable real-time measurement and control/feedback for remediation of neural anomalies. The design and performance of the combined PPD/WPT device for monitoring dopamine in a rat brain will be presented to demonstrate the current level of development. Future work on this device will involve the addition of an embedded expert system capable of performing semi-autonomous management of neural functions through a routine of sensing, processing, and control.

  12. User interface development

    Science.gov (United States)

    Aggrawal, Bharat

    1994-01-01

    This viewgraph presentation describes the development of user interfaces for OS/2 versions of computer codes for the analysis of seals. Current status, new features, work in progress, and future plans are discussed.

  13. Natural gesture interfaces

    Science.gov (United States)

    Starodubtsev, Illya

    2017-09-01

    The paper describes the implementation of the system of interaction with virtual objects based on gestures. The paper describes the common problems of interaction with virtual objects, specific requirements for the interfaces for virtual and augmented reality.

  14. Pattern formation at interfaces

    CERN Document Server

    Maier, Giulio; Nepomnyashchy, Alexander

    2010-01-01

    Applying modern nonlinear stability theory to problems of continuous media mechanics in the presence of interfaces, this text is relevant to materials science, chemical engineering, and heat transfer technologies, as well as to reaction-diffusion systems.

  15. Universal quantum interfaces

    International Nuclear Information System (INIS)

    Lloyd, Seth; Landahl, Andrew J.; Slotine, Jean-Jacques E.

    2004-01-01

    To observe or control a quantum system, one must interact with it via an interface. This article exhibits simple universal quantum interfaces--quantum input/output ports consisting of a single two-state system or quantum bit that interacts with the system to be observed or controlled. It is shown that under very general conditions the ability to observe and control the quantum bit on its own implies the ability to observe and control the system itself. The interface can also be used as a quantum communication channel, and multiple quantum systems can be connected by interfaces to become an efficient universal quantum computer. Experimental realizations are proposed, and implications for controllability, observability, and quantum information processing are explored

  16. Scalable coherent interface

    International Nuclear Information System (INIS)

    Alnaes, K.; Kristiansen, E.H.; Gustavson, D.B.; James, D.V.

    1990-01-01

    The Scalable Coherent Interface (IEEE P1596) is establishing an interface standard for very high performance multiprocessors, supporting a cache-coherent-memory model scalable to systems with up to 64K nodes. This Scalable Coherent Interface (SCI) will supply a peak bandwidth per node of 1 GigaByte/second. The SCI standard should facilitate assembly of processor, memory, I/O and bus bridge cards from multiple vendors into massively parallel systems with throughput far above what is possible today. The SCI standard encompasses two levels of interface, a physical level and a logical level. The physical level specifies electrical, mechanical and thermal characteristics of connectors and cards that meet the standard. The logical level describes the address space, data transfer protocols, cache coherence mechanisms, synchronization primitives and error recovery. In this paper we address logical level issues such as packet formats, packet transmission, transaction handshake, flow control, and cache coherence. 11 refs., 10 figs

  17. Introduction to interfaces 3

    DEFF Research Database (Denmark)

    Mortensen, Lars Boje; Høgel, Christian; Borsa, Paolo

    2017-01-01

    The Editors introduce Issue No. 3 of Interfaces: A Journal of Medieval European Literatures, dedicated to "Rediscovery and Canonization: The Roman Classics in the Middle Ages," and offer a general overview of the matter and contents of the contributions.......The Editors introduce Issue No. 3 of Interfaces: A Journal of Medieval European Literatures, dedicated to "Rediscovery and Canonization: The Roman Classics in the Middle Ages," and offer a general overview of the matter and contents of the contributions....

  18. 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.

  19. 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

  20. 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.

  1. High temperature interface superconductivity

    International Nuclear Information System (INIS)

    Gozar, A.; Bozovic, I.

    2016-01-01

    Highlight: • This review article covers the topic of high temperature interface superconductivity. • New materials and techniques used for achieving interface superconductivity are discussed. • We emphasize the role played by the differences in structure and electronic properties at the interface with respect to the bulk of the constituents. - Abstract: High-T_c superconductivity at interfaces has a history of more than a couple of decades. In this review we focus our attention on copper-oxide based heterostructures and multi-layers. We first discuss the technique, atomic layer-by-layer molecular beam epitaxy (ALL-MBE) engineering, that enabled High-T_c Interface Superconductivity (HT-IS), and the challenges associated with the realization of high quality interfaces. Then we turn our attention to the experiments which shed light on the structure and properties of interfacial layers, allowing comparison to those of single-phase films and bulk crystals. Both ‘passive’ hetero-structures as well as surface-induced effects by external gating are discussed. We conclude by comparing HT-IS in cuprates and in other classes of materials, especially Fe-based superconductors, and by examining the grand challenges currently laying ahead for the field.

  2. MER SPICE Interface

    Science.gov (United States)

    Sayfi, Elias

    2004-01-01

    MER SPICE Interface is a software module for use in conjunction with the Mars Exploration Rover (MER) mission and the SPICE software system of the Navigation and Ancillary Information Facility (NAIF) at NASA's Jet Propulsion Laboratory. (SPICE is used to acquire, record, and disseminate engineering, navigational, and other ancillary data describing circumstances under which data were acquired by spaceborne scientific instruments.) Given a Spacecraft Clock value, MER SPICE Interface extracts MER-specific data from SPICE kernels (essentially, raw data files) and calculates values for Planet Day Number, Local Solar Longitude, Local Solar Elevation, Local Solar Azimuth, and Local Solar Time (UTC). MER SPICE Interface was adapted from a subroutine, denoted m98SpiceIF written by Payam Zamani, that was intended to calculate SPICE values for the Mars Polar Lander. The main difference between MER SPICE Interface and m98SpiceIf is that MER SPICE Interface does not explicitly call CHRONOS, a time-conversion program that is part of a library of utility subprograms within SPICE. Instead, MER SPICE Interface mimics some portions of the CHRONOS code, the advantage being that it executes much faster and can efficiently be called from a pipeline of events in a parallel processing environment.

  3. Lectures on random interfaces

    CERN Document Server

    Funaki, Tadahisa

    2016-01-01

    Interfaces are created to separate two distinct phases in a situation in which phase coexistence occurs. This book discusses randomly fluctuating interfaces in several different settings and from several points of view: discrete/continuum, microscopic/macroscopic, and static/dynamic theories. The following four topics in particular are dealt with in the book. Assuming that the interface is represented as a height function measured from a fixed-reference discretized hyperplane, the system is governed by the Hamiltonian of gradient of the height functions. This is a kind of effective interface model called ∇φ-interface model. The scaling limits are studied for Gaussian (or non-Gaussian) random fields with a pinning effect under a situation in which the rate functional of the corresponding large deviation principle has non-unique minimizers. Young diagrams determine decreasing interfaces, and their dynamics are introduced. The large-scale behavior of such dynamics is studied from the points of view of the hyd...

  4. Touchfree medical interfaces.

    Science.gov (United States)

    Rossol, Nathaniel; Cheng, Irene; Rui Shen; Basu, Anup

    2014-01-01

    Real-time control of visual display systems via mid-air hand gestures offers many advantages over traditional interaction modalities. In medicine, for example, it allows a practitioner to adjust display values, e.g. contrast or zoom, on a medical visualization interface without the need to re-sterilize the interface. However, when users are holding a small tool (such as a pen, surgical needle, or computer stylus) the need to constantly put the tool down in order to make hand gesture interactions is not ideal. This work presents a novel interface that automatically adjusts for gesturing with hands and hand-held tools to precisely control medical displays. The novelty of our interface is that it uses a single set of gestures designed to be equally effective for fingers and hand-held tools without using markers. This type of interface was previously not feasible with low-resolution depth sensors such as Kinect, but is now achieved by using the recently released Leap Motion controller. Our interface is validated through a user study on a group of people given the task of adjusting parameters on a medical image.

  5. 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.

  6. 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

  7. Neural network-based sensor signal accelerator.

    Energy Technology Data Exchange (ETDEWEB)

    Vogt, M. C.

    2000-10-16

    A strategy has been developed to computationally accelerate the response time of a generic electronic sensor. The strategy can be deployed as an algorithm in a control system or as a physical interface (on an embedded microcontroller) between a slower responding external sensor and a higher-speed control system. Optional code implementations are available to adjust algorithm performance when computational capability is limited. In one option, the actual sensor signal can be sampled at the slower rate with adaptive linear neural networks predicting the sensor's future output and interpolating intermediate synthetic output values. In another option, a synchronized collection of predictors sequentially controls the corresponding synthetic output voltage. Error is adaptively corrected in both options. The core strategy has been demonstrated with automotive oxygen sensor data. A prototype interface device is under construction. The response speed increase afforded by this strategy could greatly offset the cost of developing a replacement sensor with a faster physical response time.

  8. Intention concepts and brain-machine interfacing

    Directory of Open Access Journals (Sweden)

    Franziska eThinnes-Elker

    2012-11-01

    Full Text Available Intentions, including their temporal properties and semantic content, are receiving increased attention, and neuroscientific studies in humans vary with respect to the topography of intention-related neural responses. This may reflect the fact that the kind of intentions investigated in one study may not be exactly the same kind investigated in the other. Fine-grained intention taxonomies developed in the philosophy of mind may be useful to identify the neural correlates of well-defined types of intentions, as well as to disentangle them from other related mental states, such as mere urges to perform an action. Intention-related neural signals may be exploited by brain-machine interfaces (BMIs that are currently being developed to restore speech and motor control in paralyzed subjects. Such BMI devices record the brain activity of the agent, interpret (‘decode’ the agent’s intended action, and send the corresponding execution command to an artificial effector system, e.g., a computer cursor or a robotic arm. In the present paper, we evaluate the potential of intention concepts from philosophy of mind to improve the performance and safety of BMIs based on higher-order, intention-related control signals. To this end, we address the distinction between future-, present-directed, and motor intentions, as well as the organization of intentions in time, specifically to what extent it is sequential or hierarchical. This has consequences as to whether these different types of intentions can be expected to occur simultaneously or not. We further illustrate how it may be useful or even necessary to distinguish types of intentions exposited in philosophy, including yes- vs. no-intentions and oblique vs. direct intentions, to accurately decode the agent’s intentions from neural signals in practical BMI applications.

  9. Intention concepts and brain-machine interfacing.

    Science.gov (United States)

    Thinnes-Elker, Franziska; Iljina, Olga; Apostolides, John Kyle; Kraemer, Felicitas; Schulze-Bonhage, Andreas; Aertsen, Ad; Ball, Tonio

    2012-01-01

    Intentions, including their temporal properties and semantic content, are receiving increased attention, and neuroscientific studies in humans vary with respect to the topography of intention-related neural responses. This may reflect the fact that the kind of intentions investigated in one study may not be exactly the same kind investigated in the other. Fine-grained intention taxonomies developed in the philosophy of mind may be useful to identify the neural correlates of well-defined types of intentions, as well as to disentangle them from other related mental states, such as mere urges to perform an action. Intention-related neural signals may be exploited by brain-machine interfaces (BMIs) that are currently being developed to restore speech and motor control in paralyzed patients. Such BMI devices record the brain activity of the agent, interpret ("decode") the agent's intended action, and send the corresponding execution command to an artificial effector system, e.g., a computer cursor or a robotic arm. In the present paper, we evaluate the potential of intention concepts from philosophy of mind to improve the performance and safety of BMIs based on higher-order, intention-related control signals. To this end, we address the distinction between future-, present-directed, and motor intentions, as well as the organization of intentions in time, specifically to what extent it is sequential or hierarchical. This has consequences as to whether these different types of intentions can be expected to occur simultaneously or not. We further illustrate how it may be useful or even necessary to distinguish types of intentions exposited in philosophy, including yes- vs. no-intentions and oblique vs. direct intentions, to accurately decode the agent's intentions from neural signals in practical BMI applications.

  10. Interface-based software testing

    Directory of Open Access Journals (Sweden)

    Aziz Ahmad Rais

    2016-10-01

    Full Text Available Software quality is determined by assessing the characteristics that specify how it should work, which are verified through testing. If it were possible to touch, see, or measure software, it would be easier to analyze and prove its quality. Unfortunately, software is an intangible asset, which makes testing complex. This is especially true when software quality is not a question of particular functions that can be tested through a graphical user interface. The primary objective of software architecture is to design quality of software through modeling and visualization. There are many methods and standards that define how to control and manage quality. However, many IT software development projects still fail due to the difficulties involved in measuring, controlling, and managing software quality. Software quality failure factors are numerous. Examples include beginning to test software too late in the development process, or failing properly to understand, or design, the software architecture and the software component structure. The goal of this article is to provide an interface-based software testing technique that better measures software quality, automates software quality testing, encourages early testing, and increases the software’s overall testability

  11. An adaptive interface (KNOWBOT) for nuclear power industry data bases

    International Nuclear Information System (INIS)

    Heger, A.S.

    1989-01-01

    An adaptive interface, KNOWBOT, has been designed to solve some of the problems that face the users of large centralized databases. The interface applies the neural network approach to information retrieval from a database. The database is a subset of the Nuclear Plant Reliability Data System (NPRDS). KNOWBOT preempts an existing database interface and works in conjunction with it. By design, KNOWBOT starts as a tabula rasa but acquires knowledge through its interactions with the user and the database. The interface uses its gained knowledge to personalize the database retrieval process and to induce new queries. In addition, the interface forgets the information that is no longer needed by the user. These self-organizing features of the interface reduce the scope of the database to the subsets that are highly relevant to the user needs. A proof-of-principle version of this interface has been implemented in Common LISP on a Texas Instruments Explorer I workstation. Experiments with KNOWBOT have successfully demonstrated the robustness of the model especially with induction and self-organization

  12. 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...

  13. 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.

  14. 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.

  15. EDITORIAL: Why we need a new journal in neural engineering

    Science.gov (United States)

    Durand, Dominique M.

    2004-03-01

    The field of neural engineering crystallizes for many engineers and scientists an area of research at the interface between neuroscience and engineering. For the last 15 years or so, the discipline of neural engineering (neuroengineering) has slowly appeared at conferences as a theme or track. The first conference devoted entirely to this area was the 1st International IEEE EMBS Conference on Neural Engineering which took place in Capri, Italy in 2003. Understanding how the brain works is considered the ultimate frontier and challenge in science. The complexity of the brain is so great that understanding even the most basic functions will require that we fully exploit all the tools currently at our disposal in science and engineering and simultaneously develop new methods of analysis. While neuroscientists and engineers from varied fields such as brain anatomy, neural development and electrophysiology have made great strides in the analysis of this complex organ, there remains a great deal yet to be uncovered. The potential for applications and remedies deriving from scientific discoveries and breakthroughs is extremely high. As a result of the growing availability of micromachining technology, research into neurotechnology has grown relatively rapidly in recent years and appears to be approaching a critical mass. For example, by understanding how neuronal circuits process and store information, we could design computers with capabilities beyond current limits. By understanding how neurons develop and grow, we could develop new technologies for spinal cord repair or central nervous system repair following neurological disorders. Moreover, discoveries related to higher-level cognitive function and consciousness could have a profound influence on how humans make sense of their surroundings and interact with each other. The ability to successfully interface the brain with external electronics would have enormous implications for our society and facilitate a

  16. Cultured Neural Networks: Optimization of Patterned Network Adhesiveness and Characterization of their Neural Activity

    Directory of Open Access Journals (Sweden)

    W. L. C. Rutten

    2006-01-01

    Full Text Available One type of future, improved neural interface is the “cultured probe”. It is a hybrid type of neural information transducer or prosthesis, for stimulation and/or recording of neural activity. It would consist of a microelectrode array (MEA on a planar substrate, each electrode being covered and surrounded by a local circularly confined network (“island” of cultured neurons. The main purpose of the local networks is that they act as biofriendly intermediates for collateral sprouts from the in vivo system, thus allowing for an effective and selective neuron–electrode interface. As a secondary purpose, one may envisage future information processing applications of these intermediary networks. In this paper, first, progress is shown on how substrates can be chemically modified to confine developing networks, cultured from dissociated rat cortex cells, to “islands” surrounding an electrode site. Additional coating of neurophobic, polyimide-coated substrate by triblock-copolymer coating enhances neurophilic-neurophobic adhesion contrast. Secondly, results are given on neuronal activity in patterned, unconnected and connected, circular “island” networks. For connected islands, the larger the island diameter (50, 100 or 150 μm, the more spontaneous activity is seen. Also, activity may show a very high degree of synchronization between two islands. For unconnected islands, activity may start at 22 days in vitro (DIV, which is two weeks later than in unpatterned networks.

  17. 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...

  18. 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.

  19. Proposal of a model of mammalian neural induction

    Science.gov (United States)

    Levine, Ariel J.; Brivanlou, Ali H.

    2009-01-01

    How does the vertebrate embryo make a nervous system? This complex question has been at the center of developmental biology for many years. The earliest step in this process – the induction of neural tissue – is intimately linked to patterning of the entire early embryo, and the molecular and embryological basis these processes are beginning to emerge. Here, we analyze classic and cutting-edge findings on neural induction in the mouse. We find that data from genetics, tissue explants, tissue grafting, and molecular marker expression support a coherent framework for mammalian neural induction. In this model, the gastrula organizer of the mouse embryo inhibits BMP signaling to allow neural tissue to form as a default fate – in the absence of instructive signals. The first neural tissue induced is anterior and subsequent neural tissue is posteriorized to form the midbrain, hindbrain, and spinal cord. The anterior visceral endoderm protects the pre-specified anterior neural fate from similar posteriorization, allowing formation of forebrain. This model is very similar to the default model of neural induction in the frog, thus bridging the evolutionary gap between amphibians and mammals. PMID:17585896

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

    Science.gov (United States)

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

    2015-04-01

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

  1. Environmental materials and interfaces

    International Nuclear Information System (INIS)

    1991-11-01

    A workshop that explored materials and interfaces research needs relevant to national environmental concerns was conducted at Pacific Northwest Laboratory. The purposes of the workshop were to refine the scientific research directions being planned for the Materials and Interface Program in the Molecular Science Research Center (MSRC) and further define the research and user equipment to the included as part of the proposed Environmental and Molecular Science Laboratory (EMSL). Three plenary information sessions served to outline the background, objectives, and status of the MSRC and EMSL initiatives; selected specific areas with environmentally related materials; and the status of capabilities and facilities planned for the EMSL. Attention was directed to four areas where materials and interface science can have a significant impact on prevention and remediation of environmental problems: in situ detection and characterization of hazardous wastes (sensors), minimization of hazardous waste (separation membranes, ion exchange materials, catalysts), waste containment (encapsulation and barrier materials), and fundamental understanding of contaminant transport mechanisms. During all other sessions, the participants were divided into three working groups for detailed discussion and the preparation of a written report. The working groups focused on the areas of interface structure and chemistry, materials and interface stability, and materials synthesis. These recommendations and suggestions for needed research will be useful for other researchers in proposing projects and for suggesting collaborative work with MSRC researchers. 1 fig

  2. Interfacing Sensors To Micro Controllers

    KAUST Repository

    Norain, Mohamed

    2018-01-01

    This lecture will cover the most common interface and interface techniques between sensors and microcontrollers. The presentation will introduce the pros and cons of each interface type including analogue, digital and serial output sensors. It will also cover the basic required electronics knowledge to help you in selecting and designing your next sensor to microcontroller interface.

  3. Interfacing Sensors To Micro Controllers

    KAUST Repository

    Norain, Mohamed

    2018-01-15

    This lecture will cover the most common interface and interface techniques between sensors and microcontrollers. The presentation will introduce the pros and cons of each interface type including analogue, digital and serial output sensors. It will also cover the basic required electronics knowledge to help you in selecting and designing your next sensor to microcontroller interface.

  4. An interface tracking model for droplet electrocoalescence.

    Energy Technology Data Exchange (ETDEWEB)

    Erickson, Lindsay Crowl

    2013-09-01

    This report describes an Early Career Laboratory Directed Research and Development (LDRD) project to develop an interface tracking model for droplet electrocoalescence. Many fluid-based technologies rely on electrical fields to control the motion of droplets, e.g. microfluidic devices for high-speed droplet sorting, solution separation for chemical detectors, and purification of biodiesel fuel. Precise control over droplets is crucial to these applications. However, electric fields can induce complex and unpredictable fluid dynamics. Recent experiments (Ristenpart et al. 2009) have demonstrated that oppositely charged droplets bounce rather than coalesce in the presence of strong electric fields. A transient aqueous bridge forms between approaching drops prior to pinch-off. This observation applies to many types of fluids, but neither theory nor experiments have been able to offer a satisfactory explanation. Analytic hydrodynamic approximations for interfaces become invalid near coalescence, and therefore detailed numerical simulations are necessary. This is a computationally challenging problem that involves tracking a moving interface and solving complex multi-physics and multi-scale dynamics, which are beyond the capabilities of most state-of-the-art simulations. An interface-tracking model for electro-coalescence can provide a new perspective to a variety of applications in which interfacial physics are coupled with electrodynamics, including electro-osmosis, fabrication of microelectronics, fuel atomization, oil dehydration, nuclear waste reprocessing and solution separation for chemical detectors. We present a conformal decomposition finite element (CDFEM) interface-tracking method for the electrohydrodynamics of two-phase flow to demonstrate electro-coalescence. CDFEM is a sharp interface method that decomposes elements along fluid-fluid boundaries and uses a level set function to represent the interface.

  5. User interface design considerations

    DEFF Research Database (Denmark)

    Andersen, Simon Engedal; Jakobsen, Arne; Rasmussen, Bjarne D.

    1999-01-01

    and output variables. This feature requires special attention when designing the user interface and a special approach for controlling the user selection of input and output variables are developed. To obtain a consistent system description the different input variables are grouped corresponding......When designing a user interface for a simulation model there are several important issues to consider: Who is the target user group, and which a priori information can be expected. What questions do the users want answers to and what questions are answered using a specific model?When developing...... the user interface of EESCoolTools these issues led to a series of simulation tools each with a specific purpose and a carefully selected set of input and output variables. To allow a more wide range of questions to be answered by the same model, the user can change between different sets of input...

  6. Workshop on Interface Phenomena

    CERN Document Server

    Kreuzer, Hans

    1987-01-01

    This book contains the proceedings of the first Workshop on Interface Phenomena, organized jointly by the surface science groups at Dalhousie University and the University of Maine. It was our intention to concentrate on just three topics related to the kinetics of interface reactions which, in our opinion, were frequently obscured unnecessarily in the literature and whose fundamental nature warranted an extensive discussion to help clarify the issues, very much in the spirit of the Discussions of the Faraday Society. Each session (day) saw two principal speakers expounding the different views; the session chairmen were asked to summarize the ensuing discussions. To understand the complexity of interface reactions, paradigms must be formulated to provide a framework for the interpretation of experimen­ tal data and for the construction of theoretical models. Phenomenological approaches have been based on a small number of rate equations for the concentrations or mole numbers of the various species involved i...

  7. High-bandwidth memory interface

    CERN Document Server

    Kim, Chulwoo; Song, Junyoung

    2014-01-01

    This book provides an overview of recent advances in memory interface design at both the architecture and circuit levels. Coverage includes signal integrity and testing, TSV interface, high-speed serial interface including equalization, ODT, pre-emphasis, wide I/O interface including crosstalk, skew cancellation, and clock generation and distribution. Trends for further bandwidth enhancement are also covered.   • Enables readers with minimal background in memory design to understand the basics of high-bandwidth memory interface design; • Presents state-of-the-art techniques for memory interface design; • Covers memory interface design at both the circuit level and system architecture level.

  8. An Approach to Interface Synthesis

    DEFF Research Database (Denmark)

    Madsen, Jan; Hald, Bjarne

    1995-01-01

    Presents a novel interface synthesis approach based on a one-sided interface description. Whereas most other approaches consider interface synthesis as optimizing a channel to existing client/server modules, we consider the interface synthesis as part of the client/server module synthesis (which...... may contain the re-use of existing modules). The interface synthesis approach describes the basic transformations needed to transform the server interface description into an interface description on the client side of the communication medium. The synthesis approach is illustrated through a point...

  9. Natural User Interfaces

    OpenAIRE

    Câmara , António

    2011-01-01

    Dissertação de Mestrado em Engenharia Informática apresentada à Faculdade de Ciências e Tecnologia da Universidade de Coimbra This project’s main subject are Natural User Interfaces. These interfaces’ main purpose is to allow the user to interact with computer systems in a more direct and natural way. The popularization of touch and gesture devices in the last few years has allowed for them to become increasingly common and today we are experiencing a transition of interface p...

  10. Interfacing to accelerator instrumentation

    International Nuclear Information System (INIS)

    Shea, T.J.

    1995-01-01

    As the sensory system for an accelerator, the beam instrumentation provides a tremendous amount of diagnostic information. Access to this information can vary from periodic spot checks by operators to high bandwidth data acquisition during studies. In this paper, example applications will illustrate the requirements on interfaces between the control system and the instrumentation hardware. A survey of the major accelerator facilities will identify the most popular interface standards. The impact of developments such as isochronous protocols and embedded digital signal processing will also be discussed

  11. Virtual interface environment workstations

    Science.gov (United States)

    Fisher, S. S.; Wenzel, E. M.; Coler, C.; Mcgreevy, M. W.

    1988-01-01

    A head-mounted, wide-angle, stereoscopic display system controlled by operator position, voice and gesture has been developed at NASA's Ames Research Center for use as a multipurpose interface environment. This Virtual Interface Environment Workstation (VIEW) system provides a multisensory, interactive display environment in which a user can virtually explore a 360-degree synthesized or remotely sensed environment and can viscerally interact with its components. Primary applications of the system are in telerobotics, management of large-scale integrated information systems, and human factors research. System configuration, research scenarios, and research directions are described.

  12. After Rigid Interfaces

    DEFF Research Database (Denmark)

    Troiano, Giovanni Maria

    (1) a user study with a prototype of an elastic, deformable display, and (2) a user study of deformable interfaces for performing music. The first study reports a guessability study with an elastic, deformable display where 17 participants suggested fitting gestures for 29 tasks, including navigation......, Transformation, Adaptation and Physicalization. In synthesis, the work presented in this thesis shows (1) implications of usefulness for deformable interfaces and how their new input modalities can redefine the way users interact with computers, and (2) how a systematic understanding of conventional design...

  13. Interface or Interlace?

    DEFF Research Database (Denmark)

    Hansen, Lone Koefoed; Wamberg, Jacob

    2005-01-01

    Departing from an analysis of the computer's indeterminate location between medium and machine, this paper problematises the idea of a clear-cut interface in complex computing, especially Augmented Reality. The idea and pratice of the interface is derived from the medium as a representational...... surface and thus demands the overview of an autonomous consciouness. Instead we introduce the term interlace, a mingling of representational and physical levels, thus describing the computer's ambiguous blending of imaginary and real. The proposition is demonstrated through analysis of different recent...

  14. CAMAC to GPIB interface

    International Nuclear Information System (INIS)

    Naivar, F.J.

    1978-01-01

    A CAMAC module developed at the Los Alamos Scientific Laboratory allows any device conforming to the GPIB standard to be connected to a CAMAC system. This module incorporates a microprocessor to control up to 14 GPIB-compatible instruments using a restricted set of CAMAC F-N-A commands. The marriage of a device-independent bus (IEEE Standard 488-1975) to a computer-independent bus (IEEE Standard 583-1975) provides a general method for interfacing a system of programmable instruments to any computer. This module is being used to interface a variety of interactive devices on a control console to a control computer

  15. Nonlinear optics at interfaces

    International Nuclear Information System (INIS)

    Chen, C.K.

    1980-12-01

    Two aspects of surface nonlinear optics are explored in this thesis. The first part is a theoretical and experimental study of nonlinear intraction of surface plasmons and bulk photons at metal-dielectric interfaces. The second part is a demonstration and study of surface enhanced second harmonic generation at rough metal surfaces. A general formulation for nonlinear interaction of surface plasmons at metal-dielectric interfaces is presented and applied to both second and third order nonlinear processes. Experimental results for coherent second and third harmonic generation by surface plasmons and surface coherent antiStokes Raman spectroscopy (CARS) are shown to be in good agreement with the theory

  16. Knowledge-Based Aircraft Automation: Managers Guide on the use of Artificial Intelligence for Aircraft Automation and Verification and Validation Approach for a Neural-Based Flight Controller

    Science.gov (United States)

    Broderick, Ron

    1997-01-01

    The ultimate goal of this report was to integrate the powerful tools of artificial intelligence into the traditional process of software development. To maintain the US aerospace competitive advantage, traditional aerospace and software engineers need to more easily incorporate the technology of artificial intelligence into the advanced aerospace systems being designed today. The future goal was to transition artificial intelligence from an emerging technology to a standard technology that is considered early in the life cycle process to develop state-of-the-art aircraft automation systems. This report addressed the future goal in two ways. First, it provided a matrix that identified typical aircraft automation applications conducive to various artificial intelligence methods. The purpose of this matrix was to provide top-level guidance to managers contemplating the possible use of artificial intelligence in the development of aircraft automation. Second, the report provided a methodology to formally evaluate neural networks as part of the traditional process of software development. The matrix was developed by organizing the discipline of artificial intelligence into the following six methods: logical, object representation-based, distributed, uncertainty management, temporal and neurocomputing. Next, a study of existing aircraft automation applications that have been conducive to artificial intelligence implementation resulted in the following five categories: pilot-vehicle interface, system status and diagnosis, situation assessment, automatic flight planning, and aircraft flight control. The resulting matrix provided management guidance to understand artificial intelligence as it applied to aircraft automation. The approach taken to develop a methodology to formally evaluate neural networks as part of the software engineering life cycle was to start with the existing software quality assurance standards and to change these standards to include neural network

  17. A Tensor-Product-Kernel Framework for Multiscale Neural Activity Decoding and Control

    Science.gov (United States)

    Li, Lin; Brockmeier, Austin J.; Choi, John S.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.

    2014-01-01

    Brain machine interfaces (BMIs) have attracted intense attention as a promising technology for directly interfacing computers or prostheses with the brain's motor and sensory areas, thereby bypassing the body. The availability of multiscale neural recordings including spike trains and local field potentials (LFPs) brings potential opportunities to enhance computational modeling by enriching the characterization of the neural system state. However, heterogeneity on data type (spike timing versus continuous amplitude signals) and spatiotemporal scale complicates the model integration of multiscale neural activity. In this paper, we propose a tensor-product-kernel-based framework to integrate the multiscale activity and exploit the complementary information available in multiscale neural activity. This provides a common mathematical framework for incorporating signals from different domains. The approach is applied to the problem of neural decoding and control. For neural decoding, the framework is able to identify the nonlinear functional relationship between the multiscale neural responses and the stimuli using general purpose kernel adaptive filtering. In a sensory stimulation experiment, the tensor-product-kernel decoder outperforms decoders that use only a single neural data type. In addition, an adaptive inverse controller for delivering electrical microstimulation patterns that utilizes the tensor-product kernel achieves promising results in emulating the responses to natural stimulation. PMID:24829569

  18. Neural System Prediction and Identification Challenge

    Directory of Open Access Journals (Sweden)

    Ioannis eVlachos

    2013-12-01

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

  19. Neural system prediction and identification challenge.

    Science.gov (United States)

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

    2013-01-01

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

  20. Optimal design method for a digital human–computer interface based on human reliability in a nuclear power plant. Part 3: Optimization method for interface task layout

    International Nuclear Information System (INIS)

    Jiang, Jianjun; Wang, Yiqun; Zhang, Li; Xie, Tian; Li, Min; Peng, Yuyuan; Wu, Daqing; Li, Peiyao; Ma, Congmin; Shen, Mengxu; Wu, Xing; Weng, Mengyun; Wang, Shiwei; Xie, Cen

    2016-01-01

    Highlights: • The authors present an optimization algorithm for interface task layout. • The performing process of the proposed algorithm was depicted. • The performance evaluation method adopted neural network method. • The optimization layouts of an event interface tasks were obtained by experiments. - Abstract: This is the last in a series of papers describing the optimal design for a digital human–computer interface of a nuclear power plant (NPP) from three different points based on human reliability. The purpose of this series is to propose different optimization methods from varying perspectives to decrease human factor events that arise from the defects of a human–computer interface. The present paper mainly solves the optimization method as to how to effectively layout interface tasks into different screens. The purpose of this paper is to decrease human errors by reducing the distance that an operator moves among different screens in each operation. In order to resolve the problem, the authors propose an optimization process of interface task layout for digital human–computer interface of a NPP. As to how to automatically layout each interface task into one of screens in each operation, the paper presents a shortest moving path optimization algorithm with dynamic flag based on human reliability. To test the algorithm performance, the evaluation method uses neural network based on human reliability. The less the human error probabilities are, the better the interface task layouts among different screens are. Thus, by analyzing the performance of each interface task layout, the optimization result is obtained. Finally, the optimization layouts of spurious safety injection event interface tasks of the NPP are obtained by an experiment, the proposed methods has a good accuracy and stabilization.

  1. Space as interface

    DEFF Research Database (Denmark)

    Lykke-Olesen, Andreas

    2006-01-01

    multiple projects spanning over fields such as tangible user interfaces, augmented reality, and mobile computing, a conceptual framework characterizing camera-based mixed interaction spaces is developed. To show the applicability of the framework, it is deployed on one of the presented cases and discussed...

  2. The Liquid Vapour Interface

    DEFF Research Database (Denmark)

    Als-Nielsen, Jens Aage

    1985-01-01

    In this short review we are concerned with the density variation across the liquid-vapour interface, i.e. from the bulk density of the liquid to the essentially zero density of the vapour phase. This density variation can in principle be determined from the deviation of the reflectivity from...

  3. Photochemistry at Interfaces

    Energy Technology Data Exchange (ETDEWEB)

    Eisenthal, Kenneth B [Columbia Univ., New York, NY (United States)

    2015-02-24

    We have advanced our capabilities to investigate ultrafast excited state dynamics at a liquid interface using a pump to excite molecules to higher electronic states and then probe the subsequent time evolution of the interfacial molecules with femtosecond time delayed vibrational SFG.

  4. Is the interface OK?

    Digital Repository Service at National Institute of Oceanography (India)

    Suresh, T.

    When a peripheral device fails, software methods can be initially resorted to before the usual hardware test procedures are used. A test program is presented here that allows various peripherals, inter-faced to a Norsk Data computer, to be tested...

  5. Workflow User Interfaces Patterns

    Directory of Open Access Journals (Sweden)

    Jean Vanderdonckt

    2012-03-01

    Full Text Available Este trabajo presenta una colección de patrones de diseño de interfaces de usuario para sistemas de información para el flujo de trabajo; la colección incluye cuarenta y tres patrones clasificados en siete categorías identificados a partir de la lógica del ciclo de vida de la tarea sobre la base de la oferta y la asignación de tareas a los responsables de realizarlas (i. e. recursos humanos durante el flujo de trabajo. Cada patrón de la interfaz de usuario de flujo de trabajo (WUIP, por sus siglas en inglés se caracteriza por las propiedades expresadas en el lenguaje PLML para expresar patrones y complementado por otros atributos y modelos que se adjuntan a dicho modelo: la interfaz de usuario abstracta y el modelo de tareas correspondiente. Estos modelos se especifican en un lenguaje de descripción de interfaces de usuario. Todos los WUIPs se almacenan en una biblioteca y se pueden recuperar a través de un editor de flujo de trabajo que vincula a cada patrón de asignación de trabajo a su WUIP correspondiente.A collection of user interface design patterns for workflow information systems is presented that contains forty three resource patterns classified in seven categories. These categories and their corresponding patterns have been logically identified from the task life cycle based on offering and allocation operations. Each Workflow User Interface Pattern (WUIP is characterized by properties expressed in the PLML markup language for expressing patterns and augmented by additional attributes and models attached to the pattern: the abstract user interface and the corresponding task model. These models are specified in a User Interface Description Language. All WUIPs are stored in a library and can be retrieved within a workflow editor that links each workflow pattern to its corresponding WUIP, thus giving rise to a user interface for each workflow pattern.

  6. Vibration monitoring with artificial neural networks

    International Nuclear Information System (INIS)

    Alguindigue, I.

    1991-01-01

    Vibration monitoring of components in nuclear power plants has been used for a number of years. This technique involves the analysis of vibration data coming from vital components of the plant to detect features which reflect the operational state of machinery. The analysis leads to the identification of potential failures and their causes, and makes it possible to perform efficient preventive maintenance. Earlydetection is important because it can decrease the probability of catastrophic failures, reduce forced outgage, maximize utilization of available assets, increase the life of the plant, and reduce maintenance costs. This paper documents our work on the design of a vibration monitoring methodology based on neural network technology. This technology provides an attractive complement to traditional vibration analysis because of the potential of neural network to operate in real-time mode and to handle data which may be distorted or noisy. Our efforts have been concentrated on the analysis and classification of vibration signatures collected from operating machinery. Two neural networks algorithms were used in our project: the Recirculation algorithm for data compression and the Backpropagation algorithm to perform the actual classification of the patterns. Although this project is in the early stages of development it indicates that neural networks may provide a viable methodology for monitoring and diagnostics of vibrating components. Our results to date are very encouraging

  7. 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.

  8. EDITORIAL: Special section on gaze-independent brain-computer interfaces Special section on gaze-independent brain-computer interfaces

    Science.gov (United States)

    Treder, Matthias S.

    2012-08-01

    Restoring the ability to communicate and interact with the environment in patients with severe motor disabilities is a vision that has been the main catalyst of early brain-computer interface (BCI) research. The past decade has brought a diversification of the field. BCIs have been examined as a tool for motor rehabilitation and their benefit in non-medical applications such as mental-state monitoring for improved human-computer interaction and gaming has been confirmed. At the same time, the weaknesses of some approaches have been pointed out. One of these weaknesses is gaze-dependence, that is, the requirement that the user of a BCI system voluntarily directs his or her eye gaze towards a visual target in order to efficiently operate a BCI. This not only contradicts the main doctrine of BCI research, namely that BCIs should be independent of muscle activity, but it can also limit its real-world applicability both in clinical and non-medical settings. It is only in a scenario devoid of any motor activity that a BCI solution is without alternative. Gaze-dependencies have surfaced at two different points in the BCI loop. Firstly, a BCI that relies on visual stimulation may require users to fixate on the target location. Secondly, feedback is often presented visually, which implies that the user may have to move his or her eyes in order to perceive the feedback. This special section was borne out of a BCI workshop on gaze-independent BCIs held at the 2011 Society for Applied Neurosciences (SAN) Conference and has then been extended with additional contributions from other research groups. It compiles experimental and methodological work that aims toward gaze-independent communication and mental-state monitoring. Riccio et al review the current state-of-the-art in research on gaze-independent BCIs [1]. Van der Waal et al present a tactile speller that builds on the stimulation of the fingers of the right and left hand [2]. H¨ohne et al analyze the ergonomic aspects

  9. Integration of the Residual Limb with Prostheses via Direct Skin-Bone-Peripheral Nerve Interface

    Science.gov (United States)

    2017-10-01

    AWARD NUMBER: W81XWH-16-1-0791 TITLE: Integration of the Residual Limb with Prostheses via Direct Skin- Bone-Peripheral Nerve Interface...ABOVE ADDRESS. 1. REPORT DATE October 2017 2. REPORT TYPE Annual 3. DATES COVERED 30 Sep 2016 - 29 Sep 2017 4. TITLE AND SUBTITLE Integration of the...translational study to develop Skin and Bone Integrated Pylon with Peripheral Neural Interface (SBIP-PNI) directly attached to the residuum and the

  10. 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

    directly descended from the analysis of the Hodgkin and Huxley equations in FitzHugh and Nagumo's early work. Mathematicians became increasingly interested in biological problems in general, and in the function of the nervous system in particular, during the latter part of the twentieth century. The natural tool for describing more complex neural systems whose patterns of activity unfold in time was nonlinear dynamical systems theory. Classic work from such investigators as Kolmogorov, Arnol'd, Moser, Malkin, Andronov, Hopf, Birkhoff, Hartman and others (reviewed in Izhikevich 2006) served as the basis for understanding the dynamics of neural models such as the coupling of oscillators for rhythmic behavior, leading to work such as that of Koppell and Ermentrout on the lamprey swimming system (Kopell and Ermentrout 1986, 1990), based on earlier models of Cohen et al (1982). Exploration of nonlinear interactions in neuronal populations, especially those that might be related to vision, led to the development of the Wilson-Cowan equations in the 1970s (Wilson and Cowan 1972, 1973). The advent of increasingly powerful personal computers also made it feasible to combine theoretical analyses with extensive numerical investigations of nonlinear dynamical systems. An important and influential example of such work was the detailed bifurcation analysis of Morris and Lecar's two-dimensional model of nonlinear dynamical behavior in the giant muscle fiber of the Pacific barnacle Balanus nubilis (Morris and Lecar 1981), done by Rinzel and Ermentrout in the late 1980s (Rinzel and Ermentrout 1989). The mathematical analysis of bursting behavior based on decomposition of a dynamical system into fast and slow subsystems, an application of Fenichel's geometric singular perturbation theory (Fenichel 1979, Jones 1995), continues to play an important role. Recent work on dynamical analyses of neurons and neural circuits is described in Izhikevich's recent book (Izhikevich 2006), which is based

  11. PREFACE: Water at interfaces Water at interfaces

    Science.gov (United States)

    Gallo, P.; Rovere, M.

    2010-07-01

    This special issue is devoted to illustrating important aspects and significant results in the field of modeling and simulation of water at interfaces with solutes or with confining substrates, focusing on a range of temperatures from ambient to supercooled. Understanding the behavior of water, in contact with different substrates and/or in solutions, is of pivotal importance for a wide range of applications in physics, chemistry and biochemistry. Simulations of confined and/or interfacial water are also relevant for testing how different its behavior is with respect to bulk water. Simulations and modeling in this field are of particular importance when studying supercooled regions where water shows anomalous properties. These considerations motivated the organization of a workshop at CECAM in the summer of 2009 which aimed to bring together scientists working with computer simulations on the properties of water in various environments with different methodologies. In this special issue, we collected a variety of interesting contributions from some of the speakers of the workshop. We have roughly classified the contributions into four groups. The papers of the first group address the properties of interfacial and confined water upon supercooling in an effort to understand the relation with anomalous behavior of supercooled bulk water. The second group deals with the specific problem of solvation. The next group deals with water in different environments by considering problems of great importance in technological and biological applications. Finally, the last group deals with quantum mechanical calculations related to the role of water in chemical processes. The first group of papers is introduced by the general paper of Stanley et al. The authors discuss recent progress in understanding the anomalies of water in bulk, nanoconfined, and biological environments. They present evidence that liquid water may display 'polymorphism', a property that can be present in

  12. Easy-to-use interface

    International Nuclear Information System (INIS)

    Blattner, D O; Blattner, M M; Tong, Y.

    1999-01-01

    Easy-to-use interfaces are a class of interfaces that fall between public access interfaces and graphical user interfaces in usability and cognitive difficulty. We describe characteristics of easy-to-use interfaces by the properties of four dimensions: selection, navigation, direct manipulation, and contextual metaphors. Another constraint we introduced was to include as little text as possible, and what text we have will be in at least four languages. Formative evaluations were conducted to identify and isolate these characteristics. Our application is a visual interface for a home automation system intended for a diverse set of users. The design will be expanded to accommodate the visually disabled in the near future

  13. 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.

  14. Using domain-specific basic functions for the analysis of supervised artificial neural networks

    NARCIS (Netherlands)

    van der Zwaag, B.J.

    2003-01-01

    Since the early development of artificial neural networks, researchers have tried to analyze trained neural networks in order to gain insight into their behavior. For certain applications and in certain problem domains this has been successful, for example by the development of so-called rule

  15. Characterizing root response phenotypes by neural network analysis

    OpenAIRE

    Hatzig, Sarah V.; Schiessl, Sarah; Stahl, Andreas; Snowdon, Rod J.

    2015-01-01

    Roots play an immediate role as the interface for water acquisition. To improve sustainability in low-water environments, breeders of major crops must therefore pay closer attention to advantageous root phenotypes; however, the complexity of root architecture in response to stress can be difficult to quantify. Here, the Sholl method, an established technique from neurobiology used for the characterization of neural network anatomy, was adapted to more adequately describe root responses to osm...

  16. 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....

  17. 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.

  18. 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....

  19. High school music classes enhance the neural processing of speech.

    Science.gov (United States)

    Tierney, Adam; Krizman, Jennifer; Skoe, Erika; Johnston, Kathleen; Kraus, Nina

    2013-01-01

    Should music be a priority in public education? One argument for teaching music in school is that private music instruction relates to enhanced language abilities and neural function. However, the directionality of this relationship is unclear and it is unknown whether school-based music training can produce these enhancements. Here we show that 2 years of group music classes in high school enhance the neural encoding of speech. To tease apart the relationships between music and neural function, we tested high school students participating in either music or fitness-based training. These groups were matched at the onset of training on neural timing, reading ability, and IQ. Auditory brainstem responses were collected to a synthesized speech sound presented in background noise. After 2 years of training, the neural responses of the music training group were earlier than at pre-training, while the neural timing of students in the fitness training group was unchanged. These results represent the strongest evidence to date that in-school music education can cause enhanced speech encoding. The neural benefits of musical training are, therefore, not limited to expensive private instruction early in childhood but can be elicited by cost-effective group instruction during adolescence.

  20. Robo signaling regulates the production of cranial neural crest cells.

    Science.gov (United States)

    Li, Yan; Zhang, Xiao-Tan; Wang, Xiao-Yu; Wang, Guang; Chuai, Manli; Münsterberg, Andrea; Yang, Xuesong

    2017-12-01

    Slit/Robo signaling plays an important role in the guidance of developing neurons in developing embryos. However, it remains obscure whether and how Slit/Robo signaling is involved in the production of cranial neural crest cells. In this study, we examined Robo1 deficient mice to reveal developmental defects of mouse cranial frontal and parietal bones, which are derivatives of cranial neural crest cells. Therefore, we determined the production of HNK1 + cranial neural crest cells in early chick embryo development after knock-down (KD) of Robo1 expression. Detection of markers for pre-migratory and migratory neural crest cells, PAX7 and AP-2α, showed that production of both was affected by Robo1 KD. In addition, we found that the transcription factor slug is responsible for the aberrant delamination/EMT of cranial neural crest cells induced by Robo1 KD, which also led to elevated expression of E- and N-Cadherin. N-Cadherin expression was enhanced when blocking FGF signaling with dominant-negative FGFR1 in half of the neural tube. Taken together, we show that Slit/Robo signaling influences the delamination/EMT of cranial neural crest cells, which is required for cranial bone development. Copyright © 2017. Published by Elsevier Inc.

  1. Character Recognition Using Genetically Trained Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Diniz, C.; Stantz, K.M.; Trahan, M.W.; Wagner, J.S.

    1998-10-01

    Computationally intelligent recognition of characters and symbols addresses a wide range of applications including foreign language translation and chemical formula identification. The combination of intelligent learning and optimization algorithms with layered neural structures offers powerful techniques for character recognition. These techniques were originally developed by Sandia National Laboratories for pattern and spectral analysis; however, their ability to optimize vast amounts of data make them ideal for character recognition. An adaptation of the Neural Network Designer soflsvare allows the user to create a neural network (NN_) trained by a genetic algorithm (GA) that correctly identifies multiple distinct characters. The initial successfid recognition of standard capital letters can be expanded to include chemical and mathematical symbols and alphabets of foreign languages, especially Arabic and Chinese. The FIN model constructed for this project uses a three layer feed-forward architecture. To facilitate the input of characters and symbols, a graphic user interface (GUI) has been developed to convert the traditional representation of each character or symbol to a bitmap. The 8 x 8 bitmap representations used for these tests are mapped onto the input nodes of the feed-forward neural network (FFNN) in a one-to-one correspondence. The input nodes feed forward into a hidden layer, and the hidden layer feeds into five output nodes correlated to possible character outcomes. During the training period the GA optimizes the weights of the NN until it can successfully recognize distinct characters. Systematic deviations from the base design test the network's range of applicability. Increasing capacity, the number of letters to be recognized, requires a nonlinear increase in the number of hidden layer neurodes. Optimal character recognition performance necessitates a minimum threshold for the number of cases when genetically training the net. And, the

  2. Neural Crossroads in the Hematopoietic Stem Cell Niche.

    Science.gov (United States)

    Agarwala, Sobhika; Tamplin, Owen J

    2018-05-29

    The hematopoietic stem cell (HSC) niche supports steady-state hematopoiesis and responds to changing needs during stress and disease. The nervous system is an important regulator of the niche, and its influence is established early in development when stem cells are specified. Most research has focused on direct innervation of the niche, however recent findings show there are different modes of neural control, including globally by the central nervous system (CNS) and hormone release, locally by neural crest-derived mesenchymal stem cells, and intrinsically by hematopoietic cells that express neural receptors and neurotransmitters. Dysregulation between neural and hematopoietic systems can contribute to disease, however new therapeutic opportunities may be found among neuroregulator drugs repurposed to support hematopoiesis. Copyright © 2018 Elsevier Ltd. All rights reserved.

  3. Machine Learning Interface for Medical Image Analysis.

    Science.gov (United States)

    Zhang, Yi C; Kagen, Alexander C

    2017-10-01

    TensorFlow is a second-generation open-source machine learning software library with a built-in framework for implementing neural networks in wide variety of perceptual tasks. Although TensorFlow usage is well established with computer vision datasets, the TensorFlow interface with DICOM formats for medical imaging remains to be established. Our goal is to extend the TensorFlow API to accept raw DICOM images as input; 1513 DaTscan DICOM images were obtained from the Parkinson's Progression Markers Initiative (PPMI) database. DICOM pixel intensities were extracted and shaped into tensors, or n-dimensional arrays, to populate the training, validation, and test input datasets for machine learning. A simple neural network was constructed in TensorFlow to classify images into normal or Parkinson's disease groups. Training was executed over 1000 iterations for each cross-validation set. The gradient descent optimization and Adagrad optimization algorithms were used to minimize cross-entropy between the predicted and ground-truth labels. Cross-validation was performed ten times to produce a mean accuracy of 0.938 ± 0.047 (95 % CI 0.908-0.967). The mean sensitivity was 0.974 ± 0.043 (95 % CI 0.947-1.00) and mean specificity was 0.822 ± 0.207 (95 % CI 0.694-0.950). We extended the TensorFlow API to enable DICOM compatibility in the context of DaTscan image analysis. We implemented a neural network classifier that produces diagnostic accuracies on par with excellent results from previous machine learning models. These results indicate the potential role of TensorFlow as a useful adjunct diagnostic tool in the clinical setting.

  4. Safety Parameters Graphical Interface

    International Nuclear Information System (INIS)

    Canamero, B.

    1998-01-01

    Nuclear power plant data are received at the Operations Center of the Consejo de Seguridad Nuclear in emergency situations. In order to achieve the required interface and to prepare those data to perform simulation and forecasting with already existing computer codes a Safety Parameters Graphical Interface (IGPS) has been developed. The system runs in a UNIX environment and use the Xwindows capabilities. The received data are stored in such a way that it can be easily used for further analysis and training activities. The system consists of task-oriented modules (processes) which communicate each other using well known UNIX mechanisms (signals, sockets and shared memory segments). IGPS conceptually have two different parts: Data collection and preparation, and Data monitorization. (Author)

  5. Politics at the interface

    DEFF Research Database (Denmark)

    Kannabiran, Gobinaath; Petersen, Marianne Graves

    2010-01-01

    At the birth of participatory design, there was a strong political consciousness surrounding the design of new technology, the design process in particular, establishing a rich set of methods and tools for user-centered design. Today, the term design has extended its scope of concern beyond...... the process of design and into how users interact with the designed product on a day-to-day basis. This paper is an attempt to call to attention the need for a new set of methods, attitudes and approaches, along with the existing, to discuss, analyze and reflect upon the politics at the interface....... By presenting a critical analysis of two design cases, we elicit the importance of such an agenda and the implications for design in doing so. We use the Foucauldian notion of power to analyze the power relationships in these two cases and to articulate the politics at the interface. We conclude by emphasizing...

  6. Urban Sound Interfaces

    DEFF Research Database (Denmark)

    Breinbjerg, Morten

    2012-01-01

    This paper draws on the theories of Michel de Certeau and Gaston Bachelard to discuss how media architecture, in the form of urban sound interfaces, can help us perceive the complexity of the spaces we inhabit, by exploring the history and the narratives of the places in which we live. In this pa......This paper draws on the theories of Michel de Certeau and Gaston Bachelard to discuss how media architecture, in the form of urban sound interfaces, can help us perceive the complexity of the spaces we inhabit, by exploring the history and the narratives of the places in which we live....... In this paper, three sound works are discussed in relation to the iPod, which is considered as a more private way to explore urban environments, and as a way to control the individual perception of urban spaces....

  7. The technical supervision interface

    CERN Document Server

    Sollander, P

    1998-01-01

    The Technical Control Room (TCR) is currently using 30 different applications for the remote supervision of the technical infrastructure at CERN. These applications have all been developed with the CERN made Uniform Man Machine Interface (UMMI) tools built in 1990. However, the visualization technology has evolved phenomenally since 1990, the Technical Data Server (TDS) has radically changed our control system architecture, and the standardization and the maintenance of the UMMI applications have become important issues as their number increases. The Technical Supervision Interface is intended to replace the UMMI and solve the above problems. Using a standard WWW-browser for the display, it will be inherently multi-platform and hence available for control room operators, equipment specialists and on-call personnel.

  8. Virtual button interface

    Science.gov (United States)

    Jones, J.S.

    1999-01-12

    An apparatus and method of issuing commands to a computer by a user interfacing with a virtual reality environment are disclosed. To issue a command, the user directs gaze at a virtual button within the virtual reality environment, causing a perceptible change in the virtual button, which then sends a command corresponding to the virtual button to the computer, optionally after a confirming action is performed by the user, such as depressing a thumb switch. 4 figs.

  9. Noise at the Interface

    DEFF Research Database (Denmark)

    Prior, Andrew

    2011-01-01

    The notion of noise occupies a contested territory, in which it is framed as pollution and detritus even as it makes its opposite a possibility - noise is always defined in opposition to something else, even if this ‘other’ is not quite clear. This paper explores noise in the context of ‘the...... interface’ asking what its affordances as an idea may contribute to our understanding of interface. I draw historically on information theory in particular to initiate this exploration....

  10. Noise Analysis studies with neural networks

    International Nuclear Information System (INIS)

    Seker, S.; Ciftcioglu, O.

    1996-01-01

    Noise analysis studies with neural network are aimed. Stochastic signals at the input of the network are used to obtain an algorithmic multivariate stochastic signal modeling. To this end, lattice modeling of a stochastic signal is performed to obtain backward residual noise sources which are uncorrelated among themselves. There are applied together with an additional input to the network to obtain an algorithmic model which is used for signal detection for early failure in plant monitoring. The additional input provides the information to the network to minimize the difference between the signal and the network's one-step-ahead prediction. A stochastic algorithm is used for training where the errors reflecting the measurement error during the training are also modelled so that fast and consistent convergence of network's weights is obtained. The lattice structure coupled to neural network investigated with measured signals from an actual power plant. (authors)

  11. CONSTRUCTION COST PREDICTION USING NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Smita K Magdum

    2017-10-01

    Full Text Available Construction cost prediction is important for construction firms to compete and grow in the industry. Accurate construction cost prediction in the early stage of project is important for project feasibility studies and successful completion. There are many factors that affect the cost prediction. This paper presents construction cost prediction as multiple regression model with cost of six materials as independent variables. The objective of this paper is to develop neural networks and multilayer perceptron based model for construction cost prediction. Different models of NN and MLP are developed with varying hidden layer size and hidden nodes. Four artificial neural network models and twelve multilayer perceptron models are compared. MLP and NN give better results than statistical regression method. As compared to NN, MLP works better on training dataset but fails on testing dataset. Five activation functions are tested to identify suitable function for the problem. ‘elu' transfer function gives better results than other transfer function.

  12. Planning and User Interface Affordances

    National Research Council Canada - National Science Library

    St. Amant, Robert

    1999-01-01

    .... We identify a number of similarities between executing plans and interacting with a graphical user interface, and argue that affordances for planning environments apply equally well to user interface environments...

  13. Interface Input/Output Automata

    DEFF Research Database (Denmark)

    Larsen, Kim Guldstrand; Nyman, Ulrik; Wasowski, Andrzej

    2006-01-01

    Building on the theory of interface automata by de Alfaro and Henzinger we design an interface language for Lynch’s I/O, a popular formalism used in the development of distributed asynchronous systems, not addressed by previous interface research. We introduce an explicit separation of assumptions...... a method for solving systems of relativized behavioral inequalities as used in our setup and draw a formal correspondence between our work and interface automata....

  14. 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....

  15. 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 ...

  16. 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.

  17. 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

  18. 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.

  19. 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...

  20. 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...

  1. SPR imaging combined with cyclic voltammetry for the detection of neural activity

    Directory of Open Access Journals (Sweden)

    Hui Li

    2014-03-01

    Full Text Available Surface plasmon resonance (SPR detects changes in refractive index at a metal-dielectric interface. In this study, SPR imaging (SPRi combined with cyclic voltammetry (CV was applied to detect neural activity in isolated bullfrog sciatic nerves. The neural activities induced by chemical and electrical stimulation led to an SPR response, and the activities were recorded in real time. The activities of different parts of the sciatic nerve were recorded and compared. The results demonstrated that SPR imaging combined with CV is a powerful tool for the investigation of neural activity.

  2. An application of neural networks and artificial intelligence for in-core fuel management

    International Nuclear Information System (INIS)

    Miller, L.F.; Algutifan, F.; Uhrig, R.E.

    1992-01-01

    This paper reports the feasibility of using expert systems in combination with neural networks and neutronics calculations to improve the efficiency for obtaining optimal candidate reload core designs. The general objectives of this research are as follows: (1) generate a suitable data base and ancillary software for training neural networks that duplicate neutronics calculations. (2) develop a graphical interface with neutronics software and neural networks for manual shuffling of reload cores. (3) construct an expert system for shuffling reload cores with specified rules. (4) develp neural networks that capture the nonlinear behavior of fuel depletion. (5) integrate the neural networks and neutronics software with an expert system to specify reload cores that obtain appropriate figure of merit

  3. Bidirectional Joint Representation Learning with Symmetrical Deep Neural Networks for Multimodal and Crossmodal Applications

    OpenAIRE

    Vukotic , Vedran; Raymond , Christian; Gravier , Guillaume

    2016-01-01

    International audience; Common approaches to problems involving multiple modalities (classification, retrieval, hyperlinking, etc.) are early fusion of the initial modalities and crossmodal translation from one modality to the other. Recently, deep neural networks, especially deep autoencoders, have proven promising both for crossmodal translation and for early fusion via multimodal embedding. In this work, we propose a flexible cross-modal deep neural network architecture for multimodal and ...

  4. The neural basis of speech sound discrimination from infancy to adulthood

    OpenAIRE

    Partanen, Eino

    2013-01-01

    Rapid processing of speech is facilitated by neural representations of native language phonemes. However, some disorders and developmental conditions, such as developmental dyslexia, can hamper the development of these neural memory traces, leading to language delays and poor academic achievement. While the early identification of such deficits is paramount so that interventions can be started as early as possible, there is currently no systematically used ecologically valid paradigm for the ...

  5. Research interface on a programmable ultrasound scanner.

    Science.gov (United States)

    Shamdasani, Vijay; Bae, Unmin; Sikdar, Siddhartha; Yoo, Yang Mo; Karadayi, Kerem; Managuli, Ravi; Kim, Yongmin

    2008-07-01

    Commercial ultrasound machines in the past did not provide the ultrasound researchers access to raw ultrasound data. Lack of this ability has impeded evaluation and clinical testing of novel ultrasound algorithms and applications. Recently, we developed a flexible ultrasound back-end where all the processing for the conventional ultrasound modes, such as B, M, color flow and spectral Doppler, was performed in software. The back-end has been incorporated into a commercial ultrasound machine, the Hitachi HiVision 5500. The goal of this work is to develop an ultrasound research interface on the back-end for acquiring raw ultrasound data from the machine. The research interface has been designed as a software module on the ultrasound back-end. To increase the amount of raw ultrasound data that can be spooled in the limited memory available on the back-end, we have developed a method that can losslessly compress the ultrasound data in real time. The raw ultrasound data could be obtained in any conventional ultrasound mode, including duplex and triplex modes. Furthermore, use of the research interface does not decrease the frame rate or otherwise affect the clinical usability of the machine. The lossless compression of the ultrasound data in real time can increase the amount of data spooled by approximately 2.3 times, thus allowing more than 6s of raw ultrasound data to be acquired in all the modes. The interface has been used not only for early testing of new ideas with in vitro data from phantoms, but also for acquiring in vivo data for fine-tuning ultrasound applications and conducting clinical studies. We present several examples of how newer ultrasound applications, such as elastography, vibration imaging and 3D imaging, have benefited from this research interface. Since the research interface is entirely implemented in software, it can be deployed on existing HiVision 5500 ultrasound machines and may be easily upgraded in the future. The developed research

  6. Preparing for Future Learning with a Tangible User Interface: The Case of Neuroscience

    Science.gov (United States)

    Schneider, B.; Wallace, J.; Blikstein, P.; Pea, R.

    2013-01-01

    In this paper, we describe the development and evaluation of a microworld-based learning environment for neuroscience. Our system, BrainExplorer, allows students to discover the way neural pathways work by interacting with a tangible user interface. By severing and reconfiguring connections, users can observe how the visual field is impaired and,…

  7. Alliance Approach to the Modeling of Interfaces in Complex Heterogeneous Objects

    Czech Academy of Sciences Publication Activity Database

    Votruba, Z.; Novák, Mirko

    2010-01-01

    Roč. 20, č. 5 (2010), s. 609-619 ISSN 1210-0552 R&D Projects: GA AV ČR IAA201240701 Institutional research plan: CEZ:AV0Z10300504 Keywords : system alliance * interface * dynamics * control * automaton * neural structures * complex objects * heterogeneity * wholes * agents * holons Subject RIV: BC - Control Systems Theory Impact factor: 0.511, year: 2010

  8. A Graphical User Interface (GUI) for Automated Classification of Bradley Fighting Vehicle Shock Absorbers

    National Research Council Canada - National Science Library

    Sincebaugh, Patrick

    1998-01-01

    .... We then explain the design and capabilities of the SSATS graphical user interface (GUI), which includes the integration of a neural network classification scheme. We finish by discussing recent results of utilizing the system to test and evaluate Bradley armored vehicle shock absorbers.

  9. Control of Three-Phase Grid-Connected Microgrids Using Artificial Neural Networks

    OpenAIRE

    Shuhui, L.; Fu, X.; Jaithwa, I.; Alonso, E.; Fairbank, M.; Wunsch, D. C.

    2015-01-01

    A microgrid consists of a variety of inverter-interfaced distributed energy resources (DERs). A key issue is how to control DERs within the microgrid and how to connect them to or disconnect them from the microgrid quickly. This paper presents a strategy for controlling inverter-interfaced DERs within a microgrid using an artificial neural network, which implements a dynamic programming algorithm and is trained with a new Levenberg-Marquardt backpropagation algorithm. Compared to conventional...

  10. 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.

  11. Unfolding code for neutron spectrometry based on neural nets technology

    International Nuclear Information System (INIS)

    Ortiz R, J. M.; Vega C, H. R.

    2012-10-01

    The most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. The drawbacks associated with traditional unfolding procedures have motivated the need of complementary approaches. Novel methods based on Artificial Neural Networks have been widely investigated. In this work, a neutron spectrum unfolding code based on neural nets technology is presented. This unfolding code called Neutron Spectrometry and Dosimetry by means of Artificial Neural Networks was designed in a graphical interface under LabVIEW programming environment. The core of the code is an embedded neural network architecture, previously optimized by the R obust Design of Artificial Neural Networks Methodology . The main features of the code are: is easy to use, friendly and intuitive to the user. This code was designed for a Bonner Sphere System based on a 6 Lil(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. The main feature of the code is that as entrance data, only seven rate counts measurement with a Bonner spheres spectrometer are required for simultaneously unfold the 60 energy bins of the neutron spectrum and to calculate 15 dosimetric quantities, for radiation protection porpoises. This code generates a full report in html format with all relevant information. (Author)

  12. Unfolding code for neutron spectrometry based on neural nets technology

    Energy Technology Data Exchange (ETDEWEB)

    Ortiz R, J. M.; Vega C, H. R., E-mail: morvymm@yahoo.com.mx [Universidad Autonoma de Zacatecas, Unidad Academica de Ingenieria Electrica, Apdo. Postal 336, 98000 Zacatecas (Mexico)

    2012-10-15

    The most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. The drawbacks associated with traditional unfolding procedures have motivated the need of complementary approaches. Novel methods based on Artificial Neural Networks have been widely investigated. In this work, a neutron spectrum unfolding code based on neural nets technology is presented. This unfolding code called Neutron Spectrometry and Dosimetry by means of Artificial Neural Networks was designed in a graphical interface under LabVIEW programming environment. The core of the code is an embedded neural network architecture, previously optimized by the {sup R}obust Design of Artificial Neural Networks Methodology{sup .} The main features of the code are: is easy to use, friendly and intuitive to the user. This code was designed for a Bonner Sphere System based on a {sup 6}Lil(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. The main feature of the code is that as entrance data, only seven rate counts measurement with a Bonner spheres spectrometer are required for simultaneously unfold the 60 energy bins of the neutron spectrum and to calculate 15 dosimetric quantities, for radiation protection porpoises. This code generates a full report in html format with all relevant information. (Author)

  13. NESSUS/NASTRAN Interface

    Science.gov (United States)

    Millwater, Harry; Riha, David

    1996-01-01

    The NESSUS probabilistic analysis computer program has been developed with a built-in finite element analysis program NESSUS/FEM. However, the NESSUS/FEM program is specialized for engine structures and may not contain sufficient features for other applications. In addition, users often become well acquainted with a particular finite element code and want to use that code for probabilistic structural analysis. For these reasons, this work was undertaken to develop an interface between NESSUS and NASTRAN such that NASTRAN can be used for the finite element analysis and NESSUS can be used for the probabilistic analysis. In addition, NESSUS was restructured such that other finite element codes could be more easily coupled with NESSUS. NESSUS has been enhanced such that NESSUS will modify the NASTRAN input deck for a given set of random variables, run NASTRAN and read the NASTRAN result. The coordination between the two codes is handled automatically. The work described here was implemented within NESSUS 6.2 which was delivered to NASA in September 1995. The code runs on Unix machines: Cray, HP, Sun, SGI and IBM. The new capabilities have been implemented such that a user familiar with NESSUS using NESSUS/FEM and NASTRAN can immediately use NESSUS with NASTRAN. In other words, the interface with NASTRAN has been implemented in an analogous manner to the interface with NESSUS/FEM. Only finite element specific input has been changed. This manual is written as an addendum to the existing NESSUS 6.2 manuals. We assume users have access to NESSUS manuals and are familiar with the operation of NESSUS including probabilistic finite element analysis. Update pages to the NESSUS PFEM manual are contained in Appendix E. The finite element features of the code and the probalistic analysis capabilities are summarized.

  14. Brain-computer interfaces

    DEFF Research Database (Denmark)

    Treder, Matthias S.; Miklody, Daniel; Blankertz, Benjamin

    quality measure'. We were able to show that for stimuli close to the perceptual threshold, there was sometimes a discrepancy between overt responses and brain responses, shedding light on subjects using different response criteria (e.g., more liberal or more conservative). To conclude, brain-computer...... of perceptual and cognitive biases. Furthermore, subjects can only report on stimuli if they have a clear percept of them. On the other hand, the electroencephalogram (EEG), the electrical brain activity measured with electrodes on the scalp, is a more direct measure. It allows us to tap into the ongoing neural...... auditory processing stream. In particular, it can tap brain processes that are pre-conscious or even unconscious, such as the earliest brain responses to sounds stimuli in primary auditory cortex. In a series of studies, we used a machine learning approach to show that the EEG can accurately reflect...

  15. Transport processes at fluidic interfaces

    CERN Document Server

    Reusken, Arnold

    2017-01-01

    There are several physico-chemical processes that determine the behavior of multiphase fluid systems – e.g., the fluid dynamics in the different phases and the dynamics of the interface(s), mass transport between the fluids, adsorption effects at the interface, and transport of surfactants on the interface – and result in heterogeneous interface properties. In general, these processes are strongly coupled and local properties of the interface play a crucial role. A thorough understanding of the behavior of such complex flow problems must be based on physically sound mathematical models, which especially account for the local processes at the interface. This book presents recent findings on the rigorous derivation and mathematical analysis of such models and on the development of numerical methods for direct numerical simulations. Validation results are based on specifically designed experiments using high-resolution experimental techniques. A special feature of this book is its focus on an interdisciplina...

  16. Magnons and interface magnetic substructures

    International Nuclear Information System (INIS)

    Djafari-Rouhani, B.; Dobrzynski, L.

    1975-01-01

    The localized magnons at an interface between two Heisenberg ferromagnets and the ferromagnetic stability at the interface are studied. The authors consider simple cubic crystals having the same lattice parameter and the same spin value in the fundamental state on each site, but different exchange integrals between first and second nearest neighbours. An interface by coupling two semi-infinite crystals having the same crystallographic surface is defined. The conditions for the existence of localized magnons at (001) interfaces as well as the dispersion curves of localized and resonant magnons in the high symmetry directions of the Brillouin zone are studied. The effect of the interface interactions on these modes is determined. It is shown that magnetic superstructures may exist at (110) interfaces. Such an instability is given by the existence of a soft localized mode at the interface [fr

  17. NESSUS/NASTRAN Interface

    Science.gov (United States)

    Millwater, Harry; Riha, David

    1996-01-01

    The NESSUS and NASTRAN computer codes were successfully integrated. The enhanced NESSUS code will use NASTRAN for the structural Analysis and NESSUS for the probabilistic analysis. Any quantities in the NASTRAN bulk data input can be random variables. Any NASTRAN result that is written to the output2 file can be returned to NESSUS as the finite element result. The interfacing between NESSUS and NASTRAN is handled automatically by NESSUS. NESSUS and NASTRAN can be run on different machines using the remote host option.

  18. Curriculum at the Interface

    DEFF Research Database (Denmark)

    This Symposium presents curriculum design and content issues in a Scandinavian business school at its Centenary. The aim is an exploration of an educational institution at the interface of the European Higher Education Area (EHEA) within the historical trends of the European Union. We hope...... of interdisciplinarity, use of text production as a tool in support of project and thesis writing, and the use of plurilingual content based teaching in a cooperative learning model for European studies. The history of one curriculum model initiated to educate better citizens, combining interdisciplinary methods...

  19. Virtual interface environment

    Science.gov (United States)

    Fisher, Scott S.

    1986-01-01

    A head-mounted, wide-angle, stereoscopic display system controlled by operator position, voice and gesture has been developed for use as a multipurpose interface environment. The system provides a multisensory, interactive display environment in which a user can virtually explore a 360-degree synthesized or remotely sensed environment and can viscerally interact with its components. Primary applications of the system are in telerobotics, management of large-scale integrated information systems, and human factors research. System configuration, application scenarios, and research directions are described.

  20. Interfaces para control cerebral

    OpenAIRE

    Spinelli, Enrique Mario

    2000-01-01

    La función de una interfaz para control cerebral basada en señales de electroencefalograma (EEG), en forma general denominada BCI (Brain control Interface), es establecer un enlace directo entre el cerebro y una máquina, sin utilizar acciones motoras directas. Una BCI permite realizar operaciones simples a partir de la interpretación de las señales de EEG. Su desarrollo está principalmente orientado hacia la ayuda a personas con discapacidades motoras severas, que poseen deterioros en el sist...

  1. Brain-computer interface

    DEFF Research Database (Denmark)

    2014-01-01

    A computer-implemented method of providing an interface between a user and a processing unit, the method comprising : presenting one or more stimuli to a user, each stimulus varying at a respective stimulation frequency, each stimulation frequency being associated with a respective user......-selectable input; receiving at least one signal indicative of brain activity of the user; and determining, from the received signal, which of the one or more stimuli the user attends to and selecting the user-selectable input associated with the stimulation frequency of the determined stimuli as being a user...

  2. Superconductivity at disordered interfaces

    International Nuclear Information System (INIS)

    Simanek, E.

    1979-01-01

    The increase of the superconducting transition temperature Tsub(c) due to the tunneling of conduction electrons into negative-u centers at a disordered metal-semiconductor interface is calculated. The strong dependence of the experimental increase of Tsub(c) on the Fermi energy of the metal is accounted for by the polaronic reduction of the tunneling matrix elements. The latter reduction is dynamically suppressed by the decreasing lifetime of the localized state as Esub(F) increases. The theoretical enhancement is sufficiently strong to explain the increase of Tsub(c) observed in eutectic alloys. (author)

  3. A novel asynchronous access method with binary interfaces

    Directory of Open Access Journals (Sweden)

    Torres-Solis Jorge

    2008-10-01

    Full Text Available Abstract Background Traditionally synchronous access strategies require users to comply with one or more time constraints in order to communicate intent with a binary human-machine interface (e.g., mechanical, gestural or neural switches. Asynchronous access methods are preferable, but have not been used with binary interfaces in the control of devices that require more than two commands to be successfully operated. Methods We present the mathematical development and evaluation of a novel asynchronous access method that may be used to translate sporadic activations of binary interfaces into distinct outcomes for the control of devices requiring an arbitrary number of commands to be controlled. With this method, users are required to activate their interfaces only when the device under control behaves erroneously. Then, a recursive algorithm, incorporating contextual assumptions relevant to all possible outcomes, is used to obtain an informed estimate of user intention. We evaluate this method by simulating a control task requiring a series of target commands to be tracked by a model user. Results When compared to a random selection, the proposed asynchronous access method offers a significant reduction in the number of interface activations required from the user. Conclusion This novel access method offers a variety of advantages over traditionally synchronous access strategies and may be adapted to a wide variety of contexts, with primary relevance to applications involving direct object manipulation.

  4. An artifical neural network for detection of simulated dental caries

    Energy Technology Data Exchange (ETDEWEB)

    Kositbowornchai, S. [Khon Kaen Univ. (Thailand). Dept. of Oral Diagnosis; Siriteptawee, S.; Plermkamon, S.; Bureerat, S. [Khon Kaen Univ. (Thailand). Dept. of Mechanical Engineering; Chetchotsak, D. [Khon Kaen Univ. (Thailand). Dept. of Industrial Engineering

    2006-08-15

    Objects: A neural network was developed to diagnose artificial dental caries using images from a charged-coupled device (CCD)camera and intra-oral digital radiography. The diagnostic performance of this neural network was evaluated against a gold standard. Materials and methods: The neural network design was the Learning Vector Quantization (LVQ) used to classify a tooth surface as sound or as having dental caries. The depth of the dental caries was indicated on a graphic user interface (GUI) screen developed by Matlab programming. Forty-nine images of both sound and simulated dental caries, derived from a CCD camera and by digital radiography, were used to 'train' an artificial neural network. After the 'training' process, a separate test-set comprising 322 unseen images was evaluated. Tooth sections and microscopic examinations were used to confirm the actual dental caries status.The performance of neural network was evaluated using diagnostic test. Results: The sensitivity (95%CI)/specificity (95%CI) of dental caries detection by the CCD camera and digital radiography were 0.77(0.68-0.85)/0.85(0.75-0.92) and 0.81(0.72-0.88)/0.93(0.84-0.97), respectively. The accuracy of caries depth-detection by the CCD camera and digital radiography was 58 and 40%, respectively. Conclusions: The model neural network used in this study could be a prototype for caries detection but should be improved for classifying caries depth. Our study suggests an artificial neural network can be trained to make the correct interpretations of dental caries. (orig.)

  5. An artifical neural network for detection of simulated dental caries

    International Nuclear Information System (INIS)

    Kositbowornchai, S.; Siriteptawee, S.; Plermkamon, S.; Bureerat, S.; Chetchotsak, D.

    2006-01-01

    Objects: A neural network was developed to diagnose artificial dental caries using images from a charged-coupled device (CCD)camera and intra-oral digital radiography. The diagnostic performance of this neural network was evaluated against a gold standard. Materials and methods: The neural network design was the Learning Vector Quantization (LVQ) used to classify a tooth surface as sound or as having dental caries. The depth of the dental caries was indicated on a graphic user interface (GUI) screen developed by Matlab programming. Forty-nine images of both sound and simulated dental caries, derived from a CCD camera and by digital radiography, were used to 'train' an artificial neural network. After the 'training' process, a separate test-set comprising 322 unseen images was evaluated. Tooth sections and microscopic examinations were used to confirm the actual dental caries status.The performance of neural network was evaluated using diagnostic test. Results: The sensitivity (95%CI)/specificity (95%CI) of dental caries detection by the CCD camera and digital radiography were 0.77(0.68-0.85)/0.85(0.75-0.92) and 0.81(0.72-0.88)/0.93(0.84-0.97), respectively. The accuracy of caries depth-detection by the CCD camera and digital radiography was 58 and 40%, respectively. Conclusions: The model neural network used in this study could be a prototype for caries detection but should be improved for classifying caries depth. Our study suggests an artificial neural network can be trained to make the correct interpretations of dental caries. (orig.)

  6. Optimizing the Usability of Brain-Computer Interfaces.

    Science.gov (United States)

    Zhang, Yin; Chase, Steve M

    2018-03-22

    Brain-computer interfaces are in the process of moving from the laboratory to the clinic. These devices act by reading neural activity and using it to directly control a device, such as a cursor on a computer screen. An open question in the field is how to map neural activity to device movement in order to achieve the most proficient control. This question is complicated by the fact that learning, especially the long-term skill learning that accompanies weeks of practice, can allow subjects to improve performance over time. Typical approaches to this problem attempt to maximize the biomimetic properties of the device in order to limit the need for extensive training. However, it is unclear if this approach would ultimately be superior to performance that might be achieved with a nonbiomimetic device once the subject has engaged in extended practice and learned how to use it. Here we approach this problem using ideas from optimal control theory. Under the assumption that the brain acts as an optimal controller, we present a formal definition of the usability of a device and show that the optimal postlearning mapping can be written as the solution of a constrained optimization problem. We then derive the optimal mappings for particular cases common to most brain-computer interfaces. Our results suggest that the common approach of creating biomimetic interfaces may not be optimal when learning is taken into account. More broadly, our method provides a blueprint for optimal device design in general control-theoretic contexts.

  7. Human Embryonic Stem Cells: A Model for the Study of Neural Development and Neurological Diseases

    Directory of Open Access Journals (Sweden)

    Piya Prajumwongs

    2016-01-01

    Full Text Available Although the mechanism of neurogenesis has been well documented in other organisms, there might be fundamental differences between human and those species referring to species-specific context. Based on principles learned from other systems, it is found that the signaling pathways required for neural induction and specification of human embryonic stem cells (hESCs recapitulated those in the early embryo development in vivo at certain degree. This underscores the usefulness of hESCs in understanding early human neural development and reinforces the need to integrate the principles of developmental biology and hESC biology for an efficient neural differentiation.

  8. Animal models for studying neural crest development: is the mouse different?

    Science.gov (United States)

    Barriga, Elias H; Trainor, Paul A; Bronner, Marianne; Mayor, Roberto

    2015-05-01

    The neural crest is a uniquely vertebrate cell type and has been well studied in a number of model systems. Zebrafish, Xenopus and chick embryos largely show consistent requirements for specific genes in early steps of neural crest development. By contrast, knockouts of homologous genes in the mouse often do not exhibit comparable early neural crest phenotypes. In this Spotlight article, we discuss these species-specific differences, suggest possible explanations for the divergent phenotypes in mouse and urge the community to consider these issues and the need for further research in complementary systems. © 2015. Published by The Company of Biologists Ltd.

  9. Early life diets with prebiotics and bioactive milk fractions attenuate the impact of stress on learned helplessness behaviours and alter gene expression within neural circuits important for stress resistance.

    Science.gov (United States)

    Mika, Agnieszka; Day, Heidi E W; Martinez, Alexander; Rumian, Nicole L; Greenwood, Benjamin N; Chichlowski, Maciej; Berg, Brian M; Fleshner, Monika

    2017-02-01

    Manipulating gut microbes may improve mental health. Prebiotics are indigestible compounds that increase the growth and activity of health-promoting microorganisms, yet few studies have examined how prebiotics affect CNS function. Using an acute inescapable stressor known to produce learned helplessness behaviours such as failure to escape and exaggerated fear, we tested whether early life supplementation of a blend of two prebiotics, galactooligosaccharide (GOS) and polydextrose (PDX), and the glycoprotein lactoferrin (LAC) would attenuate behavioural and biological responses to stress later in life. Juvenile, male F344 rats were fed diets containing either GOS and PDX alone, LAC alone, or GOS, PDX and LAC. All diets altered gut bacteria, while diets containing GOS and PDX increased Lactobacillus spp. After 4 weeks, rats were exposed to inescapable stress, and either immediately killed for blood and tissues, or assessed for learned helplessness 24 h later. Diets did not attenuate stress effects on spleen weight, corticosterone and blood glucose; however, all diets differentially attenuated stress-induced learned helplessness. Notably, in situ hybridization revealed that all diets reduced stress-evoked cfos mRNA in the dorsal raphe nucleus (DRN), a structure important for learned helplessness behaviours. In addition, GOS, PDX and LAC diet attenuated stress-evoked decreases in mRNA for the 5-HT 1A autoreceptor in the DRN and increased basal BDNF mRNA within the prefrontal cortex. These data suggest early life diets containing prebiotics and/or LAC promote behavioural stress resistance and uniquely modulate gene expression in corresponding circuits. © 2016 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

  10. Early Forming a Hummingbird-like Hovering Neural Network Circuitry Pattern with Reentrant Spatiotemporal Energy-Sensory Orientation Privileged to Avoid “Epilepsy” Based on a Biomimetic Acetylcholinesterase Memcapacitor Prosthesis

    Directory of Open Access Journals (Sweden)

    Ellen T. Chen

    2015-08-01

    Full Text Available The hummingbird’s significant asymmetry hovering flight with energy conservation pattern is remarkable among all vertebrates. However, little is known to human’s neuronal network circuitry current flow pattern for whether or not has this privilege during slow wave sleeping (SWS. What is the advantage in order to avoid diseases if we have this network pattern ? A memory device was developed with nanostructured biomimetic acetylcholinesterase (ACHE gorge membrane on gold chips as memcapacitor 1, served as a normal brain network prosthesis, compared with a mutated ACHE prosthesis as device 2, for evaluation of neuronal network circuitry integrity in the presence of Amyloid- beta (Ab under the conditions of free from tracers and antibodies in spiked NIST SRM 965A human serum. Three categories of Reentrant Energy-Sensory images are presented based on infused brain pulse energies in a matrix of “Sensory Biomarkers” having frequencies over 0.25-333 Hz at free and fixed Ab levels, respectively. Early non-symptomatic epilepsy was indentified and predicted by device 2 due to Pathological High Frequency Oscillation (pHFO and large areas of 38 µM Ab re-depositions. Device 1 sensitively “feels” Ab damage because of its Frequency Oscillation (HFO enhanced the hummingbird- like hovering pattern with higher reentrant energy sensitivity of 0.12 pj/bit/s/µm3 without Ab compared with Ab, 13 aj/bit/s/µm3/nM over 3.8-471 nM range over 0.003-4s. Device 1 reliably detected early CR dysfunction privileged to avoid epilepsy.

  11. Decoding the non-stationary neuron spike trains by dual Monte Carlo point process estimation in motor Brain Machine Interfaces.

    Science.gov (United States)

    Liao, Yuxi; Li, Hongbao; Zhang, Qiaosheng; Fan, Gong; Wang, Yiwen; Zheng, Xiaoxiang

    2014-01-01

    Decoding algorithm in motor Brain Machine Interfaces translates the neural signals to movement parameters. They usually assume the connection between the neural firings and movements to be stationary, which is not true according to the recent studies that observe the time-varying neuron tuning property. This property results from the neural plasticity and motor learning etc., which leads to the degeneration of the decoding performance when the model is fixed. To track the non-stationary neuron tuning during decoding, we propose a dual model approach based on Monte Carlo point process filtering method that enables the estimation also on the dynamic tuning parameters. When applied on both simulated neural signal and in vivo BMI data, the proposed adaptive method performs better than the one with static tuning parameters, which raises a promising way to design a long-term-performing model for Brain Machine Interfaces decoder.

  12. SEVERITY CLASSIFICATION OF MICROANEURYSMS USING NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    Shree Divya R

    2014-01-01

    Full Text Available Diabetic Retinopathy is one of the most common causes of blindness that leads to the loss of vision to the human eye. Several methods have been proposed to detect several defects of the human eye like hemorrhages, exudates etc. which are to be considered as the major symptoms. Among them, Microaneurysms should be considered as one of the severe condition for the early blindness. Several techniques have been proposed based on this, but they have certain drawbacks. A new technique called neural network taken for presentation, helps to detect and determine the severity of Microaneurysms which would be able to give a better performance than the existing techniques.

  13. Portraying User Interface History

    DEFF Research Database (Denmark)

    Jørgensen, Anker Helms

    2008-01-01

    history. Next the paper analyses a selected sample of papers on UI history at large. The analysis shows that the current state-of-art is featured by three aspects: Firstly internalism, in that the papers adress the tech­nologies in their own right with little con­text­ualization, secondly whiggism...... in that they largely address prevailing UI techno­logies, and thirdly history from above in that they focus on the great deeds of the visionaries. The paper then compares this state-of-art in UI history to the much more mature fields history of computing and history of technology. Based hereon, some speculations......The user interface is coming of age. Papers adressing UI history have appeared in fair amounts in the last 25 years. Most of them address particular aspects such as an in­novative interface paradigm or the contribution of a visionary or a research lab. Contrasting this, papers addres­sing UI...

  14. 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.

  15. Multiple network interface core apparatus and method

    Science.gov (United States)

    Underwood, Keith D [Albuquerque, NM; Hemmert, Karl Scott [Albuquerque, NM

    2011-04-26

    A network interface controller and network interface control method comprising providing a single integrated circuit as a network interface controller and employing a plurality of network interface cores on the single integrated circuit.

  16. 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...

  17. 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

  18. 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. .

  19. 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...

  20. 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...

  1. Transient expression of collagen type II at epitheliomesenchymal interfaces during morphogenesis of the cartilaginous neurocranium.

    Science.gov (United States)

    Thorogood, P; Bee, J; von der Mark, K

    1986-08-01

    In the avian embryo a matrix-mediated tissue interaction between retinal pigmented epithelium and neural crest-derived periocular mesenchyme leads to the differentiation of (scleral) cartilage. The composition of the extracellular matrix at the interface between these two tissues has been examined immunohistochemically, both during and after the interaction has taken place. Of the matrix components studied (fibronectin, laminin, and collagen types I, II, IV, and V) only collagen type II displayed a dramatic change in distribution between the two stages. During the interaction, at stage 15, type II was present in the extracellular compartment basal to the epithelium. After completion of the interaction, collagen type II was no longer detectable at the interface even though it was readily detectable in the vitreous humor, cornea, and perinotochordal sheath, and subsequently will be expressed by the chondrogenic tissue itself as overt differentiation commences. These results suggest that collagen type II might be causally involved in this particular epitheliomesenchymal interaction. Examination of the spatial and temporal patterns of collagen type II expression elsewhere in the developing craniofacial complex revealed a hitherto unreported pattern of distribution. In addition to its predictable locations (i.e., cornea, vitreous, and perinotochordal sheath) it was found to be present at certain other sites, for example, at the basal surfaces of some neuroepithelia. These additional locations are all known to be sites of chondrogenesis-promoting tissue interactions which result in the formation of the elements of the cartilaginous neurocranium (e.g., otic vesicle). Furthermore this spatial distribution exhibits a changing temporal pattern in that it is detectable at the time that the interactions are known to be taking place, but subsequently is no longer detectable by the immunohistochemical means employed. This definable pattern of transient collagen type II

  2. Emergent coordination underlying learning to reach to grasp with a brain-machine interface.

    Science.gov (United States)

    Vaidya, Mukta; Balasubramanian, Karthikeyan; Southerland, Joshua; Badreldin, Islam; Eleryan, Ahmed; Shattuck, Kelsey; Gururangan, Suchin; Slutzky, Marc; Osborne, Leslie; Fagg, Andrew; Oweiss, Karim; Hatsopoulos, Nicholas G

    2018-04-01

    The development of coordinated reach-to-grasp movement has been well studied in infants and children. However, the role of motor cortex during this development is unclear because it is difficult to study in humans. We took the approach of using a brain-machine interface (BMI) paradigm in rhesus macaques with prior therapeutic amputations to examine the emergence of novel, coordinated reach to grasp. Previous research has shown that after amputation, the cortical area previously involved in the control of the lost limb undergoes reorganization, but prior BMI work has largely relied on finding neurons that already encode specific movement-related information. In this study, we taught macaques to cortically control a robotic arm and hand through operant conditioning, using neurons that were not explicitly reach or grasp related. Over the course of training, stereotypical patterns emerged and stabilized in the cross-covariance between the reaching and grasping velocity profiles, between pairs of neurons involved in controlling reach and grasp, and to a comparable, but lesser, extent between other stable neurons in the network. In fact, we found evidence of this structured coordination between pairs composed of all combinations of neurons decoding reach or grasp and other stable neurons in the network. The degree of and participation in coordination was highly correlated across all pair types. Our approach provides a unique model for studying the development of novel, coordinated reach-to-grasp movement at the behavioral and cortical levels. NEW & NOTEWORTHY Given that motor cortex undergoes reorganization after amputation, our work focuses on training nonhuman primates with chronic amputations to use neurons that are not reach or grasp related to control a robotic arm to reach to grasp through the use of operant conditioning, mimicking early development. We studied the development of a novel, coordinated behavior at the behavioral and cortical level, and the neural

  3. Amphioxus and lamprey AP-2 genes: implications for neural crest evolution and migration patterns

    Science.gov (United States)

    Meulemans, Daniel; Bronner-Fraser, Marianne

    2002-01-01

    The neural crest is a uniquely vertebrate cell type present in the most basal vertebrates, but not in cephalochordates. We have studied differences in regulation of the neural crest marker AP-2 across two evolutionary transitions: invertebrate to vertebrate, and agnathan to gnathostome. Isolation and comparison of amphioxus, lamprey and axolotl AP-2 reveals its extensive expansion in the vertebrate dorsal neural tube and pharyngeal arches, implying co-option of AP-2 genes by neural crest cells early in vertebrate evolution. Expression in non-neural ectoderm is a conserved feature in amphioxus and vertebrates, suggesting an ancient role for AP-2 genes in this tissue. There is also common expression in subsets of ventrolateral neurons in the anterior neural tube, consistent with a primitive role in brain development. Comparison of AP-2 expression in axolotl and lamprey suggests an elaboration of cranial neural crest patterning in gnathostomes. However, migration of AP-2-expressing neural crest cells medial to the pharyngeal arch mesoderm appears to be a primitive feature retained in all vertebrates. Because AP-2 has essential roles in cranial neural crest differentiation and proliferation, the co-option of AP-2 by neural crest cells in the vertebrate lineage was a potentially crucial event in vertebrate evolution.

  4. Ethics in published brain-computer interface research

    Science.gov (United States)

    Specker Sullivan, L.; Illes, J.

    2018-02-01

    Objective. Sophisticated signal processing has opened the doors to more research with human subjects than ever before. The increase in the use of human subjects in research comes with a need for increased human subjects protections. Approach. We quantified the presence or absence of ethics language in published reports of brain-computer interface (BCI) studies that involved human subjects and qualitatively characterized ethics statements. Main results. Reports of BCI studies with human subjects that are published in neural engineering and engineering journals are anchored in the rationale of technological improvement. Ethics language is markedly absent, omitted from 31% of studies published in neural engineering journals and 59% of studies in biomedical engineering journals. Significance. As the integration of technological tools with the capacities of the mind deepens, explicit attention to ethical issues will ensure that broad human benefit is embraced and not eclipsed by technological exclusiveness.

  5. Transient analysis for PWR reactor core using neural networks predictors

    International Nuclear Information System (INIS)

    Gueray, B.S.

    2001-01-01

    In this study, transient analysis for a Pressurized Water Reactor core has been performed. A lumped parameter approximation is preferred for that purpose, to describe the reactor core together with mechanism which play an important role in dynamic analysis. The dynamic behavior of the reactor core during transients is analyzed considering the transient initiating events, wich are an essential part of Safety Analysis Reports. several transients are simulated based on the employed core model. Simulation results are in accord the physical expectations. A neural network is developed to predict the future response of the reactor core, in advance. The neural network is trained using the simulation results of a number of representative transients. Structure of the neural network is optimized by proper selection of transfer functions for the neurons. Trained neural network is used to predict the future responses following an early observation of the changes in system variables. Estimated behaviour using the neural network is in good agreement with the simulation results for various for types of transients. Results of this study indicate that the designed neural network can be used as an estimator of the time dependent behavior of the reactor core under transient conditions

  6. Identifying Broadband Rotational Spectra with Neural Networks

    Science.gov (United States)

    Zaleski, Daniel P.; Prozument, Kirill

    2017-06-01

    A typical broadband rotational spectrum may contain several thousand observable transitions, spanning many species. Identifying the individual spectra, particularly when the dynamic range reaches 1,000:1 or even 10,000:1, can be challenging. One approach is to apply automated fitting routines. In this approach, combinations of 3 transitions can be created to form a "triple", which allows fitting of the A, B, and C rotational constants in a Watson-type Hamiltonian. On a standard desktop computer, with a target molecule of interest, a typical AUTOFIT routine takes 2-12 hours depending on the spectral density. A new approach is to utilize machine learning to train a computer to recognize the patterns (frequency spacing and relative intensities) inherit in rotational spectra and to identify the individual spectra in a raw broadband rotational spectrum. Here, recurrent neural networks have been trained to identify different types of rotational spectra and classify them accordingly. Furthermore, early results in applying convolutional neural networks for spectral object recognition in broadband rotational spectra appear promising. Perez et al. "Broadband Fourier transform rotational spectroscopy for structure determination: The water heptamer." Chem. Phys. Lett., 2013, 571, 1-15. Seifert et al. "AUTOFIT, an Automated Fitting Tool for Broadband Rotational Spectra, and Applications to 1-Hexanal." J. Mol. Spectrosc., 2015, 312, 13-21. Bishop. "Neural networks for pattern recognition." Oxford university press, 1995.

  7. Artificial Neural Network Model for Predicting Compressive

    Directory of Open Access Journals (Sweden)

    Salim T. Yousif

    2013-05-01

    Full Text Available   Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature.    The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor  affecting the output of the model.     The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.

  8. 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

  9. 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

  10. Combinatorial Nano-Bio Interfaces.

    Science.gov (United States)

    Cai, Pingqiang; Zhang, Xiaoqian; Wang, Ming; Wu, Yun-Long; Chen, Xiaodong

    2018-06-08

    Nano-bio interfaces are emerging from the convergence of engineered nanomaterials and biological entities. Despite rapid growth, clinical translation of biomedical nanomaterials is heavily compromised by the lack of comprehensive understanding of biophysicochemical interactions at nano-bio interfaces. In the past decade, a few investigations have adopted a combinatorial approach toward decoding nano-bio interfaces. Combinatorial nano-bio interfaces comprise the design of nanocombinatorial libraries and high-throughput bioevaluation. In this Perspective, we address challenges in combinatorial nano-bio interfaces and call for multiparametric nanocombinatorics (composition, morphology, mechanics, surface chemistry), multiscale bioevaluation (biomolecules, organelles, cells, tissues/organs), and the recruitment of computational modeling and artificial intelligence. Leveraging combinatorial nano-bio interfaces will shed light on precision nanomedicine and its potential applications.

  11. Urban Media and Interfaces

    DEFF Research Database (Denmark)

    2013-01-01

    For ten weeks in 2013, nineteen eclectic students from Anthropology, Ethnology and Design formed cross-disciplinary teams to research existing practices and possible futures in Blågården. Social media is radically changing how urban space is explored, experienced and communicated. For example...... for current and potential visitors as mentioned in the social housing plan for the area. On the other hand, the area's mixed ethnicity, colorful shops and cafes are valued by city tourists and other visitors who seek authentic experiences in local contexts. Against this background, Det Gode Naboskab......, Wonderful Copenhagen and Socialsquare jointly raise these questions: What is the role of social media as interface between the area around Blågårds Plads, its local communities and (potential) visitors, considering perspectives of security, control and planning? What are the challenges and opportunities...

  12. Embodiment and Interface

    DEFF Research Database (Denmark)

    Gregersen, Andreas Lindegaard; Grodal, Torben Kragh

    2008-01-01

    The article discusses – based on neurological and phenomenological theory - how the human embodiment supports and constrains the interaction between players and video games. It analyses embodied interaction with the specific hardware/software configuration of the Nintendo Wii and Wii Tennis as well...... as other game system configurations. The article argues that playing video games may provide experiences of extended embodiment where players may experience ownership of both actions and virtual bodies related to the represented game world. The article shows how ownership may be related to differences...... of the player as patient, i.e. being the object of another agent’s actions.  Keywords: Video games, embodiment, interface, agency, action, control, cognition  ...

  13. Porphyrins at interfaces

    Science.gov (United States)

    Auwärter, Willi; Écija, David; Klappenberger, Florian; Barth, Johannes V.

    2015-02-01

    Porphyrins and other tetrapyrrole macrocycles possess an impressive variety of functional properties that have been exploited in natural and artificial systems. Different metal centres incorporated within the tetradentate ligand are key for achieving and regulating vital processes, including reversible axial ligation of adducts, electron transfer, light-harvesting and catalytic transformations. Tailored substituents optimize their performance, dictating their arrangement in specific environments and mediating the assembly of molecular nanoarchitectures. Here we review the current understanding of these species at well-defined interfaces, disclosing exquisite insights into their structural and chemical properties, and also discussing methods by which to manipulate their intramolecular and organizational features. The distinct characteristics arising from the interfacial confinement offer intriguing prospects for molecular science and advanced materials. We assess the role of surface interactions with respect to electronic and physicochemical characteristics, and describe in situ metallation pathways, molecular magnetism, rotation and switching. The engineering of nanostructures, organized layers, interfacial hybrid and bio-inspired systems is also addressed.

  14. Neural Networks for Modeling and Control of Particle Accelerators

    Science.gov (United States)

    Edelen, A. L.; Biedron, S. G.; Chase, B. E.; Edstrom, D.; Milton, S. V.; Stabile, P.

    2016-04-01

    Particle accelerators are host to myriad nonlinear and complex physical phenomena. They often involve a multitude of interacting systems, are subject to tight performance demands, and should be able to run for extended periods of time with minimal interruptions. Often times, traditional control techniques cannot fully meet these requirements. One promising avenue is to introduce machine learning and sophisticated control techniques inspired by artificial intelligence, particularly in light of recent theoretical and practical advances in these fields. Within machine learning and artificial intelligence, neural networks are particularly well-suited to modeling, control, and diagnostic analysis of complex, nonlinear, and time-varying systems, as well as systems with large parameter spaces. Consequently, the use of neural network-based modeling and control techniques could be of significant benefit to particle accelerators. For the same reasons, particle accelerators are also ideal test-beds for these techniques. Many early attempts to apply neural networks to particle accelerators yielded mixed results due to the relative immaturity of the technology for such tasks. The purpose of this paper is to re-introduce neural networks to the particle accelerator community and report on some work in neural network control that is being conducted as part of a dedicated collaboration between Fermilab and Colorado State University (CSU). We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.

  15. APST interfaces in LINCS

    Energy Technology Data Exchange (ETDEWEB)

    Fletcher, J.G.

    1995-07-01

    APST is an acronym for the four highest of the seven layers of the LINCS hierarchy of communication protocols: (from high to low) Application, Presentation, Session, and Transport. Routines in each but the lowest of these APST layers can utilize the facilities of any lower APST layer (normally, but not necessarily, the immediately next lower layer) by invoking various primitives (macros that in most cases are subroutine calls) defining the upper interface of the lower layer. So there are three APST interfaces: Presentation layer, used by the Application layer; Session layer, normally used by the Presentation layer; and Transport layer, normally used by the Session layer. Logically, each end of a stream (unidirectional sequence of transmitted information) is handled by three modules, one module each for the Presentation, Session, and Transport layers, and each of these modules deals with only that one end of that one stream. The internal workings of the layers, particularly the Transport layer, do not necessarily exhibit this same modularization; for example, the two oppositely directed streams between the same two ends (constituting an association) may interact within a layer. However, such interaction is an implementational detail of no direct interest to those utilizing the layer. The present document does not describe implementation, nor does it discuss in any detail how the modules employ packet headings and data formats to communicate with their partner modules at the other end of a stream. There being one logical module per end of stream is a characteristic only of the Presentation, Session, and Transport layers. An Application layer module usually manages several streams, orchestrating them to achieve some desired purpose. The modules of the layers (Network, Link, and Physical) below the APST layers each handle many streams, multiplexing them through the nodes and channels of the network to transmit them from their origins to their destinations.

  16. SpikingLab: modelling agents controlled by Spiking Neural Networks in Netlogo.

    Science.gov (United States)

    Jimenez-Romero, Cristian; Johnson, Jeffrey

    2017-01-01

    The scientific interest attracted by Spiking Neural Networks (SNN) has lead to the development of tools for the simulation and study of neuronal dynamics ranging from phenomenological models to the more sophisticated and biologically accurate Hodgkin-and-Huxley-based and multi-compartmental models. However, despite the multiple features offered by neural modelling tools, their integration with environments for the simulation of robots and agents can be challenging and time consuming. The implementation of artificial neural circuits to control robots generally involves the following tasks: (1) understanding the simulation tools, (2) creating the neural circuit in the neural simulator, (3) linking the simulated neural circuit with the environment of the agent and (4) programming the appropriate interface in the robot or agent to use the neural controller. The accomplishment of the above-mentioned tasks can be challenging, especially for undergraduate students or novice researchers. This paper presents an alternative tool which facilitates the simulation of simple SNN circuits using the multi-agent simulation and the programming environment Netlogo (educational software that simplifies the study and experimentation of complex systems). The engine proposed and implemented in Netlogo for the simulation of a functional model of SNN is a simplification of integrate and fire (I&F) models. The characteristics of the engine (including neuronal dynamics, STDP learning and synaptic delay) are demonstrated through the implementation of an agent representing an artificial insect controlled by a simple neural circuit. The setup of the experiment and its outcomes are described in this work.

  17. Acid chat: gestural interface design

    OpenAIRE

    Gökhan, Ali Oytun; Gokhan, Ali Oytun

    2005-01-01

    AcidChat is an experimental design project that aims to create an innovative computer software interface for Internet chat software using today's well known technologies; Adobe Photoshop, Macromedia Freehand and digital photography. The aim of the project is to create new understandings of interface and it's usage, by adding new conceptions to chat based interfaces which creates a totally new look at the computer software and application. One of the key features is to add a gestural approach ...

  18. Continuity and change in children's longitudinal neural responses to numbers.

    Science.gov (United States)

    Emerson, Robert W; Cantlon, Jessica F

    2015-03-01

    Human children possess the ability to approximate numerical quantity nonverbally from a young age. Over the course of early childhood, children develop increasingly precise representations of numerical values, including a symbolic number system that allows them to conceive of numerical information as Arabic numerals or number words. Functional brain imaging studies of adults report that activity in bilateral regions of the intraparietal sulcus (IPS) represents a key neural correlate of numerical cognition. Developmental neuroimaging studies indicate that the right IPS develops its number-related neural response profile more rapidly than the left IPS during early childhood. One prediction that can be derived from previous findings is that there is longitudinal continuity in the number-related neural responses of the right IPS over development while the development of the left IPS depends on the acquisition of numerical skills. We tested this hypothesis using fMRI in a longitudinal design with children ages 4 to 9. We found that neural responses in the right IPS are correlated over a 1-2-year period in young children whereas left IPS responses change systematically as a function of children's numerical discrimination acuity. The data are consistent with the hypothesis that functional properties of the right IPS in numerical processing are stable over early childhood whereas the functions of the left IPS are dynamically modulated by the development of numerical skills. © 2014 John Wiley & Sons Ltd.

  19. 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

  20. The Future of Brain-Computer Interfacing (keynote paper)

    NARCIS (Netherlands)

    Nijholt, Antinus

    In this paper we survey some early applications and research on brain-computer interfacing. We emphasize and revalue the role the views on artistic and playful applications have played. In previous years various road maps for BCI research appeared. The interest in medical applications has guided BCI

  1. The Neural Foundations of Reaction and Action in Aversive Motivation.

    Science.gov (United States)

    Campese, Vincent D; Sears, Robert M; Moscarello, Justin M; Diaz-Mataix, Lorenzo; Cain, Christopher K; LeDoux, Joseph E

    2016-01-01

    Much of the early research in aversive learning concerned motivation and reinforcement in avoidance conditioning and related paradigms. When the field transitioned toward the focus on Pavlovian threat conditioning in isolation, this paved the way for the clear understanding of the psychological principles and neural and molecular mechanisms responsible for this type of learning and memory that has unfolded over recent decades. Currently, avoidance conditioning is being revisited, and with what has been learned about associative aversive learning, rapid progress is being made. We review, below, the literature on the neural substrates critical for learning in instrumental active avoidance tasks and conditioned aversive motivation.

  2. Weed Growth Stage Estimator Using Deep Convolutional Neural Networks

    DEFF Research Database (Denmark)

    Teimouri, Nima; Dyrmann, Mads; Nielsen, Per Rydahl

    2018-01-01

    conditions with regards to soil types, resolution and light settings. Then, 9649 of these images were used for training the computer, which automatically divided the weeds into nine growth classes. The performance of this proposed convolutional neural network approach was evaluated on a further set of 2516...... in estimating the number of leaves and 96% accuracy when accepting a deviation of two leaves. These results show that this new method of using deep convolutional neural networks has a relatively high ability to estimate early growth stages across a wide variety of weed species....

  3. Neural network for prediction of superheater fireside corrosion

    Energy Technology Data Exchange (ETDEWEB)

    Makkonen, P. [Foster Wheeler Energia Oy, Karhula R and D Center, Karhula (Finland)

    1998-12-31

    Superheater corrosion causes vast annual losses to the power companies. If the corrosion could be reliably predicted, new power plants could be designed accordingly, and knowledge of fuel selection and determination of process conditions could be utilized to minimize superheater corrosion. If relations between inputs and the output are poorly known, conventional models depending on corrosion theories will fail. A prediction model based on a neural network is capable of learning from errors and improving its performance as the amount of data increases. The neural network developed during this study predicts superheater corrosion with 80 % accuracy at early stage of the project. (orig.) 10 refs.

  4. Neural network for prediction of superheater fireside corrosion

    Energy Technology Data Exchange (ETDEWEB)

    Makkonen, P [Foster Wheeler Energia Oy, Karhula R and D Center, Karhula (Finland)

    1999-12-31

    Superheater corrosion causes vast annual losses to the power companies. If the corrosion could be reliably predicted, new power plants could be designed accordingly, and knowledge of fuel selection and determination of process conditions could be utilized to minimize superheater corrosion. If relations between inputs and the output are poorly known, conventional models depending on corrosion theories will fail. A prediction model based on a neural network is capable of learning from errors and improving its performance as the amount of data increases. The neural network developed during this study predicts superheater corrosion with 80 % accuracy at early stage of the project. (orig.) 10 refs.

  5. Selectivity and Longevity of Peripheral-Nerve and Machine Interfaces: A Review

    Directory of Open Access Journals (Sweden)

    Usman Ghafoor

    2017-10-01

    Full Text Available For those individuals with upper-extremity amputation, a daily normal living activity is no longer possible or it requires additional effort and time. With the aim of restoring their sensory and motor functions, theoretical and technological investigations have been carried out in the field of neuroprosthetic systems. For transmission of sensory feedback, several interfacing modalities including indirect (non-invasive, direct-to-peripheral-nerve (invasive, and cortical stimulation have been applied. Peripheral nerve interfaces demonstrate an edge over the cortical interfaces due to the sensitivity in attaining cortical brain signals. The peripheral nerve interfaces are highly dependent on interface designs and are required to be biocompatible with the nerves to achieve prolonged stability and longevity. Another criterion is the selection of nerves that allows minimal invasiveness and damages as well as high selectivity for a large number of nerve fascicles. In this paper, we review the nerve-machine interface modalities noted above with more focus on peripheral nerve interfaces, which are responsible for provision of sensory feedback. The invasive interfaces for recording and stimulation of electro-neurographic signals include intra-fascicular, regenerative-type interfaces that provide multiple contact channels to a group of axons inside the nerve and the extra-neural-cuff-type interfaces that enable interaction with many axons around the periphery of the nerve. Section Current Prosthetic Technology summarizes the advancements made to date in the field of neuroprosthetics toward the achievement of a bidirectional nerve-machine interface with more focus on sensory feedback. In the Discussion section, the authors propose a hybrid interface technique for achieving better selectivity and long-term stability using the available nerve interfacing techniques.

  6. Selectivity and Longevity of Peripheral-Nerve and Machine Interfaces: A Review

    Science.gov (United States)

    Ghafoor, Usman; Kim, Sohee; Hong, Keum-Shik

    2017-01-01

    For those individuals with upper-extremity amputation, a daily normal living activity is no longer possible or it requires additional effort and time. With the aim of restoring their sensory and motor functions, theoretical and technological investigations have been carried out in the field of neuroprosthetic systems. For transmission of sensory feedback, several interfacing modalities including indirect (non-invasive), direct-to-peripheral-nerve (invasive), and cortical stimulation have been applied. Peripheral nerve interfaces demonstrate an edge over the cortical interfaces due to the sensitivity in attaining cortical brain signals. The peripheral nerve interfaces are highly dependent on interface designs and are required to be biocompatible with the nerves to achieve prolonged stability and longevity. Another criterion is the selection of nerves that allows minimal invasiveness and damages as well as high selectivity for a large number of nerve fascicles. In this paper, we review the nerve-machine interface modalities noted above with more focus on peripheral nerve interfaces, which are responsible for provision of sensory feedback. The invasive interfaces for recording and stimulation of electro-neurographic signals include intra-fascicular, regenerative-type interfaces that provide multiple contact channels to a group of axons inside the nerve and the extra-neural-cuff-type interfaces that enable interaction with many axons around the periphery of the nerve. Section Current Prosthetic Technology summarizes the advancements made to date in the field of neuroprosthetics toward the achievement of a bidirectional nerve-machine interface with more focus on sensory feedback. In the Discussion section, the authors propose a hybrid interface technique for achieving better selectivity and long-term stability using the available nerve interfacing techniques. PMID:29163122

  7. Playful User Interfaces. Interfaces that Invite Social and Physical Interaction.

    NARCIS (Netherlands)

    Nijholt, Antinus; Unknown, [Unknown

    2014-01-01

    This book is about user interfaces to applications that can be considered as ‘playful’. The interfaces to such applications should be ‘playful’ as well. The application should be fun, and interacting with such an application should, of course, be fun as well. Maybe more. Why not expect that the

  8. Configurations of NPD : production interfaces and interface integration mechanisms

    NARCIS (Netherlands)

    Smulders, F.E.H.M.; Boer, H.; Hansen, P.H.K.; Gubi, E.; Dorst, C.H.

    2002-01-01

    This paper describes and illustrates different configurations of the interface between new product development and production processes, including both intra–firm and inter–firm interfaces. These configurations are partly based on a process view of product innovation and partly on a structural view

  9. Estimation of Muscle Force Based on Neural Drive in a Hemispheric Stroke Survivor.

    Science.gov (United States)

    Dai, Chenyun; Zheng, Yang; Hu, Xiaogang

    2018-01-01

    Robotic assistant-based therapy holds great promise to improve the functional recovery of stroke survivors. Numerous neural-machine interface techniques have been used to decode the intended movement to control robotic systems for rehabilitation therapies. In this case report, we tested the feasibility of estimating finger extensor muscle forces of a stroke survivor, based on the decoded descending neural drive through population motoneuron discharge timings. Motoneuron discharge events were obtained by decomposing high-density surface electromyogram (sEMG) signals of the finger extensor muscle. The neural drive was extracted from the normalized frequency of the composite discharge of the motoneuron pool. The neural-drive-based estimation was also compared with the classic myoelectric-based estimation. Our results showed that the neural-drive-based approach can better predict the force output, quantified by lower estimation errors and higher correlations with the muscle force, compared with the myoelectric-based estimation. Our findings suggest that the neural-drive-based approach can potentially be used as a more robust interface signal for robotic therapies during the stroke rehabilitation.

  10. Apple cuticle: the perfect interface

    Science.gov (United States)

    Curry, Eric; Arey, Bruce

    2010-06-01

    The domestic apple might well be called an 'extreme' fruit. In the arid Northwest United States, the fruit often tolerates surface temperatures ranging from -2 °C in the early spring to 50 °C in the heat of summer, and again to -2 °C during controlled postharvest storage for up to 12 months. During its 18-month existence, the apple maintains a cuticle that is dynamic and environmentally responsive to protect against 1) cellular water loss during desiccation stress and 2) excessive uptake of standing surface moisture. Physiological disorders of the peel such as russeting, cracking, splitting, flecking and lenticel marking, develop as epidermal cells respond to rapid changes in ambient conditions at specific developmental stages during the growing season. Resultant market losses underlie research investigating the nature of apple cuticle growth and development. Ultrastructural analysis of the pro-cuticle using scanning electron microscopy indicates an overlapping network of lipid-based distally-elongating microtubules--produced by and connected to epidermal cells--which co-polymerize to form an organic solvent-insoluble semi-permeable cutin matrix. Microtubule elongation, aggregation, and polymerization function together as long as the fruit continues to enlarge. The nature of lipid transport from the epidermal cells through the cell wall to become part of the cuticular matrix was explored using an FEI Helios NanoLabTM DualBeamTM focused ion beam/scanning electron microscope on chemically- and cryo-fixed peel tissue from mature or freshly harvested apples. Based on microtubule dimensions, regular projections found at the cell/cuticle interface suggest an array of microtubule-like structures associated with the epidermal cell.

  11. UWB based low-cost and non-invasive practical breast cancer early detection

    Science.gov (United States)

    Vijayasarveswari, V.; Khatun, S.; Fakir, M. M.; Jusoh, M.; Ali, S.

    2017-03-01

    Breast cancer is one of the main causes of women death worldwide. Breast tumor is an early stage of cancer that locates in cells of a human breast. As there is no remedy, early detection is crucial. Towards this, Ultra-Wideband (UWB) is a prominent candidate. It is a wireless communication technology which can achieve high bandwidth with low power utilization. UWB is suitable to be used for short range communication systems including breast cancer detection since it is secure, non-invasive and human health friendly. This paper presents the low-cost and non-invasive early breast cancer detection strategy using UWB sensor (or antenna). Emphasis is given here to detect breast tumor in 2D and 3D environments. The developed system consisted of hardware and software. Hardware included UWB transceiver and a pair of home-made directional sensor/antenna. The software included feed-forward back propagation Neural Network (NN) module to detect the tumor existence, size and location along with soft interface between software and hardware. Forward scattering technique was used by placing two sensors diagonally opposite sides of a breast phantom. UWB pulses were transmitted from one side of phantom and received from other side, controlled by the software interface in PC environment. Collected received signals were then fed into the NN module for training, testing and validation. The system exhibited detection efficiency on tumor existence, location (x, y, z), and size were approximately 100%, (78.17%, 70.66%, 92.46%), 85.86% respectively. The proposed UWB based early breast cancer detection system could be more practical with low-cost, user friendly and non-harmful features. This project may help users to monitor their breast health regularly at their home.

  12. Neural Point-and-Click Communication by a Person With Incomplete Locked-In Syndrome.

    Science.gov (United States)

    Bacher, Daniel; Jarosiewicz, Beata; Masse, Nicolas Y; Stavisky, Sergey D; Simeral, John D; Newell, Katherine; Oakley, Erin M; Cash, Sydney S; Friehs, Gerhard; Hochberg, Leigh R

    2015-06-01

    A goal of brain-computer interface research is to develop fast and reliable means of communication for individuals with paralysis and anarthria. We evaluated the ability of an individual with incomplete locked-in syndrome enrolled in the BrainGate Neural Interface System pilot clinical trial to communicate using neural point-and-click control. A general-purpose interface was developed to provide control of a computer cursor in tandem with one of two on-screen virtual keyboards. The novel BrainGate Radial Keyboard was compared to a standard QWERTY keyboard in a balanced copy-spelling task. The Radial Keyboard yielded a significant improvement in typing accuracy and speed-enabling typing rates over 10 correct characters per minute. The participant used this interface to communicate face-to-face with research staff by using text-to-speech conversion, and remotely using an internet chat application. This study demonstrates the first use of an intracortical brain-computer interface for neural point-and-click communication by an individual with incomplete locked-in syndrome. © The Author(s) 2014.

  13. Mediator Med23 deficiency enhances neural differentiation of murine embryonic stem cells through modulating BMP signaling.

    Science.gov (United States)

    Zhu, Wanqu; Yao, Xiao; Liang, Yan; Liang, Dan; Song, Lu; Jing, Naihe; Li, Jinsong; Wang, Gang

    2015-02-01

    Unraveling the mechanisms underlying early neural differentiation of embryonic stem cells (ESCs) is crucial to developing cell-based therapies of neurodegenerative diseases. Neural fate acquisition is proposed to be controlled by a 'default' mechanism, for which the molecular regulation is not well understood. In this study, we investigated the functional roles of Mediator Med23 in pluripotency and lineage commitment of murine ESCs. Unexpectedly, we found that, despite the largely unchanged pluripotency and self-renewal of ESCs, Med23 depletion rendered the cells prone to neural differentiation in different differentiation assays. Knockdown of two other Mediator subunits, Med1 and Med15, did not alter the neural differentiation of ESCs. Med15 knockdown selectively inhibited endoderm differentiation, suggesting the specificity of cell fate control by distinctive Mediator subunits. Gene profiling revealed that Med23 depletion attenuated BMP signaling in ESCs. Mechanistically, MED23 modulated Bmp4 expression by controlling the activity of ETS1, which is involved in Bmp4 promoter-enhancer communication. Interestingly, med23 knockdown in zebrafish embryos also enhanced neural development at early embryogenesis, which could be reversed by co-injection of bmp4 mRNA. Taken together, our study reveals an intrinsic, restrictive role of MED23 in early neural development, thus providing new molecular insights for neural fate determination. © 2015. Published by The Company of Biologists Ltd.

  14. Furthering interface design in services

    NARCIS (Netherlands)

    Secomandi, F.; Snelders, Dirk

    2010-01-01

    This paper critically discusses ideas from the book Interface: An Approach to Design (Bonsiepe 1999) as a springboard for thinking through the design and use of services. We introduce Bonsiepe’s take on phenomenological philosophy of technology in his conception of the user interface. Next to that,

  15. Human-machine interface upgrade

    International Nuclear Information System (INIS)

    Kropik, M.; Matejka, K.; Sklenka, L.; Chab, V.

    2002-01-01

    The article describes a new human-machine interface that was installed at the VR-1 training reactor. The human-machine interface upgrade was completed in the summer 2001. The interface was designed with respect to functional, ergonomic and aesthetic requirements. The interface is based on a personal computer equipped with two displays. One display enables alphanumeric communication between the reactor operator and the nuclear reactor I and C. The second display is a graphical one. It presents the status of the reactor, principal parameters (as power, period), control rods positions, course of the reactor power. Furthermore, it is possible to set parameters, to show the active core configuration, to perform reactivity calculations, etc. The software for the new human-machine interface was produced with the InTouch developing tool of the Wonder-Ware Company. It is possible to switch the language of the interface between Czech and English because of many foreign students and visitors to the reactor. Microcomputer based communication units with proper software were developed to connect the new human-machine interface with the present reactor I and C. The new human-machine interface at the VR-1 training reactor improves the comfort and safety of the reactor utilisation, facilitates experiments and training, and provides better support for foreign visitors. (orig.)

  16. Modeling soft interface dominated systems

    NARCIS (Netherlands)

    Lamorgese, A.; Mauri, R.; Sagis, L.M.C.

    2017-01-01

    The two main continuum frameworks used for modeling the dynamics of soft multiphase systems are the Gibbs dividing surface model, and the diffuse interface model. In the former the interface is modeled as a two dimensional surface, and excess properties such as a surface density, or surface energy

  17. GRAPHIC INTERFACES FOR ENGINEERING APPLICATIONS

    Directory of Open Access Journals (Sweden)

    Ion PANA,

    2012-05-01

    Full Text Available Using effective the method of calculating Fitness for Service requires the achievement of graphical interfaces. This paper presents an example of such interfaces, made with Visual Basic program and used in the evaluation of pipelines in a research contract [4

  18. Preface (to Playful User Interfaces)

    NARCIS (Netherlands)

    Unknown, [Unknown; Nijholt, A.; Nijholt, Antinus

    2014-01-01

    This book is about user interfaces to applications that can be considered as ‘playful’. The interfaces to such applications should be ‘playful’ as well. The application should be fun, and interacting with such an application should, of course, be fun as well. Maybe more. Why not expect that the

  19. Overview of Graphical User Interfaces.

    Science.gov (United States)

    Hulser, Richard P.

    1993-01-01

    Discussion of graphical user interfaces for online public access catalogs (OPACs) covers the history of OPACs; OPAC front-end design, including examples from Indiana University and the University of Illinois; and planning and implementation of a user interface. (10 references) (EA)

  20. Playful Interfaces : Introduction and History

    NARCIS (Netherlands)

    Nijholt, Anton; Nijholt, A.

    2014-01-01

    In this short survey we have some historical notes about human-computer interface development with an emphasis on interface technology that has allowed us to design playful interactions with applications. The applications do not necessarily have to be entertainment applications. We can have playful