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.

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.

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)

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.

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

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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.

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.

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

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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

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

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

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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.

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

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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.

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.

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

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

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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.

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

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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.

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.

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

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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.

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

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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.

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.

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

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

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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.

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.

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.

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

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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

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.

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.

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.

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

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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.

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

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

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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.

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.

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

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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.

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

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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.

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...

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

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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.

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

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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.

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

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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.

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

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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.

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

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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.

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

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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.

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.

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

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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.

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

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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.

Modality-Specific Axonal Regeneration: Towards selective regenerative neural interfaces

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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.

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

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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.

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

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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

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

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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

Neural changes underlying early stages of L2 vocabulary acquisition.

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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.

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

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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.

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

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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.

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

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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.

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.

Neural control of finger movement via intracortical brain-machine interface

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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

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

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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

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

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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

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.

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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

Neurophysiology and neural engineering: a review.

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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.

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

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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.

Integration of active devices on smart polymers for neural interfaces

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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

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

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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.

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

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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.

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

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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.

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

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

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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.

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

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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.

Evolvable Neural Software System

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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.

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

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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.

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

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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.

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

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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.

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

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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.

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...

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.

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.

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.

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...

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.

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.

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.

Microchannel neural interface manufacture by stacking silicone and metal foil laminae

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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.

High-Density Stretchable Electrode Grids for Chronic Neural Recording.

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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.

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

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.

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.

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.

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.

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

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.

Neural Basis of Brain Dysfunction Produced by Early Sleep Problems

Directory of Open Access Journals (Sweden)

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.

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.

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.

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.

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.

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.

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.

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.

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...

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.

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.

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

Improving Neural Recording Technology at the Nanoscale

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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.

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

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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

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

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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].

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

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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.

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

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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.

Invasive Intraneural Interfaces: Foreign Body Reaction Issues

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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

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.

Characterization of Early Cortical Neural Network ...

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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

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.

Automatic Speech Recognition from Neural Signals: A Focused Review

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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.

Neural Point-and-Click Communication by a Person With Incomplete Locked-In Syndrome.

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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.

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.

Emotional sounds modulate early neural processing of emotional pictures

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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.

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

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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.

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.

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 ...

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

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...

Shaping Early Reorganization of Neural Networks Promotes Motor Function after Stroke

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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

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.

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

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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.

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.;

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...

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

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

NeuroMEMS: Neural Probe Microtechnologies

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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.

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

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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

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

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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.

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

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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.

Entropy Learning in Neural Network

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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.

Estimation of Muscle Force Based on Neural Drive in a Hemispheric Stroke Survivor.

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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.

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

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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.

Knowledge synthesis with maps of neural connectivity.

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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.

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.

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

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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.

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

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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

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.

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)

Early perception and structural identity: neural implementation

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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.

Kernel Temporal Differences for Neural Decoding

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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

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.

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

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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. �

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.

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.

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«.

Neural decoding of collective wisdom with multi-brain computing.

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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

Rodent Zic Genes in Neural Network Wiring.

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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.

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.

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

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.

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

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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

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

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.

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.

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.

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

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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.

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.

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

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

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.

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.

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

Comparison of Mono-, Bi-, and Tripolar Configurations for Stimulation and Recording With an Interfascicular Interface.

Science.gov (United States)

Nielsen, Thomas N; Sevcencu, Cristian; Struijk, Johannes J

2014-01-01

Previous studies have indicated that electrodes placed between fascicles can provide nerve recruitment with high topological selectivity if the areas of interest in the nerve are separated with passive elements. In this study, we investigated if this separation of fascicles also can provide topologically selective nerve recordings and compared the performance of mono-, bi-, and tripolar configurations for stimulation and recording with an intra-neural interface. The interface was implanted in the sciatic nerve of 10 rabbits and achieved a median selectivity of Ŝ=0.98-0.99 for all stimulation configurations, while recording selectivity configurations was in the range of Ŝ=0.70-0.80 with the monopolar configuration providing the lowest and the average reference configuration the highest recording selectivity. Interfascicular electrodes could provide an interesting addition to the bulk of peripheral nerve interfaces available for neural prosthetic devices. The separation of the nerve into chambers by the passive elements of the electrode could ensure a higher selectivity than comparable cuff electrodes and the intra-neural location could provide an option of targeting mainly central fascicles. Further studies are, however, still required to develop biocompatible electrodes and test their stability and safety in chronic experiments.

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.

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

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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.

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.

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.

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

Recent Advances in Neural Recording Microsystems

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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.

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.

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.

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 ...

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

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...

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...

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.

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.

The Neural-fuzzy Thermal Error Compensation Controller on CNC Machining Center

Science.gov (United States)

Tseng, Pai-Chung; Chen, Shen-Len

The geometric errors and structural thermal deformation are factors that influence the machining accuracy of Computer Numerical Control (CNC) machining center. Therefore, researchers pay attention to thermal error compensation technologies on CNC machine tools. Some real-time error compensation techniques have been successfully demonstrated in both laboratories and industrial sites. The compensation results still need to be enhanced. In this research, the neural-fuzzy theory has been conducted to derive a thermal prediction model. An IC-type thermometer has been used to detect the heat sources temperature variation. The thermal drifts are online measured by a touch-triggered probe with a standard bar. A thermal prediction model is then derived by neural-fuzzy theory based on the temperature variation and the thermal drifts. A Graphic User Interface (GUI) system is also built to conduct the user friendly operation interface with Insprise C++ Builder. The experimental results show that the thermal prediction model developed by neural-fuzzy theory methodology can improve machining accuracy from 80µm to 3µm. Comparison with the multi-variable linear regression analysis the compensation accuracy is increased from ±10µm to ±3µm.

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.

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

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.

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

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...

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.

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.

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.

Encoder-decoder optimization for brain-computer interfaces.

Science.gov (United States)

Merel, Josh; Pianto, Donald M; Cunningham, John P; Paninski, Liam

2015-06-01

Neuroprosthetic brain-computer interfaces are systems that decode neural activity into useful control signals for effectors, such as a cursor on a computer screen. It has long been recognized that both the user and decoding system can adapt to increase the accuracy of the end effector. Co-adaptation is the process whereby a user learns to control the system in conjunction with the decoder adapting to learn the user's neural patterns. We provide a mathematical framework for co-adaptation and relate co-adaptation to the joint optimization of the user's control scheme ("encoding model") and the decoding algorithm's parameters. When the assumptions of that framework are respected, co-adaptation cannot yield better performance than that obtainable by an optimal initial choice of fixed decoder, coupled with optimal user learning. For a specific case, we provide numerical methods to obtain such an optimized decoder. We demonstrate our approach in a model brain-computer interface system using an online prosthesis simulator, a simple human-in-the-loop pyschophysics setup which provides a non-invasive simulation of the BCI setting. These experiments support two claims: that users can learn encoders matched to fixed, optimal decoders and that, once learned, our approach yields expected performance advantages.

Encoder-decoder optimization for brain-computer interfaces.

Directory of Open Access Journals (Sweden)

Josh Merel

2015-06-01

Full Text Available Neuroprosthetic brain-computer interfaces are systems that decode neural activity into useful control signals for effectors, such as a cursor on a computer screen. It has long been recognized that both the user and decoding system can adapt to increase the accuracy of the end effector. Co-adaptation is the process whereby a user learns to control the system in conjunction with the decoder adapting to learn the user's neural patterns. We provide a mathematical framework for co-adaptation and relate co-adaptation to the joint optimization of the user's control scheme ("encoding model" and the decoding algorithm's parameters. When the assumptions of that framework are respected, co-adaptation cannot yield better performance than that obtainable by an optimal initial choice of fixed decoder, coupled with optimal user learning. For a specific case, we provide numerical methods to obtain such an optimized decoder. We demonstrate our approach in a model brain-computer interface system using an online prosthesis simulator, a simple human-in-the-loop pyschophysics setup which provides a non-invasive simulation of the BCI setting. These experiments support two claims: that users can learn encoders matched to fixed, optimal decoders and that, once learned, our approach yields expected performance advantages.

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.

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.

Higher-order neural network software for distortion invariant object recognition

Science.gov (United States)

Reid, Max B.; Spirkovska, Lilly

1991-01-01

The state-of-the-art in pattern recognition for such applications as automatic target recognition and industrial robotic vision relies on digital image processing. We present a higher-order neural network model and software which performs the complete feature extraction-pattern classification paradigm required for automatic pattern recognition. Using a third-order neural network, we demonstrate complete, 100 percent accurate invariance to distortions of scale, position, and in-plate rotation. In a higher-order neural network, feature extraction is built into the network, and does not have to be learned. Only the relatively simple classification step must be learned. This is key to achieving very rapid training. The training set is much smaller than with standard neural network software because the higher-order network only has to be shown one view of each object to be learned, not every possible view. The software and graphical user interface run on any Sun workstation. Results of the use of the neural software in autonomous robotic vision systems are presented. Such a system could have extensive application in robotic manufacturing.

Beyond the VWFA: The orthography-semantics interface in spelling and reading

Science.gov (United States)

Purcell, Jeremy J.; Shea, Jennifer; Rapp, Brenda

2014-01-01

Lexical orthographic information provides the basis for recovering the meanings of words in reading and for generating correct word spellings in writing. Research has provided evidence that an area of the left ventral temporal cortex, a sub-region of what is often referred to as the Visual Word Form Area (VWFA), plays a significant role specifically in lexical orthographic processing. The current investigation goes beyond this previous work by examining the neurotopography of the interface of lexical orthography with semantics. We apply a novel lesion mapping approach with three individuals with acquired dysgraphia and dyslexia who suffered lesions to left ventral temporal cortex. To map cognitive processes to their neural substrates, this lesion mapping approach applies similar logical constraints as used in cognitive neuropsychological research. Using this approach, this investigation: (1) Identifies a region anterior to the VWFA that is important in the interface of orthographic information with semantics for reading and spelling; (2) Determines that, within this Orthography-Semantics Interface Region (OSIR), access to orthography from semantics (spelling) is topographically distinct from access to semantics from orthography (reading); (3) Provides evidence that, within this region, there is modality-specific access to and from lexical semantics for both spoken and written modalities, in both word production and comprehension. Overall, this study contributes to our understanding of the neural architecture at the lexical orthography-semantic-phonological interface within left ventral temporal cortex. PMID:24833190

Towards a miniaturized brain-machine-spinal cord interface (BMSI) for restoration of function after spinal cord injury.

Science.gov (United States)

Shahdoost, Shahab; Frost, Shawn; Van Acker, Gustaf; DeJong, Stacey; Dunham, Caleb; Barbay, Scott; Nudo, Randolph; Mohseni, Pedram

2014-01-01

Nearly 6 million people in the United States are currently living with paralysis in which 23% of the cases are related to spinal cord injury (SCI). Miniaturized closed-loop neural interfaces have the potential for restoring function and mobility lost to debilitating neural injuries such as SCI by leveraging recent advancements in bioelectronics and a better understanding of the processes that underlie functional and anatomical reorganization in an injured nervous system. This paper describes our current progress towards developing a miniaturized brain-machine-spinal cord interface (BMSI) that is envisioned to convert in real time the neural command signals recorded from the brain to electrical stimuli delivered to the spinal cord below the injury level. Specifically, the paper reports on a corticospinal interface integrated circuit (IC) as a core building block for such a BMSI that is capable of low-noise recording of extracellular neural spikes from the cerebral cortex as well as muscle activation using intraspinal microstimulation (ISMS) in a rat with contusion injury to the thoracic spinal cord. The paper further presents results from a neurobiological study conducted in both normal and SCI rats to investigate the effect of various ISMS parameters on movement thresholds in the rat hindlimb. Coupled with proper signal-processing algorithms in the future for the transformation between the cortically recorded data and ISMS parameters, such a BMSI has the potential to facilitate functional recovery after an SCI by re-establishing corticospinal communication channels lost due to the injury.

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).…

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.

Toward multi-area distributed network of implanted neural interrogators

Science.gov (United States)

Powell, Marc P.; Hou, Xiaoxiao; Galligan, Craig; Ashe, Jeffrey; Borton, David A.

2017-08-01

As we aim to improve our understanding of the brain, it is critical that researchers have simultaneous multi-area, large-scale access to the brain. Information processing in the brain occurs through close and distant coupling of functional sub-domains, as opposed to within isolated single neurons. However, commercially available neural interfaces capable of sensing electrophysiology of single neurons, currently allow access to only a small, mm3 volume of cortical cells, are not scalable to recording from orders of magnitude more neurons, and leverage bulky, skull mounted hardware and cabling sensitive to relative movements of the skull and brain. In this work, we propose a system capable of recording from many individual distributed neural interrogator nodes, untethered from any external electronics. Using an array of epidural inductive coils to wirelessly power the implanted electronics, the system is intended to be agnostic to the surgical placement of any individual node. Here, we demonstrate the ability to transmit nearly 15mW of power with greater than 50% power transfer efficiency, benchtop testing of individual subcircuit system components showing successful digitization of neural signals, and wireless transmission currently supporting a data rate of 3.84Mbps. We leverage a software defined radio based RF receiver to demodulate the data which can be stored in memory for later retrieval. Finally, we introduce a packaging technology capable of isolating active electronics from the surrounding tissue while providing capability for electrical feed-through assemblies for external neural interfacing. We expect, based on the presented preliminary findings, that the system can be integrated into a platform technology for the study of the intricate interactions between cortical domains.

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

Sensory System for Implementing a Human—Computer Interface Based on Electrooculography

Directory of Open Access Journals (Sweden)

Sergio Ortega

2010-12-01

Full Text Available This paper describes a sensory system for implementing a human–computer interface based on electrooculography. An acquisition system captures electrooculograms and transmits them via the ZigBee protocol. The data acquired are analysed in real time using a microcontroller-based platform running the Linux operating system. The continuous wavelet transform and neural network are used to process and analyse the signals to obtain highly reliable results in real time. To enhance system usability, the graphical interface is projected onto special eyewear, which is also used to position the signal-capturing electrodes.

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.

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

Brain-computer interfaces increase whole-brain signal to noise.

Science.gov (United States)

Papageorgiou, T Dorina; Lisinski, Jonathan M; McHenry, Monica A; White, Jason P; LaConte, Stephen M

2013-08-13

Brain-computer interfaces (BCIs) can convert mental states into signals to drive real-world devices, but it is not known if a given covert task is the same when performed with and without BCI-based control. Using a BCI likely involves additional cognitive processes, such as multitasking, attention, and conflict monitoring. In addition, it is challenging to measure the quality of covert task performance. We used whole-brain classifier-based real-time functional MRI to address these issues, because the method provides both classifier-based maps to examine the neural requirements of BCI and classification accuracy to quantify the quality of task performance. Subjects performed a covert counting task at fast and slow rates to control a visual interface. Compared with the same task when viewing but not controlling the interface, we observed that being in control of a BCI improved task classification of fast and slow counting states. Additional BCI control increased subjects' whole-brain signal-to-noise ratio compared with the absence of control. The neural pattern for control consisted of a positive network comprised of dorsal parietal and frontal regions and the anterior insula of the right hemisphere as well as an expansive negative network of regions. These findings suggest that real-time functional MRI can serve as a platform for exploring information processing and frontoparietal and insula network-based regulation of whole-brain task signal-to-noise ratio.

Neural-net based real-time economic dispatch for thermal power plants

Energy Technology Data Exchange (ETDEWEB)

Djukanovic, M.; Milosevic, B. [Inst. Nikola Tesla, Belgrade (Yugoslavia). Dept. of Power Systems; Calovic, M. [Univ. of Belgrade (Yugoslavia). Dept. of Electrical Engineering; Sobajic, D.J. [Electric Power Research Inst., Palo Alto, CA (United States)

1996-12-01

This paper proposes the application of artificial neural networks to real-time optimal generation dispatch of thermal units. The approach can take into account the operational requirements and network losses. The proposed economic dispatch uses an artificial neural network (ANN) for generation of penalty factors, depending on the input generator powers and identified system load change. Then, a few additional iterations are performed within an iterative computation procedure for the solution of coordination equations, by using reference-bus penalty-factors derived from the Newton-Raphson load flow. A coordination technique for environmental and economic dispatch of pure thermal systems, based on the neural-net theory for simplified solution algorithms and improved man-machine interface is introduced. Numerical results on two test examples show that the proposed algorithm can efficiently and accurately develop optimal and feasible generator output trajectories, by applying neural-net forecasts of system load patterns.

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

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 ...

Implantable fiber-optic interface for parallel multisite long-term optical dynamic brain interrogation in freely moving mice

Science.gov (United States)

Doronina-Amitonova, L. V.; Fedotov, I. V.; Ivashkina, O. I.; Zots, M. A.; Fedotov, A. B.; Anokhin, K. V.; Zheltikov, A. M.

2013-01-01

Seeing the big picture of functional responses within large neural networks in a freely functioning brain is crucial for understanding the cellular mechanisms behind the higher nervous activity, including the most complex brain functions, such as cognition and memory. As a breakthrough toward meeting this challenge, implantable fiber-optic interfaces integrating advanced optogenetic technologies and cutting-edge fiber-optic solutions have been demonstrated, enabling a long-term optogenetic manipulation of neural circuits in freely moving mice. Here, we show that a specifically designed implantable fiber-optic interface provides a powerful tool for parallel long-term optical interrogation of distinctly separate, functionally different sites in the brain of freely moving mice. This interface allows the same groups of neurons lying deeply in the brain of a freely behaving mouse to be reproducibly accessed and optically interrogated over many weeks, providing a long-term dynamic detection of genome activity in response to a broad variety of pharmacological and physiological stimuli. PMID:24253232

Nanowire electrodes for high-density stimulation and measurement of neural circuits

Directory of Open Access Journals (Sweden)

Jacob T. Robinson

2013-03-01

Full Text Available Brain-machine interfaces (BMIs that can precisely monitor and control neural activity will likely require new hardware with improved resolution and specificity. New nanofabricated electrodes with feature sizes and densities comparable to neural circuits may lead to such improvements. In this perspective, we review the recent development of vertical nanowire (NW electrodes that could provide highly parallel single-cell recording and stimulation for future BMIs. We compare the advantages of these devices and discuss some of the technical challenges that must be overcome for this technology to become a platform for next-generation closed-loop BMIs.

Two pore channel 2 differentially modulates neural differentiation of mouse embryonic stem cells.

Directory of Open Access Journals (Sweden)

Zhe-Hao Zhang

Full Text Available Nicotinic acid adenine dinucleotide phosphate (NAADP is an endogenous Ca(2+ mobilizing nucleotide presented in various species. NAADP mobilizes Ca(2+ from acidic organelles through two pore channel 2 (TPC2 in many cell types and it has been previously shown that NAADP can potently induce neuronal differentiation in PC12 cells. Here we examined the role of TPC2 signaling in the neural differentiation of mouse embryonic stem (ES cells. We found that the expression of TPC2 was markedly decreased during the initial ES cell entry into neural progenitors, and the levels of TPC2 gradually rebounded during the late stages of neurogenesis. Correspondingly, TPC2 knockdown accelerated mouse ES cell differentiation into neural progenitors but inhibited these neural progenitors from committing to neurons. Overexpression of TPC2, on the other hand, inhibited mouse ES cell from entering the early neural lineage. Interestingly, TPC2 knockdown had no effect on the differentiation of astrocytes and oligodendrocytes of mouse ES cells. Taken together, our data indicate that TPC2 signaling plays a temporal and differential role in modulating the neural lineage entry of mouse ES cells, in that TPC2 signaling inhibits ES cell entry to early neural progenitors, but is required for late neuronal differentiation.

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.

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.

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.

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

Deep Web Search Interface Identification: A Semi-Supervised Ensemble Approach

Directory of Open Access Journals (Sweden)

Hong Wang

2014-12-01

Full Text Available To surface the Deep Web, one crucial task is to predict whether a given web page has a search interface (searchable HyperText Markup Language (HTML form or not. Previous studies have focused on supervised classification with labeled examples. However, labeled data are scarce, hard to get and requires tediousmanual work, while unlabeled HTML forms are abundant and easy to obtain. In this research, we consider the plausibility of using both labeled and unlabeled data to train better models to identify search interfaces more effectively. We present a semi-supervised co-training ensemble learning approach using both neural networks and decision trees to deal with the search interface identification problem. We show that the proposed model outperforms previous methods using only labeled data. We also show that adding unlabeled data improves the effectiveness of the proposed model.

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.

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.

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.

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.

Sub-meninges implantation reduces immune response to neural implants.

Science.gov (United States)

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

2013-04-15

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

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.

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

Meninges harbor cells expressing neural precursor markers during development and adulthood.

Science.gov (United States)

Bifari, Francesco; Berton, Valeria; Pino, Annachiara; Kusalo, Marijana; Malpeli, Giorgio; Di Chio, Marzia; Bersan, Emanuela; Amato, Eliana; Scarpa, Aldo; Krampera, Mauro; Fumagalli, Guido; Decimo, Ilaria

2015-01-01

Brain and skull developments are tightly synchronized, allowing the cranial bones to dynamically adapt to the brain shape. At the brain-skull interface, meninges produce the trophic signals necessary for normal corticogenesis and bone development. Meninges harbor different cell populations, including cells forming the endosteum of the cranial vault. Recently, we and other groups have described the presence in meninges of a cell population endowed with neural differentiation potential in vitro and, after transplantation, in vivo. However, whether meninges may be a niche for neural progenitor cells during embryonic development and in adulthood remains to be determined. In this work we provide the first description of the distribution of neural precursor markers in rat meninges during development up to adulthood. We conclude that meninges share common properties with the classical neural stem cell niche, as they: (i) are a highly proliferating tissue; (ii) host cells expressing neural precursor markers such as nestin, vimentin, Sox2 and doublecortin; and (iii) are enriched in extracellular matrix components (e.g., fractones) known to bind and concentrate growth factors. This study underlines the importance of meninges as a potential niche for endogenous precursor cells during development and in adulthood.

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...

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.

Man-system interface based on automatic speech recognition: integration to a virtual control desk

Energy Technology Data Exchange (ETDEWEB)

Jorge, Carlos Alexandre F.; Mol, Antonio Carlos A.; Pereira, Claudio M.N.A.; Aghina, Mauricio Alves C., E-mail: calexandre@ien.gov.b, E-mail: mol@ien.gov.b, E-mail: cmnap@ien.gov.b, E-mail: mag@ien.gov.b [Instituto de Engenharia Nuclear (IEN/CNEN-RJ), Rio de Janeiro, RJ (Brazil); Nomiya, Diogo V., E-mail: diogonomiya@gmail.co [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil)

2009-07-01

This work reports the implementation of a man-system interface based on automatic speech recognition, and its integration to a virtual nuclear power plant control desk. The later is aimed to reproduce a real control desk using virtual reality technology, for operator training and ergonomic evaluation purpose. An automatic speech recognition system was developed to serve as a new interface with users, substituting computer keyboard and mouse. They can operate this virtual control desk in front of a computer monitor or a projection screen through spoken commands. The automatic speech recognition interface developed is based on a well-known signal processing technique named cepstral analysis, and on artificial neural networks. The speech recognition interface is described, along with its integration with the virtual control desk, and results are presented. (author)

Man-system interface based on automatic speech recognition: integration to a virtual control desk

International Nuclear Information System (INIS)

Jorge, Carlos Alexandre F.; Mol, Antonio Carlos A.; Pereira, Claudio M.N.A.; Aghina, Mauricio Alves C.; Nomiya, Diogo V.

2009-01-01

This work reports the implementation of a man-system interface based on automatic speech recognition, and its integration to a virtual nuclear power plant control desk. The later is aimed to reproduce a real control desk using virtual reality technology, for operator training and ergonomic evaluation purpose. An automatic speech recognition system was developed to serve as a new interface with users, substituting computer keyboard and mouse. They can operate this virtual control desk in front of a computer monitor or a projection screen through spoken commands. The automatic speech recognition interface developed is based on a well-known signal processing technique named cepstral analysis, and on artificial neural networks. The speech recognition interface is described, along with its integration with the virtual control desk, and results are presented. (author)

Comparison of Back propagation neural network and Back propagation neural network Based Particle Swarm intelligence in Diagnostic Breast Cancer

Directory of Open Access Journals (Sweden)

Farahnaz SADOUGHI

2014-03-01

Full Text Available Breast cancer is the most commonly diagnosed cancer and the most common cause of death in women all over the world. Use of computer technology supporting breast cancer diagnosing is now widespread and pervasive across a broad range of medical areas. Early diagnosis of this disease can greatly enhance the chances of long-term survival of breast cancer victims. Artificial Neural Networks (ANN as mainly method play important role in early diagnoses breast cancer. This paper studies Levenberg Marquardet Backpropagation (LMBP neural network and Levenberg Marquardet Backpropagation based Particle Swarm Optimization(LMBP-PSO for the diagnosis of breast cancer. The obtained results show that LMBP and LMBP based PSO system provides higher classification efficiency. But LMBP based PSO needs minimum training and testing time. It helps in developing Medical Decision System (MDS for breast cancer diagnosing. It can also be used as secondary observer in clinical decision making.

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.

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

SSVEP and ANN based optimal speller design for Brain Computer Interface

Directory of Open Access Journals (Sweden)

Irshad Ahmad Ansari

2015-07-01

Full Text Available This work put forwards an optimal BCI (Brain Computer Interface speller design based on Steady State Visual Evoked Potentials (SSVEP and Artificial Neural Network (ANN in order to help the people with severe motor impairments. This work is carried out to enhance the accuracy and communication rate of  BCI system. To optimize the BCI system, the work has been divided into two steps: First, designing of an encoding technique to choose characters from the speller interface and the second is the development and implementation of feature extraction algorithm to acquire optimal features, which is used to train the BCI system for classification using neural network. Optimization of speller interface is focused on representation of character matrix and its designing parameters. Then again, a lot of deliberations made in order to optimize selection of features and user’s time window. Optimized system works nearly the same with the new user and gives character per minute (CPM of 13 ± 2 with an average accuracy of 94.5% by choosing first two harmonics of power spectral density as the feature vectors and using the 2 second time window for each selection. Optimized BCI performs better with experienced users with an average accuracy of 95.1%. Such a good accuracy has not been reported before in account of fair enough CPM.DOI: 10.15181/csat.v2i2.1059

Application of neural networks to software quality modeling of a very large telecommunications system.

Science.gov (United States)

Khoshgoftaar, T M; Allen, E B; Hudepohl, J P; Aud, S J

1997-01-01

Society relies on telecommunications to such an extent that telecommunications software must have high reliability. Enhanced measurement for early risk assessment of latent defects (EMERALD) is a joint project of Nortel and Bell Canada for improving the reliability of telecommunications software products. This paper reports a case study of neural-network modeling techniques developed for the EMERALD system. The resulting neural network is currently in the prototype testing phase at Nortel. Neural-network models can be used to identify fault-prone modules for extra attention early in development, and thus reduce the risk of operational problems with those modules. We modeled a subset of modules representing over seven million lines of code from a very large telecommunications software system. The set consisted of those modules reused with changes from the previous release. The dependent variable was membership in the class of fault-prone modules. The independent variables were principal components of nine measures of software design attributes. We compared the neural-network model with a nonparametric discriminant model and found the neural-network model had better predictive accuracy.

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,…

Improved head direction command classification using an optimised Bayesian neural network.

Science.gov (United States)

Nguyen, Son T; Nguyen, Hung T; Taylor, Philip B; Middleton, James

2006-01-01

Assistive technologies have recently emerged to improve the quality of life of severely disabled people by enhancing their independence in daily activities. Since many of those individuals have limited or non-existing control from the neck downward, alternative hands-free input modalities have become very important for these people to access assistive devices. In hands-free control, head movement has been proved to be a very effective user interface as it can provide a comfortable, reliable and natural way to access the device. Recently, neural networks have been shown to be useful not only for real-time pattern recognition but also for creating user-adaptive models. Since multi-layer perceptron neural networks trained using standard back-propagation may cause poor generalisation, the Bayesian technique has been proposed to improve the generalisation and robustness of these networks. This paper describes the use of Bayesian neural networks in developing a hands-free wheelchair control system. The experimental results show that with the optimised architecture, classification Bayesian neural networks can detect head commands of wheelchair users accurately irrespective to their levels of injuries.

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.

Antagonism between the transcription factors NANOG and OTX2 specifies rostral or caudal cell fate during neural patterning transition.

Science.gov (United States)

Su, Zhenghui; Zhang, Yanqi; Liao, Baojian; Zhong, Xiaofen; Chen, Xin; Wang, Haitao; Guo, Yiping; Shan, Yongli; Wang, Lihui; Pan, Guangjin

2018-03-23

During neurogenesis, neural patterning is a critical step during which neural progenitor cells differentiate into neurons with distinct functions. However, the molecular determinants that regulate neural patterning remain poorly understood. Here we optimized the "dual SMAD inhibition" method to specifically promote differentiation of human pluripotent stem cells (hPSCs) into forebrain and hindbrain neural progenitor cells along the rostral-caudal axis. We report that neural patterning determination occurs at the very early stage in this differentiation. Undifferentiated hPSCs expressed basal levels of the transcription factor orthodenticle homeobox 2 (OTX2) that dominantly drove hPSCs into the "default" rostral fate at the beginning of differentiation. Inhibition of glycogen synthase kinase 3β (GSK3β) through CHIR99021 application sustained transient expression of the transcription factor NANOG at early differentiation stages through Wnt signaling. Wnt signaling and NANOG antagonized OTX2 and, in the later stages of differentiation, switched the default rostral cell fate to the caudal one. Our findings have uncovered a mutual antagonism between NANOG and OTX2 underlying cell fate decisions during neural patterning, critical for the regulation of early neural development in humans. © 2018 by The American Society for Biochemistry and Molecular Biology, Inc.

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.

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

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)

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...

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

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.)

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.)

SOX10-Nano-Lantern Reporter Human iPS Cells; A Versatile Tool for Neural Crest Research.

Directory of Open Access Journals (Sweden)

Tomoko Horikiri

Full Text Available The neural crest is a source to produce multipotent neural crest stem cells that have a potential to differentiate into diverse cell types. The transcription factor SOX10 is expressed through early neural crest progenitors and stem cells in vertebrates. Here we report the generation of SOX10-Nano-lantern (NL reporter human induced pluripotent stem cells (hiPS by using CRISPR/Cas9 systems, that are beneficial to investigate the generation and maintenance of neural crest progenitor cells. SOX10-NL positive cells are produced transiently from hiPS cells by treatment with TGFβ inhibitor SB431542 and GSK3 inhibitor CHIR99021. We found that all SOX10-NL-positive cells expressed an early neural crest marker NGFR, however SOX10-NL-positive cells purified from differentiated hiPS cells progressively attenuate their NL-expression under proliferation. We therefore attempted to maintain SOX10-NL-positive cells with additional signaling on the plane and sphere culture conditions. These SOX10-NL cells provide us to investigate mass culture with neural crest cells for stem cell research.

Regulation of Msx genes by a Bmp gradient is essential for neural crest specification.

Science.gov (United States)

Tribulo, Celeste; Aybar, Manuel J; Nguyen, Vu H; Mullins, Mary C; Mayor, Roberto

2003-12-01

There is evidence in Xenopus and zebrafish embryos that the neural crest/neural folds are specified at the border of the neural plate by a precise threshold concentration of a Bmp gradient. In order to understand the molecular mechanism by which a gradient of Bmp is able to specify the neural crest, we analyzed how the expression of Bmp targets, the Msx genes, is regulated and the role that Msx genes has in neural crest specification. As Msx genes are directly downstream of Bmp, we analyzed Msx gene expression after experimental modification in the level of Bmp activity by grafting a bead soaked with noggin into Xenopus embryos, by expressing in the ectoderm a dominant-negative Bmp4 or Bmp receptor in Xenopus and zebrafish embryos, and also through Bmp pathway component mutants in the zebrafish. All the results show that a reduction in the level of Bmp activity leads to an increase in the expression of Msx genes in the neural plate border. Interestingly, by reaching different levels of Bmp activity in animal cap ectoderm, we show that a specific concentration of Bmp induces msx1 expression to a level similar to that required to induce neural crest. Our results indicate that an intermediate level of Bmp activity specifies the expression of Msx genes in the neural fold region. In addition, we have analyzed the role that msx1 plays on neural crest specification. As msx1 has a role in dorsoventral pattering, we have carried out conditional gain- and loss-of-function experiments using different msx1 constructs fused to a glucocorticoid receptor element to avoid an early effect of this factor. We show that msx1 expression is able to induce all other early neural crest markers tested (snail, slug, foxd3) at the time of neural crest specification. Furthermore, the expression of a dominant negative of Msx genes leads to the inhibition of all the neural crest markers analyzed. It has been previously shown that snail is one of the earliest genes acting in the neural crest

SpikingLab: modelling agents controlled by Spiking Neural Networks in Netlogo.

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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.

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

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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.

Enhancing neural activity to drive respiratory plasticity following cervical spinal cord injury

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Hormigo, Kristiina M.; Zholudeva, Lyandysha V.; Spruance, Victoria M.; Marchenko, Vitaliy; Cote, Marie-Pascale; Vinit, Stephane; Giszter, Simon; Bezdudnaya, Tatiana; Lane, Michael A.

2016-01-01

Cervical spinal cord injury (SCI) results in permanent life-altering sensorimotor deficits, among which impaired breathing is one of the most devastating and life-threatening. While clinical and experimental research has revealed that some spontaneous respiratory improvement (functional plasticity) can occur post-SCI, the extent of the recovery is limited and significant deficits persist. Thus, increasing effort is being made to develop therapies that harness and enhance this neuroplastic potential to optimize long-term recovery of breathing in injured individuals. One strategy with demonstrated therapeutic potential is the use of treatments that increase neural and muscular activity (e.g. locomotor training, neural and muscular stimulation) and promote plasticity. With a focus on respiratory function post-SCI, this review will discuss advances in the use of neural interfacing strategies and activity-based treatments, and highlights some recent results from our own research. PMID:27582085

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.

Construction of implantable optical fibers for long-term optogenetic manipulation of neural circuits.

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Sparta, Dennis R; Stamatakis, Alice M; Phillips, Jana L; Hovelsø, Nanna; van Zessen, Ruud; Stuber, Garret D

2011-12-08

In vivo optogenetic strategies have redefined our ability to assay how neural circuits govern behavior. Although acutely implanted optical fibers have previously been used in such studies, long-term control over neuronal activity has been largely unachievable. Here we describe a method to construct implantable optical fibers to readily manipulate neural circuit elements with minimal tissue damage or change in light output over time (weeks to months). Implanted optical fibers readily interface with in vivo electrophysiological arrays or electrochemical detection electrodes. The procedure described here, from implant construction to the start of behavioral experimentation, can be completed in approximately 2-6 weeks. Successful use of implantable optical fibers will allow for long-term control of mammalian neural circuits in vivo, which is integral to the study of the neurobiology of behavior.

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

Isolation and culture of neural crest cells from embryonic murine neural tube.

Science.gov (United States)

Pfaltzgraff, Elise R; Mundell, Nathan A; Labosky, Patricia A

2012-06-02

The embryonic neural crest (NC) is a multipotent progenitor population that originates at the dorsal aspect of the neural tube, undergoes an epithelial to mesenchymal transition (EMT) and migrates throughout the embryo, giving rise to diverse cell types. NC also has the unique ability to influence the differentiation and maturation of target organs. When explanted in vitro, NC progenitors undergo self-renewal, migrate and differentiate into a variety of tissue types including neurons, glia, smooth muscle cells, cartilage and bone. NC multipotency was first described from explants of the avian neural tube. In vitro isolation of NC cells facilitates the study of NC dynamics including proliferation, migration, and multipotency. Further work in the avian and rat systems demonstrated that explanted NC cells retain their NC potential when transplanted back into the embryo. Because these inherent cellular properties are preserved in explanted NC progenitors, the neural tube explant assay provides an attractive option for studying the NC in vitro. To attain a better understanding of the mammalian NC, many methods have been employed to isolate NC populations. NC-derived progenitors can be cultured from post-migratory locations in both the embryo and adult to study the dynamics of post-migratory NC progenitors, however isolation of NC progenitors as they emigrate from the neural tube provides optimal preservation of NC cell potential and migratory properties. Some protocols employ fluorescence activated cell sorting (FACS) to isolate a NC population enriched for particular progenitors. However, when starting with early stage embryos, cell numbers adequate for analyses are difficult to obtain with FACS, complicating the isolation of early NC populations from individual embryos. Here, we describe an approach that does not rely on FACS and results in an approximately 96% pure NC population based on a Wnt1-Cre activated lineage reporter. The method presented here is adapted from

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.

A developmental perspective on the neural bases of human empathy.

Science.gov (United States)

Tousignant, Béatrice; Eugène, Fanny; Jackson, Philip L

2017-08-01

While empathy has been widely studied in philosophical and psychological literatures, recent advances in social neuroscience have shed light on the neural correlates of this complex interpersonal phenomenon. In this review, we provide an overview of brain imaging studies that have investigated the neural substrates of human empathy. Based on existing models of the functional architecture of empathy, we review evidence of the neural underpinnings of each main component, as well as their development from infancy. Although early precursors of affective sharing and self-other distinction appear to be present from birth, recent findings also suggest that even higher-order components of empathy such as perspective-taking and emotion regulation demonstrate signs of development during infancy. This merging of developmental and social neuroscience literature thus supports the view that ontogenic development of empathy is rooted in early infancy, well before the emergence of verbal abilities. With age, the refinement of top-down mechanisms may foster more appropriate empathic responses, thus promoting greater altruistic motivation and prosocial behaviors. Copyright © 2016 Elsevier Inc. All rights reserved.

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)

IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK FOR FACE RECOGNITION USING GABOR FEATURE EXTRACTION

Directory of Open Access Journals (Sweden)

Muthukannan K

2013-11-01

Full Text Available Face detection and recognition is the first step for many applications in various fields such as identification and is used as a key to enter into the various electronic devices, video surveillance, and human computer interface and image database management. This paper focuses on feature extraction in an image using Gabor filter and the extracted image feature vector is then given as an input to the neural network. The neural network is trained with the input data. The Gabor wavelet concentrates on the important components of the face including eye, mouth, nose, cheeks. The main requirement of this technique is the threshold, which gives privileged sensitivity. The threshold values are the feature vectors taken from the faces. These feature vectors are given into the feed forward neural network to train the network. Using the feed forward neural network as a classifier, the recognized and unrecognized faces are classified. This classifier attains a higher face deduction rate. By training more input vectors the system proves to be effective. The effectiveness of the proposed method is demonstrated by the experimental results.

Neural System Prediction and Identification Challenge

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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.

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.

Breast Cancer Diagnosis using Artificial Neural Networks with Extreme Learning Techniques

OpenAIRE

Chandra Prasetyo Utomo; Aan Kardiana; Rika Yuliwulandari

2014-01-01

Breast cancer is the second cause of dead among women. Early detection followed by appropriate cancer treatment can reduce the deadly risk. Medical professionals can make mistakes while identifying a disease. The help of technology such as data mining and machine learning can substantially improve the diagnosis accuracy. Artificial Neural Networks (ANN) has been widely used in intelligent breast cancer diagnosis. However, the standard Gradient-Based Back Propagation Artificial Neural Networks...

Application of hierarchical dissociated neural network in closed-loop hybrid system integrating biological and mechanical intelligence.

Directory of Open Access Journals (Sweden)

Yongcheng Li

Full Text Available Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including 'random' and '4Q' (cultured neurons artificially divided into four interconnected parts neural network. Compared to the random cultures, the '4Q' cultures presented absolutely different activities, and the robot controlled by the '4Q' network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems.

Application of Hierarchical Dissociated Neural Network in Closed-Loop Hybrid System Integrating Biological and Mechanical Intelligence

Science.gov (United States)

Zhang, Bin; Wang, Yuechao; Li, Hongyi

2015-01-01

Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including ‘random’ and ‘4Q’ (cultured neurons artificially divided into four interconnected parts) neural network. Compared to the random cultures, the ‘4Q’ cultures presented absolutely different activities, and the robot controlled by the ‘4Q’ network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems. PMID:25992579

Application of hierarchical dissociated neural network in closed-loop hybrid system integrating biological and mechanical intelligence.

Science.gov (United States)

Li, Yongcheng; Sun, Rong; Zhang, Bin; Wang, Yuechao; Li, Hongyi

2015-01-01

Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including 'random' and '4Q' (cultured neurons artificially divided into four interconnected parts) neural network. Compared to the random cultures, the '4Q' cultures presented absolutely different activities, and the robot controlled by the '4Q' network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems.

A bidirectional brain-machine interface algorithm that approximates arbitrary force-fields.

Directory of Open Access Journals (Sweden)

Alessandro Vato

Full Text Available We examine bidirectional brain-machine interfaces that control external devices in a closed loop by decoding motor cortical activity to command the device and by encoding the state of the device by delivering electrical stimuli to sensory areas. Although it is possible to design this artificial sensory-motor interaction while maintaining two independent channels of communication, here we propose a rule that closes the loop between flows of sensory and motor information in a way that approximates a desired dynamical policy expressed as a field of forces acting upon the controlled external device. We previously developed a first implementation of this approach based on linear decoding of neural activity recorded from the motor cortex into a set of forces (a force field applied to a point mass, and on encoding of position of the point mass into patterns of electrical stimuli delivered to somatosensory areas. However, this previous algorithm had the limitation that it only worked in situations when the position-to-force map to be implemented is invertible. Here we overcome this limitation by developing a new non-linear form of the bidirectional interface that can approximate a virtually unlimited family of continuous fields. The new algorithm bases both the encoding of position information and the decoding of motor cortical activity on an explicit map between spike trains and the state space of the device computed with Multi-Dimensional-Scaling. We present a detailed computational analysis of the performance of the interface and a validation of its robustness by using synthetic neural responses in a simulated sensory-motor loop.

Deletion of OTX2 in neural ectoderm delays anterior pituitary development

Science.gov (United States)

Mortensen, Amanda H.; Schade, Vanessa; Lamonerie, Thomas; Camper, Sally A.

2015-01-01

OTX2 is a homeodomain transcription factor that is necessary for normal head development in mouse and man. Heterozygosity for loss-of-function alleles causes an incompletely penetrant, haploinsufficiency disorder. Affected individuals exhibit a spectrum of features that range from developmental defects in eye and/or pituitary development to acephaly. To investigate the mechanism underlying the pituitary defects, we used different cre lines to inactivate Otx2 in early head development and in the prospective anterior and posterior lobes. Mice homozygous for Otx2 deficiency in early head development and pituitary oral ectoderm exhibit craniofacial defects and pituitary gland dysmorphology, but normal pituitary cell specification. The morphological defects mimic those observed in humans and mice with OTX2 heterozygous mutations. Mice homozygous for Otx2 deficiency in the pituitary neural ectoderm exhibited altered patterning of gene expression and ablation of FGF signaling. The posterior pituitary lobe and stalk, which normally arise from neural ectoderm, were extremely hypoplastic. Otx2 expression was intact in Rathke's pouch, the precursor to the anterior lobe, but the anterior lobe was hypoplastic. The lack of FGF signaling from the neural ectoderm was sufficient to impair anterior lobe growth, but not the differentiation of hormone-producing cells. This study demonstrates that Otx2 expression in the neural ectoderm is important intrinsically for the development of the posterior lobe and pituitary stalk, and it has significant extrinsic effects on anterior pituitary growth. Otx2 expression early in head development is important for establishing normal craniofacial features including development of the brain, eyes and pituitary gland. PMID:25315894

Neural predictors of sensorimotor adaptation rate and savings.

Science.gov (United States)

Cassady, Kaitlin; Ruitenberg, Marit; Koppelmans, Vincent; Reuter-Lorenz, Patricia; De Dios, Yiri; Gadd, Nichole; Wood, Scott; Riascos Castenada, Roy; Kofman, Igor; Bloomberg, Jacob; Mulavara, Ajitkumar; Seidler, Rachael

2018-04-01

In this study, we investigate whether individual variability in the rate of visuomotor adaptation and multiday savings is associated with differences in regional gray matter volume and resting-state functional connectivity. Thirty-four participants performed a manual adaptation task during two separate test sessions, on average 9 days apart. Functional connectivity strength between sensorimotor, dorsal cingulate, and temporoparietal regions of the brain was found to predict the rate of learning during the early phase of the adaptation task. In contrast, default mode network connectivity strength was found to predict both the rate of learning during the late adaptation phase and savings. As for structural predictors, greater gray matter volume in temporoparietal and occipital regions predicted faster early learning, whereas greater gray matter volume in superior posterior regions of the cerebellum predicted faster late learning. These findings suggest that the offline neural predictors of early adaptation may facilitate the cognitive aspects of sensorimotor adaptation, supported by the involvement of temporoparietal and cingulate networks. The offline neural predictors of late adaptation and savings, including the default mode network and the cerebellum, likely support the storage and modification of newly acquired sensorimotor representations. © 2017 Wiley Periodicals, Inc.

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.

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.

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

Science.gov (United States)

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

2013-01-01

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

Vorticity generation and evolution in shock-accelerated density-stratified interfaces

International Nuclear Information System (INIS)

Yang, X.; Chern, I.; Zabusky, N.J.; Samtaney, R.; Hawley, J.F.

1992-01-01

The results of direct numerical simulations of inviscid planar shock-accelerated density-stratified interfaces in two dimensions are presented and compared with shock tube experiments of Haas [(private communication, 1988)] and Sturtevant [in Shock Tubes and Waves, edited by H. Gronig (VCH, Berlin, 1987), p. 89] . Heavy-to-light (''slow/fast or s/f) and light-to-heavy (''fast/slow,'' or f/s) gas interfaces are examined and early-time impulsive vorticity deposition and the evolution of coherent vortex structures are emphasized and quantified. The present second-order Godunov scheme yields excellent agreement with shock-polar analyses at early time. A more physical vortex interpretation explains the commonly used (i.e., linear paradigm) designations of ''unstable'' and ''stable'' for the f/s and s/f interfaces, respectively. The later time events are Rayleigh--Taylor like and can be described in terms of the evolution of a vortex layer (large-scale translation and rotation): asymmetric tip vortex ''roll-up'' and ''binding;'' layer ''instability;'' convective mixing; and baroclinic vorticity generation from secondary shock--interface interactions

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).

Concurrent heterogeneous neural model simulation on real-time neuromimetic hardware.

Science.gov (United States)

Rast, Alexander; Galluppi, Francesco; Davies, Sergio; Plana, Luis; Patterson, Cameron; Sharp, Thomas; Lester, David; Furber, Steve

2011-11-01

Dedicated hardware is becoming increasingly essential to simulate emerging very-large-scale neural models. Equally, however, it needs to be able to support multiple models of the neural dynamics, possibly operating simultaneously within the same system. This may be necessary either to simulate large models with heterogeneous neural types, or to simplify simulation and analysis of detailed, complex models in a large simulation by isolating the new model to a small subpopulation of a larger overall network. The SpiNNaker neuromimetic chip is a dedicated neural processor able to support such heterogeneous simulations. Implementing these models on-chip uses an integrated library-based tool chain incorporating the emerging PyNN interface that allows a modeller to input a high-level description and use an automated process to generate an on-chip simulation. Simulations using both LIF and Izhikevich models demonstrate the ability of the SpiNNaker system to generate and simulate heterogeneous networks on-chip, while illustrating, through the network-scale effects of wavefront synchronisation and burst gating, methods that can provide effective behavioural abstractions for large-scale hardware modelling. SpiNNaker's asynchronous virtual architecture permits greater scope for model exploration, with scalable levels of functional and temporal abstraction, than conventional (or neuromorphic) computing platforms. The complete system illustrates a potential path to understanding the neural model of computation, by building (and breaking) neural models at various scales, connecting the blocks, then comparing them against the biology: computational cognitive neuroscience. Copyright © 2011 Elsevier Ltd. All rights reserved.

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.

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....

EEG signal classification based on artificial neural networks and amplitude spectra features

Science.gov (United States)

Chojnowski, K.; FrÄ czek, J.

BCI (called Brain-Computer Interface) is an interface that allows direct communication between human brain and an external device. It bases on EEG signal collection, processing and classification. In this paper a complete BCI system is presented which classifies EEG signal using artificial neural networks. For this purpose we used a multi-layered perceptron architecture trained with the RProp algorithm. Furthermore a simple multi-threaded method for automatic network structure optimizing was shown. We presented the results of our system in the opening and closing eyes recognition task. We also showed how our system could be used for controlling devices basing on imaginary hand movements.

Early visual experience and the recognition of basic facial expressions: involvement of the middle temporal and inferior frontal gyri during haptic identification by the early blind.

Science.gov (United States)

Kitada, Ryo; Okamoto, Yuko; Sasaki, Akihiro T; Kochiyama, Takanori; Miyahara, Motohide; Lederman, Susan J; Sadato, Norihiro

2013-01-01

Face perception is critical for social communication. Given its fundamental importance in the course of evolution, the innate neural mechanisms can anticipate the computations necessary for representing faces. However, the effect of visual deprivation on the formation of neural mechanisms that underlie face perception is largely unknown. We previously showed that sighted individuals can recognize basic facial expressions by haptics surprisingly well. Moreover, the inferior frontal gyrus (IFG) and posterior superior temporal sulcus (pSTS) in the sighted subjects are involved in haptic and visual recognition of facial expressions. Here, we conducted both psychophysical and functional magnetic-resonance imaging (fMRI) experiments to determine the nature of the neural representation that subserves the recognition of basic facial expressions in early blind individuals. In a psychophysical experiment, both early blind and sighted subjects haptically identified basic facial expressions at levels well above chance. In the subsequent fMRI experiment, both groups haptically identified facial expressions and shoe types (control). The sighted subjects then completed the same task visually. Within brain regions activated by the visual and haptic identification of facial expressions (relative to that of shoes) in the sighted group, corresponding haptic identification in the early blind activated regions in the inferior frontal and middle temporal gyri. These results suggest that the neural system that underlies the recognition of basic facial expressions develops supramodally even in the absence of early visual experience.

Zebrafish msxB, msxC and msxE function together to refine the neural-nonneural border and regulate cranial placodes and neural crest development.

Science.gov (United States)

Phillips, Bryan T; Kwon, Hye-Joo; Melton, Colt; Houghtaling, Paul; Fritz, Andreas; Riley, Bruce B

2006-06-15

The zebrafish muscle segment homeobox genes msxB, msxC and msxE are expressed in partially overlapping domains in the neural crest and preplacodal ectoderm. We examined the roles of these msx genes in early development. Disrupting individual msx genes causes modest variable defects, whereas disrupting all three produces a reproducible severe phenotype, suggesting functional redundancy. Neural crest differentiation is blocked at an early stage. Preplacodal development begins normally, but placodes arising from the msx expression domain later show elevated apoptosis and are reduced in size. Cell proliferation is normal in these tissues. Unexpectedly, Msx-deficient embryos become ventralized by late gastrulation whereas misexpression of msxB dorsalizes the embryo. These effects appear to involve Distal-less (Dlx) protein activity, as loss of dlx3b and dlx4b suppresses ventralization in Msx-depleted embryos. At the same time, Msx-depletion restores normal preplacodal gene expression to dlx3b-dlx4b mutants. These data suggest that mutual antagonism between Msx and Dlx proteins achieves a balance of function required for normal preplacodal differentiation and placement of the neural-nonneural border.

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.

Neural Alterations in Acquired Age-Related Hearing Loss

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Raksha Anand Mudar

2016-06-01

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

Comparison of 2D and 3D neural induction methods for the generation of neural progenitor cells from human induced pluripotent stem cells.

Science.gov (United States)

Chandrasekaran, Abinaya; Avci, Hasan X; Ochalek, Anna; Rösingh, Lone N; Molnár, Kinga; László, Lajos; Bellák, Tamás; Téglási, Annamária; Pesti, Krisztina; Mike, Arpad; Phanthong, Phetcharat; Bíró, Orsolya; Hall, Vanessa; Kitiyanant, Narisorn; Krause, Karl-Heinz; Kobolák, Julianna; Dinnyés, András

2017-12-01

Neural progenitor cells (NPCs) from human induced pluripotent stem cells (hiPSCs) are frequently induced using 3D culture methodologies however, it is unknown whether spheroid-based (3D) neural induction is actually superior to monolayer (2D) neural induction. Our aim was to compare the efficiency of 2D induction with 3D induction method in their ability to generate NPCs, and subsequently neurons and astrocytes. Neural differentiation was analysed at the protein level qualitatively by immunocytochemistry and quantitatively by flow cytometry for NPC (SOX1, PAX6, NESTIN), neuronal (MAP2, TUBB3), cortical layer (TBR1, CUX1) and glial markers (SOX9, GFAP, AQP4). Electron microscopy demonstrated that both methods resulted in morphologically similar neural rosettes. However, quantification of NPCs derived from 3D neural induction exhibited an increase in the number of PAX6/NESTIN double positive cells and the derived neurons exhibited longer neurites. In contrast, 2D neural induction resulted in more SOX1 positive cells. While 2D monolayer induction resulted in slightly less mature neurons, at an early stage of differentiation, the patch clamp analysis failed to reveal any significant differences between the electrophysiological properties between the two induction methods. In conclusion, 3D neural induction increases the yield of PAX6 + /NESTIN + cells and gives rise to neurons with longer neurites, which might be an advantage for the production of forebrain cortical neurons, highlighting the potential of 3D neural induction, independent of iPSCs' genetic background. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

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.

Simbrain 3.0: A flexible, visually-oriented neural network simulator.

Science.gov (United States)

Tosi, Zachary; Yoshimi, Jeffrey

2016-11-01

Simbrain 3.0 is a software package for neural network design and analysis, which emphasizes flexibility (arbitrarily complex networks can be built using a suite of basic components) and a visually rich, intuitive interface. These features support both students and professionals. Students can study all of the major classes of neural networks in a familiar graphical setting, and can easily modify simulations, experimenting with networks and immediately seeing the results of their interventions. With the 3.0 release, Simbrain supports models on the order of thousands of neurons and a million synapses. This allows the same features that support education to support research professionals, who can now use the tool to quickly design, run, and analyze the behavior of large, highly customizable simulations. Copyright © 2016 Elsevier Ltd. All rights reserved.

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.

Neural substrate expansion for the restoration of brain function

Directory of Open Access Journals (Sweden)

Han-Chiao Isaac Chen

2016-01-01

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

Requirement for Foxd3 in the maintenance of neural crest progenitors.

Science.gov (United States)

Teng, Lu; Mundell, Nathan A; Frist, Audrey Y; Wang, Qiaohong; Labosky, Patricia A

2008-05-01

Understanding the molecular mechanisms of stem cell maintenance is crucial for the ultimate goal of manipulating stem cells for the treatment of disease. Foxd3 is required early in mouse embryogenesis; Foxd3(-/-) embryos fail around the time of implantation, cells of the inner cell mass cannot be maintained in vitro, and blastocyst-derived stem cell lines cannot be established. Here, we report that Foxd3 is required for maintenance of the multipotent mammalian neural crest. Using tissue-specific deletion of Foxd3 in the neural crest, we show that Foxd3(flox/-); Wnt1-Cre mice die perinatally with a catastrophic loss of neural crest-derived structures. Cranial neural crest tissues are either missing or severely reduced in size, the peripheral nervous system consists of reduced dorsal root ganglia and cranial nerves, and the entire gastrointestinal tract is devoid of neural crest derivatives. These results demonstrate a global role for this transcriptional repressor in all aspects of neural crest maintenance along the anterior-posterior axis, and establish an unprecedented molecular link between multiple divergent progenitor lineages of the mammalian embryo.

Autoregressive Integrated Adaptive Neural Networks Classifier for EEG-P300 Classification

Directory of Open Access Journals (Sweden)

Demi Soetraprawata

2013-06-01

Full Text Available Brain Computer Interface has a potency to be applied in mechatronics apparatus and vehicles in the future. Compared to the other techniques, EEG is the most preferred for BCI designs. In this paper, a new adaptive neural network classifier of different mental activities from EEG-based P300 signals is proposed. To overcome the over-training that is caused by noisy and non-stationary data, the EEG signals are filtered and extracted using autoregressive models before passed to the adaptive neural networks classifier. To test the improvement in the EEG classification performance with the proposed method, comparative experiments were conducted using Bayesian Linear Discriminant Analysis. The experiment results show that the all subjects achieve a classification accuracy of 100%.

A bidirectional brain-machine interface featuring a neuromorphic hardware decoder

Directory of Open Access Journals (Sweden)

Fabio Boi

2016-12-01

Full Text Available Bidirectional brain-machine interfaces (BMIs establish a two-way direct communication link4 between the brain and the external world. A decoder translates recorded neural activity into motor5 commands and an encoder delivers sensory information collected from the environment directly6 to the brain creating a closed-loop system. These two modules are typically integrated in bulky7 external devices. However, the clinical support of patients with severe motor and sensory deficits8 requires compact, low-power, and fully implantable systems that can decode neural signals to9 control external devices. As a first step toward this goal, we developed a modular bidirectional BMI10 setup that uses a compact neuromorphic processor as a decoder. On this chip we implemented11 a network of spiking neurons built using its ultra-low-power mixed-signal analog/digital circuits.12 On-chip on-line spike-timing-dependent plasticity synapse circuits enabled the network to learn13 to decode neural signals recorded from the brain into motor outputs controlling the movements14 of an external device. The modularity of the BMI allowed us to tune the individual components15 of the setup without modifying the whole system. In this paper we present the features of16 this modular BMI, and describe how we configured the network of spiking neuron circuits to17 implement the decoder and to coordinate it with the encoder in an experimental BMI paradigm18 that connects bidirectionally the brain of an anesthetized rat with an external object. We show that19 the chip learned the decoding task correctly, allowing the interfaced brain to control the object’s20 trajectories robustly. Based on our demonstration, we propose that neuromorphic technology is21 mature enough for the development of BMI modules that are sufficiently low-power and compact,22 while being highly computationally powerful and adaptive.

A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder.

Science.gov (United States)

Boi, Fabio; Moraitis, Timoleon; De Feo, Vito; Diotalevi, Francesco; Bartolozzi, Chiara; Indiveri, Giacomo; Vato, Alessandro

2016-01-01

Bidirectional brain-machine interfaces (BMIs) establish a two-way direct communication link between the brain and the external world. A decoder translates recorded neural activity into motor commands and an encoder delivers sensory information collected from the environment directly to the brain creating a closed-loop system. These two modules are typically integrated in bulky external devices. However, the clinical support of patients with severe motor and sensory deficits requires compact, low-power, and fully implantable systems that can decode neural signals to control external devices. As a first step toward this goal, we developed a modular bidirectional BMI setup that uses a compact neuromorphic processor as a decoder. On this chip we implemented a network of spiking neurons built using its ultra-low-power mixed-signal analog/digital circuits. On-chip on-line spike-timing-dependent plasticity synapse circuits enabled the network to learn to decode neural signals recorded from the brain into motor outputs controlling the movements of an external device. The modularity of the BMI allowed us to tune the individual components of the setup without modifying the whole system. In this paper, we present the features of this modular BMI and describe how we configured the network of spiking neuron circuits to implement the decoder and to coordinate it with the encoder in an experimental BMI paradigm that connects bidirectionally the brain of an anesthetized rat with an external object. We show that the chip learned the decoding task correctly, allowing the interfaced brain to control the object's trajectories robustly. Based on our demonstration, we propose that neuromorphic technology is mature enough for the development of BMI modules that are sufficiently low-power and compact, while being highly computationally powerful and adaptive.

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

A brain-machine-muscle interface for restoring hindlimb locomotion after complete spinal transection in rats.

Directory of Open Access Journals (Sweden)

Monzurul Alam

Full Text Available A brain-machine interface (BMI is a neuroprosthetic device that can restore motor function of individuals with paralysis. Although the feasibility of BMI control of upper-limb neuroprostheses has been demonstrated, a BMI for the restoration of lower-limb motor functions has not yet been developed. The objective of this study was to determine if gait-related information can be captured from neural activity recorded from the primary motor cortex of rats, and if this neural information can be used to stimulate paralysed hindlimb muscles after complete spinal cord transection. Neural activity was recorded from the hindlimb area of the primary motor cortex of six female Sprague Dawley rats during treadmill locomotion before and after mid-thoracic transection. Before spinal transection there was a strong association between neural activity and the step cycle. This association decreased after spinal transection. However, the locomotive state (standing vs. walking could still be successfully decoded from neural recordings made after spinal transection. A novel BMI device was developed that processed this neural information in real-time and used it to control electrical stimulation of paralysed hindlimb muscles. This system was able to elicit hindlimb muscle contractions that mimicked forelimb stepping. We propose this lower-limb BMI as a future neuroprosthesis for human paraplegics.

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)

Feature extraction using extrema sampling of discrete derivatives for spike sorting in implantable upper-limb neural prostheses.

Science.gov (United States)

Zamani, Majid; Demosthenous, Andreas

2014-07-01

Next generation neural interfaces for upper-limb (and other) prostheses aim to develop implantable interfaces for one or more nerves, each interface having many neural signal channels that work reliably in the stump without harming the nerves. To achieve real-time multi-channel processing it is important to integrate spike sorting on-chip to overcome limitations in transmission bandwidth. This requires computationally efficient algorithms for feature extraction and clustering suitable for low-power hardware implementation. This paper describes a new feature extraction method for real-time spike sorting based on extrema analysis (namely positive peaks and negative peaks) of spike shapes and their discrete derivatives at different frequency bands. Employing simulation across different datasets, the accuracy and computational complexity of the proposed method are assessed and compared with other methods. The average classification accuracy of the proposed method in conjunction with online sorting (O-Sort) is 91.6%, outperforming all the other methods tested with the O-Sort clustering algorithm. The proposed method offers a better tradeoff between classification error and computational complexity, making it a particularly strong choice for on-chip spike sorting.

Neural mechanisms of information storage in visual short-term memory.

Science.gov (United States)

Serences, John T

2016-11-01

The capacity to briefly memorize fleeting sensory information supports visual search and behavioral interactions with relevant stimuli in the environment. Traditionally, studies investigating the neural basis of visual short term memory (STM) have focused on the role of prefrontal cortex (PFC) in exerting executive control over what information is stored and how it is adaptively used to guide behavior. However, the neural substrates that support the actual storage of content-specific information in STM are more controversial, with some attributing this function to PFC and others to the specialized areas of early visual cortex that initially encode incoming sensory stimuli. In contrast to these traditional views, I will review evidence suggesting that content-specific information can be flexibly maintained in areas across the cortical hierarchy ranging from early visual cortex to PFC. While the factors that determine exactly where content-specific information is represented are not yet entirely clear, recognizing the importance of task-demands and better understanding the operation of non-spiking neural codes may help to constrain new theories about how memories are maintained at different resolutions, across different timescales, and in the presence of distracting information. Copyright © 2016 Elsevier Ltd. All rights reserved.

Neural Mechanisms of Information Storage in Visual Short-Term Memory

Science.gov (United States)

Serences, John T.

2016-01-01

The capacity to briefly memorize fleeting sensory information supports visual search and behavioral interactions with relevant stimuli in the environment. Traditionally, studies investigating the neural basis of visual short term memory (STM) have focused on the role of prefrontal cortex (PFC) in exerting executive control over what information is stored and how it is adaptively used to guide behavior. However, the neural substrates that support the actual storage of content-specific information in STM are more controversial, with some attributing this function to PFC and others to the specialized areas of early visual cortex that initially encode incoming sensory stimuli. In contrast to these traditional views, I will review evidence suggesting that content-specific information can be flexibly maintained in areas across the cortical hierarchy ranging from early visual cortex to PFC. While the factors that determine exactly where content-specific information is represented are not yet entirely clear, recognizing the importance of task-demands and better understanding the operation of non-spiking neural codes may help to constrain new theories about how memories are maintained at different resolutions, across different timescales, and in the presence of distracting information. PMID:27668990

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

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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...

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

Advances in neural networks computational intelligence for ICT

CERN Document Server

Esposito, Anna; Morabito, Francesco; Pasero, Eros

2016-01-01

This carefully edited book is putting emphasis on computational and artificial intelligent methods for learning and their relative applications in robotics, embedded systems, and ICT interfaces for psychological and neurological diseases. The book is a follow-up of the scientific workshop on Neural Networks (WIRN 2015) held in Vietri sul Mare, Italy, from the 20th to the 22nd of May 2015. The workshop, at its 27th edition became a traditional scientific event that brought together scientists from many countries, and several scientific disciplines. Each chapter is an extended version of the original contribution presented at the workshop, and together with the reviewers’ peer revisions it also benefits from the live discussion during the presentation. The content of book is organized in the following sections. 1. Introduction, 2. Machine Learning, 3. Artificial Neural Networks: Algorithms and models, 4. Intelligent Cyberphysical and Embedded System, 5. Computational Intelligence Methods for Biomedical ICT in...

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)

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)

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.

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.

Nuclear reactor pump diagnostics via noise analysis/artificial neural networks

International Nuclear Information System (INIS)

Keyvan, S.; Rabelo, L.C.

1991-01-01

A feasibility study is performed on the utilization of artificial neural networks as a tool for reactor diagnostics. Reactor pump signals utilized in a wear-out monitoring system developed for early detection of degradation of pump shaft are analyzed as a semi-benchmark test to study the feasibility of neural networks for pattern recognition. The Adaptive Resonance Theory (ART 2) paradigm of artificial neural networks is applied in this study. The signals are collected signals as well as generated signals simulating the wear progress. The wear-out monitoring system applies noise analysis techniques, and is capable of distinguishing between these signals and providing a measure of the progress of the degradation. This paper presents the results of the analysis of these data via the ART 2 paradigm

Time-recovering PCI-AER interface for bio-inspired spiking systems

Science.gov (United States)

Paz-Vicente, R.; Linares-Barranco, A.; Cascado, D.; Vicente, S.; Jimenez, G.; Civit, A.

2005-06-01

Address Event Representation (AER) is an emergent neuromorphic interchip communication protocol that allows for real-time virtual massive connectivity between huge number neurons located on different chips. By exploiting high speed digital communication circuits (with nano-seconds timings), synaptic neural connections can be time multiplexed, while neural activity signals (with mili-seconds timings) are sampled at low frequencies. Also, neurons generate 'events' according to their activity levels. More active neurons generate more events per unit time, and access the interchip communication channel more frequently, while neurons with low activity consume less communication bandwidth. When building multi-chip muti-layered AER systems it is absolutely necessary to have a computer interface that allows (a) to read AER interchip traffic into the computer and visualize it on screen, and (b) inject a sequence of events at some point of the AER structure. This is necessary for testing and debugging complex AER systems. This paper presents a PCI to AER interface, that dispatches a sequence of events received from the PCI bus with embedded timing information to establish when each event will be delivered. A set of specialized states machines has been introduced to recovery the possible time delays introduced by the asynchronous AER bus. On the input channel, the interface capture events assigning a timestamp and delivers them through the PCI bus to MATLAB applications. It has been implemented in real time hardware using VHDL and it has been tested in a PCI-AER board, developed by authors, that includes a Spartan II 200 FPGA. The demonstration hardware is currently capable to send and receive events at a peak rate of 8,3 Mev/sec, and a typical rate of 1 Mev/sec.

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

Science.gov (United States)

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

2007-01-01

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

Non-invasive brain-to-brain interface (BBI: establishing functional links between two brains.

Directory of Open Access Journals (Sweden)

Seung-Schik Yoo

Full Text Available Transcranial focused ultrasound (FUS is capable of modulating the neural activity of specific brain regions, with a potential role as a non-invasive computer-to-brain interface (CBI. In conjunction with the use of brain-to-computer interface (BCI techniques that translate brain function to generate computer commands, we investigated the feasibility of using the FUS-based CBI to non-invasively establish a functional link between the brains of different species (i.e. human and Sprague-Dawley rat, thus creating a brain-to-brain interface (BBI. The implementation was aimed to non-invasively translate the human volunteer's intention to stimulate a rat's brain motor area that is responsible for the tail movement. The volunteer initiated the intention by looking at a strobe light flicker on a computer display, and the degree of synchronization in the electroencephalographic steady-state-visual-evoked-potentials (SSVEP with respect to the strobe frequency was analyzed using a computer. Increased signal amplitude in the SSVEP, indicating the volunteer's intention, triggered the delivery of a burst-mode FUS (350 kHz ultrasound frequency, tone burst duration of 0.5 ms, pulse repetition frequency of 1 kHz, given for 300 msec duration to excite the motor area of an anesthetized rat transcranially. The successful excitation subsequently elicited the tail movement, which was detected by a motion sensor. The interface was achieved at 94.0±3.0% accuracy, with a time delay of 1.59±1.07 sec from the thought-initiation to the creation of the tail movement. Our results demonstrate the feasibility of a computer-mediated BBI that links central neural functions between two biological entities, which may confer unexplored opportunities in the study of neuroscience with potential implications for therapeutic applications.

Control Strategies for the DAB Based PV Interface System.

Directory of Open Access Journals (Sweden)

Hadi M El-Helw

Full Text Available This paper presents an interface system based on the Dual Active Bridge (DAB converter for Photovoltaic (PV arrays. Two control strategies are proposed for the DAB converter to harvest the maximum power from the PV array. The first strategy is based on a simple PI controller to regulate the terminal PV voltage through the phase shift angle of the DAB converter. The Perturb and Observe (P&O Maximum Power Point Tracking (MPPT technique is utilized to set the reference of the PV terminal voltage. The second strategy presented in this paper employs the Artificial Neural Network (ANN to directly set the phase shift angle of the DAB converter that results in harvesting maximum power. This feed-forward strategy overcomes the stability issues of the feedback strategy. The proposed PV interface systems are modeled and simulated using MATLAB/SIMULINK and the EMTDC/PSCAD software packages. The simulation results reveal accurate and fast response of the proposed systems. The dynamic performance of the proposed feed-forward strategy outdoes that of the feedback strategy in terms of accuracy and response time. Moreover, an experimental prototype is built to test and validate the proposed PV interface system.

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

Latent Factors Limiting the Performance of sEMG-Interfaces

Directory of Open Access Journals (Sweden)

Sergey Lobov

2018-04-01

Full Text Available Recent advances in recording and real-time analysis of surface electromyographic signals (sEMG have fostered the use of sEMG human–machine interfaces for controlling personal computers, prostheses of upper limbs, and exoskeletons among others. Despite a relatively high mean performance, sEMG-interfaces still exhibit strong variance in the fidelity of gesture recognition among different users. Here, we systematically study the latent factors determining the performance of sEMG-interfaces in synthetic tests and in an arcade game. We show that the degree of muscle cooperation and the amount of the body fatty tissue are the decisive factors in synthetic tests. Our data suggest that these factors can only be adjusted by long-term training, which promotes fine-tuning of low-level neural circuits driving the muscles. Short-term training has no effect on synthetic tests, but significantly increases the game scoring. This implies that it works at a higher decision-making level, not relevant for synthetic gestures. We propose a procedure that enables quantification of the gestures’ fidelity in a dynamic gaming environment. For each individual subject, the approach allows identifying “problematic” gestures that decrease gaming performance. This information can be used for optimizing the training strategy and for adapting the signal processing algorithms to individual users, which could be a way for a qualitative leap in the development of future sEMG-interfaces.

Early visual experience and the recognition of basic facial expressions: involvement of the middle temporal and inferior frontal gyri during haptic identification by the early blind

Science.gov (United States)

Kitada, Ryo; Okamoto, Yuko; Sasaki, Akihiro T.; Kochiyama, Takanori; Miyahara, Motohide; Lederman, Susan J.; Sadato, Norihiro

2012-01-01

Face perception is critical for social communication. Given its fundamental importance in the course of evolution, the innate neural mechanisms can anticipate the computations necessary for representing faces. However, the effect of visual deprivation on the formation of neural mechanisms that underlie face perception is largely unknown. We previously showed that sighted individuals can recognize basic facial expressions by haptics surprisingly well. Moreover, the inferior frontal gyrus (IFG) and posterior superior temporal sulcus (pSTS) in the sighted subjects are involved in haptic and visual recognition of facial expressions. Here, we conducted both psychophysical and functional magnetic-resonance imaging (fMRI) experiments to determine the nature of the neural representation that subserves the recognition of basic facial expressions in early blind individuals. In a psychophysical experiment, both early blind and sighted subjects haptically identified basic facial expressions at levels well above chance. In the subsequent fMRI experiment, both groups haptically identified facial expressions and shoe types (control). The sighted subjects then completed the same task visually. Within brain regions activated by the visual and haptic identification of facial expressions (relative to that of shoes) in the sighted group, corresponding haptic identification in the early blind activated regions in the inferior frontal and middle temporal gyri. These results suggest that the neural system that underlies the recognition of basic facial expressions develops supramodally even in the absence of early visual experience. PMID:23372547

Extended passaging increases the efficiency of neural differentiation from induced pluripotent stem cells

Directory of Open Access Journals (Sweden)

Koehler Karl R

2011-08-01

Full Text Available Abstract Background The use of induced pluripotent stem cells (iPSCs for the functional replacement of damaged neurons and in vitro disease modeling is of great clinical relevance. Unfortunately, the capacity of iPSC lines to differentiate into neurons is highly variable, prompting the need for a reliable means of assessing the differentiation capacity of newly derived iPSC cell lines. Extended passaging is emerging as a method of ensuring faithful reprogramming. We adapted an established and efficient embryonic stem cell (ESC neural induction protocol to test whether iPSCs (1 have the competence to give rise to functional neurons with similar efficiency as ESCs and (2 whether the extent of neural differentiation could be altered or enhanced by increased passaging. Results Our gene expression and morphological analyses revealed that neural conversion was temporally delayed in iPSC lines and some iPSC lines did not properly form embryoid bodies during the first stage of differentiation. Notably, these deficits were corrected by continual passaging in an iPSC clone. iPSCs with greater than 20 passages (late-passage iPSCs expressed higher expression levels of pluripotency markers and formed larger embryoid bodies than iPSCs with fewer than 10 passages (early-passage iPSCs. Moreover, late-passage iPSCs started to express neural marker genes sooner than early-passage iPSCs after the initiation of neural induction. Furthermore, late-passage iPSC-derived neurons exhibited notably greater excitability and larger voltage-gated currents than early-passage iPSC-derived neurons, although these cells were morphologically indistinguishable. Conclusions These findings strongly suggest that the efficiency neuronal conversion depends on the complete reprogramming of iPSCs via extensive passaging.

PSA-NCAM-Negative Neural Crest Cells Emerging during Neural Induction of Pluripotent Stem Cells Cause Mesodermal Tumors and Unwanted Grafts

Science.gov (United States)

Lee, Dongjin R.; Yoo, Jeong-Eun; Lee, Jae Souk; Park, Sanghyun; Lee, Junwon; Park, Chul-Yong; Ji, Eunhyun; Kim, Han-Soo; Hwang, Dong-Youn; Kim, Dae-Sung; Kim, Dong-Wook

2015-01-01

Summary Tumorigenic potential of human pluripotent stem cells (hPSCs) is an important issue in clinical applications. Despite many efforts, PSC-derived neural precursor cells (NPCs) have repeatedly induced tumors in animal models even though pluripotent cells were not detected. We found that polysialic acid-neural cell adhesion molecule (PSA-NCAM)− cells among the early NPCs caused tumors, whereas PSA-NCAM+ cells were nontumorigenic. Molecular profiling, global gene analysis, and multilineage differentiation of PSA-NCAM− cells confirm that they are multipotent neural crest stem cells (NCSCs) that could differentiate into both ectodermal and mesodermal lineages. Transplantation of PSA-NCAM− cells in a gradient manner mixed with PSA-NCAM+ cells proportionally increased mesodermal tumor formation and unwanted grafts such as PERIPHERIN+ cells or pigmented cells in the rat brain. Therefore, we suggest that NCSCs are a critical target for tumor prevention in hPSC-derived NPCs, and removal of PSA-NCAM− cells eliminates the tumorigenic potential originating from NCSCs after transplantation. PMID:25937368

Volunteers Oriented Interface Design for the Remote Navigation of Rescue Robots at Large-Scale Disaster Sites

Science.gov (United States)

Yang, Zhixiao; Ito, Kazuyuki; Saijo, Kazuhiko; Hirotsune, Kazuyuki; Gofuku, Akio; Matsuno, Fumitoshi

This paper aims at constructing an efficient interface being similar to those widely used in human daily life, to fulfill the need of many volunteer rescuers operating rescue robots at large-scale disaster sites. The developed system includes a force feedback steering wheel interface and an artificial neural network (ANN) based mouse-screen interface. The former consists of a force feedback steering control and a six monitors’ wall. It provides a manual operation like driving cars to navigate a rescue robot. The latter consists of a mouse and a camera’s view displayed in a monitor. It provides a semi-autonomous operation by mouse clicking to navigate a rescue robot. Results of experiments show that a novice volunteer can skillfully navigate a tank rescue robot through both interfaces after 20 to 30 minutes of learning their operation respectively. The steering wheel interface has high navigating speed in open areas, without restriction of terrains and surface conditions of a disaster site. The mouse-screen interface is good at exact navigation in complex structures, while bringing little tension to operators. The two interfaces are designed to switch into each other at any time to provide a combined efficient navigation method.

A preferential design approach for energy-efficient and robust implantable neural signal processing hardware.

Science.gov (United States)

Narasimhan, Seetharam; Chiel, Hillel J; Bhunia, Swarup

2009-01-01

For implantable neural interface applications, it is important to compress data and analyze spike patterns across multiple channels in real time. Such a computational task for online neural data processing requires an innovative circuit-architecture level design approach for low-power, robust and area-efficient hardware implementation. Conventional microprocessor or Digital Signal Processing (DSP) chips would dissipate too much power and are too large in size for an implantable system. In this paper, we propose a novel hardware design approach, referred to as "Preferential Design" that exploits the nature of the neural signal processing algorithm to achieve a low-voltage, robust and area-efficient implementation using nanoscale process technology. The basic idea is to isolate the critical components with respect to system performance and design them more conservatively compared to the noncritical ones. This allows aggressive voltage scaling for low power operation while ensuring robustness and area efficiency. We have applied the proposed approach to a neural signal processing algorithm using the Discrete Wavelet Transform (DWT) and observed significant improvement in power and robustness over conventional design.

Biophysical Neural Spiking, Bursting, and Excitability Dynamics in Reconfigurable Analog VLSI.

Science.gov (United States)

Yu, T; Sejnowski, T J; Cauwenberghs, G

2011-10-01

We study a range of neural dynamics under variations in biophysical parameters underlying extended Morris-Lecar and Hodgkin-Huxley models in three gating variables. The extended models are implemented in NeuroDyn, a four neuron, twelve synapse continuous-time analog VLSI programmable neural emulation platform with generalized channel kinetics and biophysical membrane dynamics. The dynamics exhibit a wide range of time scales extending beyond 100 ms neglected in typical silicon models of tonic spiking neurons. Circuit simulations and measurements show transition from tonic spiking to tonic bursting dynamics through variation of a single conductance parameter governing calcium recovery. We similarly demonstrate transition from graded to all-or-none neural excitability in the onset of spiking dynamics through the variation of channel kinetic parameters governing the speed of potassium activation. Other combinations of variations in conductance and channel kinetic parameters give rise to phasic spiking and spike frequency adaptation dynamics. The NeuroDyn chip consumes 1.29 mW and occupies 3 mm × 3 mm in 0.5 μm CMOS, supporting emerging developments in neuromorphic silicon-neuron interfaces.

Auditory-neurophysiological responses to speech during early childhood: Effects of background noise.

Science.gov (United States)

White-Schwoch, Travis; Davies, Evan C; Thompson, Elaine C; Woodruff Carr, Kali; Nicol, Trent; Bradlow, Ann R; Kraus, Nina

2015-10-01

Early childhood is a critical period of auditory learning, during which children are constantly mapping sounds to meaning. But this auditory learning rarely occurs in ideal listening conditions-children are forced to listen against a relentless din. This background noise degrades the neural coding of these critical sounds, in turn interfering with auditory learning. Despite the importance of robust and reliable auditory processing during early childhood, little is known about the neurophysiology underlying speech processing in children so young. To better understand the physiological constraints these adverse listening scenarios impose on speech sound coding during early childhood, auditory-neurophysiological responses were elicited to a consonant-vowel syllable in quiet and background noise in a cohort of typically-developing preschoolers (ages 3-5 yr). Overall, responses were degraded in noise: they were smaller, less stable across trials, slower, and there was poorer coding of spectral content and the temporal envelope. These effects were exacerbated in response to the consonant transition relative to the vowel, suggesting that the neural coding of spectrotemporally-dynamic speech features is more tenuous in noise than the coding of static features-even in children this young. Neural coding of speech temporal fine structure, however, was more resilient to the addition of background noise than coding of temporal envelope information. Taken together, these results demonstrate that noise places a neurophysiological constraint on speech processing during early childhood by causing a breakdown in neural processing of speech acoustics. These results may explain why some listeners have inordinate difficulties understanding speech in noise. Speech-elicited auditory-neurophysiological responses offer objective insight into listening skills during early childhood by reflecting the integrity of neural coding in quiet and noise; this paper documents typical response

Automated radial basis function neural network based image classification system for diabetic retinopathy detection in retinal images

Science.gov (United States)

Anitha, J.; Vijila, C. Kezi Selva; Hemanth, D. Jude

2010-02-01

Diabetic retinopathy (DR) is a chronic eye disease for which early detection is highly essential to avoid any fatal results. Image processing of retinal images emerge as a feasible tool for this early diagnosis. Digital image processing techniques involve image classification which is a significant technique to detect the abnormality in the eye. Various automated classification systems have been developed in the recent years but most of them lack high classification accuracy. Artificial neural networks are the widely preferred artificial intelligence technique since it yields superior results in terms of classification accuracy. In this work, Radial Basis function (RBF) neural network based bi-level classification system is proposed to differentiate abnormal DR Images and normal retinal images. The results are analyzed in terms of classification accuracy, sensitivity and specificity. A comparative analysis is performed with the results of the probabilistic classifier namely Bayesian classifier to show the superior nature of neural classifier. Experimental results show promising results for the neural classifier in terms of the performance measures.

Knowledge engineering tools for reasoning with scientific observations and interpretations: a neural connectivity use case.

Science.gov (United States)

Russ, Thomas A; Ramakrishnan, Cartic; Hovy, Eduard H; Bota, Mihail; Burns, Gully A P C

2011-08-22

We address the goal of curating observations from published experiments in a generalizable form; reasoning over these observations to generate interpretations and then querying this interpreted knowledge to supply the supporting evidence. We present web-application software as part of the 'BioScholar' project (R01-GM083871) that fully instantiates this process for a well-defined domain: using tract-tracing experiments to study the neural connectivity of the rat brain. The main contribution of this work is to provide the first instantiation of a knowledge representation for experimental observations called 'Knowledge Engineering from Experimental Design' (KEfED) based on experimental variables and their interdependencies. The software has three parts: (a) the KEfED model editor - a design editor for creating KEfED models by drawing a flow diagram of an experimental protocol; (b) the KEfED data interface - a spreadsheet-like tool that permits users to enter experimental data pertaining to a specific model; (c) a 'neural connection matrix' interface that presents neural connectivity as a table of ordinal connection strengths representing the interpretations of tract-tracing data. This tool also allows the user to view experimental evidence pertaining to a specific connection. BioScholar is built in Flex 3.5. It uses Persevere (a noSQL database) as a flexible data store and PowerLoom® (a mature First Order Logic reasoning system) to execute queries using spatial reasoning over the BAMS neuroanatomical ontology. We first introduce the KEfED approach as a general approach and describe its possible role as a way of introducing structured reasoning into models of argumentation within new models of scientific publication. We then describe the design and implementation of our example application: the BioScholar software. This is presented as a possible biocuration interface and supplementary reasoning toolkit for a larger, more specialized bioinformatics system: the Brain

Alteration in neonatal nutrition causes perturbations in hypothalamic neural circuits controlling reproductive function.

Science.gov (United States)

Caron, Emilie; Ciofi, Philippe; Prevot, Vincent; Bouret, Sebastien G

2012-08-15

It is increasingly accepted that alterations of the early life environment may have lasting impacts on physiological functions. In particular, epidemiological and animal studies have indicated that changes in growth and nutrition during childhood and adolescence can impair reproductive function. However, the precise biological mechanisms that underlie these programming effects of neonatal nutrition on reproduction are still poorly understood. Here, we used a mouse model of divergent litter size to investigate the effects of early postnatal overnutrition and undernutrition on the maturation of hypothalamic circuits involved in reproductive function. Neonatally undernourished females display attenuated postnatal growth associated with delayed puberty and defective development of axonal projections from the arcuate nucleus to the preoptic region. These alterations persist into adulthood and specifically affect the organization of neural projections containing kisspeptin, a key neuropeptide involved in pubertal activation and fertility. Neonatal overfeeding also perturbs the development of neural projections from the arcuate nucleus to the preoptic region, but it does not result in alterations in kisspeptin projections. These studies indicate that alterations in the early nutritional environment cause lasting and deleterious effects on the organization of neural circuits involved in the control of reproduction, and that these changes are associated with lifelong functional perturbations.

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

Science.gov (United States)

Takiyama, Ken

2017-12-01

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

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.

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.

Older adults benefit from music training early in life: biological evidence for long-term training-driven plasticity.

Science.gov (United States)

White-Schwoch, Travis; Woodruff Carr, Kali; Anderson, Samira; Strait, Dana L; Kraus, Nina

2013-11-06

Aging results in pervasive declines in nervous system function. In the auditory system, these declines include neural timing delays in response to fast-changing speech elements; this causes older adults to experience difficulty understanding speech, especially in challenging listening environments. These age-related declines are not inevitable, however: older adults with a lifetime of music training do not exhibit neural timing delays. Yet many people play an instrument for a few years without making a lifelong commitment. Here, we examined neural timing in a group of human older adults who had nominal amounts of music training early in life, but who had not played an instrument for decades. We found that a moderate amount (4-14 years) of music training early in life is associated with faster neural timing in response to speech later in life, long after training stopped (>40 years). We suggest that early music training sets the stage for subsequent interactions with sound. These experiences may interact over time to sustain sharpened neural processing in central auditory nuclei well into older age.

Processing of chromatic information in a deep convolutional neural network.

Science.gov (United States)

Flachot, Alban; Gegenfurtner, Karl R

2018-04-01

Deep convolutional neural networks are a class of machine-learning algorithms capable of solving non-trivial tasks, such as object recognition, with human-like performance. Little is known about the exact computations that deep neural networks learn, and to what extent these computations are similar to the ones performed by the primate brain. Here, we investigate how color information is processed in the different layers of the AlexNet deep neural network, originally trained on object classification of over 1.2M images of objects in their natural contexts. We found that the color-responsive units in the first layer of AlexNet learned linear features and were broadly tuned to two directions in color space, analogously to what is known of color responsive cells in the primate thalamus. Moreover, these directions are decorrelated and lead to statistically efficient representations, similar to the cardinal directions of the second-stage color mechanisms in primates. We also found, in analogy to the early stages of the primate visual system, that chromatic and achromatic information were segregated in the early layers of the network. Units in the higher layers of AlexNet exhibit on average a lower responsivity for color than units at earlier stages.

Richtmyer-Meshkov instability of a sinusoidal interface driven by a cylindrical shock

Science.gov (United States)

Liu, L.; Ding, J.; Zhai, Z.; Luo, X.

2018-04-01

Evolution of a single-mode interface triggered by a cylindrically converging shock in a V-shaped geometry is investigated numerically using an adaptive multi-phase solver. Several physical mechanisms, including the Bell-Plesset (BP) effect, the Rayleigh-Taylor (RT) effect, the nonlinearity, and the compressibility are found to be pronounced in the converging environment. Generally, the BP and nonlinear effects play an important role at early stages, while the RT effect and the compressibility dominate the late-stage evolution. Four sinusoidal interfaces with different initial amplitudes (a_0 ) and wavelengths (λ ) are found to evolve differently in the converging geometry. For the very small a_0 /λ interfaces, nonlinearity is negligible at the early stages and the sole presence of the BP effect results in an increasing growth rate, confining the linear growth of the instability to a relatively small amount of time. For the moderately small a_0 /λ cases, the BP and nonlinear effects, which, respectively, promote and inhibit the perturbation development, coexist in the early stage. The counterbalancing effects between them produce a very long period of growth that is linear in time, even to a moment when the amplitude over wavelength ratio approaches 0.6. The RT stabilization effect at late stages due to the interface deceleration significantly inhibits the perturbation growth, which can be reasonably predicted by a modified Bell model.

ADAM13 Induces Cranial Neural Crest by Cleaving Class B Ephrins and Regulating Wnt Signaling

Science.gov (United States)

Wei, Shuo; Xu, Guofeng; Bridges, Lance C.; Williams, Phoebe; White, Judith M.; DeSimone, Douglas W.

2010-01-01

SUMMARY The cranial neural crest (CNC) are multipotent embryonic cells that contribute to craniofacial structures and other cells and tissues of the vertebrate head. During embryogenesis, CNC is induced at the neural plate boundary through the interplay of several major signaling pathways. Here we report that the metalloproteinase activity of ADAM13 is required for early induction of CNC in Xenopus. In both cultured cells and X. tropicalis embryos, membrane-bound Ephrins (Efns) B1 and B2 were identified as substrates for ADAM13. ADAM13 upregulates canonical Wnt signaling and early expression of the transcription factor snail2, whereas EfnB1 inhibits the canonical Wnt pathway and snail2 expression. We propose that by cleaving class B Efns, ADAM13 promotes canonical Wnt signaling and early CNC induction. PMID:20708595

GROUT-CONCRETE INTERFACE BOND PERFORMANCE: EFFECT OF INTERFACE MOISTURE ON THE TENSILE BOND STRENGTH AND GROUT MICROSTRUCTURE.

Science.gov (United States)

De la Varga, I; Muñoz, J F; Bentz, D P; Spragg, R P; Stutzman, P E; Graybeal, B A

2018-05-01

Bond between two cementitious materials is crucial in applications such as repairs, overlays, and connections of prefabricated bridge elements (PBEs), to name just a few. It is the latter that has special interest to the authors of this paper. After performing a dimensional stability study on grout-like materials commonly used as connections between PBEs, it was observed that the so-called 'non-shrink' cementitious grouts showed a considerable amount of early-age shrinkage. This might have negative effects on the integrity of the structure, due not only to the grout material's early degradation, but also to a possible loss of bond between the grout and the prefabricated concrete element. Many factors affect the bond strength between two cementitious materials (e.g., grout-concrete), the presence of moisture at the existing concrete substrate surface being one of them. In this regard, pre-moistening the concrete substrate surface prior to the application of the grout material is sometimes recommended for bond enhancement. This topic has been the focus of numerous research studies in the past; however, there is still controversy among practitioners on the real benefits that this practice might provide. This paper evaluates the tensile bond performance of two non-shrink cementitious grouts applied to the exposed aggregate surface of a concrete substrate, and how the supply of moisture at the grout-concrete interface affects the bond strength. "Pull-off" bond results show increased tensile bond strength when the concrete surface is pre-moistened. Reasons to explain the observed increased bond strength are given after a careful microstructural analysis of the grout-concrete interface. Interfaces where sufficient moisture is provided to the concrete substrate such that moisture movement from the grout is prevented show reduced porosity and increased hydration on the grout side of the interface, which is thought to directly contribute to the increased tensile bond

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.

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.

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.

Correlation-based model of artificially induced plasticity in motor cortex by a bidirectional brain-computer interface.

Science.gov (United States)

Lajoie, Guillaume; Krouchev, Nedialko I; Kalaska, John F; Fairhall, Adrienne L; Fetz, Eberhard E

2017-02-01

Experiments show that spike-triggered stimulation performed with Bidirectional Brain-Computer-Interfaces (BBCI) can artificially strengthen connections between separate neural sites in motor cortex (MC). When spikes from a neuron recorded at one MC site trigger stimuli at a second target site after a fixed delay, the connections between sites eventually strengthen. It was also found that effective spike-stimulus delays are consistent with experimentally derived spike-timing-dependent plasticity (STDP) rules, suggesting that STDP is key to drive these changes. However, the impact of STDP at the level of circuits, and the mechanisms governing its modification with neural implants remain poorly understood. The present work describes a recurrent neural network model with probabilistic spiking mechanisms and plastic synapses capable of capturing both neural and synaptic activity statistics relevant to BBCI conditioning protocols. Our model successfully reproduces key experimental results, both established and new, and offers mechanistic insights into spike-triggered conditioning. Using analytical calculations and numerical simulations, we derive optimal operational regimes for BBCIs, and formulate predictions concerning the efficacy of spike-triggered conditioning in different regimes of cortical activity.

Neural Correlates of Processing Negative and Sexually Arousing Pictures

Science.gov (United States)

Bailey, Kira; West, Robert; Mullaney, Kellie M.

2012-01-01

Recent work has questioned whether the negativity bias is a distinct component of affective picture processing. The current study was designed to determine whether there are different neural correlates of processing positive and negative pictures using event-related brain potentials. The early posterior negativity and late positive potential were greatest in amplitude for erotic pictures. Partial Least Squares analysis revealed one latent variable that distinguished erotic pictures from neutral and positive pictures and another that differentiated negative pictures from neutral and positive pictures. The effects of orienting task on the neural correlates of processing negative and erotic pictures indicate that affective picture processing is sensitive to both stimulus-driven, and attentional or decision processes. The current data, together with other recent findings from our laboratory, lead to the suggestion that there are distinct neural correlates of processing negative and positive stimuli during affective picture processing. PMID:23029071

Neural Correlates of Phrase Rhythm: An EEG Study of Bipartite vs. Rondo Sonata Form

Directory of Open Access Journals (Sweden)

Antonio Fernández-Caballero

2017-04-01

Full Text Available This paper introduces the neural correlates of phrase rhythm. In short, phrase rhythm is the rhythmic aspect of phrase construction and the relationships between phrases. For the sake of establishing the neural correlates, a musical experiment has been designed to induce music-evoked stimuli related to phrase rhythm. Brain activity is monitored through electroencephalography (EEG by using a brain–computer interface. The power spectral value of each EEG channel is estimated to obtain how power variance distributes as a function of frequency. Our experiment shows statistical differences in theta and alpha bands in the phrase rhythm variations of two classical sonatas, one in bipartite form and the other in rondo form.

Addition of visual noise boosts evoked potential-based brain-computer interface.

Science.gov (United States)

Xie, Jun; Xu, Guanghua; Wang, Jing; Zhang, Sicong; Zhang, Feng; Li, Yeping; Han, Chengcheng; Li, Lili

2014-05-14

Although noise has a proven beneficial role in brain functions, there have not been any attempts on the dedication of stochastic resonance effect in neural engineering applications, especially in researches of brain-computer interfaces (BCIs). In our study, a steady-state motion visual evoked potential (SSMVEP)-based BCI with periodic visual stimulation plus moderate spatiotemporal noise can achieve better offline and online performance due to enhancement of periodic components in brain responses, which was accompanied by suppression of high harmonics. Offline results behaved with a bell-shaped resonance-like functionality and 7-36% online performance improvements can be achieved when identical visual noise was adopted for different stimulation frequencies. Using neural encoding modeling, these phenomena can be explained as noise-induced input-output synchronization in human sensory systems which commonly possess a low-pass property. Our work demonstrated that noise could boost BCIs in addressing human needs.

Tangible interfaces for virtual nuclear power plant control desk

Energy Technology Data Exchange (ETDEWEB)

Aghina, Mauricio Alves C.; Mol, Antonio Carlos A.; Jorge, Carlos Alexandre F.; Lapa, Celso M.F. [Instituto de Engenharia Nuclear (IEN/CNEN-RJ), Rio de Janeiro, RJ (Brazil); Nomiya, Diogo [Engenharia Eletrica (UFRJ), Universidade Federal do Rio de Janeiro, RJ (Brazil); Cunha, Gerson G.; Landau, Luiz [Programa de Engenharia Civil (PEC/COPPE/UFRJ), Universidade Federal do Rio de Janeiro, RJ (Brazil)

2011-07-01

Due to the high safety requirements for nuclear power plant operation, control desks must be designed in such a way operators can take all the procedures safely, with a good overview of all variable indicators and easy access to actuator controls. Also, operators must see alarms indication in a way they can easily identify any abnormal conditions and bring the NPP back to normal operation. The ergonomics and human factors fields have helped evaluations to improve the design of nuclear power plant control systems. Lately, the use of virtual control desks have helped even more such evaluations, by integrating in one platform both nuclear power plant dynamics simulator with a high visual fidelity control desk proto typing. Operators can interact with these virtual control desks in a similar way as with real ones. Such a virtual control desk has been under development at Instituto de Engenharia Nuclear, IEN/CNEN. This paper reports the latest improvements, with the use of more interaction modes, to turn operation a friendlier task. An automatic speech recognition interface has been implemented as a self-contained system, by accessing directly MS Windows Application Interface, and with online neural network training for spoken commend recognition. Thus, operators can switch among different desk views. Besides this, head tracking interfaces have been integrated with the virtual control desk, to move within desk views according to users head movements. Both marker and markerless-based head tracking interfaces have been implemented. Results are shown and commented. (author)

Tangible interfaces for virtual nuclear power plant control desk

International Nuclear Information System (INIS)

Aghina, Mauricio Alves C.; Mol, Antonio Carlos A.; Jorge, Carlos Alexandre F.; Lapa, Celso M.F.; Nomiya, Diogo; Cunha, Gerson G.; Landau, Luiz

2011-01-01

Due to the high safety requirements for nuclear power plant operation, control desks must be designed in such a way operators can take all the procedures safely, with a good overview of all variable indicators and easy access to actuator controls. Also, operators must see alarms indication in a way they can easily identify any abnormal conditions and bring the NPP back to normal operation. The ergonomics and human factors fields have helped evaluations to improve the design of nuclear power plant control systems. Lately, the use of virtual control desks have helped even more such evaluations, by integrating in one platform both nuclear power plant dynamics simulator with a high visual fidelity control desk proto typing. Operators can interact with these virtual control desks in a similar way as with real ones. Such a virtual control desk has been under development at Instituto de Engenharia Nuclear, IEN/CNEN. This paper reports the latest improvements, with the use of more interaction modes, to turn operation a friendlier task. An automatic speech recognition interface has been implemented as a self-contained system, by accessing directly MS Windows Application Interface, and with online neural network training for spoken commend recognition. Thus, operators can switch among different desk views. Besides this, head tracking interfaces have been integrated with the virtual control desk, to move within desk views according to users head movements. Both marker and markerless-based head tracking interfaces have been implemented. Results are shown and commented. (author)

An integrated neuro-robotic interface for stroke rehabilitation using the NASA X1 powered lower limb exoskeleton.

Science.gov (United States)

He, Yongtian; Nathan, Kevin; Venkatakrishnan, Anusha; Rovekamp, Roger; Beck, Christopher; Ozdemir, Recep; Francisco, Gerard E; Contreras-Vidal, Jose L

2014-01-01

Stroke remains a leading cause of disability, limiting independent ambulation in survivors, and consequently affecting quality of life (QOL). Recent technological advances in neural interfacing with robotic rehabilitation devices are promising in the context of gait rehabilitation. Here, the X1, NASA's powered robotic lower limb exoskeleton, is introduced as a potential diagnostic, assistive, and therapeutic tool for stroke rehabilitation. Additionally, the feasibility of decoding lower limb joint kinematics and kinetics during walking with the X1 from scalp electroencephalographic (EEG) signals--the first step towards the development of a brain-machine interface (BMI) system to the X1 exoskeleton--is demonstrated.

A Parallel Supercomputer Implementation of a Biological Inspired Neural Network and its use for Pattern Recognition

International Nuclear Information System (INIS)

De Ladurantaye, Vincent; Lavoie, Jean; Bergeron, Jocelyn; Parenteau, Maxime; Lu Huizhong; Pichevar, Ramin; Rouat, Jean

2012-01-01

A parallel implementation of a large spiking neural network is proposed and evaluated. The neural network implements the binding by synchrony process using the Oscillatory Dynamic Link Matcher (ODLM). Scalability, speed and performance are compared for 2 implementations: Message Passing Interface (MPI) and Compute Unified Device Architecture (CUDA) running on clusters of multicore supercomputers and NVIDIA graphical processing units respectively. A global spiking list that represents at each instant the state of the neural network is described. This list indexes each neuron that fires during the current simulation time so that the influence of their spikes are simultaneously processed on all computing units. Our implementation shows a good scalability for very large networks. A complex and large spiking neural network has been implemented in parallel with success, thus paving the road towards real-life applications based on networks of spiking neurons. MPI offers a better scalability than CUDA, while the CUDA implementation on a GeForce GTX 285 gives the best cost to performance ratio. When running the neural network on the GTX 285, the processing speed is comparable to the MPI implementation on RQCHP's Mammouth parallel with 64 notes (128 cores).

A Neural Networks Based Operation Guidance System for Procedure Presentation and Validation

International Nuclear Information System (INIS)

Seung, Kun Mo; Lee, Seung Jun; Seong, Poong Hyun

2006-01-01

In this paper, a neural network based operator support system is proposed to reduce operator's errors in abnormal situations in nuclear power plants (NPPs). There are many complicated situations, in which regular and suitable operations should be done by operators accordingly. In order to regulate and validate operators' operations, it is necessary to develop an operator support system which includes computer based procedures with the functions for operation validation. Many computerized procedures systems (CPS) have been recently developed. Focusing on the human machine interface (HMI) design and procedures' computerization, most of CPSs used various methodologies to enhance system's convenience, reliability and accessibility. Other than only showing procedures, the proposed system integrates a simple CPS and an operation validation system (OVS) by using artificial neural network (ANN) for operational permission and quantitative evaluation

Visual communication interface for severe physically disabled patients

Science.gov (United States)

Savino, M. J.; Fernández, E. A.

2007-11-01

During the last years several interfaces have been developed to allow communication to those patients suffering serious physical disabilities. In this work, a computer based communication interface is presented. It was designed to allow communication to those patients that cannot use neither their hands nor their voice but they can do it through their eyes. The system monitors the eyes movements by means of a webcam. Then, by means of an Artificial Neural Network, the system allows the identification of specified position on the screen through the identification of the eyes positions. This way the user can control a virtual keyboard on a screen that allows him to write and browse the system and enables him to send e-mails, SMS, activate video/music programs and control environmental devices. A patient was simulated to evaluate the versatility of the system. Its operation was satisfactory and it allowed the evaluation of the system potential. The development of this system requires low cost elements that are easily found in the market.

Visual communication interface for severe physically disabled patients

International Nuclear Information System (INIS)

Savino, M J; Fernandez, E A

2007-01-01

During the last years several interfaces have been developed to allow communication to those patients suffering serious physical disabilities. In this work, a computer based communication interface is presented. It was designed to allow communication to those patients that cannot use neither their hands nor their voice but they can do it through their eyes. The system monitors the eyes movements by means of a webcam. Then, by means of an Artificial Neural Network, the system allows the identification of specified position on the screen through the identification of the eyes positions. This way the user can control a virtual keyboard on a screen that allows him to write and browse the system and enables him to send e-mails, SMS, activate video/music programs and control environmental devices. A patient was simulated to evaluate the versatility of the system. Its operation was satisfactory and it allowed the evaluation of the system potential. The development of this system requires low cost elements that are easily found in the market

Visual communication interface for severe physically disabled patients

Energy Technology Data Exchange (ETDEWEB)

Savino, M J [Fac. de Ingenieria, Universidad Catolica de Cordoba, Camino a Alta Gracia km. 10 (5000) Cordoba, Cordoba (Argentina); Fernandez, E A [Fac. de Ingenieria, Universidad Catolica de Cordoba, Camino a Alta Gracia km. 10 (5000) Cordoba, Cordoba (Argentina)

2007-11-15

During the last years several interfaces have been developed to allow communication to those patients suffering serious physical disabilities. In this work, a computer based communication interface is presented. It was designed to allow communication to those patients that cannot use neither their hands nor their voice but they can do it through their eyes. The system monitors the eyes movements by means of a webcam. Then, by means of an Artificial Neural Network, the system allows the identification of specified position on the screen through the identification of the eyes positions. This way the user can control a virtual keyboard on a screen that allows him to write and browse the system and enables him to send e-mails, SMS, activate video/music programs and control environmental devices. A patient was simulated to evaluate the versatility of the system. Its operation was satisfactory and it allowed the evaluation of the system potential. The development of this system requires low cost elements that are easily found in the market.

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

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

Science.gov (United States)

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

2018-02-01

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

Absence of Rybp Compromises Neural Differentiation of Embryonic Stem Cells

Directory of Open Access Journals (Sweden)

Gergo Kovacs

2016-01-01

Full Text Available Rybp (Ring1 and Yy1 Binding Protein is a transcriptional regulator and member of the noncanonical polycomb repressive complex 1 with essential role in early embryonic development. We have previously described that alteration of Rybp dosage in mouse models induced striking neural tube defects (NTDs, exencephaly, and disorganized neurocortex. In this study we further investigated the role of Rybp in neural differentiation by utilising wild type (rybp+/+ and rybp null mutant (rybp-/- embryonic stem cells (ESCs and tried to uncover underlying molecular events that are responsible for the observed phenotypic changes. We found that rybp null mutant ESCs formed less matured neurons, astrocytes, and oligodendrocytes from existing progenitors than wild type cells. Furthermore, lack of rybp coincided with altered gene expression of key neural markers including Pax6 and Plagl1 pinpointing a possible transcriptional circuit among these genes.

Digital implementation of a neural network for imaging

Science.gov (United States)

Wood, Richard; McGlashan, Alex; Yatulis, Jay; Mascher, Peter; Bruce, Ian

2012-10-01

This paper outlines the design and testing of a digital imaging system that utilizes an artificial neural network with unsupervised and supervised learning to convert streaming input (real time) image space into parameter space. The primary objective of this work is to investigate the effectiveness of using a neural network to significantly reduce the information density of streaming images so that objects can be readily identified by a limited set of primary parameters and act as an enhanced human machine interface (HMI). Many applications are envisioned including use in biomedical imaging, anomaly detection and as an assistive device for the visually impaired. A digital circuit was designed and tested using a Field Programmable Gate Array (FPGA) and an off the shelf digital camera. Our results indicate that the networks can be readily trained when subject to limited sets of objects such as the alphabet. We can also separate limited object sets with rotational and positional invariance. The results also show that limited visual fields form with only local connectivity.

Spatial and Time Domain Feature of ERP Speller System Extracted via Convolutional Neural Network.

Science.gov (United States)

Yoon, Jaehong; Lee, Jungnyun; Whang, Mincheol

2018-01-01

Feature of event-related potential (ERP) has not been completely understood and illiteracy problem remains unsolved. To this end, P300 peak has been used as the feature of ERP in most brain-computer interface applications, but subjects who do not show such peak are common. Recent development of convolutional neural network provides a way to analyze spatial and temporal features of ERP. Here, we train the convolutional neural network with 2 convolutional layers whose feature maps represented spatial and temporal features of event-related potential. We have found that nonilliterate subjects' ERP show high correlation between occipital lobe and parietal lobe, whereas illiterate subjects only show correlation between neural activities from frontal lobe and central lobe. The nonilliterates showed peaks in P300, P500, and P700, whereas illiterates mostly showed peaks in around P700. P700 was strong in both subjects. We found that P700 peak may be the key feature of ERP as it appears in both illiterate and nonilliterate subjects.

The development of functional network organization in early childhood and early adolescence: A resting-state fNIRS study

OpenAIRE

Lin Cai; Qi Dong; Haijing Niu

2018-01-01

Early childhood (7–8 years old) and early adolescence (11–12 years old) constitute two landmark developmental stages that comprise considerable changes in neural cognition. However, very limited information from functional neuroimaging studies exists on the functional topological configuration of the human brain during specific developmental periods. In the present study, we utilized continuous resting-state functional near-infrared spectroscopy (rs-fNIRS) imaging data to examine topological ...

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

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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.

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

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)

DynaSim: A MATLAB Toolbox for Neural Modeling and Simulation.

Science.gov (United States)

Sherfey, Jason S; Soplata, Austin E; Ardid, Salva; Roberts, Erik A; Stanley, David A; Pittman-Polletta, Benjamin R; Kopell, Nancy J

2018-01-01

DynaSim is an open-source MATLAB/GNU Octave toolbox for rapid prototyping of neural models and batch simulation management. It is designed to speed up and simplify the process of generating, sharing, and exploring network models of neurons with one or more compartments. Models can be specified by equations directly (similar to XPP or the Brian simulator) or by lists of predefined or custom model components. The higher-level specification supports arbitrarily complex population models and networks of interconnected populations. DynaSim also includes a large set of features that simplify exploring model dynamics over parameter spaces, running simulations in parallel using both multicore processors and high-performance computer clusters, and analyzing and plotting large numbers of simulated data sets in parallel. It also includes a graphical user interface (DynaSim GUI) that supports full functionality without requiring user programming. The software has been implemented in MATLAB to enable advanced neural modeling using MATLAB, given its popularity and a growing interest in modeling neural systems. The design of DynaSim incorporates a novel schema for model specification to facilitate future interoperability with other specifications (e.g., NeuroML, SBML), simulators (e.g., NEURON, Brian, NEST), and web-based applications (e.g., Geppetto) outside MATLAB. DynaSim is freely available at http://dynasimtoolbox.org. This tool promises to reduce barriers for investigating dynamics in large neural models, facilitate collaborative modeling, and complement other tools being developed in the neuroinformatics community.

A Symbiotic Brain-Machine Interface through Value-Based Decision Making

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Mahmoudi, Babak; Sanchez, Justin C.

2011-01-01

Background In the development of Brain Machine Interfaces (BMIs), there is a great need to enable users to interact with changing environments during the activities of daily life. It is expected that the number and scope of the learning tasks encountered during interaction with the environment as well as the pattern of brain activity will vary over time. These conditions, in addition to neural reorganization, pose a challenge to decoding neural commands for BMIs. We have developed a new BMI framework in which a computational agent symbiotically decoded users' intended actions by utilizing both motor commands and goal information directly from the brain through a continuous Perception-Action-Reward Cycle (PARC). Methodology The control architecture designed was based on Actor-Critic learning, which is a PARC-based reinforcement learning method. Our neurophysiology studies in rat models suggested that Nucleus Accumbens (NAcc) contained a rich representation of goal information in terms of predicting the probability of earning reward and it could be translated into an evaluative feedback for adaptation of the decoder with high precision. Simulated neural control experiments showed that the system was able to maintain high performance in decoding neural motor commands during novel tasks or in the presence of reorganization in the neural input. We then implanted a dual micro-wire array in the primary motor cortex (M1) and the NAcc of rat brain and implemented a full closed-loop system in which robot actions were decoded from the single unit activity in M1 based on an evaluative feedback that was estimated from NAcc. Conclusions Our results suggest that adapting the BMI decoder with an evaluative feedback that is directly extracted from the brain is a possible solution to the problem of operating BMIs in changing environments with dynamic neural signals. During closed-loop control, the agent was able to solve a reaching task by capturing the action and reward

A symbiotic brain-machine interface through value-based decision making.

Directory of Open Access Journals (Sweden)

Babak Mahmoudi

Full Text Available BACKGROUND: In the development of Brain Machine Interfaces (BMIs, there is a great need to enable users to interact with changing environments during the activities of daily life. It is expected that the number and scope of the learning tasks encountered during interaction with the environment as well as the pattern of brain activity will vary over time. These conditions, in addition to neural reorganization, pose a challenge to decoding neural commands for BMIs. We have developed a new BMI framework in which a computational agent symbiotically decoded users' intended actions by utilizing both motor commands and goal information directly from the brain through a continuous Perception-Action-Reward Cycle (PARC. METHODOLOGY: The control architecture designed was based on Actor-Critic learning, which is a PARC-based reinforcement learning method. Our neurophysiology studies in rat models suggested that Nucleus Accumbens (NAcc contained a rich representation of goal information in terms of predicting the probability of earning reward and it could be translated into an evaluative feedback for adaptation of the decoder with high precision. Simulated neural control experiments showed that the system was able to maintain high performance in decoding neural motor commands during novel tasks or in the presence of reorganization in the neural input. We then implanted a dual micro-wire array in the primary motor cortex (M1 and the NAcc of rat brain and implemented a full closed-loop system in which robot actions were decoded from the single unit activity in M1 based on an evaluative feedback that was estimated from NAcc. CONCLUSIONS: Our results suggest that adapting the BMI decoder with an evaluative feedback that is directly extracted from the brain is a possible solution to the problem of operating BMIs in changing environments with dynamic neural signals. During closed-loop control, the agent was able to solve a reaching task by capturing the action and

Evolution in the design of a low sheath-flow interface for CE-MS and application to biological samples.

Science.gov (United States)

González-Ruiz, Víctor; Codesido, Santiago; Rudaz, Serge; Schappler, Julie

2018-03-01

Although several interfaces for CE-MS hyphenation are commercially available, the development of new versatile, simple and yet efficient and sensitive alternatives remains an important field of research. In a previous work, a simple low sheath-flow interface was developed from inexpensive parts. This interface features a design easy to build, maintain, and adapt to particular needs. The present work introduces an improved design of the previous interface. By reducing the diameter of the separation capillary and the emitter, a smaller Taylor cone is spontaneously formed, minimizing the zone dispersion while the analytes go through the interface and leading to less peak broadening associated to the ESI process. Numerical modeling allowed studying the mixing and diffusion processes taking place in the Taylor cone. The analytical performance of this new interface was tested with pharmaceutically relevant molecules and endogenous metabolites. The interface was eventually applied to the analysis of neural cell culture samples, allowing the identification of a panel of neurotransmission-related molecules. An excellent migration time repeatability was obtained (intra-day RSD 10 with an injected volume of 6.7 nL of biological extract. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Convolutional Neural Network for Multi-Category Rapid Serial Visual Presentation BCI

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Ran eManor

2015-12-01

Full Text Available Brain computer interfaces rely on machine learning algorithms to decode the brain's electrical activity into decisions. For example, in rapid serial visual presentation (RSVP tasks, the subject is presented with a continuous stream of images containing rare target images among standard images, while the algorithm has to detect brain activity associated with target images. Here, we continue our previous work, presenting a deep neural network model for the use of single trial EEG classification in RSVP tasks. Deep neural networks have shown state of the art performance in computer vision and speech recognition and thus have great promise for other learning tasks, like classification of EEG samples. In our model, we introduce a novel spatio-temporal regularization for EEG data to reduce overfitting. We show improved classification performance compared to our earlier work on a five categories RSVP experiment. In addition, we compare performance on data from different sessions and validate the model on a public benchmark data set of a P300 speller task. Finally, we discuss the advantages of using neural network models compared to manually designing feature extraction algorithms.

Vascular pattern of the dentate gyrus is regulated by neural progenitors.

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Pombero, Ana; Garcia-Lopez, Raquel; Estirado, Alicia; Martinez, Salvador

2018-05-01

Neurogenesis is a vital process that begins during early embryonic development and continues until adulthood, though in the latter case, it is restricted to the subventricular zone and the subgranular zone of the dentate gyrus (DG). In particular, the DG's neurogenic properties are structurally and functionally unique, which may be related to its singular vascular pattern. Neurogenesis and angiogenesis share molecular signals and act synergistically, supporting the concept of a neurogenic niche as a functional unit between neural precursors cells and their environment, in which the blood vessels play an important role. Whereas it is well known that vascular development controls neural proliferation in the embryonary and in the adult brain, by releasing neurotrophic factors; the potential influence of neural cells on vascular components during angiogenesis is largely unknown. We have demonstrated that the reduction of neural progenitors leads to a significant impairment of vascular development. Since VEGF is a potential regulator in the neurogenesis-angiogenesis crosstalk, we were interested in assessing the possible role of this molecule in the hippocampal neurovascular development. Our results showed that VEGF is the molecule involved in the regulation of vascular development by neural progenitor cells in the DG.

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

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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.

Neural electrical activity and neural network growth.

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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.

High school music classes enhance the neural processing of speech

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Adam eTierney

2013-12-01

Full Text Available 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 two years of group music classes in high school enhance the subcortical 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 subcortical responses of the music training group were earlier than at pretraining, 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.

Semantic Congruence Accelerates the Onset of the Neural Signals of Successful Memory Encoding.

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Packard, Pau A; Rodríguez-Fornells, Antoni; Bunzeck, Nico; Nicolás, Berta; de Diego-Balaguer, Ruth; Fuentemilla, Lluís

2017-01-11

As the stream of experience unfolds, our memory system rapidly transforms current inputs into long-lasting meaningful memories. A putative neural mechanism that strongly influences how input elements are transformed into meaningful memory codes relies on the ability to integrate them with existing structures of knowledge or schemas. However, it is not yet clear whether schema-related integration neural mechanisms occur during online encoding. In the current investigation, we examined the encoding-dependent nature of this phenomenon in humans. We showed that actively integrating words with congruent semantic information provided by a category cue enhances memory for words and increases false recall. The memory effect of such active integration with congruent information was robust, even with an interference task occurring right after each encoding word list. In addition, via electroencephalography, we show in 2 separate studies that the onset of the neural signals of successful encoding appeared early (∼400 ms) during the encoding of congruent words. That the neural signals of successful encoding of congruent and incongruent information followed similarly ∼200 ms later suggests that this earlier neural response contributed to memory formation. We propose that the encoding of events that are congruent with readily available contextual semantics can trigger an accelerated onset of the neural mechanisms, supporting the integration of semantic information with the event input. This faster onset would result in a long-lasting and meaningful memory trace for the event but, at the same time, make it difficult to distinguish it from plausible but never encoded events (i.e., related false memories). Conceptual or schema congruence has a strong influence on long-term memory. However, the question of whether schema-related integration neural mechanisms occur during online encoding has yet to be clarified. We investigated the neural mechanisms reflecting how the active

Combining BMI stimulation and mathematical modeling for acute stroke recovery and neural repair

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Sara L Gonzalez Andino

2011-07-01

Full Text Available Rehabilitation is a neural plasticity-exploiting approach that forces undamaged neural circuits to undertake the functionality of other circuits damaged by stroke. It aims to partial restoration of the neural functions by circuit remodeling rather than by the regeneration of damaged circuits. The core hypothesis of the present paper is that - in stroke - Brain Machine Interfaces can be designed to target neural repair instead of rehabilitation. To support this hypothesis we first review existing evidence on the role of endogenous or externally applied electric fields on all processes involved in CNS repair. We then describe our own results to illustrate the neuroprotective and neuroregenerative effects of BMI- electrical stimulation on sensory deprivation-related degenerative processes of the CNS. Finally, we discuss three of the crucial issues involved in the design of neural repair-oriented BMIs: when to stimulate, where to stimulate and - the particularly important but unsolved issue of - how to stimulate. We argue that optimal parameters for the electrical stimulation can be determined from studying and modeling the dynamics of the electric fields that naturally emerge at the central and peripheral nervous system during spontaneous healing in both, experimental animals and human patients. We conclude that a closed-loop BMI that defines the optimal stimulation parameters from a priori developed experimental models of the dynamics of spontaneous repair and the on-line monitoring of neural activity might place BMIs as an alternative or complement to stem-cell transplantation or pharmacological approaches, intensively pursued nowadays.

Artificial Neural Network Model for Predicting Compressive

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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.

Abrogation of Early Apoptosis Does Not Alter Late Inhibition of Hippocampal Neurogenesis After Irradiation

International Nuclear Information System (INIS)

Li Yuqing; Aubert, Isabelle; Wong, C. Shun

2010-01-01

Purpose: Irradiation of the adult brain results in acute apoptosis of neural progenitors and vascular endothelial cells, as well as late dysfunction of neural progenitors and inhibition of neurogenesis. We sought to determine whether the early apoptotic response has a causative role in late inhibition of neurogenesis after cranial irradiation. Methods and Materials: Using a genetic approach with p53 and smpd1 transgenic mice and a pharmacologic approach with basic fibroblast growth factor (bFGF) to abrogate the early apoptotic response, we evaluated the late inhibition of neurogenesis in the hippocampal dentate gyrus after cranial irradiation. Results: In dentate gyrus, subgranular neural progenitors underwent p53-dependent apoptosis within 24 h after irradiation. Despite a near abrogation of neural progenitor apoptosis in p53-/- mice, the reduction in newborn neurons in dentate gyrus at 9 weeks after irradiation in p53-/- mice was not different from that observed in wildtype controls. Endothelial cell apoptosis after radiation is mediated by membrane damage initiated by activation of acid sphingomyelinase (ASMase). Deletion of the smpd1 gene (which encodes ASMase) attenuated the apoptotic response of endothelial cells. At 9 weeks after irradiation, the inhibition of hippocampal neurogenesis was not rescued by ASMase deficiency. Intravenous administration of bFGF protected both endothelial cells and neural progenitors against radiation-induced apoptosis. There was no protection against inhibition of neurogenesis at 9 weeks after irradiation in bFGF-treated mice. Conclusion: Early apoptotic death of neural progenitors, endothelial cells, or both does not have a causative association with late inhibition of neurogenesis after irradiation.

How age of bilingual exposure can change the neural systems for language in the developing brain: a functional near infrared spectroscopy investigation of syntactic processing in monolingual and bilingual children.

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Jasinska, K K; Petitto, L A

2013-10-01

Is the developing bilingual brain fundamentally similar to the monolingual brain (e.g., neural resources supporting language and cognition)? Or, does early-life bilingual language experience change the brain? If so, how does age of first bilingual exposure impact neural activation for language? We compared how typically-developing bilingual and monolingual children (ages 7-10) and adults recruit brain areas during sentence processing using functional Near Infrared Spectroscopy (fNIRS) brain imaging. Bilingual participants included early-exposed (bilingual exposure from birth) and later-exposed individuals (bilingual exposure between ages 4-6). Both bilingual children and adults showed greater neural activation in left-hemisphere classic language areas, and additionally, right-hemisphere homologues (Right Superior Temporal Gyrus, Right Inferior Frontal Gyrus). However, important differences were observed between early-exposed and later-exposed bilinguals in their earliest-exposed language. Early bilingual exposure imparts fundamental changes to classic language areas instead of alterations to brain regions governing higher cognitive executive functions. However, age of first bilingual exposure does matter. Later-exposed bilinguals showed greater recruitment of the prefrontal cortex relative to early-exposed bilinguals and monolinguals. The findings provide fascinating insight into the neural resources that facilitate bilingual language use and are discussed in terms of how early-life language experiences can modify the neural systems underlying human language processing. Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.

Transmission of wireless neural signals through a 0.18 µm CMOS low-power amplifier.

Science.gov (United States)

Gazziro, M; Braga, C F R; Moreira, D A; Carvalho, A C P L F; Rodrigues, J F; Navarro, J S; Ardila, J C M; Mioni, D P; Pessatti, M; Fabbro, P; Freewin, C; Saddow, S E

2015-01-01

In the field of Brain Machine Interfaces (BMI) researchers still are not able to produce clinically viable solutions that meet the requirements of long-term operation without the use of wires or batteries. Another problem is neural compatibility with the electrode probes. One of the possible ways of approaching these problems is the use of semiconductor biocompatible materials (silicon carbide) combined with an integrated circuit designed to operate with low power consumption. This paper describes a low-power neural signal amplifier chip, named Cortex, fabricated using 0.18 μm CMOS process technology with all electronics integrated in an area of 0.40 mm(2). The chip has 4 channels, total power consumption of only 144 μW, and is impedance matched to silicon carbide biocompatible electrodes.

A primer on brain-machine interfaces, concepts, and technology: a key element in the future of functional neurorestoration.

Science.gov (United States)

Lee, Brian; Liu, Charles Y; Apuzzo, Michael L J

2013-01-01

Conventionally, the practice of neurosurgery has been characterized by the removal of pathology, congenital or acquired. The emerging complement to the removal of pathology is surgery for the specific purpose of restoration of function. Advents in neuroscience, technology, and the understanding of neural circuitry are creating opportunities to intervene in disease processes in a reparative manner, thereby advancing toward the long-sought-after concept of neurorestoration. Approaching the issue of neurorestoration from a biomedical engineering perspective is the rapidly growing arena of implantable devices. Implantable devices are becoming more common in medicine and are making significant advancements to improve a patient's functional outcome. Devices such as deep brain stimulators, vagus nerve stimulators, and spinal cord stimulators are now becoming more commonplace in neurosurgery as we utilize our understanding of the nervous system to interpret neural activity and restore function. One of the most exciting prospects in neurosurgery is the technologically driven field of brain-machine interface, also known as brain-computer interface, or neuroprosthetics. The successful development of this technology will have far-reaching implications for patients suffering from a great number of diseases, including but not limited to spinal cord injury, paralysis, stroke, or loss of limb. This article provides an overview of the issues related to neurorestoration using implantable devices with a specific focus on brain-machine interface technology. Copyright © 2013 Elsevier Inc. All rights reserved.

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.

Brain-computer interface for alertness estimation and improving

Science.gov (United States)

Hramov, Alexander; Maksimenko, Vladimir; Hramova, Marina

2018-02-01

Using wavelet analysis of the signals of electrical brain activity (EEG), we study the processes of neural activity, associated with perception of visual stimuli. We demonstrate that the brain can process visual stimuli in two scenarios: (i) perception is characterized by destruction of the alpha-waves and increase in the high-frequency (beta) activity, (ii) the beta-rhythm is not well pronounced, while the alpha-wave energy remains unchanged. The special experiments show that the motivation factor initiates the first scenario, explained by the increasing alertness. Based on the obtained results we build the brain-computer interface and demonstrate how the degree of the alertness can be estimated and controlled in real experiment.

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

Science.gov (United States)

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

2018-02-01

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

Closed loop interactions between spiking neural network and robotic simulators based on MUSIC and ROS

Directory of Open Access Journals (Sweden)

Philipp Weidel

2016-08-01

Full Text Available In order to properly assess the function and computational properties of simulated neural systems, it is necessary to account for the nature of the stimuli that drive the system. However, providing stimuli that are rich and yet both reproducible and amenable to experimental manipulations is technically challenging, and even more so if a closed-loop scenario is required. In this work, we present a novel approach to solve this problem, connecting robotics and neural network simulators. We implement a middleware solution that bridges the Robotic Operating System (ROS to the Multi-Simulator Coordinator (MUSIC. This enables any robotic and neural simulators that implement the corresponding interfaces to be efficiently coupled, allowing real-time performance for a wide range of configurations. This work extends the toolset available for researchers in both neurorobotics and computational neuroscience, and creates the opportunity to perform closed-loop experiments of arbitrary complexity to address questions in multiple areas, including embodiment, agency, and reinforcement learning.

A neutron spectrum unfolding computer code based on artificial neural networks

International Nuclear Information System (INIS)

Ortiz-Rodríguez, J.M.; Reyes Alfaro, A.; Reyes Haro, A.; Cervantes Viramontes, J.M.; Vega-Carrillo, H.R.

2014-01-01

The Bonner Spheres Spectrometer consists of a thermal neutron sensor placed at the center of a number of moderating polyethylene spheres of different diameters. From the measured readings, information can be derived about the spectrum of the neutron field where measurements were made. Disadvantages of the Bonner system are the weight associated with each sphere and the need to sequentially irradiate the spheres, requiring long exposure periods. Provided a well-established response matrix and adequate irradiation conditions, 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 Intelligence, mainly Artificial Neural Networks, have been widely investigated. In this work, a neutron spectrum unfolding code based on neural nets technology is presented. This code is called Neutron Spectrometry and Dosimetry with Artificial Neural networks unfolding code that was designed in a graphical interface. The core of the code is an embedded neural network architecture previously optimized using the robust design of artificial neural networks methodology. The main features of the code are: easy to use, friendly and intuitive to the user. This code was designed for a Bonner Sphere System based on a 6 LiI(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, for unfolding the neutron spectrum, only seven rate counts measured with seven Bonner spheres are required; simultaneously the code calculates 15 dosimetric quantities as well as the total flux for radiation protection purposes. This code generates a full report with all information of the unfolding

Ultra-low noise miniaturized neural amplifier with hardware averaging.

Science.gov (United States)

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

2015-08-01

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

CONSTRUCTION COST PREDICTION USING NEURAL NETWORKS

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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.

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%.

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.

Correlation-based model of artificially induced plasticity in motor cortex by a bidirectional brain-computer interface.

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Guillaume Lajoie

2017-02-01

Full Text Available Experiments show that spike-triggered stimulation performed with Bidirectional Brain-Computer-Interfaces (BBCI can artificially strengthen connections between separate neural sites in motor cortex (MC. When spikes from a neuron recorded at one MC site trigger stimuli at a second target site after a fixed delay, the connections between sites eventually strengthen. It was also found that effective spike-stimulus delays are consistent with experimentally derived spike-timing-dependent plasticity (STDP rules, suggesting that STDP is key to drive these changes. However, the impact of STDP at the level of circuits, and the mechanisms governing its modification with neural implants remain poorly understood. The present work describes a recurrent neural network model with probabilistic spiking mechanisms and plastic synapses capable of capturing both neural and synaptic activity statistics relevant to BBCI conditioning protocols. Our model successfully reproduces key experimental results, both established and new, and offers mechanistic insights into spike-triggered conditioning. Using analytical calculations and numerical simulations, we derive optimal operational regimes for BBCIs, and formulate predictions concerning the efficacy of spike-triggered conditioning in different regimes of cortical activity.

Towards building hybrid biological/in silico neural networks for motor neuroprosthetic control

Directory of Open Access Journals (Sweden)

Mehmet eKocaturk

2015-08-01

Full Text Available In this article, we introduce the Bioinspired Neuroprosthetic Design Environment (BNDE as a practical platform for the development of novel brain machine interface (BMI controllers which are based on spiking model neurons. We built the BNDE around a hard real-time system so that it is capable of creating simulated synapses from extracellularly recorded neurons to model neurons. In order to evaluate the practicality of the BNDE for neuroprosthetic control experiments, a novel, adaptive BMI controller was developed and tested using real-time closed-loop simulations. The present controller consists of two in silico medium spiny neurons which receive simulated synaptic inputs from recorded motor cortical neurons. In the closed-loop simulations, the recordings from the cortical neurons were imitated using an external, hardware-based neural signal synthesizer. By implementing a reward-modulated spike timing-dependent plasticity rule, the controller achieved perfect target reach accuracy for a two target reaching task in one dimensional space. The BNDE combines the flexibility of software-based spiking neural network (SNN simulations with powerful online data visualization tools and is a low-cost, PC-based and all-in-one solution for developing neurally-inspired BMI controllers. We believe the BNDE is the first implementation which is capable of creating hybrid biological/in silico neural networks for motor neuroprosthetic control and utilizes multiple CPU cores for computationally intensive real-time SNN simulations.

A PIV Study of Drop-interface Coalescence with Surfactants

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Weheliye, Weheliye Hashi; Dong, Teng; Angeli, Panagiota

2017-11-01

In this work, the coalescence of a drop with an aqueous-organic interface was studied by Particle Image Velocimetry (PIV). The effect of surfactants on the drop surface evolution, the vorticity field and the kinetic energy distribution in the drop during coalescence were investigated. The coalescence took place in an acrylic rectangular box with 79% glycerol solution at the bottom and Exxsol D80 oil above. The glycerol solution drop was generated through a nozzle fixed at 2cm above the aqueous/oil interface and was seeded with Rhodamine particles. The whole process was captured by a high-speed camera. Different mass ratios of non-ionic surfactant Span80 to oil were studied. The increase of surfactant concentration promoted deformation of the interface before the rupture of the trapped oil film. At the early stages after film rupture, two counter-rotating vortices appeared at the bottom of the drop which then travelled to the upper part. The propagation rates, as well as the intensities of the vortices decreased at high surfactant concentrations. At early stages, the kinetic energy was mainly distributed near the bottom part of the droplet, while at later stages it was distributed near the upper part of the droplet. Programme Grant MEMPHIS, Chinese Scholarship Council (CSC).

Knowledge engineering tools for reasoning with scientific observations and interpretations: a neural connectivity use case

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Bota Mihail

2011-08-01

Full Text Available Abstract Background We address the goal of curating observations from published experiments in a generalizable form; reasoning over these observations to generate interpretations and then querying this interpreted knowledge to supply the supporting evidence. We present web-application software as part of the 'BioScholar' project (R01-GM083871 that fully instantiates this process for a well-defined domain: using tract-tracing experiments to study the neural connectivity of the rat brain. Results The main contribution of this work is to provide the first instantiation of a knowledge representation for experimental observations called 'Knowledge Engineering from Experimental Design' (KEfED based on experimental variables and their interdependencies. The software has three parts: (a the KEfED model editor - a design editor for creating KEfED models by drawing a flow diagram of an experimental protocol; (b the KEfED data interface - a spreadsheet-like tool that permits users to enter experimental data pertaining to a specific model; (c a 'neural connection matrix' interface that presents neural connectivity as a table of ordinal connection strengths representing the interpretations of tract-tracing data. This tool also allows the user to view experimental evidence pertaining to a specific connection. BioScholar is built in Flex 3.5. It uses Persevere (a noSQL database as a flexible data store and PowerLoom® (a mature First Order Logic reasoning system to execute queries using spatial reasoning over the BAMS neuroanatomical ontology. Conclusions We first introduce the KEfED approach as a general approach and describe its possible role as a way of introducing structured reasoning into models of argumentation within new models of scientific publication. We then describe the design and implementation of our example application: the BioScholar software. This is presented as a possible biocuration interface and supplementary reasoning toolkit for a larger

Knowledge engineering tools for reasoning with scientific observations and interpretations: a neural connectivity use case

Science.gov (United States)

2011-01-01

Background We address the goal of curating observations from published experiments in a generalizable form; reasoning over these observations to generate interpretations and then querying this interpreted knowledge to supply the supporting evidence. We present web-application software as part of the 'BioScholar' project (R01-GM083871) that fully instantiates this process for a well-defined domain: using tract-tracing experiments to study the neural connectivity of the rat brain. Results The main contribution of this work is to provide the first instantiation of a knowledge representation for experimental observations called 'Knowledge Engineering from Experimental Design' (KEfED) based on experimental variables and their interdependencies. The software has three parts: (a) the KEfED model editor - a design editor for creating KEfED models by drawing a flow diagram of an experimental protocol; (b) the KEfED data interface - a spreadsheet-like tool that permits users to enter experimental data pertaining to a specific model; (c) a 'neural connection matrix' interface that presents neural connectivity as a table of ordinal connection strengths representing the interpretations of tract-tracing data. This tool also allows the user to view experimental evidence pertaining to a specific connection. BioScholar is built in Flex 3.5. It uses Persevere (a noSQL database) as a flexible data store and PowerLoom® (a mature First Order Logic reasoning system) to execute queries using spatial reasoning over the BAMS neuroanatomical ontology. Conclusions We first introduce the KEfED approach as a general approach and describe its possible role as a way of introducing structured reasoning into models of argumentation within new models of scientific publication. We then describe the design and implementation of our example application: the BioScholar software. This is presented as a possible biocuration interface and supplementary reasoning toolkit for a larger, more specialized

The social-sensory interface: category interactions in person perception.

Science.gov (United States)

Freeman, Jonathan B; Johnson, Kerri L; Adams, Reginald B; Ambady, Nalini

2012-01-01

Research is increasingly challenging the claim that distinct sources of social information-such as sex, race, and emotion-are processed in discrete fashion. Instead, there appear to be functionally relevant interactions that occur. In the present article, we describe research examining how cues conveyed by the human face, voice, and body interact to form the unified representations that guide our perceptions of and responses to other people. We explain how these information sources are often thrown into interaction through bottom-up forces (e.g., phenotypic cues) as well as top-down forces (e.g., stereotypes and prior knowledge). Such interactions point to a person perception process that is driven by an intimate interface between bottom-up perceptual and top-down social processes. Incorporating data from neuroimaging, event-related potentials (ERP), computational modeling, computer mouse-tracking, and other behavioral measures, we discuss the structure of this interface, and we consider its implications and adaptive purposes. We argue that an increased understanding of person perception will likely require a synthesis of insights and techniques, from social psychology to the cognitive, neural, and vision sciences.

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.

Alteration of gene expression by alcohol exposure at early neurulation.

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Zhou, Feng C; Zhao, Qianqian; Liu, Yunlong; Goodlett, Charles R; Liang, Tiebing; McClintick, Jeanette N; Edenberg, Howard J; Li, Lang

2011-02-21

We have previously demonstrated that alcohol exposure at early neurulation induces growth retardation, neural tube abnormalities, and alteration of DNA methylation. To explore the global gene expression changes which may underline these developmental defects, microarray analyses were performed in a whole embryo mouse culture model that allows control over alcohol and embryonic variables. Alcohol caused teratogenesis in brain, heart, forelimb, and optic vesicle; a subset of the embryos also showed cranial neural tube defects. In microarray analysis (accession number GSM9545), adopting hypothesis-driven Gene Set Enrichment Analysis (GSEA) informatics and intersection analysis of two independent experiments, we found that there was a collective reduction in expression of neural specification genes (neurogenin, Sox5, Bhlhe22), neural growth factor genes [Igf1, Efemp1, Klf10 (Tieg), and Edil3], and alteration of genes involved in cell growth, apoptosis, histone variants, eye and heart development. There was also a reduction of retinol binding protein 1 (Rbp1), and de novo expression of aldehyde dehydrogenase 1B1 (Aldh1B1). Remarkably, four key hematopoiesis genes (glycophorin A, adducin 2, beta-2 microglobulin, and ceruloplasmin) were absent after alcohol treatment, and histone variant genes were reduced. The down-regulation of the neurospecification and the neurotrophic genes were further confirmed by quantitative RT-PCR. Furthermore, the gene expression profile demonstrated distinct subgroups which corresponded with two distinct alcohol-related neural tube phenotypes: an open (ALC-NTO) and a closed neural tube (ALC-NTC). Further, the epidermal growth factor signaling pathway and histone variants were specifically altered in ALC-NTO, and a greater number of neurotrophic/growth factor genes were down-regulated in the ALC-NTO than in the ALC-NTC embryos. This study revealed a set of genes vulnerable to alcohol exposure and genes that were associated with neural tube

Stage-specific control of neural crest stem cell proliferation by the small rho GTPases Cdc42 and Rac1

DEFF Research Database (Denmark)

Fuchs, Sebastian; Herzog, Dominik; Sumara, Grzegorz

2009-01-01

-renewal and proliferation of later stage, but not early migratory NCSCs. This stage-specific requirement for small Rho GTPases is due to changes in NCSCs that, during development, acquire responsiveness to mitogenic EGF acting upstream of both Cdc42 and Rac1. Thus, our data reveal distinct mechanisms for growth control......The neural crest (NC) generates a variety of neural and non-neural tissues during vertebrate development. Both migratory NC cells and their target structures contain cells with stem cell features. Here we show that these populations of neural crest-derived stem cells (NCSCs) are differentially...

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.

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

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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…

Neural correlates of threat perception: neural equivalence of conspecific and heterospecific mobbing calls is learned.

Directory of Open Access Journals (Sweden)

Marc T Avey

Full Text Available Songbird auditory areas (i.e., CMM and NCM are preferentially activated to playback of conspecific vocalizations relative to heterospecific and arbitrary noise. Here, we asked if the neural response to auditory stimulation is not simply preferential for conspecific vocalizations but also for the information conveyed by the vocalization. Black-capped chickadees use their chick-a-dee mobbing call to recruit conspecifics and other avian species to mob perched predators. Mobbing calls produced in response to smaller, higher-threat predators contain more "D" notes compared to those produced in response to larger, lower-threat predators and thus convey the degree of threat of predators. We specifically asked whether the neural response varies with the degree of threat conveyed by the mobbing calls of chickadees and whether the neural response is the same for actual predator calls that correspond to the degree of threat of the chickadee mobbing calls. Our results demonstrate that, as degree of threat increases in conspecific chickadee mobbing calls, there is a corresponding increase in immediate early gene (IEG expression in telencephalic auditory areas. We also demonstrate that as the degree of threat increases for the heterospecific predator, there is a corresponding increase in IEG expression in the auditory areas. Furthermore, there was no significant difference in the amount IEG expression between conspecific mobbing calls or heterospecific predator calls that were the same degree of threat. In a second experiment, using hand-reared chickadees without predator experience, we found more IEG expression in response to mobbing calls than corresponding predator calls, indicating that degree of threat is learned. Our results demonstrate that degree of threat corresponds to neural activity in the auditory areas and that threat can be conveyed by different species signals and that these signals must be learned.

Neural Correlates of Threat Perception: Neural Equivalence of Conspecific and Heterospecific Mobbing Calls Is Learned

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Avey, Marc T.; Hoeschele, Marisa; Moscicki, Michele K.; Bloomfield, Laurie L.; Sturdy, Christopher B.

2011-01-01

Songbird auditory areas (i.e., CMM and NCM) are preferentially activated to playback of conspecific vocalizations relative to heterospecific and arbitrary noise [1]–[2]. Here, we asked if the neural response to auditory stimulation is not simply preferential for conspecific vocalizations but also for the information conveyed by the vocalization. Black-capped chickadees use their chick-a-dee mobbing call to recruit conspecifics and other avian species to mob perched predators [3]. Mobbing calls produced in response to smaller, higher-threat predators contain more “D” notes compared to those produced in response to larger, lower-threat predators and thus convey the degree of threat of predators [4]. We specifically asked whether the neural response varies with the degree of threat conveyed by the mobbing calls of chickadees and whether the neural response is the same for actual predator calls that correspond to the degree of threat of the chickadee mobbing calls. Our results demonstrate that, as degree of threat increases in conspecific chickadee mobbing calls, there is a corresponding increase in immediate early gene (IEG) expression in telencephalic auditory areas. We also demonstrate that as the degree of threat increases for the heterospecific predator, there is a corresponding increase in IEG expression in the auditory areas. Furthermore, there was no significant difference in the amount IEG expression between conspecific mobbing calls or heterospecific predator calls that were the same degree of threat. In a second experiment, using hand-reared chickadees without predator experience, we found more IEG expression in response to mobbing calls than corresponding predator calls, indicating that degree of threat is learned. Our results demonstrate that degree of threat corresponds to neural activity in the auditory areas and that threat can be conveyed by different species signals and that these signals must be learned. PMID:21909363

Neural correlates of threat perception: neural equivalence of conspecific and heterospecific mobbing calls is learned.

Science.gov (United States)

Avey, Marc T; Hoeschele, Marisa; Moscicki, Michele K; Bloomfield, Laurie L; Sturdy, Christopher B

2011-01-01

Songbird auditory areas (i.e., CMM and NCM) are preferentially activated to playback of conspecific vocalizations relative to heterospecific and arbitrary noise. Here, we asked if the neural response to auditory stimulation is not simply preferential for conspecific vocalizations but also for the information conveyed by the vocalization. Black-capped chickadees use their chick-a-dee mobbing call to recruit conspecifics and other avian species to mob perched predators. Mobbing calls produced in response to smaller, higher-threat predators contain more "D" notes compared to those produced in response to larger, lower-threat predators and thus convey the degree of threat of predators. We specifically asked whether the neural response varies with the degree of threat conveyed by the mobbing calls of chickadees and whether the neural response is the same for actual predator calls that correspond to the degree of threat of the chickadee mobbing calls. Our results demonstrate that, as degree of threat increases in conspecific chickadee mobbing calls, there is a corresponding increase in immediate early gene (IEG) expression in telencephalic auditory areas. We also demonstrate that as the degree of threat increases for the heterospecific predator, there is a corresponding increase in IEG expression in the auditory areas. Furthermore, there was no significant difference in the amount IEG expression between conspecific mobbing calls or heterospecific predator calls that were the same degree of threat. In a second experiment, using hand-reared chickadees without predator experience, we found more IEG expression in response to mobbing calls than corresponding predator calls, indicating that degree of threat is learned. Our results demonstrate that degree of threat corresponds to neural activity in the auditory areas and that threat can be conveyed by different species signals and that these signals must be learned.

All-trans retinoic acid promotes neural lineage entry by pluripotent embryonic stem cells via multiple pathways

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Fang Bo

2009-07-01

Full Text Available Abstract Background All-trans retinoic acid (RA is one of the most important morphogens with pleiotropic actions. Its embryonic distribution correlates with neural differentiation in the developing central nervous system. To explore the precise effects of RA on neural differentiation of mouse embryonic stem cells (ESCs, we detected expression of RA nuclear receptors and RA-metabolizing enzymes in mouse ESCs and investigated the roles of RA in adherent monolayer culture. Results Upon addition of RA, cell differentiation was directed rapidly and exclusively into the neural lineage. Conversely, pharmacological interference with RA signaling suppressed this neural differentiation. Inhibition of fibroblast growth factor (FGF signaling did not suppress significantly neural differentiation in RA-treated cultures. Pharmacological interference with extracellular signal-regulated kinase (ERK pathway or activation of Wnt pathway effectively blocked the RA-promoted neural specification. ERK phosphorylation was enhanced in RA-treated cultures at the early stage of differentiation. Conclusion RA can promote neural lineage entry by ESCs in adherent monolayer culture systems. This effect depends on RA signaling and its crosstalk with the ERK and Wnt pathways.

Fault diagnosis in nuclear power plants using an artificial neural network technique

International Nuclear Information System (INIS)

Chou, H.P.; Prock, J.; Bonfert, J.P.

1993-01-01

Application of artificial intelligence (AI) computational techniques, such as expert systems, fuzzy logic, and neural networks in diverse areas has taken place extensively. In the nuclear industry, the intended goal for these AI techniques is to improve power plant operational safety and reliability. As a computerized operator support tool, the artificial neural network (ANN) approach is an emerging technology that currently attracts a large amount of interest. The ability of ANNs to extract the input/output relation of a complicated process and the superior execution speed of a trained ANN motivated this study. The goal was to develop neural networks for sensor and process faults diagnosis with the potential of implementing as a component of a real-time operator support system LYDIA, early sensor and process fault detection and diagnosis

What will this do to me and my brain? Ethical issues in brain-to-brain interfacing

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Elisabeth eHildt

2015-02-01

Full Text Available For several years now, brain-computer interfaces (BCIs in which brain signals are used to navigate a computer, a prostheses or a technical device, have been developed in various experimental contexts (Wolpaw & Wolpaw 2012; Grübler & Hildt 2014. Researchers have recently taken the next step and run experiments based on connections between two brains. These so-called brain-to-brain interfaces (abbreviation: BBIs or BTBIs involve not only a BCI component deriving information from a brain and sending it to a computer, but also a computer-brain interface (CBI component delivering information to another brain. What results is technology-mediated brain-to-brain communication (B2B communication, i.e. direct communication between two brains that does not involve any activity of the peripheral nervous system. In what follows, ethical issues that arise in neural interfacing will be discussed after a short introduction to recent BBI experiments. In this, the focus will be on the implications BBIs may have on the individual at the CBI side of the BBI, i.e. on the recipient.

Brain-Machine Interface control of a robot arm using actor-critic rainforcement learning.

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

2012-01-01

Here we demonstrate how a marmoset monkey can use a reinforcement learning (RL) Brain-Machine Interface (BMI) to effectively control the movements of a robot arm for a reaching task. In this work, an actor-critic RL algorithm used neural ensemble activity in the monkey's motor cortext to control the robot movements during a two-target decision task. This novel approach to decoding offers unique advantages for BMI control applications. Compared to supervised learning decoding methods, the actor-critic RL algorithm does not require an explicit set of training data to create a static control model, but rather it incrementally adapts the model parameters according to its current performance, in this case requiring only a very basic feedback signal. We show how this algorithm achieved high performance when mapping the monkey's neural states (94%) to robot actions, and only needed to experience a few trials before obtaining accurate real-time control of the robot arm. Since RL methods responsively adapt and adjust their parameters, they can provide a method to create BMIs that are robust against perturbations caused by changes in either the neural input space or the output actions they generate under different task requirements or goals.

Operant conditioning of a multiple degree-of-freedom brain-machine interface in a primate model of amputation.

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Balasubramanian, Karthikeyan; Southerland, Joshua; Vaidya, Mukta; Qian, Kai; Eleryan, Ahmed; Fagg, Andrew H; Sluzky, Marc; Oweiss, Karim; Hatsopoulos, Nicholas

2013-01-01

Operant conditioning with biofeedback has been shown to be an effective method to modify neural activity to generate goal-directed actions in a brain-machine interface. It is particularly useful when neural activity cannot be mathematically mapped to motor actions of the actual body such as in the case of amputation. Here, we implement an operant conditioning approach with visual feedback in which an amputated monkey is trained to control a multiple degree-of-freedom robot to perform a reach-to-grasp behavior. A key innovation is that each controlled dimension represents a behaviorally relevant synergy among a set of joint degrees-of-freedom. We present a number of behavioral metrics by which to assess improvements in BMI control with exposure to the system. The use of non-human primates with chronic amputation is arguably the most clinically-relevant model of human amputation that could have direct implications for developing a neural prosthesis to treat humans with missing upper limbs.

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

Direct observation of ionic structure at solid-liquid interfaces

DEFF Research Database (Denmark)

Siretanu, Igor; Ebeling, Daniel; Andersson, Martin Peter

2014-01-01

The distribution of ions and charge at solid-water interfaces plays an essential role in a wide range of processes in biology, geology and technology. While theoretical models of the solid-electrolyte interface date back to the early 20th century, a detailed picture of the structure of the electric...... double layer has remained elusive, largely because of experimental techniques have not allowed direct observation of the behaviour of ions, i.e. with subnanometer resolution. We have made use of recent advances in high-resolution Atomic Force Microscopy to reveal, with atomic level precision, the ordered...

A flexible user-interface for audiovisual presentation and interactive control in neurobehavioral experiments [v1; ref status: indexed, http://f1000r.es/wt

Directory of Open Access Journals (Sweden)

Christopher T Noto

2013-01-01

Full Text Available A major problem facing behavioral neuroscientists is a lack of unified, vendor-distributed data acquisition systems that allow stimulus presentation and behavioral monitoring while recording neural activity. Numerous systems perform one of these tasks well independently, but to our knowledge, a useful package with a straightforward user interface does not exist. Here we describe the development of a flexible, script-based user interface that enables customization for real-time stimulus presentation, behavioral monitoring and data acquisition. The experimental design can also incorporate neural microstimulation paradigms. We used this interface to deliver multimodal, auditory and visual (images or video stimuli to a nonhuman primate and acquire single-unit data. Our design is cost-effective and works well with commercially available hardware and software. Our design incorporates a script, providing high-level control of data acquisition via a sequencer running on a digital signal processor to enable behaviorally triggered control of the presentation of visual and auditory stimuli. Our experiments were conducted in combination with eye-tracking hardware. The script, however, is designed to be broadly useful to neuroscientists who may want to deliver stimuli of different modalities using any animal model.

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.

Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting.

Science.gov (United States)

Werner, Thilo; Vianello, Elisa; Bichler, Olivier; Garbin, Daniele; Cattaert, Daniel; Yvert, Blaise; De Salvo, Barbara; Perniola, Luca

2016-01-01

In this paper, we present an alternative approach to perform spike sorting of complex brain signals based on spiking neural networks (SNN). The proposed architecture is suitable for hardware implementation by using resistive random access memory (RRAM) technology for the implementation of synapses whose low latency (spike sorting. This offers promising advantages to conventional spike sorting techniques for brain-computer interfaces (BCI) and neural prosthesis applications. Moreover, the ultra-low power consumption of the RRAM synapses of the spiking neural network (nW range) may enable the design of autonomous implantable devices for rehabilitation purposes. We demonstrate an original methodology to use Oxide based RRAM (OxRAM) as easy to program and low energy (Spike Timing Dependent Plasticity. Real spiking data have been recorded both intra- and extracellularly from an in-vitro preparation of the Crayfish sensory-motor system and used for validation of the proposed OxRAM based SNN. This artificial SNN is able to identify, learn, recognize and distinguish between different spike shapes in the input signal with a recognition rate about 90% without any supervision.

Spatial and Time Domain Feature of ERP Speller System Extracted via Convolutional Neural Network

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Jaehong Yoon

2018-01-01

Full Text Available Feature of event-related potential (ERP has not been completely understood and illiteracy problem remains unsolved. To this end, P300 peak has been used as the feature of ERP in most brain–computer interface applications, but subjects who do not show such peak are common. Recent development of convolutional neural network provides a way to analyze spatial and temporal features of ERP. Here, we train the convolutional neural network with 2 convolutional layers whose feature maps represented spatial and temporal features of event-related potential. We have found that nonilliterate subjects’ ERP show high correlation between occipital lobe and parietal lobe, whereas illiterate subjects only show correlation between neural activities from frontal lobe and central lobe. The nonilliterates showed peaks in P300, P500, and P700, whereas illiterates mostly showed peaks in around P700. P700 was strong in both subjects. We found that P700 peak may be the key feature of ERP as it appears in both illiterate and nonilliterate subjects.

Neural mechanisms of oculomotor abnormalities in the infantile strabismus syndrome.

Science.gov (United States)

Walton, Mark M G; Pallus, Adam; Fleuriet, Jérome; Mustari, Michael J; Tarczy-Hornoch, Kristina

2017-07-01

Infantile strabismus is characterized by numerous visual and oculomotor abnormalities. Recently nonhuman primate models of infantile strabismus have been established, with characteristics that closely match those observed in human patients. This has made it possible to study the neural basis for visual and oculomotor symptoms in infantile strabismus. In this review, we consider the available evidence for neural abnormalities in structures related to oculomotor pathways ranging from visual cortex to oculomotor nuclei. These studies provide compelling evidence that a disturbance of binocular vision during a sensitive period early in life, whatever the cause, results in a cascade of abnormalities through numerous brain areas involved in visual functions and eye movements. Copyright © 2017 the American Physiological Society.

Neural substrates of reliability-weighted visual-tactile multisensory integration

Directory of Open Access Journals (Sweden)

Michael S Beauchamp

2010-06-01

Full Text Available As sensory systems deteriorate in aging or disease, the brain must relearn the appropriate weights to assign each modality during multisensory integration. Using blood-oxygen level dependent functional magnetic resonance imaging (BOLD fMRI of human subjects, we tested a model for the neural mechanisms of sensory weighting, termed “weighted connections”. This model holds that the connection weights between early and late areas vary depending on the reliability of the modality, independent of the level of early sensory cortex activity. When subjects detected viewed and felt touches to the hand, a network of brain areas was active, including visual areas in lateral occipital cortex, somatosensory areas in inferior parietal lobe, and multisensory areas in the intraparietal sulcus (IPS. In agreement with the weighted connection model, the connection weight measured with structural equation modeling between somatosensory cortex and IPS increased for somatosensory-reliable stimuli, and the connection weight between visual cortex and IPS increased for visual-reliable stimuli. This double dissociation of connection strengths was similar to the pattern of behavioral responses during incongruent multisensory stimulation, suggesting that weighted connections may be a neural mechanism for behavioral reliability weighting.for behavioral reliability weighting.

Cracking the neural code, treating paralysis and the future of bioelectronic medicine.

Science.gov (United States)

Bouton, C

2017-07-01

The human nervous system is a vast network carrying not only sensory and movement information, but also information to and from our organs, intimately linking it to our overall health. Scientists and engineers have been working for decades to tap into this network and 'crack the neural code' by decoding neural signals and learning how to 'speak' the language of the nervous system. Progress has been made in developing neural decoding methods to decipher brain activity and bioelectronic technologies to treat rheumatoid arthritis, paralysis, epilepsy and for diagnosing brain-related diseases such as Parkinson's and Alzheimer's disease. In a recent first-in-human study involving paralysis, a paralysed male study participant regained movement in his hand, years after his injury, through the use of a bioelectronic neural bypass. This work combined neural decoding and neurostimulation methods to translate and re-route signals around damaged neural pathways within the central nervous system. By extending these methods to decipher neural messages in the peripheral nervous system, status information from our bodily functions and specific organs could be gained. This, one day, could allow real-time diagnostics to be performed to give us a deeper insight into a patient's condition, or potentially even predict disease or allow early diagnosis. The future of bioelectronic medicine is extremely bright and is wide open as new diagnostic and treatment options are developed for patients around the world. © 2017 The Association for the Publication of the Journal of Internal Medicine.

A neural-based remote eye gaze tracker under natural head motion.

Science.gov (United States)

Torricelli, Diego; Conforto, Silvia; Schmid, Maurizio; D'Alessio, Tommaso

2008-10-01

A novel approach to view-based eye gaze tracking for human computer interface (HCI) is presented. The proposed method combines different techniques to address the problems of head motion, illumination and usability in the framework of low cost applications. Feature detection and tracking algorithms have been designed to obtain an automatic setup and strengthen the robustness to light conditions. An extensive analysis of neural solutions has been performed to deal with the non-linearity associated with gaze mapping under free-head conditions. No specific hardware, such as infrared illumination or high-resolution cameras, is needed, rather a simple commercial webcam working in visible light spectrum suffices. The system is able to classify the gaze direction of the user over a 15-zone graphical interface, with a success rate of 95% and a global accuracy of around 2 degrees , comparable with the vast majority of existing remote gaze trackers.

An implantable integrated low-power amplifier-microelectrode array for Brain-Machine Interfaces.

Science.gov (United States)

Patrick, Erin; Sankar, Viswanath; Rowe, William; Sanchez, Justin C; Nishida, Toshikazu

2010-01-01

One of the important challenges in designing Brain-Machine Interfaces (BMI) is to build implantable systems that have the ability to reliably process the activity of large ensembles of cortical neurons. In this paper, we report the design, fabrication, and testing of a polyimide-based microelectrode array integrated with a low-power amplifier as part of the Florida Wireless Integrated Recording Electrode (FWIRE) project at the University of Florida developing a fully implantable neural recording system for BMI applications. The electrode array was fabricated using planar micromachining MEMS processes and hybrid packaged with the amplifier die using a flip-chip bonding technique. The system was tested both on bench and in-vivo. Acute and chronic neural recordings were obtained from a rodent for a period of 42 days. The electrode-amplifier performance was analyzed over the chronic recording period with the observation of a noise floor of 4.5 microVrms, and an average signal-to-noise ratio of 3.8.

Parameter estimation in space systems using recurrent neural networks

Science.gov (United States)

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

1991-01-01

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

PyNEST: a convenient interface to the NEST simulator

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Jochen M Eppler

2009-01-01

Full Text Available The neural simulation tool NEST (http://www.nest-initiative.org is a simulator for heterogeneous networks of point neurons or neurons with a small number of compartments. It aims at simulations of large neural systems with more than 10^4 neurons and 10^7 to 10^9 synapses. NEST is implemented in C++ and can be used on a large range of architectures from single-core laptops over multi-core desktop computers to super-computers with thousands of processor cores. Python (http://www.python.org is a modern programming language that has recently received considerable attention in Computational Neuroscience. Python is easy to learn and has many extension modules for scientific computing (e.g. http://www.scipy.org. In this contribution we describe PyNEST, the new user interface to NEST. PyNEST combines NEST’s efficient simulation kernel with the simplicity and flexibility of Python. Compared to NEST’s native simulation language SLI, PyNEST makes it easier to set up simulations, generate stimuli, and analyze simulation results. We describe how PyNEST connects NEST and Python and how it is implemented. With a number of examples, we illustrate how it is used.

Raingauge-Based Rainfall Nowcasting with Artificial Neural Network

Science.gov (United States)

Liong, Shie-Yui; He, Shan

2010-05-01

Rainfall forecasting and nowcasting are of great importance, for instance, in real-time flood early warning systems. Long term rainfall forecasting demands global climate, land, and sea data, thus, large computing power and storage capacity are required. Rainfall nowcasting's computing requirement, on the other hand, is much less. Rainfall nowcasting may use data captured by radar and/or weather stations. This paper presents the application of Artificial Neural Network (ANN) on rainfall nowcasting using data observed at weather and/or rainfall stations. The study focuses on the North-East monsoon period (December, January and February) in Singapore. Rainfall and weather data from ten stations, between 2000 and 2006, were selected and divided into three groups for training, over-fitting test and validation of the ANN. Several neural network architectures were tried in the study. Two architectures, Backpropagation ANN and Group Method of Data Handling ANN, yielded better rainfall nowcasting, up to two hours, than the other architectures. The obtained rainfall nowcasts were then used by a catchment model to forecast catchment runoff. The results of runoff forecast are encouraging and promising.With ANN's high computational speed, the proposed approach may be deliverable for creating the real-time flood early warning system.

A Kinect-Based Gesture Recognition Approach for a Natural Human Robot Interface

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Grazia Cicirelli

2015-03-01

Full Text Available In this paper, we present a gesture recognition system for the development of a human-robot interaction (HRI interface. Kinect cameras and the OpenNI framework are used to obtain real-time tracking of a human skeleton. Ten different gestures, performed by different persons, are defined. Quaternions of joint angles are first used as robust and significant features. Next, neural network (NN classifiers are trained to recognize the different gestures. This work deals with different challenging tasks, such as the real-time implementation of a gesture recognition system and the temporal resolution of gestures. The HRI interface developed in this work includes three Kinect cameras placed at different locations in an indoor environment and an autonomous mobile robot that can be remotely controlled by one operator standing in front of one of the Kinects. Moreover, the system is supplied with a people re-identification module which guarantees that only one person at a time has control of the robot. The system's performance is first validated offline, and then online experiments are carried out, proving the real-time operation of the system as required by a HRI interface.

ADAM13 Induces Cranial Neural Crest by Cleaving Class B Ephrins and Regulating Wnt Signaling

OpenAIRE

Wei, Shuo; Xu, Guofeng; Bridges, Lance C.; Williams, Phoebe; White, Judith M.; DeSimone, Douglas W.

2010-01-01

The cranial neural crest (CNC) are multipotent embryonic cells that contribute to craniofacial structures and other cells and tissues of the vertebrate head. During embryogenesis, CNC is induced at the neural plate boundary through the interplay of several major signaling pathways. Here we report that the metalloproteinase activity of ADAM13 is required for early induction of CNC in Xenopus. In both cultured cells and X. tropicalis embryos, membrane-bound Ephrins (Efns) B1 and B2 were identif...

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

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....

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

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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

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

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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.

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.

Anosmin-1 is essential for neural crest and cranial placodes formation in Xenopus.

Science.gov (United States)

Bae, Chang-Joon; Hong, Chang-Soo; Saint-Jeannet, Jean-Pierre

2018-01-15

During embryogenesis vertebrates develop a complex craniofacial skeleton associated with sensory organs. These structures are primarily derived from two embryonic cell populations the neural crest and cranial placodes, respectively. Neural crest cells and cranial placodes are specified through the integrated action of several families of signaling molecules, and the subsequent activation of a complex network of transcription factors. Here we describe the expression and function of Anosmin-1 (Anos1), an extracellular matrix protein, during neural crest and cranial placodes development in Xenopus laevis. Anos1 was identified as a target of Pax3 and Zic1, two transcription factors necessary and sufficient to generate neural crest and cranial placodes. Anos1 is expressed in cranial neural crest progenitors at early neurula stage and in cranial placode derivatives later in development. We show that Anos1 function is required for neural crest and sensory organs development in Xenopus, consistent with the defects observed in Kallmann syndrome patients carrying a mutation in ANOS1. These findings indicate that anos1 has a conserved function in the development of craniofacial structures, and indicate that anos1-depleted Xenopus embryos represent a useful model to analyze the pathogenesis of Kallmann syndrome. Copyright © 2017. Published by Elsevier Inc.

As We May Think and Be: Brain-computer interfaces to expand the substrate of mind

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Mijail Demian Serruya

2015-04-01

Full Text Available Over a half-century ago, the scientist Vannevar Bush explored the conundrum of how to tap the exponentially rising sea of human knowledge for the betterment of humanity. In his description of a hypothetical electronic library he dubbed the memex, he anticipated internet search and online encyclopedias (Bush, 1945. By blurring the boundary between brain and computer, brain-computer interfaces (BCI could lead to more efficient use of electronic resources (Schalk, 2008. We could expand the substrate of the mind itself rather than merely interfacing it to external computers. Components of brain-computer interfaces could be re-arranged to create brain-brain interfaces, or tightly interconnected links between a person’s brain and ectopic neural modules. Such modules – whether sitting in a bubbling Petri dish, rendered in reciprocally linked integrated circuits, or implanted in our belly – would mark the first step on to a path of breaking out of the limitations imposed by our phylogenetic past Novel BCI architectures could generate novel abilities to navigate and access information that might speed translational science efforts and push the boundaries of human knowledge in an unprecedented manner.

Quantitative Analysis of Human Pluripotency and Neural Specification by In-Depth (PhosphoProteomic Profiling

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Ilyas Singec

2016-09-01

Full Text Available Controlled differentiation of human embryonic stem cells (hESCs can be utilized for precise analysis of cell type identities during early development. We established a highly efficient neural induction strategy and an improved analytical platform, and determined proteomic and phosphoproteomic profiles of hESCs and their specified multipotent neural stem cell derivatives (hNSCs. This quantitative dataset (nearly 13,000 proteins and 60,000 phosphorylation sites provides unique molecular insights into pluripotency and neural lineage entry. Systems-level comparative analysis of proteins (e.g., transcription factors, epigenetic regulators, kinase families, phosphorylation sites, and numerous biological pathways allowed the identification of distinct signatures in pluripotent and multipotent cells. Furthermore, as predicted by the dataset, we functionally validated an autocrine/paracrine mechanism by demonstrating that the secreted protein midkine is a regulator of neural specification. This resource is freely available to the scientific community, including a searchable website, PluriProt.

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

Two critical periods in early visual cortex during figure–ground segregation

Science.gov (United States)

Wokke, Martijn E; Sligte, Ilja G; Steven Scholte, H; Lamme, Victor A F

2012-01-01

The ability to distinguish a figure from its background is crucial for visual perception. To date, it remains unresolved where and how in the visual system different stages of figure–ground segregation emerge. Neural correlates of figure border detection have consistently been found in early visual cortex (V1/V2). However, areas V1/V2 have also been frequently associated with later stages of figure–ground segregation (such as border ownership or surface segregation). To causally link activity in early visual cortex to different stages of figure–ground segregation, we briefly disrupted activity in areas V1/V2 at various moments in time using transcranial magnetic stimulation (TMS). Prior to stimulation we presented stimuli that made it possible to differentiate between figure border detection and surface segregation. We concurrently recorded electroencephalographic (EEG) signals to examine how neural correlates of figure–ground segregation were affected by TMS. Results show that disruption of V1/V2 in an early time window (96–119 msec) affected detection of figure stimuli and affected neural correlates of figure border detection, border ownership, and surface segregation. TMS applied in a relatively late time window (236–259 msec) selectively deteriorated performance associated with surface segregation. We conclude that areas V1/V2 are not only essential in an early stage of figure–ground segregation when figure borders are detected, but subsequently causally contribute to more sophisticated stages of figure–ground segregation such as surface segregation. PMID:23170239

Two critical periods in early visual cortex during figure-ground segregation.

Science.gov (United States)

Wokke, Martijn E; Sligte, Ilja G; Steven Scholte, H; Lamme, Victor A F

2012-11-01

The ability to distinguish a figure from its background is crucial for visual perception. To date, it remains unresolved where and how in the visual system different stages of figure-ground segregation emerge. Neural correlates of figure border detection have consistently been found in early visual cortex (V1/V2). However, areas V1/V2 have also been frequently associated with later stages of figure-ground segregation (such as border ownership or surface segregation). To causally link activity in early visual cortex to different stages of figure-ground segregation, we briefly disrupted activity in areas V1/V2 at various moments in time using transcranial magnetic stimulation (TMS). Prior to stimulation we presented stimuli that made it possible to differentiate between figure border detection and surface segregation. We concurrently recorded electroencephalographic (EEG) signals to examine how neural correlates of figure-ground segregation were affected by TMS. Results show that disruption of V1/V2 in an early time window (96-119 msec) affected detection of figure stimuli and affected neural correlates of figure border detection, border ownership, and surface segregation. TMS applied in a relatively late time window (236-259 msec) selectively deteriorated performance associated with surface segregation. We conclude that areas V1/V2 are not only essential in an early stage of figure-ground segregation when figure borders are detected, but subsequently causally contribute to more sophisticated stages of figure-ground segregation such as surface segregation.

Development of human nervous tissue upon differentiation of embryonic stem cells in three-dimensional culture.

Science.gov (United States)

Preynat-Seauve, Olivier; Suter, David M; Tirefort, Diderik; Turchi, Laurent; Virolle, Thierry; Chneiweiss, Herve; Foti, Michelangelo; Lobrinus, Johannes-Alexander; Stoppini, Luc; Feki, Anis; Dubois-Dauphin, Michel; Krause, Karl Heinz

2009-03-01

Researches on neural differentiation using embryonic stem cells (ESC) require analysis of neurogenesis in conditions mimicking physiological cellular interactions as closely as possible. In this study, we report an air-liquid interface-based culture of human ESC. This culture system allows three-dimensional cell expansion and neural differentiation in the absence of added growth factors. Over a 3-month period, a macroscopically visible, compact tissue developed. Histological coloration revealed a dense neural-like neural tissue including immature tubular structures. Electron microscopy, immunochemistry, and electrophysiological recordings demonstrated a dense network of neurons, astrocytes, and oligodendrocytes able to propagate signals. Within this tissue, tubular structures were niches of cells resembling germinal layers of human fetal brain. Indeed, the tissue contained abundant proliferating cells expressing markers of neural progenitors. Finally, the capacity to generate neural tissues on air-liquid interface differed for different ESC lines, confirming variations of their neurogenic potential. In conclusion, this study demonstrates in vitro engineering of a human neural-like tissue with an organization that bears resemblance to early developing brain. As opposed to previously described methods, this differentiation (a) allows three-dimensional organization, (b) yields dense interconnected neural tissue with structurally and functionally distinct areas, and (c) is spontaneously guided by endogenous developmental cues.

The lamprey: a jawless vertebrate model system for examining origin of the neural crest and other vertebrate traits.

Science.gov (United States)

Green, Stephen A; Bronner, Marianne E

2014-01-01

Lampreys are a group of jawless fishes that serve as an important point of comparison for studies of vertebrate evolution. Lampreys and hagfishes are agnathan fishes, the cyclostomes, which sit at a crucial phylogenetic position as the only living sister group of the jawed vertebrates. Comparisons between cyclostomes and jawed vertebrates can help identify shared derived (i.e. synapomorphic) traits that might have been inherited from ancestral early vertebrates, if unlikely to have arisen convergently by chance. One example of a uniquely vertebrate trait is the neural crest, an embryonic tissue that produces many cell types crucial to vertebrate features, such as the craniofacial skeleton, pigmentation of the skin, and much of the peripheral nervous system (Gans and Northcutt, 1983). Invertebrate chordates arguably lack unambiguous neural crest homologs, yet have cells with some similarities, making comparisons with lampreys and jawed vertebrates essential for inferring characteristics of development in early vertebrates, and how they may have evolved from nonvertebrate chordates. Here we review recent research on cyclostome neural crest development, including research on lamprey gene regulatory networks and differentiated neural crest fates. Copyright © 2014 International Society of Differentiation. Published by Elsevier B.V. All rights reserved.

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

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…

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.

Characterizing water-metal interfaces and machine learning potential energy surfaces

Science.gov (United States)

Ryczko, Kevin

In this thesis, we first discuss the fundamentals of ab initio electronic structure theory and density functional theory (DFT). We also discuss statistics related to computing thermodynamic averages of molecular dynamics (MD). We then use this theory to analyze and compare the structural, dynamical, and electronic properties of liquid water next to prototypical metals including platinum, graphite, and graphene. Our results are built on Born-Oppenheimer molecular dynamics (BOMD) generated using density functional theory (DFT) which explicitly include van der Waals (vdW) interactions within a first principles approach. All calculations reported use large simulation cells, allowing for an accurate treatment of the water-electrode interfaces. We have included vdW interactions through the use of the optB86b-vdW exchange correlation functional. Comparisons with the Perdew-Burke-Ernzerhof (PBE) exchange correlation functional are also shown. We find an initial peak, due to chemisorption, in the density profile of the liquid water-Pt interface not seen in the liquid water-graphite interface, liquid watergraphene interface, nor interfaces studied previously. To further investigate this chemisorption peak, we also report differences in the electronic structure of single water molecules on both Pt and graphite surfaces. We find that a covalent bond forms between the single water molecule and the platinum surface, but not between the single water molecule and the graphite surface. We also discuss the effects that defects and dopants in the graphite and graphene surfaces have on the structure and dynamics of liquid water. Lastly, we introduce artificial neural networks (ANNs), and demonstrate how they can be used to machine learn electronic structure calculations. As a proof of principle, we show the success of an ANN potential energy surfaces for a dimer molecule with a Lennard-Jones potential.

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

Neural correlates of the food/non-food visual distinction.

Science.gov (United States)

Tsourides, Kleovoulos; Shariat, Shahriar; Nejati, Hossein; Gandhi, Tapan K; Cardinaux, Annie; Simons, Christopher T; Cheung, Ngai-Man; Pavlovic, Vladimir; Sinha, Pawan

2016-03-01

An evolutionarily ancient skill we possess is the ability to distinguish between food and non-food. Our goal here is to identify the neural correlates of visually driven 'edible-inedible' perceptual distinction. We also investigate correlates of the finer-grained likability assessment. Our stimuli depicted food or non-food items with sub-classes of appealing or unappealing exemplars. Using data-classification techniques drawn from machine-learning, as well as evoked-response analyses, we sought to determine whether these four classes of stimuli could be distinguished based on the patterns of brain activity they elicited. Subjects viewed 200 images while in a MEG scanner. Our analyses yielded two successes and a surprising failure. The food/non-food distinction had a robust neural counterpart and emerged as early as 85 ms post-stimulus onset. The likable/non-likable distinction too was evident in the neural signals when food and non-food stimuli were grouped together, or when only the non-food stimuli were included in the analyses. However, we were unable to identify any neural correlates of this distinction when limiting the analyses only to food stimuli. Taken together, these positive and negative results further our understanding of the substrates of a set of ecologically important judgments and have clinical implications for conditions like eating-disorders and anhedonia. Copyright © 2016 Elsevier B.V. All rights reserved.

Vibration monitoring of EDF rotating machinery using artificial neural networks

International Nuclear Information System (INIS)

Alguindigue, I.E.; Loskiewicz-Buczak, A.; Uhrig, R.E.; Hamon, L.; Lefevre, F.

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 networks 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 by Electricite de France (EDF). Two neural networks algorithms were used in our project: the Recirculation algorithm and the Backpropagation algorithm. 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 are very encouraging

NSDann2BS, a neutron spectrum unfolding code based on neural networks technology and two bonner spheres

Energy Technology Data Exchange (ETDEWEB)

Ortiz-Rodriguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.; Solis Sanches, L. O.; Miranda, R. Castaneda; Cervantes Viramontes, J. M. [Universidad Autonoma de Zacatecas, Unidad Academica de Ingenieria Electrica. Av. Ramon Lopez Velarde 801. Col. Centro Zacatecas, Zac (Mexico); Vega-Carrillo, H. R. [Universidad Autonoma de Zacatecas, Unidad Academica de Ingenieria Electrica. Av. Ramon Lopez Velarde 801. Col. Centro Zacatecas, Zac., Mexico. and Unidad Academica de Estudios Nucleares. C. Cip (Mexico)

2013-07-03

In this work a neutron spectrum unfolding code, based on artificial intelligence technology is presented. The code called ''Neutron Spectrometry and Dosimetry with Artificial Neural Networks and two Bonner spheres'', (NSDann2BS), was designed in a graphical user interface under the LabVIEW programming environment. The main features of this code are to use an embedded artificial neural network architecture optimized with the ''Robust design of artificial neural networks methodology'' and to use two Bonner spheres as the only piece of information. In order to build the code here presented, once the net topology was optimized and properly trained, knowledge stored at synaptic weights was extracted and using a graphical framework build on the LabVIEW programming environment, the NSDann2BS code was designed. This code is friendly, intuitive and easy to use for the end user. The code is freely available upon request to authors. To demonstrate the use of the neural net embedded in the NSDann2BS code, the rate counts of {sup 252}Cf, {sup 241}AmBe and {sup 239}PuBe neutron sources measured with a Bonner spheres system.

NSDann2BS, a neutron spectrum unfolding code based on neural networks technology and two bonner spheres

International Nuclear Information System (INIS)

Ortiz-Rodríguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.; Solís Sánches, L. O.; Miranda, R. Castañeda; Cervantes Viramontes, J. M.; Vega-Carrillo, H. R.

2013-01-01

In this work a neutron spectrum unfolding code, based on artificial intelligence technology is presented. The code called ''Neutron Spectrometry and Dosimetry with Artificial Neural Networks and two Bonner spheres'', (NSDann2BS), was designed in a graphical user interface under the LabVIEW programming environment. The main features of this code are to use an embedded artificial neural network architecture optimized with the ''Robust design of artificial neural networks methodology'' and to use two Bonner spheres as the only piece of information. In order to build the code here presented, once the net topology was optimized and properly trained, knowledge stored at synaptic weights was extracted and using a graphical framework build on the LabVIEW programming environment, the NSDann2BS code was designed. This code is friendly, intuitive and easy to use for the end user. The code is freely available upon request to authors. To demonstrate the use of the neural net embedded in the NSDann2BS code, the rate counts of 252 Cf, 241 AmBe and 239 PuBe neutron sources measured with a Bonner spheres system

NSDann2BS, a neutron spectrum unfolding code based on neural networks technology and two bonner spheres

Science.gov (United States)

Ortiz-Rodríguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.; Solís Sánches, L. O.; Miranda, R. Castañeda; Cervantes Viramontes, J. M.; Vega-Carrillo, H. R.

2013-07-01

In this work a neutron spectrum unfolding code, based on artificial intelligence technology is presented. The code called "Neutron Spectrometry and Dosimetry with Artificial Neural Networks and two Bonner spheres", (NSDann2BS), was designed in a graphical user interface under the LabVIEW programming environment. The main features of this code are to use an embedded artificial neural network architecture optimized with the "Robust design of artificial neural networks methodology" and to use two Bonner spheres as the only piece of information. In order to build the code here presented, once the net topology was optimized and properly trained, knowledge stored at synaptic weights was extracted and using a graphical framework build on the LabVIEW programming environment, the NSDann2BS code was designed. This code is friendly, intuitive and easy to use for the end user. The code is freely available upon request to authors. To demonstrate the use of the neural net embedded in the NSDann2BS code, the rate counts of 252Cf, 241AmBe and 239PuBe neutron sources measured with a Bonner spheres system.

Embedded System for Prosthetic Control Using Implanted Neuromuscular Interfaces Accessed Via an Osseointegrated Implant.

Science.gov (United States)

Mastinu, Enzo; Doguet, Pascal; Botquin, Yohan; Hakansson, Bo; Ortiz-Catalan, Max

2017-08-01

Despite the technological progress in robotics achieved in the last decades, prosthetic limbs still lack functionality, reliability, and comfort. Recently, an implanted neuromusculoskeletal interface built upon osseointegration was developed and tested in humans, namely the Osseointegrated Human-Machine Gateway. Here, we present an embedded system to exploit the advantages of this technology. Our artificial limb controller allows for bioelectric signals acquisition, processing, decoding of motor intent, prosthetic control, and sensory feedback. It includes a neurostimulator to provide direct neural feedback based on sensory information. The system was validated using real-time tasks characterization, power consumption evaluation, and myoelectric pattern recognition performance. Functionality was proven in a first pilot patient from whom results of daily usage were obtained. The system was designed to be reliably used in activities of daily living, as well as a research platform to monitor prosthesis usage and training, machine-learning-based control algorithms, and neural stimulation paradigms.

cables1 Is Required for Embryonic Neural Development: Molecular, Cellular, and Behavioral Evidence From the Zebrafish

Science.gov (United States)

GROENEWEG, JOLIJN W.; WHITE, YVONNE A.R.; KOKEL, DAVID; PETERSON, RANDALL T.; ZUKERBERG, LAWRENCE R.; BERIN, INNA; RUEDA, BO R.; WOOD, ANTONY W.

2014-01-01

SUMMARY In vitro studies have suggested that the Cables1 gene regulates epithelial cell proliferation, whereas other studies suggest a role in promoting neural differentiation. In efforts to clarify the functions of Cables1 in vivo, we conducted gain- and loss-of-function studies targeting its ortholog (cables1) in the zebrafish embryo. Similar to rodents, zebrafish cables1 mRNA expression is detected most robustly in embryonic neural tissues. Antisense knockdown of cables1 leads to increased numbers of apoptotic cells, particularly in brain tissue, in addition to a distinct behavioral phenotype, characterized by hyperactivity in response to stimulation. Apoptosis and the behavioral abnormality could be rescued by co-expression of a morpholino-resistant cables1 construct. Suppression of p53 expression in cables1 morphants partially rescued both apoptosis and the behavioral phenotype, suggesting that the phenotype of cables1 morphants is due in part to p53-dependent apoptosis. Alterations in the expression patterns of several neural transcription factors were observed in cables1 morphants during early neurulation, suggesting that cables1 is required for early neural differentiation. Ectopic overexpression of cables1 strongly disrupted embryonic morphogenesis, while overexpression of a cables1 mutant lacking the C-terminal cyclin box had little effect, suggesting functional importance of the cyclin box. Lastly, marked reductions in p35, but not Cdk5, were observed in cables1 morphants. Collectively, these data suggest that cables1 is important for neural differentiation during embryogenesis, in a mechanism that likely involves interactions with the Cdk5/p35 kinase pathway. PMID:21268180