WorldWideScience

Sample records for warfighters neural processing

  1. Differential brain activation to angry faces by elite warfighters: neural processing evidence for enhanced threat detection.

    Directory of Open Access Journals (Sweden)

    Martin P Paulus

    Full Text Available BACKGROUND: Little is known about the neural basis of elite performers and their optimal performance in extreme environments. The purpose of this study was to examine brain processing differences between elite warfighters and comparison subjects in brain structures that are important for emotion processing and interoception. METHODOLOGY/PRINCIPAL FINDINGS: Navy Sea, Air, and Land Forces (SEALs while off duty (n = 11 were compared with n = 23 healthy male volunteers while performing a simple emotion face-processing task during functional magnetic resonance imaging. Irrespective of the target emotion, elite warfighters relative to comparison subjects showed relatively greater right-sided insula, but attenuated left-sided insula, activation. Navy SEALs showed selectively greater activation to angry target faces relative to fearful or happy target faces bilaterally in the insula. This was not accounted for by contrasting positive versus negative emotions. Finally, these individuals also showed slower response latencies to fearful and happy target faces than did comparison subjects. CONCLUSIONS/SIGNIFICANCE: These findings support the hypothesis that elite warfighters deploy greater processing resources toward potential threat-related facial expressions and reduced processing resources to non-threat-related facial expressions. Moreover, rather than expending more effort in general, elite warfighters show more focused neural and performance tuning. In other words, greater neural processing resources are directed toward threat stimuli and processing resources are conserved when facing a nonthreat stimulus situation.

  2. Warfighter Performance

    Science.gov (United States)

    2012-10-23

    Synthetic and Systems Biology, Microbial Fuel Cells Sentinel Cells,  Biomaterials Dr. Linda Chrisey, Dr. Laura Kienker Warfighter Performance  CASEVAC...Personnel Command • Deputy Chief, Navy Dental Corps • CO, Navy Expeditionary Medical Unit • COO, Rutgers / Cleveland Clinic Regen Med research consortium...Commander, Carrier Air Wing 9 • CO, Electronic Attack Squadrons VAQ-139, VAQ-129 Education • DDS, Doctor of Dental Surgery • PhD, Cell

  3. Warfighter IT Interoperability Standards Study

    Science.gov (United States)

    2012-07-22

    Data Model JCA Joint Capability Area JCB Joint Capabilities Board JCPAT-E Joint C4I Program Assessment Tool-Empowered JCR Joint Capabilities...Joint Capabilities Release ( JCR ) FBCB2 Movement Tracking System (MTS) JCR -Log x FBCB2 Tactical Ground Reporting System (TIGR) Began FY11 x MC FSC2...Configuration Management Process Mounted MTS ( JCR LOG) Joint Capabilities No data model No TIGR Warfighting Mission Area, Joint

  4. Neural Network Communications Signal Processing

    Science.gov (United States)

    1994-08-01

    Technical Information Report for the Neural Network Communications Signal Processing Program, CDRL A003, 31 March 1993. Software Development Plan for...track changing jamming conditions to provide the decoder with the best log- likelihood ratio metrics at a given time. As part of our development plan we...Artificial Neural Networks (ICANN-91) Volume 2, June 24-28, 1991, pp. 1677-1680. Kohonen, Teuvo, Raivio, Kimmo, Simula, Oli, Venta , 011i, Henriksson

  5. Principles of neural information processing

    CERN Document Server

    Seelen, Werner v

    2016-01-01

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

  6. Handbook on neural information processing

    CERN Document Server

    Maggini, Marco; Jain, Lakhmi

    2013-01-01

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

  7. Neural overlap in processing music and speech.

    Science.gov (United States)

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

    2015-03-19

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

  8. Neural overlap in processing music and speech

    Science.gov (United States)

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

    2015-01-01

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

  9. Process Neural Networks Theory and Applications

    CERN Document Server

    He, Xingui

    2010-01-01

    "Process Neural Networks - Theory and Applications" proposes the concept and model of a process neural network for the first time, showing how it expands the mapping relationship between the input and output of traditional neural networks, and enhancing the expression capability for practical problems, with broad applicability to solving problems relating to process in practice. Some theoretical problems such as continuity, functional approximation capability, and computing capability, are strictly proved. The application methods, network construction principles, and optimization alg

  10. Strength training for the warfighter.

    Science.gov (United States)

    Kraemer, William J; Szivak, Tunde K

    2012-07-01

    Optimizing strength training for the warfighter is challenged by past training philosophies that no longer serve the modern warfighter facing the "anaerobic battlefield." Training approaches for integration of strength with other needed physical capabilities have been shown to require a periodization model that has the flexibility for changes and is able to adapt to ever-changing circumstances affecting the quality of workouts. Additionally, sequencing of workouts to limit over-reaching and development of overtraining syndromes that end in loss of duty time and injury are paramount to long-term success. Allowing adequate time for rest and recovery and recognizing the negative influences of extreme exercise programs and excessive endurance training will be vital in moving physical training programs into a more modern perspective as used by elite strength-power anaerobic athletes in sports today. Because the warfighter is an elite athlete, it is time that training approaches that are scientifically based are updated within the military to match the functional demands of modern warfare and are given greater credence and value at the command levels. A needs analysis, development of periodized training modules, and individualization of programs are needed to optimize the strength of the modern warfighter. We now have the knowledge, professional coaches and nonprofit organization certifications with continuing education units, and modern training technology to allow this to happen. Ultimately, it only takes command decisions and implementation to make this possible.

  11. South Korea Leads the Warfight

    Science.gov (United States)

    2007-01-01

    warfighting command in a doctrinally supporting relationship to the ROK armed forces. The evolution to a Korean -led defense of the Republic of Korea is a...T he Republic of Korea –U.S. alliance is embarking on the most profound transformation affecting American forces on the peninsula since the Korean ...War. For the last 57 years, the United States has led the war- fighting command responsible for the defense of the Republic of Korea (ROK). As the ROK

  12. Neural inhibition enables selection during language processing.

    Science.gov (United States)

    Snyder, Hannah R; Hutchison, Natalie; Nyhus, Erika; Curran, Tim; Banich, Marie T; O'Reilly, Randall C; Munakata, Yuko

    2010-09-21

    Whether grocery shopping or choosing words to express a thought, selecting between options can be challenging, especially for people with anxiety. We investigate the neural mechanisms supporting selection during language processing and its breakdown in anxiety. Our neural network simulations demonstrate a critical role for competitive, inhibitory dynamics supported by GABAergic interneurons. As predicted by our model, we find that anxiety (associated with reduced neural inhibition) impairs selection among options and associated prefrontal cortical activity, even in a simple, nonaffective verb-generation task, and the GABA agonist midazolam (which increases neural inhibition) improves selection, whereas retrieval from semantic memory is unaffected when selection demands are low. Neural inhibition is key to choosing our words.

  13. NARX neural networks for sequence processing tasks

    OpenAIRE

    Hristev, Eugen

    2012-01-01

    This project aims at researching and implementing a neural network architecture system for the NARX (Nonlinear AutoRegressive with eXogenous inputs) model, used in sequence processing tasks and particularly in time series prediction. The model can fallback to different types of architectures including time-delay neural networks and multi layer perceptron. The NARX simulator tests and compares the different architectures for both synthetic and real data, including the time series o...

  14. Toward a warfighter's associate: eliminating the operator control unit

    Science.gov (United States)

    Everett, Hobart R.; Pacis, Estrellina B.; Kogut, Greg; Farrington, Nathan M.; Khurana, S.

    2004-12-01

    In addition to the challenges of equipping a mobile robot with the appropriate sensors, actuators, and processing electronics necessary to perform some useful function, there coexists the equally important challenge of effectively controlling the system"s desired actions. This need is particularly critical if the intent is to operate in conjunction with human forces in a military application, as any low-level distractions can seriously reduce a warfighter"s chances of survival in hostile environments. Historically there can be seen a definitive trend towards making the robot smarter in order to reduce the control burden on the operator, and while much progress has been made in laboratory prototypes, all equipment deployed in theatre to date has been strictly teleoperated. There exists a definite tradeoff between the value added by the robot, in terms of how it contributes to the performance of the mission, and the loss of effectiveness associated with the operator control unit. From a command-and-control perspective, the ultimate goal would be to eliminate the need for a separate robot controller altogether, since it represents an unwanted burden and potential liability from the operator"s perspective. This paper introduces the long-term concept of a supervised autonomous Warfighter"s Associate, which employs a natural-language interface for communication with (and oversight by) its human counterpart. More realistic near-term solutions to achieve intermediate success are then presented, along with actual results to date. The primary application discussed is military, but the concept also applies to law enforcement, space exploration, and search-and-rescue scenarios.

  15. Hafnium transistor process design for neural interfacing.

    Science.gov (United States)

    Parent, David W; Basham, Eric J

    2009-01-01

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

  16. Diagnosing process faults using neural network models

    Energy Technology Data Exchange (ETDEWEB)

    Buescher, K.L.; Jones, R.D.; Messina, M.J.

    1993-11-01

    In order to be of use for realistic problems, a fault diagnosis method should have the following three features. First, it should apply to nonlinear processes. Second, it should not rely on extensive amounts of data regarding previous faults. Lastly, it should detect faults promptly. The authors present such a scheme for static (i.e., non-dynamic) systems. It involves using a neural network to create an associative memory whose fixed points represent the normal behavior of the system.

  17. Neural substrates of sublexical processing for spelling.

    Science.gov (United States)

    DeMarco, Andrew T; Wilson, Stephen M; Rising, Kindle; Rapcsak, Steven Z; Beeson, Pélagie M

    2017-01-01

    We used fMRI to examine the neural substrates of sublexical phoneme-grapheme conversion during spelling in a group of healthy young adults. Participants performed a writing-to-dictation task involving irregular words (e.g., choir), plausible nonwords (e.g., kroid), and a control task of drawing familiar geometric shapes (e.g., squares). Written production of both irregular words and nonwords engaged a left-hemisphere perisylvian network associated with reading/spelling and phonological processing skills. Effects of lexicality, manifested by increased activation during nonword relative to irregular word spelling, were noted in anterior perisylvian regions (posterior inferior frontal gyrus/operculum/precentral gyrus/insula), and in left ventral occipito-temporal cortex. In addition to enhanced neural responses within domain-specific components of the language network, the increased cognitive demands associated with spelling nonwords engaged domain-general frontoparietal cortical networks involved in selective attention and executive control. These results elucidate the neural substrates of sublexical processing during written language production and complement lesion-deficit correlation studies of phonological agraphia. Copyright © 2016 Elsevier Inc. All rights reserved.

  18. Neural processing-type displacement sensor employing multimode waveguide

    Science.gov (United States)

    Aisawa, Shigeki; Noguchi, Kazuhiro; Matsumoto, Takao

    1991-04-01

    A novel neural processing-type displacement sensor, consisting of a multimode waveguide and a neural network, is demonstrated. This sensor detects displacement using changes in the interference output image of the waveguide. The interference image is directly processed by a three-layer perceptron neural network. Environmental change, such as the intensity fluctuation, and change of the temperature can be followed by training the neural network. Experimental results show that the sensor has a resolution of 1 micron.

  19. Active voltammetric microsensors with neural signal processing.

    Energy Technology Data Exchange (ETDEWEB)

    Vogt, M. C.

    1998-12-11

    Many industrial and environmental processes, including bioremediation, would benefit from the feedback and control information provided by a local multi-analyte chemical sensor. For most processes, such a sensor would need to be rugged enough to be placed in situ for long-term remote monitoring, and inexpensive enough to be fielded in useful numbers. The multi-analyte capability is difficult to obtain from common passive sensors, but can be provided by an active device that produces a spectrum-type response. Such new active gas microsensor technology has been developed at Argonne National Laboratory. The technology couples an electrocatalytic ceramic-metallic (cermet) microsensor with a voltammetric measurement technique and advanced neural signal processing. It has been demonstrated to be flexible, rugged, and very economical to produce and deploy. Both narrow interest detectors and wide spectrum instruments have been developed around this technology. Much of this technology's strength lies in the active measurement technique employed. The technique involves applying voltammetry to a miniature electrocatalytic cell to produce unique chemical ''signatures'' from the analytes. These signatures are processed with neural pattern recognition algorithms to identify and quantify the components in the analyte. The neural signal processing allows for innovative sampling and analysis strategies to be employed with the microsensor. In most situations, the whole response signature from the voltammogram can be used to identify, classify, and quantify an analyte, without dissecting it into component parts. This allows an instrument to be calibrated once for a specific gas or mixture of gases by simple exposure to a multi-component standard rather than by a series of individual gases. The sampled unknown analytes can vary in composition or in concentration, the calibration, sensing, and processing methods of these active voltammetric microsensors can

  20. Neural processing of reward in adolescent rodents

    Directory of Open Access Journals (Sweden)

    Nicholas W. Simon

    2015-02-01

    Full Text Available Immaturities in adolescent reward processing are thought to contribute to poor decision making and increased susceptibility to develop addictive and psychiatric disorders. Very little is known; however, about how the adolescent brain processes reward. The current mechanistic theories of reward processing are derived from adult models. Here we review recent research focused on understanding of how the adolescent brain responds to rewards and reward-associated events. A critical aspect of this work is that age-related differences are evident in neuronal processing of reward-related events across multiple brain regions even when adolescent rats demonstrate behavior similar to adults. These include differences in reward processing between adolescent and adult rats in orbitofrontal cortex and dorsal striatum. Surprisingly, minimal age related differences are observed in ventral striatum, which has been a focal point of developmental studies. We go on to discuss the implications of these differences for behavioral traits affected in adolescence, such as impulsivity, risk-taking, and behavioral flexibility. Collectively, this work suggests that reward-evoked neural activity differs as a function of age and that regions such as the dorsal striatum that are not traditionally associated with affective processing in adults may be critical for reward processing and psychiatric vulnerability in adolescents.

  1. Speech Processing Disorder in Neural Hearing Loss

    Directory of Open Access Journals (Sweden)

    Joseph P. Pillion

    2012-01-01

    Full Text Available Deficits in central auditory processing may occur in a variety of clinical conditions including traumatic brain injury, neurodegenerative disease, auditory neuropathy/dyssynchrony syndrome, neurological disorders associated with aging, and aphasia. Deficits in central auditory processing of a more subtle nature have also been studied extensively in neurodevelopmental disorders in children with learning disabilities, ADD, and developmental language disorders. Illustrative cases are reviewed demonstrating the use of an audiological test battery in patients with auditory neuropathy/dyssynchrony syndrome, bilateral lesions to the inferior colliculi, and bilateral lesions to the temporal lobes. Electrophysiological tests of auditory function were utilized to define the locus of dysfunction at neural levels ranging from the auditory nerve, midbrain, and cortical levels.

  2. Neural Adaptation Effects in Conceptual Processing

    Directory of Open Access Journals (Sweden)

    Barbara F. M. Marino

    2015-07-01

    Full Text Available We investigated the conceptual processing of nouns referring to objects characterized by a highly typical color and orientation. We used a go/no-go task in which we asked participants to categorize each noun as referring or not to natural entities (e.g., animals after a selective adaptation of color-edge neurons in the posterior LV4 region of the visual cortex was induced by means of a McCollough effect procedure. This manipulation affected categorization: the green-vertical adaptation led to slower responses than the green-horizontal adaptation, regardless of the specific color and orientation of the to-be-categorized noun. This result suggests that the conceptual processing of natural entities may entail the activation of modality-specific neural channels with weights proportional to the reliability of the signals produced by these channels during actual perception. This finding is discussed with reference to the debate about the grounded cognition view.

  3. Neural network training as a dissipative process.

    Science.gov (United States)

    Gori, Marco; Maggini, Marco; Rossi, Alessandro

    2016-09-01

    This paper analyzes the practical issues and reports some results on a theory in which learning is modeled as a continuous temporal process driven by laws describing the interactions of intelligent agents with their own environment. The classic regularization framework is paired with the idea of temporal manifolds by introducing the principle of least cognitive action, which is inspired by the related principle of mechanics. The introduction of the counterparts of the kinetic and potential energy leads to an interpretation of learning as a dissipative process. As an example, we apply the theory to supervised learning in neural networks and show that the corresponding Euler-Lagrange differential equations can be connected to the classic gradient descent algorithm on the supervised pairs. We give preliminary experiments to confirm the soundness of the theory.

  4. Theory of Neural Information Processing Systems

    Energy Technology Data Exchange (ETDEWEB)

    Galla, Tobias [Abdus Salam International Centre for Theoretical Physics and INFM/CNR SISSA-Unit, Strada Costiera 11, I-34014 Trieste (Italy)

    2006-04-07

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

  5. Introduction to spiking neural networks: Information processing, learning and applications.

    Science.gov (United States)

    Ponulak, Filip; Kasinski, Andrzej

    2011-01-01

    The concept that neural information is encoded in the firing rate of neurons has been the dominant paradigm in neurobiology for many years. This paradigm has also been adopted by the theory of artificial neural networks. Recent physiological experiments demonstrate, however, that in many parts of the nervous system, neural code is founded on the timing of individual action potentials. This finding has given rise to the emergence of a new class of neural models, called spiking neural networks. In this paper we summarize basic properties of spiking neurons and spiking networks. Our focus is, specifically, on models of spike-based information coding, synaptic plasticity and learning. We also survey real-life applications of spiking models. The paper is meant to be an introduction to spiking neural networks for scientists from various disciplines interested in spike-based neural processing.

  6. Neural Networks for Signal Processing and Control

    Science.gov (United States)

    Hesselroth, Ted Daniel

    Neural networks are developed for controlling a robot-arm and camera system and for processing images. The networks are based upon computational schemes that may be found in the brain. In the first network, a neural map algorithm is employed to control a five-joint pneumatic robot arm and gripper through feedback from two video cameras. The pneumatically driven robot arm employed shares essential mechanical characteristics with skeletal muscle systems. To control the position of the arm, 200 neurons formed a network representing the three-dimensional workspace embedded in a four-dimensional system of coordinates from the two cameras, and learned a set of pressures corresponding to the end effector positions, as well as a set of Jacobian matrices for interpolating between these positions. Because of the properties of the rubber-tube actuators of the arm, the position as a function of supplied pressure is nonlinear, nonseparable, and exhibits hysteresis. Nevertheless, through the neural network learning algorithm the position could be controlled to an accuracy of about one pixel (~3 mm) after two hundred learning steps. Applications of repeated corrections in each step via the Jacobian matrices leads to a very robust control algorithm since the Jacobians learned by the network have to satisfy the weak requirement that they yield a reduction of the distance between gripper and target. The second network is proposed as a model for the mammalian vision system in which backward connections from the primary visual cortex (V1) to the lateral geniculate nucleus play a key role. The application of hebbian learning to the forward and backward connections causes the formation of receptive fields which are sensitive to edges, bars, and spatial frequencies of preferred orientations. The receptive fields are learned in such a way as to maximize the rate of transfer of information from the LGN to V1. Orientational preferences are organized into a feature map in the primary visual

  7. Using neural networks for dynamic light scattering time series processing

    Science.gov (United States)

    Chicea, Dan

    2017-04-01

    A basic experiment to record dynamic light scattering (DLS) time series was assembled using basic components. The DLS time series processing using the Lorentzian function fit was considered as reference. A Neural Network was designed and trained using simulated frequency spectra for spherical particles in the range 0–350 nm, assumed to be scattering centers, and the neural network design and training procedure are described in detail. The neural network output accuracy was tested both on simulated and on experimental time series. The match with the DLS results, considered as reference, was good serving as a proof of concept for using neural networks in fast DLS time series processing.

  8. Linking neural and symbolic representation and processing of conceptual structures

    NARCIS (Netherlands)

    van der Velde, Frank; Forth, Jamie; Nazareth, Deniece S.; Wiggins, Geraint A.

    2017-01-01

    We compare and discuss representations in two cognitive architectures aimed at representing and processing complex conceptual (sentence-like) structures. First is the Neural Blackboard Architecture (NBA), which aims to account for representation and processing of complex and combinatorial conceptual

  9. Learning Processes of Layered Neural Networks

    OpenAIRE

    Fujiki, Sumiyoshi; FUJIKI, Nahomi, M.

    1995-01-01

    A positive reinforcement type learning algorithm is formulated for a stochastic feed-forward neural network, and a learning equation similar to that of the Boltzmann machine algorithm is obtained. By applying a mean field approximation to the same stochastic feed-forward neural network, a deterministic analog feed-forward network is obtained and the back-propagation learning rule is re-derived.

  10. Maximizing Space Investment: Tightening Link between Warfighters and Materiel for Space-Based Information

    Science.gov (United States)

    2011-01-24

    development and planned enhancements (based on the author’s personal experience). 35 ― Business Process Modeling ( BPM ) is the representation of...to their businesses .‖ Business Process Modeling Forum. http://www.bpmodeling.com/faq (accessed 16 January 2011). BPM can help the EC author...considers economic, political, and 2 military pressures on fielding space capability for the warfighter and proposes small changes, coupled with a

  11. Powder processing of hybrid titanium neural electrodes

    Science.gov (United States)

    Lopez, Jose Luis, Jr.

    A preliminary investigation into the powder production of a novel hybrid titanium neural electrode for EEG is presented. The rheological behavior of titanium powder suspensions using sodium alginate as a dispersant are examined for optimal slip casting conditions. Electrodes were slip cast and sintered at 950°C for 1 hr, 1000°C for 1, 3, and 6 hrs, and 1050°C for 1 hr. Residual porosities from sintering are characterized using Archimedes' technique and image analysis. The pore network is gel impregnated by submerging the electrodes in electrically conductive gel and placing them in a chamber under vacuum. Gel evaporation of the impregnated electrodes is examined. Electrodes are characterized in the dry and gelled states using impedance spectrometry and compared to a standard silver- silver chloride electrode. Power spectral densities for the sensors in the dry and gelled state are also compared. Residual porosities for the sintered specimens were between 50.59% and 44.81%. Gel evaporation tests show most of the impregnated gel evaporating within 20 min of exposure to atmospheric conditions with prolonged evaporation times for electrodes with higher impregnated gel mass. Impedance measurements of the produced electrodes indicate the low impedance of the hybrid electrodes are due to the increased contact area of the porous electrode. Power spectral densities of the titanium electrode behave similar to a standard silver-silver chloride electrode. Tests suggest the powder processed hybrid titanium electrode's performance is better than current dry contact electrodes and comparable to standard gelled silver-silver chloride electrodes.

  12. An information theoretic approach for combining neural network process models.

    Science.gov (United States)

    Sridhar, D V.; Bartlett, E B.; Seagrave, R C.

    1999-07-01

    Typically neural network modelers in chemical engineering focus on identifying and using a single, hopefully optimal, neural network model. Using a single optimal model implicitly assumes that one neural network model can extract all the information available in a given data set and that the other candidate models are redundant. In general, there is no assurance that any individual model has extracted all relevant information from the data set. Recently, Wolpert (Neural Networks, 5(2), 241 (1992)) proposed the idea of stacked generalization to combine multiple models. Sridhar, Seagrave and Barlett (AIChE J., 42, 2529 (1996)) implemented the stacked generalization for neural network models by integrating multiple neural networks into an architecture known as stacked neural networks (SNNs). SNNs consist of a combination of the candidate neural networks and were shown to provide improved modeling of chemical processes. However, in Sridhar's work SNNs were limited to using a linear combination of artificial neural networks. While a linear combination is simple and easy to use, it can utilize only those model outputs that have a high linear correlation to the output. Models that are useful in a nonlinear sense are wasted if a linear combination is used. In this work we propose an information theoretic stacking (ITS) algorithm for combining neural network models. The ITS algorithm identifies and combines useful models regardless of the nature of their relationship to the actual output. The power of the ITS algorithm is demonstrated through three examples including application to a dynamic process modeling problem. The results obtained demonstrate that the SNNs developed using the ITS algorithm can achieve highly improved performance as compared to selecting and using a single hopefully optimal network or using SNNs based on a linear combination of neural networks.

  13. Optimization of underwater wet welding process parameters using neural network

    National Research Council Canada - National Science Library

    Omajene, Joshua Emuejevoke; Martikainen, Jukka; Wu, Huapeng; Kah, Paul

    2014-01-01

    .... The soundness of a weld can be predicted from the weld bead geometry.This paper illustrates the application of artificial neural network approach in the optimization of the welding process parameter and the influence of the water environment...

  14. Doubly stochastic Poisson processes in artificial neural learning.

    Science.gov (United States)

    Card, H C

    1998-01-01

    This paper investigates neuron activation statistics in artificial neural networks employing stochastic arithmetic. It is shown that a doubly stochastic Poisson process is an appropriate model for the signals in these circuits.

  15. Designing neural networks that process mean values of random variables

    Energy Technology Data Exchange (ETDEWEB)

    Barber, Michael J. [AIT Austrian Institute of Technology, Innovation Systems Department, 1220 Vienna (Austria); Clark, John W. [Department of Physics and McDonnell Center for the Space Sciences, Washington University, St. Louis, MO 63130 (United States); Centro de Ciências Matemáticas, Universidade de Madeira, 9000-390 Funchal (Portugal)

    2014-06-13

    We develop a class of neural networks derived from probabilistic models posed in the form of Bayesian networks. Making biologically and technically plausible assumptions about the nature of the probabilistic models to be represented in the networks, we derive neural networks exhibiting standard dynamics that require no training to determine the synaptic weights, that perform accurate calculation of the mean values of the relevant random variables, that can pool multiple sources of evidence, and that deal appropriately with ambivalent, inconsistent, or contradictory evidence. - Highlights: • High-level neural computations are specified by Bayesian belief networks of random variables. • Probability densities of random variables are encoded in activities of populations of neurons. • Top-down algorithm generates specific neural network implementation of given computation. • Resulting “neural belief networks” process mean values of random variables. • Such networks pool multiple sources of evidence and deal properly with inconsistent evidence.

  16. Know Your Place: Neural Processing of Social Hierarchy in Humans

    Science.gov (United States)

    Zink, Caroline F.; Tong, Yunxia; Chen, Qiang; Bassett, Danielle S.; Stein, Jason L.; Meyer-Lindenberg, Andreas

    2008-01-01

    Summary Social hierarchies guide behavior in many species, including humans, where status also has an enormous impact on motivation and health. However, little is known about the underlying neural representation of social hierarchies in humans. In the present study, we identify dissociable neural responses to perceived social rank using functional magnetic resonance imaging (fMRI) in an interactive simulated social context. In both stable and unstable social hierarchies, viewing a superior individual differentially engaged perceptual-attentional, saliency, and cognitive systems, notably dorsolateral prefrontal cortex. In the unstable hierarchy setting, additional regions were recruited related to emotional processing (amygdala), social cognition (medial prefrontal cortex), and behavioral readiness. Furthermore, social hierarchical consequences of performance were neurally dissociable and of comparable salience to monetary reward, providing a neural basis for the high motivational value of status. Our results identify neural mechanisms that may mediate the enormous influence of social status on human behavior and health. PMID:18439411

  17. Neural correlates of processing negative and sexually arousing pictures.

    Directory of Open Access Journals (Sweden)

    Kira Bailey

    Full Text Available 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.

  18. 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

  19. Neural networks in windprofiler data processing

    Science.gov (United States)

    Weber, H.; Richner, H.; Kretzschmar, R.; Ruffieux, D.

    2003-04-01

    Wind profilers are basically Doppler radars yielding 3-dimensional wind profiles that are deduced from the Doppler shift caused by turbulent elements in the atmosphere. These signals can be contaminated by other airborne elements such as birds or hydrometeors. Using a feed-forward neural network with one hidden layer and one output unit, birds and hydrometeors can be successfully identified in non-averaged single spectra; theses are subsequently removed in the wind computation. An infrared camera was used to identify birds in one of the beams of the wind profiler. After training the network with about 6000 contaminated data sets, it was able to identify contaminated data in a test data set with a reliability of 96 percent. The assumption was made that the neural network parameters obtained in the beam for which bird data was collected can be transferred to the other beams (at least three beams are needed for computing wind vectors). Comparing the evolution of a wind field with and without the neural network shows a significant improvement of wind data quality. Current work concentrates on training the network also for hydrometeors. It is hoped that the instrument's capability can thus be expanded to measure not only correct winds, but also observe bird migration, estimate precipitation and -- by combining precipitation information with vertical velocity measurement -- the monitoring of the height of the melting layer.

  20. Neurale Netværk anvendt indenfor Proceskontrol. Neural Network for Process Control

    DEFF Research Database (Denmark)

    Madsen, Per Printz

    Dette projekt omhandler anvendelsen af neurale netværksmodeller til proceskontrol. Neurale netværksmodeller er simple modeller af de processer, der forløber i det biologiske neurale netværk. Det biologiske neurale netværk er det netværk af nerveceller, der tilsammen danner centralnervesystemet hos...... beskrivelige inputsignaler. Det biologiske neurale netværk dvs. hjernen er således gennem indlæring i stand til at læse, hvorledes der skal stryes og reguleres på baggrund af disse inputsignaler, så det ønskede resultat opnås. Det er derfor nærliggende at undersøge, hvorvidt neurale netværk er anvendelige...... indenfor proceskontrol i almindelighed. Med anvendelser til proceskontrol menes der her anvendeler til prediction, simulering og regulering af dynamiske systemer. For at teste, hvorvidt neurale netværk er anvendelig til prediction og simulering, er der anvendt en tre-trinsoverheder simulator til...

  1. Linking Neural and Symbolic Representation and Processing of Conceptual Structures.

    Science.gov (United States)

    van der Velde, Frank; Forth, Jamie; Nazareth, Deniece S; Wiggins, Geraint A

    2017-01-01

    We compare and discuss representations in two cognitive architectures aimed at representing and processing complex conceptual (sentence-like) structures. First is the Neural Blackboard Architecture (NBA), which aims to account for representation and processing of complex and combinatorial conceptual structures in the brain. Second is IDyOT (Information Dynamics of Thinking), which derives sentence-like structures by learning statistical sequential regularities over a suitable corpus. Although IDyOT is designed at a level more abstract than the neural, so it is a model of cognitive function, rather than neural processing, there are strong similarities between the composite structures developed in IDyOT and the NBA. We hypothesize that these similarities form the basis of a combined architecture in which the individual strengths of each architecture are integrated. We outline and discuss the characteristics of this combined architecture, emphasizing the representation and processing of conceptual structures.

  2. Linking Neural and Symbolic Representation and Processing of Conceptual Structures

    Directory of Open Access Journals (Sweden)

    Frank van der Velde

    2017-08-01

    Full Text Available We compare and discuss representations in two cognitive architectures aimed at representing and processing complex conceptual (sentence-like structures. First is the Neural Blackboard Architecture (NBA, which aims to account for representation and processing of complex and combinatorial conceptual structures in the brain. Second is IDyOT (Information Dynamics of Thinking, which derives sentence-like structures by learning statistical sequential regularities over a suitable corpus. Although IDyOT is designed at a level more abstract than the neural, so it is a model of cognitive function, rather than neural processing, there are strong similarities between the composite structures developed in IDyOT and the NBA. We hypothesize that these similarities form the basis of a combined architecture in which the individual strengths of each architecture are integrated. We outline and discuss the characteristics of this combined architecture, emphasizing the representation and processing of conceptual structures.

  3. Linking Neural and Symbolic Representation and Processing of Conceptual Structures

    Science.gov (United States)

    van der Velde, Frank; Forth, Jamie; Nazareth, Deniece S.; Wiggins, Geraint A.

    2017-01-01

    We compare and discuss representations in two cognitive architectures aimed at representing and processing complex conceptual (sentence-like) structures. First is the Neural Blackboard Architecture (NBA), which aims to account for representation and processing of complex and combinatorial conceptual structures in the brain. Second is IDyOT (Information Dynamics of Thinking), which derives sentence-like structures by learning statistical sequential regularities over a suitable corpus. Although IDyOT is designed at a level more abstract than the neural, so it is a model of cognitive function, rather than neural processing, there are strong similarities between the composite structures developed in IDyOT and the NBA. We hypothesize that these similarities form the basis of a combined architecture in which the individual strengths of each architecture are integrated. We outline and discuss the characteristics of this combined architecture, emphasizing the representation and processing of conceptual structures. PMID:28848460

  4. Characteristic functions and process identification by neural networks

    CERN Document Server

    Dente, J A

    1997-01-01

    Principal component analysis (PCA) algorithms use neural networks to extract the eigenvectors of the correlation matrix from the data. However, if the process is non-Gaussian, PCA algorithms or their higher order generalisations provide only incomplete or misleading information on the statistical properties of the data. To handle such situations we propose neural network algorithms, with an hybrid (supervised and unsupervised) learning scheme, which constructs the characteristic function of the probability distribution and the transition functions of the stochastic process. Illustrative examples are presented, which include Cauchy and Levy-type processes

  5. High level cognitive information processing in neural networks

    Science.gov (United States)

    Barnden, John A.; Fields, Christopher A.

    1992-01-01

    Two related research efforts were addressed: (1) high-level connectionist cognitive modeling; and (2) local neural circuit modeling. The goals of the first effort were to develop connectionist models of high-level cognitive processes such as problem solving or natural language understanding, and to understand the computational requirements of such models. The goals of the second effort were to develop biologically-realistic model of local neural circuits, and to understand the computational behavior of such models. In keeping with the nature of NASA's Innovative Research Program, all the work conducted under the grant was highly innovative. For instance, the following ideas, all summarized, are contributions to the study of connectionist/neural networks: (1) the temporal-winner-take-all, relative-position encoding, and pattern-similarity association techniques; (2) the importation of logical combinators into connection; (3) the use of analogy-based reasoning as a bridge across the gap between the traditional symbolic paradigm and the connectionist paradigm; and (4) the application of connectionism to the domain of belief representation/reasoning. The work on local neural circuit modeling also departs significantly from the work of related researchers. In particular, its concentration on low-level neural phenomena that could support high-level cognitive processing is unusual within the area of biological local circuit modeling, and also serves to expand the horizons of the artificial neural net field.

  6. Neural correlates of successful semantic processing during propofol sedation

    NARCIS (Netherlands)

    Adapa, Ram M.; Davis, Matthew H.; Stamatakis, Emmanuel A.; Absalom, Anthony R.; Menon, David K.

    2014-01-01

    Sedation has a graded effect on brain responses to auditory stimuli: perceptual processing persists at sedation levels that attenuate more complex processing. We used fMRI in healthy volunteers sedated with propofol to assess changes in neural responses to spoken stimuli. Volunteers were scanned awa

  7. Fluid Intelligence and Automatic Neural Processes in Facial Expression Perception

    DEFF Research Database (Denmark)

    Liu, Tongran; Xiao, Tong; Li, Xiaoyan

    2015-01-01

    The relationship between human fluid intelligence and social-emotional abilities has been a topic of considerable interest. The current study investigated whether adolescents with different intellectual levels had different automatic neural processing of facial expressions. Two groups of adolesce......-attentive change detection on social-emotional information.......The relationship between human fluid intelligence and social-emotional abilities has been a topic of considerable interest. The current study investigated whether adolescents with different intellectual levels had different automatic neural processing of facial expressions. Two groups of adolescent...

  8. Further results in multiset processing with neural networks.

    Science.gov (United States)

    McGregor, Simon

    2008-08-01

    This paper presents new experimental results on the variadic neural network (VNN) [McGregor, S. (2007). Neural network processing for multiset data. In Proceedings: Vol. 4668. Artificial neural networks - ICANN 2007, 17th international conference (pp. 460-470). Springer]. The inputs to a variadic network are an arbitrary-length list of n-tuples of real numbers, where n is fixed, and the function computed by the network is unaffected by permutation of the inputs. This paper describes improvements in the training algorithm for the variadic perceptron, based on a constructive cascade topology, and performance of the improved networks on geometric problems inspired by vector graphics. Further development may allow practical application of these or similar networks to vector graphics processing and statistical analysis.

  9. Control of the Coagulation Process in a Paper-mill Wastewater Treatment Process Using a Fuzzy Neural Network

    OpenAIRE

    Wan, J.-Q.; Huang, M.-Z.; Ma, Y.-W.; Guo, W. J.; Y. Wang; Zhang, H.-P.

    2010-01-01

    In this paper, an integrated neural-fuzzy process controller was developed to study the coagulation of wastewater treatment in a paper mill. In order to improve the fuzzy neural network performance, the self-learning ability embedded in the fuzzy neural network model was emphasized for improving the rule extraction performance. It proves the fuzzy neural network more effective in modeling the coagulation performance than artificial neural networks (ANN). For comparing between the fuzzy neural...

  10. Studying the role of synchronized and chaotic spiking neural ensembles in neural information processing.

    Science.gov (United States)

    Rosselló, Josep L; Canals, Vicens; Oliver, Antoni; Morro, Antoni

    2014-08-01

    The brain is characterized by performing many diverse processing tasks ranging from elaborate processes such as pattern recognition, memory or decision making to more simple functionalities such as linear filtering in image processing. Understanding the mechanisms by which the brain is able to produce such a different range of cortical operations remains a fundamental problem in neuroscience. Here we show a study about which processes are related to chaotic and synchronized states based on the study of in-silico implementation of Stochastic Spiking Neural Networks (SSNN). The measurements obtained reveal that chaotic neural ensembles are excellent transmission and convolution systems since mutual information between signals is minimized. At the same time, synchronized cells (that can be understood as ordered states of the brain) can be associated to more complex nonlinear computations. In this sense, we experimentally show that complex and quick pattern recognition processes arise when both synchronized and chaotic states are mixed. These measurements are in accordance with in vivo observations related to the role of neural synchrony in pattern recognition and to the speed of the real biological process. We also suggest that the high-level adaptive mechanisms of the brain that are the Hebbian and non-Hebbian learning rules can be understood as processes devoted to generate the appropriate clustering of both synchronized and chaotic ensembles. The measurements obtained from the hardware implementation of different types of neural systems suggest that the brain processing can be governed by the superposition of these two complementary states with complementary functionalities (nonlinear processing for synchronized states and information convolution and parallelization for chaotic).

  11. Warfighter information services: lessons learned in the intelligence domain

    Science.gov (United States)

    Bray, S. E.

    2014-05-01

    A vision was presented in a previous paper of how a common set of services within a framework could be used to provide all the information processing needs of Warfighters. Central to that vision was the concept of a "Virtual Knowledge Base". The paper presents an implementation of these ideas in the intelligence domain. Several innovative technologies were employed in the solution, which are presented and their benefits explained. The project was successful, validating many of the design principles for such a system which had been proposed in earlier work. Many of these principles are discussed in detail, explaining lessons learned. The results showed that it is possible to make vast improvements in the ability to exploit available data, making it discoverable and queryable wherever it is from anywhere within a participating network; and to exploit machine reasoning to make faster and better inferences from available data, enabling human analysts to spend more of their time doing more difficult analytical tasks rather than searching for relevant data. It was also demonstrated that a small number of generic Information Processing services can be combined and configured in a variety of ways (without changing any software code) to create "fact-processing" workflows, in this case to create different intelligence analysis capabilities. It is yet to be demonstrated that the same generic services can be reused to create analytical/situational awareness capabilities for logistics, operations, planning or other military functions but this is considered likely.

  12. Culture, gaze and the neural processing of fear expressions.

    Science.gov (United States)

    Adams, Reginald B; Franklin, Robert G; Rule, Nicholas O; Freeman, Jonathan B; Kveraga, Kestutis; Hadjikhani, Nouchine; Yoshikawa, Sakiko; Ambady, Nalini

    2010-06-01

    The direction of others' eye gaze has important influences on how we perceive their emotional expressions. Here, we examined differences in neural activation to direct- versus averted-gaze fear faces as a function of culture of the participant (Japanese versus US Caucasian), culture of the stimulus face (Japanese versus US Caucasian), and the relation between the two. We employed a previously validated paradigm to examine differences in neural activation in response to rapidly presented direct- versus averted-fear expressions, finding clear evidence for a culturally determined role of gaze in the processing of fear. Greater neural responsivity was apparent to averted- versus direct-gaze fear in several regions related to face and emotion processing, including bilateral amygdalae, when posed on same-culture faces, whereas greater response to direct- versus averted-gaze fear was apparent in these same regions when posed on other-culture faces. We also found preliminary evidence for intercultural variation including differential responses across participants to Japanese versus US Caucasian stimuli, and to a lesser degree differences in how Japanese and US Caucasian participants responded to these stimuli. These findings reveal a meaningful role of culture in the processing of eye gaze and emotion, and highlight their interactive influences in neural processing.

  13. Recurrent Artificial Neural Networks and Finite State Natural Language Processing.

    Science.gov (United States)

    Moisl, Hermann

    It is argued that pessimistic assessments of the adequacy of artificial neural networks (ANNs) for natural language processing (NLP) on the grounds that they have a finite state architecture are unjustified, and that their adequacy in this regard is an empirical issue. First, arguments that counter standard objections to finite state NLP on the…

  14. Combinatorial structures and processing in neural blackboard architectures

    NARCIS (Netherlands)

    van der Velde, Frank; van der Velde, Frank; de Kamps, Marc; Besold, Tarek R.; d'Avila Garcez, Artur; Marcus, Gary F.; Miikkulainen, Risto

    2015-01-01

    We discuss and illustrate Neural Blackboard Architectures (NBAs) as the basis for variable binding and combinatorial processing the brain. We focus on the NBA for sentence structure. NBAs are based on the notion that conceptual representations are in situ, hence cannot be copied or transported.

  15. Music Signal Processing Using Vector Product Neural Networks

    Science.gov (United States)

    Fan, Z. C.; Chan, T. S.; Yang, Y. H.; Jang, J. S. R.

    2017-05-01

    We propose a novel neural network model for music signal processing using vector product neurons and dimensionality transformations. Here, the inputs are first mapped from real values into three-dimensional vectors then fed into a three-dimensional vector product neural network where the inputs, outputs, and weights are all three-dimensional values. Next, the final outputs are mapped back to the reals. Two methods for dimensionality transformation are proposed, one via context windows and the other via spectral coloring. Experimental results on the iKala dataset for blind singing voice separation confirm the efficacy of our model.

  16. Natural Language Processing Neural Network Considering Deep Cases

    Science.gov (United States)

    Sagara, Tsukasa; Hagiwara, Masafumi

    In this paper, we propose a novel neural network considering deep cases. It can learn knowledge from natural language documents and can perform recall and inference. Various techniques of natural language processing using Neural Network have been proposed. However, natural language sentences used in these techniques consist of about a few words, and they cannot handle complicated sentences. In order to solve these problems, the proposed network divides natural language sentences into a sentence layer, a knowledge layer, ten kinds of deep case layers and a dictionary layer. It can learn the relations among sentences and among words by dividing sentences. The advantages of the method are as follows: (1) ability to handle complicated sentences; (2) ability to restructure sentences; (3) usage of the conceptual dictionary, Goi-Taikei, as the long term memory in a brain. Two kinds of experiments were carried out by using goo dictionary and Wikipedia as knowledge sources. Superior performance of the proposed neural network has been confirmed.

  17. Prediction of Electrochemical Machining Process Parameters using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Hoda Hosny Abuzied

    2012-01-01

    Full Text Available Electrochemical machining (ECM is a non-traditional machining process used mainly to cut hard or difficult to cut metals, where the application of a more traditional process is not convenient. It offers several special advantages including higher machining rate, better precision and control, and a wider range of materials that can be machined. A suitable selection of machining parameters for the ECM process relies heavily on the operator’s technologies and experience because of their numerous and diverse range. Machining parameters provided by the machine tool builder cannot meet the operator’s requirements. So, artificial neural networks were introduced as an efficient approach to predict the values of resulting surface roughness and material removal rate. Many researchers usedartificial neural networks (ANN in improvement of ECM process and also in other nontraditional machining processes as well be seen in later sections. The present study is, initiated to predict values of some of resulting process parameters such as metal removal rate(MRR, and surface roughness (Ra using artificial neural networks based on variation of certain predominant parameters of an electrochemical broaching process such as applied voltage, feed rate and electrolyte flow rate. ANN was found to be an efficient approach as it reduced time & effort required to predict material removal rate & surface roughness if they were found experimentally using trial & error method. To validate the proposed approach the predicted values of surface roughness and material removal rate were compared with a previously obtained ones from the experimental work.

  18. Neural correlates of processing "self-conscious" vs. "basic" emotions.

    Science.gov (United States)

    Gilead, Michael; Katzir, Maayan; Eyal, Tal; Liberman, Nira

    2016-01-29

    Self-conscious emotions are prevalent in our daily lives and play an important role in both normal and pathological behavior. Despite their immense significance, the neural substrates that are involved in the processing of such emotions are surprisingly under-studied. In light of this, we conducted an fMRI study in which participants thought of various personal events which elicited feelings of negative and positive self-conscious (i.e., guilt, pride) or basic (i.e., anger, joy) emotions. We performed a conjunction analysis to investigate the neural correlates associated with processing events that are related to self-conscious vs. basic emotions, irrespective of valence. The results show that processing self-conscious emotions resulted in activation within frontal areas associated with self-processing and self-control, namely, the mPFC extending to the dACC, and within the lateral-dorsal prefrontal cortex. Processing basic emotions resulted in activation throughout relatively phylogenetically-ancient regions of the cortex, namely in visual and tactile processing areas and in the insular cortex. Furthermore, self-conscious emotions differentially activated the mPFC such that the negative self-conscious emotion (guilt) was associated with a more dorsal activation, and the positive self-conscious emotion (pride) was associated with a more ventral activation. We discuss how these results shed light on the nature of mental representations and neural systems involved in self-reflective and affective processing.

  19. [Chronic pain : Perception, reward and neural processing].

    Science.gov (United States)

    Becker, S; Diers, M

    2016-10-01

    Many chronic pain syndromes are characterized by enhanced perception of painful stimuli as well as alterations in cortical processing in sensory and motor regions. In this review article the alterations in muscle pain and neuropathic pain are described. Alterations in patients with fibromyalgia and chronic back pain are described as examples for musculoskeletal pain and also in patients with phantom limb pain after amputation and complex regional pain syndrome as examples for neuropathic pain. In addition to altered pain perception, cumulative evidence on alterations in the processing of reward and the underlying mechanisms in chronic pain has been described. A description is given of what is known on how pain and reward interact and affect each other. The relevance of such interactions for chronic pain is discussed. The implications of these findings for therapeutic approaches are delineated with respect to sensorimotor training and behavioral therapy, focusing on the effectiveness of these approaches, mechanisms and future developments. In particular, we discuss operant behavioral therapy in patients with chronic back pain and fibromyalgia as well as prosthesis training in patients with phantom limb pain and discrimination, mirror and imaginary training in patients with phantom limb pain and complex regional pain syndrome. With respect to the processing of reward, the focus of the discussion is on the role of reward and associated learning in pain therapy.

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

    Science.gov (United States)

    Simons, Laura; Elman, Igor; Borsook, David

    2014-01-01

    Our understanding of chronic pain involves complex brain circuits that include sensory, emotional, cognitive and interoceptive processing. The feed-forward interactions between physical (e.g., trauma) and emotional pain and the consequences of altered psychological status on the expression of pain have made the evaluation and treatment of chronic pain a challenge in the clinic. By understanding the neural circuits involved in psychological processes, a mechanistic approach to the implementation of psychology-based treatments may be better understood. In this review we evaluate some of the principle processes that may be altered as a consequence of chronic pain in the context of localized and integrated neural networks. These changes are ongoing, vary in their magnitude, and their hierarchical manifestations, and may be temporally and sequentially altered by treatments, and all contribute to an overall pain phenotype. Furthermore, we link altered psychological processes to specific evidence-based treatments to put forth a model of pain neuroscience psychology. PMID:24374383

  1. Review of neural network modelling of cracking process

    Science.gov (United States)

    Rosli, M. N.; Aziz, N.

    2016-11-01

    Cracking process is a very important process that converts low value products into high value products such as conversion of naphtha into ethylene and propylene. The process is nonlinear with extensive reaction network. Thus, nonlinear technique such as artificial neural network is explored to develop the model of the system. The paper will review and discuss the research works done on the technique in modelling cracking process using artificial neural network starting from early 1990s until recent development in 2015. Timeline is provided to show progression of work done throughout the years, the main issues addressed, and the proposed techniques for each. In the next section, the main objective of each work and each techniques explored by previous researchers is discussed in more detail. A table that summarizes previous works is provided to show common works done throughout the years. Lastly, potential gap for future works in the area is highlighted.

  2. Neural network connectivity and response latency modelled by stochastic processes

    DEFF Research Database (Denmark)

    Tamborrino, Massimiliano

    is connected to thousands of other neurons. The rst question is: how to model neural networks through stochastic processes? A multivariate Ornstein-Uhlenbeck process, obtained as a diffusion approximation of a jump process, is the proposed answer. Obviously, dependencies between neurons imply dependencies......Stochastic processes and their rst passage times have been widely used to describe the membrane potential dynamics of single neurons and to reproduce neuronal spikes, respectively.However, cerebral cortex in human brains is estimated to contain 10-20 billions of neurons and each of them...... between their spike times. Therefore, the second question is: how to detect neural network connectivity from simultaneously recorded spike trains? Answering this question corresponds to investigate the joint distribution of sequences of rst passage times. A non-parametric method based on copulas...

  3. Activated sludge process based on artificial neural network

    Institute of Scientific and Technical Information of China (English)

    张文艺; 蔡建安

    2002-01-01

    Considering the difficulty of creating water quality model for activated sludge system, a typical BP artificial neural network model has been established to simulate the operation of a waste water treatment facilities. The comparison of prediction results with the on-spot measurements shows the model, the model is accurate and this model can also be used to realize intelligentized on-line control of the wastewater processing process.

  4. Sports experience changes the neural processing of action language.

    Science.gov (United States)

    Beilock, Sian L; Lyons, Ian M; Mattarella-Micke, Andrew; Nusbaum, Howard C; Small, Steven L

    2008-09-09

    Experience alters behavior by producing enduring changes in the neural processes that support performance. For example, performing a specific action improves the execution of that action via changes in associated sensory and motor neural circuitry, and experience using language improves language comprehension by altering the anatomy and physiology of perisylvian neocortical brain regions. Here we provide evidence that specialized (sports) motor experience enhances action-related language understanding by recruitment of left dorsal lateral premotor cortex, a region normally devoted to higher-level action selection and implementation-even when there is no intention to perform a real action. Experience playing and watching sports has enduring effects on language understanding by changing the neural networks that subserve comprehension to incorporate areas active in performing sports skills. Without such experience, sport novices recruit lower-level sensory-motor regions, thought to support the instantiation of movement, during language processing, and activity in primary motor areas does not help comprehension. Thus, the language system is sufficiently plastic and dynamic to encompass expertise-related neural recruitment outside core language networks.

  5. Nutritional state modulates the neural processing of visual motion.

    Science.gov (United States)

    Longden, Kit D; Muzzu, Tomaso; Cook, Daniel J; Schultz, Simon R; Krapp, Holger G

    2014-04-14

    Food deprivation alters the processing of sensory information, increasing neural activity in the olfactory and gustatory systems in animals across phyla. Neural signaling is metabolically costly, and a hungry animal has limited energy reserves, so we hypothesized that neural activity in other systems may be downregulated by food deprivation. We investigated this hypothesis in the motion vision pathway of the blowfly. Like other animals, flies augment their motion vision when moving: they increase the resting activity and gain of visual interneurons supporting the control of locomotion and gaze. In the present study, walking-induced changes in visual processing depended on the nutritional state-they decreased with food deprivation and recovered after subsequent feeding. We found that changes in the motion vision pathway depended on walking speed in a manner dependent on the nutritional state. Walking also reduced response latencies in visual interneurons, an effect not altered by food deprivation. Finally, the optomotor reflex that compensates for visual wide-field motion was reduced in food-deprived flies. Thus, walking augmented motion vision, but the effect was decreased when energy reserves were low. Our results suggest that energy limitations may drive the rebalancing of neural activity with changes in the nutritional state.

  6. Expert,Neural and Fuzzy Systems in Process Planning

    Institute of Scientific and Technical Information of China (English)

    1999-01-01

    Computer aided process planning (CAPP) aims at improving efficiency, quali t y, and productivity in a manufacturing concern through reducing lead-times and costs by utilizing better manufacturing practices thus improving competitiveness in the market. CAPP attempts to capture the thoughts and methods of the experie nced process planner. Variant systems are understandable, generative systems can plan new parts. Expert systems increase flexibility, fuzzy logic captures vague knowledge while neural networks learn. The combination of fuzzy, neural and exp ert system technologies is necessary to capture and utilize the process planning logic. A system that maintains the dependability and clarity of variant systems , is capable of planning new parts, and improves itself through learning is neede d by industry.

  7. Time series prediction using wavelet process neural network

    Institute of Scientific and Technical Information of China (English)

    Ding Gang; Zhong Shi-Sheng; Li Yang

    2008-01-01

    In the real world, the inputs of many complicated systems are time-varying functions or processes. In order to predict the outputs of these systems with high speed and accuracy, this paper proposes a time series prediction model based on the wavelet process neural network, and develops the corresponding learning algorithm based on the expansion of the orthogonal basis functions. The effectiveness of the proposed time series prediction model and its learning algorithm is proved by the Mackey-Glass time series prediction, and the comparative prediction results indicate that the proposed time series prediction model based on the wavelet process neural network seems to perform well and appears suitable for using as a good tool to predict the highly complex nonlinear time series.

  8. Neural network connectivity and response latency modelled by stochastic processes

    DEFF Research Database (Denmark)

    Tamborrino, Massimiliano

    is connected to thousands of other neurons. The rst question is: how to model neural networks through stochastic processes? A multivariate Ornstein-Uhlenbeck process, obtained as a diffusion approximation of a jump process, is the proposed answer. Obviously, dependencies between neurons imply dependencies...... between their spike times. Therefore, the second question is: how to detect neural network connectivity from simultaneously recorded spike trains? Answering this question corresponds to investigate the joint distribution of sequences of rst passage times. A non-parametric method based on copulas...... generation of pikes. When a stimulus is applied to the network, the spontaneous rings may prevail and hamper detection of the effects of the stimulus. Therefore, the spontaneous rings cannot be ignored and the response latency has to be detected on top of a background signal. Everything becomes more dicult...

  9. Neural mechanisms of order information processing in working memory

    Directory of Open Access Journals (Sweden)

    Barbara Dolenc

    2013-11-01

    Full Text Available The ability to encode and maintain the exact order of short sequences of stimuli or events is often crucial to our ability for effective high-order planning. However, it is not yet clear which neural mechanisms underpin this process. Several studies suggest that in comparison with item recognition temporal order coding activates prefrontal and parietal brain regions. Results of various studies tend to favour the hypothesis that the order of the stimuli is represented and encoded on several stages, from primacy and recency estimates to the exact position of the item in a sequence. Different brain regions play a different role in this process. Dorsolateral prefrontal cortex has a more general role in attention, while the premotor cortex is more involved in the process of information grouping. Parietal lobe and hippocampus also play a significant role in order processing as they enable the representation of distance. Moreover, order maintenance is associated with the existence of neural oscillators that operate at different frequencies. Electrophysiological studies revealed that theta and alpha oscillations play an important role in the maintenance of temporal order information. Those EEG oscillations are differentially associated with processes that support the maintenance of order information and item recognition. Various studies suggest a link between prefrontal areas and memory for temporal order, implying that EEG neural oscillations in the prefrontal cortex may play a role in the maintenance of information on temporal order.

  10. A novel neural network for the prediction of process variables

    Institute of Scientific and Technical Information of China (English)

    赵晓光; 陈丙珍; 何小荣

    1995-01-01

    A novel neural network, which forms a complex function in whole via multiple compounding of simple quadratic functions is presented. Generally, it approximates a process relationship by complicated polynomials. In practical problems, quite a lot of processes exhibit a polynomial relation, and within a certain range, processes without polynomial relations can also be approximated completely by polynomials. Therefore, this network can predict process variables more accurately. Theoretically, it can approximate any continuous functions. In this artide this network is investigated and its characteristics are analysed, then some examples to demonstrate its efficiency are presented.

  11. Delivering SSA Capabilities to the Warfighter

    Science.gov (United States)

    van Weezendonk, J.; Sherk, J.; Ryan, T.; McGuire, R.

    The Space Superiority Systems Wing at the Space and Missile Center (SMC/SY) equips US forces with Offensive Counterspace (OCS), Defensive Counterspace (DCS), and Space Situation Awareness (SSA) systems that further enhance space superiority. The Technology Division (SYT) mission is to identify, develop, and transition cutting-edge technologies to the warfighter. SYT invests in the most relevant technologies for SSA, DCS and OCS that enhance SMC/SY's portfolio. This presentation will provide an overview of the SMC/SY SSA Technology being worked and highlights several key programs. The presentation will also highlight how the SMC/SY SSA efforts fit into to a Space Superiority Architecture. SYT executes its own Space Control Technology program line and leverages technologies from various DoD and national laboratories, Federally Funded Research and Development Companies, national agencies, industry and academia to accomplish their mission. The portions of the SY FY06 SSA portfolio that will be discussed are: Precision Metrics, Star Sensor Studies, Multi-mission Deployable Optical System, Intelligent Agent Data Fusion efforts, ESSA ACTD and the GReAT tech demo.

  12. Neural processes underlying cultural differences in cognitive persistence.

    Science.gov (United States)

    Telzer, Eva H; Qu, Yang; Lin, Lynda C

    2017-08-01

    Self-improvement motivation, which occurs when individuals seek to improve upon their competence by gaining new knowledge and improving upon their skills, is critical for cognitive, social, and educational adjustment. While many studies have delineated the neural mechanisms supporting extrinsic motivation induced by monetary rewards, less work has examined the neural processes that support intrinsically motivated behaviors, such as self-improvement motivation. Because cultural groups traditionally vary in terms of their self-improvement motivation, we examined cultural differences in the behavioral and neural processes underlying motivated behaviors during cognitive persistence in the absence of extrinsic rewards. In Study 1, 71 American (47 females, M=19.68 years) and 68 Chinese (38 females, M=19.37 years) students completed a behavioral cognitive control task that required cognitive persistence across time. In Study 2, 14 American and 15 Chinese students completed the same cognitive persistence task during an fMRI scan. Across both studies, American students showed significant declines in cognitive performance across time, whereas Chinese participants demonstrated effective cognitive persistence. These behavioral effects were explained by cultural differences in self-improvement motivation and paralleled by increasing activation and functional coupling between the inferior frontal gyrus (IFG) and ventral striatum (VS) across the task among Chinese participants, neural activation and coupling that remained low in American participants. These findings suggest a potential neural mechanism by which the VS and IFG work in concert to promote cognitive persistence in the absence of extrinsic rewards. Thus, frontostriatal circuitry may be a neurobiological signal representing intrinsic motivation for self-improvement that serves an adaptive function, increasing Chinese students' motivation to engage in cognitive persistence. Copyright © 2017 Elsevier Inc. All rights

  13. Group Influences on Young Adult Warfighters’ Risk Taking

    Science.gov (United States)

    2016-12-01

    Award Number: W81XWH-12-2-0124, HRPO Log Number A-17574 TITLE: "Group Influences on Young Adult Warfighters’ Risk -Taking" CONTRACTING ORGANIZATION...PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. 1. REPORT DATE December 2016 2. REPORT TYPE Final 3. DATES COVERED 30Sep2012 - 29Sep2016 4...TITLE AND SUBTITLE Group Influences on Young Adult Warfighters’ Risk -Taking 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT

  14. Neural PID Control Strategy for Networked Process Control

    Directory of Open Access Journals (Sweden)

    Jianhua Zhang

    2013-01-01

    Full Text Available A new method with a two-layer hierarchy is presented based on a neural proportional-integral-derivative (PID iterative learning method over the communication network for the closed-loop automatic tuning of a PID controller. It can enhance the performance of the well-known simple PID feedback control loop in the local field when real networked process control applied to systems with uncertain factors, such as external disturbance or randomly delayed measurements. The proposed PID iterative learning method is implemented by backpropagation neural networks whose weights are updated via minimizing tracking error entropy of closed-loop systems. The convergence in the mean square sense is analysed for closed-loop networked control systems. To demonstrate the potential applications of the proposed strategies, a pressure-tank experiment is provided to show the usefulness and effectiveness of the proposed design method in network process control systems.

  15. Processing directed acyclic graphs with recursive neural networks.

    Science.gov (United States)

    Bianchini, M; Gori, M; Scarselli, F

    2001-01-01

    Recursive neural networks are conceived for processing graphs and extend the well-known recurrent model for processing sequences. In Frasconi et al. (1998), recursive neural networks can deal only with directed ordered acyclic graphs (DOAGs), in which the children of any given node are ordered. While this assumption is reasonable in some applications, it introduces unnecessary constraints in others. In this paper, it is shown that the constraint on the ordering can be relaxed by using an appropriate weight sharing, that guarantees the independence of the network output with respect to the permutations of the arcs leaving from each node. The method can be used with graphs having low connectivity and, in particular, few outcoming arcs. Some theoretical properties of the proposed architecture are given. They guarantee that the approximation capabilities are maintained, despite the weight sharing.

  16. Neural dynamics of phonological processing in the dorsal auditory stream.

    Science.gov (United States)

    Liebenthal, Einat; Sabri, Merav; Beardsley, Scott A; Mangalathu-Arumana, Jain; Desai, Anjali

    2013-09-25

    Neuroanatomical models hypothesize a role for the dorsal auditory pathway in phonological processing as a feedforward efferent system (Davis and Johnsrude, 2007; Rauschecker and Scott, 2009; Hickok et al., 2011). But the functional organization of the pathway, in terms of time course of interactions between auditory, somatosensory, and motor regions, and the hemispheric lateralization pattern is largely unknown. Here, ambiguous duplex syllables, with elements presented dichotically at varying interaural asynchronies, were used to parametrically modulate phonological processing and associated neural activity in the human dorsal auditory stream. Subjects performed syllable and chirp identification tasks, while event-related potentials and functional magnetic resonance images were concurrently collected. Joint independent component analysis was applied to fuse the neuroimaging data and study the neural dynamics of brain regions involved in phonological processing with high spatiotemporal resolution. Results revealed a highly interactive neural network associated with phonological processing, composed of functional fields in posterior temporal gyrus (pSTG), inferior parietal lobule (IPL), and ventral central sulcus (vCS) that were engaged early and almost simultaneously (at 80-100 ms), consistent with a direct influence of articulatory somatomotor areas on phonemic perception. Left hemispheric lateralization was observed 250 ms earlier in IPL and vCS than pSTG, suggesting that functional specialization of somatomotor (and not auditory) areas determined lateralization in the dorsal auditory pathway. The temporal dynamics of the dorsal auditory pathway described here offer a new understanding of its functional organization and demonstrate that temporal information is essential to resolve neural circuits underlying complex behaviors.

  17. Development during adolescence of the neural processing of social emotion

    OpenAIRE

    Burnett, Stephanie; Bird, Geoffrey; Moll, Jorge; Frith, Chris; Blakemore, Sarah-Jayne

    2009-01-01

    In this fMRI study, we investigated the development between adolescence and adulthood of the neural processing of social emotions. Unlike basic emotions (such as disgust and fear), social emotions (such as guilt and embarrassment) require the representation of another’s mental states. Nineteen adolescents (10–18 years) and 10 adults (22–32 years) were scanned while thinking about scenarios featuring either social or basic emotions. In both age groups, the anterior rostral medial prefrontal co...

  18. Radar signal design problem with neural network processing

    Indian Academy of Sciences (India)

    C Krishnamohan Rao; P S Moharir

    2001-06-01

    Binary and ternary sequences with peaky autocorrelation, measured in terms of high discrimination and merit factor have been searched earlier, using optimization techniques. It is shown that the use of neural network processing of the return signal is much more advantageous. It opens up a new signal design problem, which is solved by an optimization technique called Hamming scan, for both binary and ternary sequences.

  19. Process for forming synapses in neural networks and resistor therefor

    Science.gov (United States)

    Fu, C.Y.

    1996-07-23

    Customizable neural network in which one or more resistors form each synapse is disclosed. All the resistors in the synaptic array are identical, thus simplifying the processing issues. Highly doped, amorphous silicon is used as the resistor material, to create extremely high resistances occupying very small spaces. Connected in series with each resistor in the array is at least one severable conductor whose uppermost layer has a lower reflectivity of laser energy than typical metal conductors at a desired laser wavelength. 5 figs.

  20. Process for forming synapses in neural networks and resistor therefor

    Energy Technology Data Exchange (ETDEWEB)

    Fu, Chi Y. (San Francisco, CA)

    1996-01-01

    Customizable neural network in which one or more resistors form each synapse. All the resistors in the synaptic array are identical, thus simplifying the processing issues. Highly doped, amorphous silicon is used as the resistor material, to create extremely high resistances occupying very small spaces. Connected in series with each resistor in the array is at least one severable conductor whose uppermost layer has a lower reflectivity of laser energy than typical metal conductors at a desired laser wavelength.

  1. A Miniaturized System for Neural Signal Acquiring and Processing

    Institute of Scientific and Technical Information of China (English)

    WANG Min; GAO Guang-hong; XIANG Dong-sheng; CAO Mao-yong; JIA Ai-bin; DING Lei; KONG Hui-min

    2008-01-01

    To collect neural activity data from awake, behaving freely animals, we develop miniaturized implantable recording system by the modern chip:Programmable System on Chip(PSoC) and through chronic electrodes in the cortex. With PSoC family member CY8C29466,the system completed operational and instrument amplifiers, filters, timers, AD convertors, and serial communication, etc. The signal processing was dealt with virtual instrument technology. All of these factors can significantly affect the price and development cycle of the project. The result showed that the system was able to record and analyze neural extrocellular discharge generated by neurons continuously for a week or more. This is very useful for the interdisciplinary research of neuroscience and information engineering technique.The circuits and architecture of the devices can be adapted for neurobiology and research with other small animals.

  2. Vicarious neural processing of outcomes during observational learning.

    Directory of Open Access Journals (Sweden)

    Elisabetta Monfardini

    Full Text Available Learning what behaviour is appropriate in a specific context by observing the actions of others and their outcomes is a key constituent of human cognition, because it saves time and energy and reduces exposure to potentially dangerous situations. Observational learning of associative rules relies on the ability to map the actions of others onto our own, process outcomes, and combine these sources of information. Here, we combined newly developed experimental tasks and functional magnetic resonance imaging (fMRI to investigate the neural mechanisms that govern such observational learning. Results show that the neural systems involved in individual trial-and-error learning and in action observation and execution both participate in observational learning. In addition, we identified brain areas that specifically activate for others' incorrect outcomes during learning in the posterior medial frontal cortex (pMFC, the anterior insula and the posterior superior temporal sulcus (pSTS.

  3. Acute Stress Influences Neural Circuits of Reward Processing

    Directory of Open Access Journals (Sweden)

    Anthony John Porcelli

    2012-11-01

    Full Text Available People often make decisions under aversive conditions such as acute stress. Yet, less is known about the process in which acute stress can influence decision-making. A growing body of research has established that reward-related information associated with the outcomes of decisions exerts a powerful influence over the choices people make and that an extensive network of brain regions, prominently featuring the striatum, is involved in the processing of this reward-related information. Thus, an important step in research on the nature of acute stress’ influence over decision-making is to examine how it may modulate responses to rewards and punishments within reward-processing neural circuitry. In the current experiment, we employed a simple reward processing paradigm – where participants received monetary rewards and punishments – known to evoke robust striatal responses. Immediately prior to performing each of two task runs, participants were exposed to acute stress (i.e., cold pressor or a no stress control procedure in a between-subjects fashion. No stress group participants exhibited a pattern of activity within the dorsal striatum and orbitofrontal cortex consistent with past research on outcome processing – specifically, differential responses for monetary rewards over punishments. In contrast, acute stress group participants’ dorsal striatum and orbitofrontal cortex demonstrated decreased sensitivity to monetary outcomes and a lack of differential activity. These findings provide insight into how neural circuits may process rewards and punishments associated with simple decisions under acutely stressful conditions.

  4. Native language shapes automatic neural processing of speech.

    Science.gov (United States)

    Intartaglia, Bastien; White-Schwoch, Travis; Meunier, Christine; Roman, Stéphane; Kraus, Nina; Schön, Daniele

    2016-08-01

    The development of the phoneme inventory is driven by the acoustic-phonetic properties of one's native language. Neural representation of speech is known to be shaped by language experience, as indexed by cortical responses, and recent studies suggest that subcortical processing also exhibits this attunement to native language. However, most work to date has focused on the differences between tonal and non-tonal languages that use pitch variations to convey phonemic categories. The aim of this cross-language study is to determine whether subcortical encoding of speech sounds is sensitive to language experience by comparing native speakers of two non-tonal languages (French and English). We hypothesized that neural representations would be more robust and fine-grained for speech sounds that belong to the native phonemic inventory of the listener, and especially for the dimensions that are phonetically relevant to the listener such as high frequency components. We recorded neural responses of American English and French native speakers, listening to natural syllables of both languages. Results showed that, independently of the stimulus, American participants exhibited greater neural representation of the fundamental frequency compared to French participants, consistent with the importance of the fundamental frequency to convey stress patterns in English. Furthermore, participants showed more robust encoding and more precise spectral representations of the first formant when listening to the syllable of their native language as compared to non-native language. These results align with the hypothesis that language experience shapes sensory processing of speech and that this plasticity occurs as a function of what is meaningful to a listener.

  5. A fuzzy neural network for intelligent data processing

    Science.gov (United States)

    Xie, Wei; Chu, Feng; Wang, Lipo; Lim, Eng Thiam

    2005-03-01

    In this paper, we describe an incrementally generated fuzzy neural network (FNN) for intelligent data processing. This FNN combines the features of initial fuzzy model self-generation, fast input selection, partition validation, parameter optimization and rule-base simplification. A small FNN is created from scratch -- there is no need to specify the initial network architecture, initial membership functions, or initial weights. Fuzzy IF-THEN rules are constantly combined and pruned to minimize the size of the network while maintaining accuracy; irrelevant inputs are detected and deleted, and membership functions and network weights are trained with a gradient descent algorithm, i.e., error backpropagation. Experimental studies on synthesized data sets demonstrate that the proposed Fuzzy Neural Network is able to achieve accuracy comparable to or higher than both a feedforward crisp neural network, i.e., NeuroRule, and a decision tree, i.e., C4.5, with more compact rule bases for most of the data sets used in our experiments. The FNN has achieved outstanding results for cancer classification based on microarray data. The excellent classification result for Small Round Blue Cell Tumors (SRBCTs) data set is shown. Compared with other published methods, we have used a much fewer number of genes for perfect classification, which will help researchers directly focus their attention on some specific genes and may lead to discovery of deep reasons of the development of cancers and discovery of drugs.

  6. Modeling cognitive and emotional processes: a novel neural network architecture.

    Science.gov (United States)

    Khashman, Adnan

    2010-12-01

    In our continuous attempts to model natural intelligence and emotions in machine learning, many research works emerge with different methods that are often driven by engineering concerns and have the common goal of modeling human perception in machines. This paper aims to go further in that direction by investigating the integration of emotion at the structural level of cognitive systems using the novel emotional DuoNeural Network (DuoNN). This network has hidden layer DuoNeurons, where each has two embedded neurons: a dorsal neuron and a ventral neuron for cognitive and emotional data processing, respectively. When input visual stimuli are presented to the DuoNN, the dorsal cognitive neurons process local features while the ventral emotional neurons process the entire pattern. We present the computational model and the learning algorithm of the DuoNN, the input information-cognitive and emotional-parallel streaming method, and a comparison between the DuoNN and a recently developed emotional neural network. Experimental results show that the DuoNN architecture, configuration, and the additional emotional information processing, yield higher recognition rates and faster learning and decision making.

  7. Signal Processing in Periodically Forced Gradient Frequency Neural Networks.

    Science.gov (United States)

    Kim, Ji Chul; Large, Edward W

    2015-01-01

    Oscillatory instability at the Hopf bifurcation is a dynamical phenomenon that has been suggested to characterize active non-linear processes observed in the auditory system. Networks of oscillators poised near Hopf bifurcation points and tuned to tonotopically distributed frequencies have been used as models of auditory processing at various levels, but systematic investigation of the dynamical properties of such oscillatory networks is still lacking. Here we provide a dynamical systems analysis of a canonical model for gradient frequency neural networks driven by a periodic signal. We use linear stability analysis to identify various driven behaviors of canonical oscillators for all possible ranges of model and forcing parameters. The analysis shows that canonical oscillators exhibit qualitatively different sets of driven states and transitions for different regimes of model parameters. We classify the parameter regimes into four main categories based on their distinct signal processing capabilities. This analysis will lead to deeper understanding of the diverse behaviors of neural systems under periodic forcing and can inform the design of oscillatory network models of auditory signal processing.

  8. Neural Processing of Emotional Prosody across the Adult Lifespan

    Directory of Open Access Journals (Sweden)

    Liliana Ramona Demenescu

    2015-01-01

    Full Text Available Emotion recognition deficits emerge with the increasing age, in particular, a decline in the identification of sadness. However, little is known about the age-related changes of emotion processing in sensory, affective, and executive brain areas. This functional magnetic resonance imaging (fMRI study investigated neural correlates of auditory processing of prosody across adult lifespan. Unattended detection of emotional prosody changes was assessed in 21 young (age range: 18–35 years, 19 middle-aged (age range: 36–55 years, and 15 older (age range: 56–75 years adults. Pseudowords uttered with neutral prosody were standards in an oddball paradigm with angry, sad, happy, and gender deviants (total 20% deviants. Changes in emotional prosody and voice gender elicited bilateral superior temporal gyri (STG responses reflecting automatic encoding of prosody. At the right STG, responses to sad deviants decreased linearly with age, whereas happy events exhibited a nonlinear relationship. In contrast to behavioral data, no age by sex interaction emerged on the neural networks. The aging decline of emotion processing of prosodic cues emerges already at an early automatic stage of information processing at the level of the auditory cortex. However, top-down modulation may lead to an additional perceptional bias, for example, towards positive stimuli, and may depend on context factors such as the listener’s sex.

  9. Neural Processing of Emotional Prosody across the Adult Lifespan.

    Science.gov (United States)

    Demenescu, Liliana Ramona; Kato, Yutaka; Mathiak, Klaus

    2015-01-01

    Emotion recognition deficits emerge with the increasing age, in particular, a decline in the identification of sadness. However, little is known about the age-related changes of emotion processing in sensory, affective, and executive brain areas. This functional magnetic resonance imaging (fMRI) study investigated neural correlates of auditory processing of prosody across adult lifespan. Unattended detection of emotional prosody changes was assessed in 21 young (age range: 18-35 years), 19 middle-aged (age range: 36-55 years), and 15 older (age range: 56-75 years) adults. Pseudowords uttered with neutral prosody were standards in an oddball paradigm with angry, sad, happy, and gender deviants (total 20% deviants). Changes in emotional prosody and voice gender elicited bilateral superior temporal gyri (STG) responses reflecting automatic encoding of prosody. At the right STG, responses to sad deviants decreased linearly with age, whereas happy events exhibited a nonlinear relationship. In contrast to behavioral data, no age by sex interaction emerged on the neural networks. The aging decline of emotion processing of prosodic cues emerges already at an early automatic stage of information processing at the level of the auditory cortex. However, top-down modulation may lead to an additional perceptional bias, for example, towards positive stimuli, and may depend on context factors such as the listener's sex.

  10. Fairness influences early signatures of reward-related neural processing.

    Science.gov (United States)

    Massi, Bart; Luhmann, Christian C

    2015-12-01

    Many humans exhibit a strong preference for fairness during decision-making. Although there is evidence that social factors influence reward-related and affective neural processing, it is unclear if this effect is mediated by compulsory outcome evaluation processes or results from slower deliberate cognition. Here we show that the feedback-related negativity (FRN) and late positive potential (LPP), two signatures of early hedonic processing, are modulated by the fairness of rewards during a passive rating task. We find that unfair payouts elicit larger FRNs than fair payouts, whereas fair payouts elicit larger LPPs than unfair payouts. This is true both in the time-domain, where the FRN and LPP are related, and in the time-frequency domain, where the two signals are largely independent. Ultimately, this work demonstrates that fairness affects the early stages of reward and affective processing, suggesting a common biological mechanism for social and personal reward evaluation.

  11. Neural markers of opposite-sex bias in face processing

    Directory of Open Access Journals (Sweden)

    Alice Mado eProverbio

    2010-10-01

    Full Text Available Some behavioral and neuroimaging studies suggest that adults prefer to view attractive faces of the opposite sex more than attractive faces of the same sex. However, unlike the other-race face effect (ORE; Caldara et al., 2004, little is known regarding the existence of an opposite-/same-sex bias in face processing. In this study, the faces of 130 attractive male and female adults were foveally presented to 40 heterosexual university students (20 men and 20 women who were engaged in a secondary perceptual task (landscape detection. The automatic processing of face gender was investigated by recording ERPs from 128 scalp sites. Neural markers of opposite- vs. same-sex bias in face processing included larger and earlier centro-parietal N400s in response to faces of the opposite sex and a larger late positivity (LP to same-sex faces. Analysis of intra-cortical neural generators (swLORETA showed that facial processing-related (FG, BA37, BA20/21 and emotion-related brain areas (the right parahippocampal gyrus, BA35; uncus, BA36/38; and the cingulate gyrus, BA24 had higher activations in response to opposite- than same-sex faces. The results of this analysis, along with data obtained from ERP recordings, support the hypothesis that both genders process opposite-sex faces differently than same-sex faces. The data also suggest a hemispheric asymmetry in the processing of opposite-/same-sex faces, with the right hemisphere involved in processing same-sex faces and the left hemisphere involved in processing faces of the opposite sex. The data support previous literature suggesting a right lateralization for the representation of self-image and body awareness.

  12. Living ordered neural networks as model systems for signal processing

    Science.gov (United States)

    Villard, C.; Amblard, P. O.; Becq, G.; Gory-Fauré, S.; Brocard, J.; Roth, S.

    2007-06-01

    Neural circuit architecture is a fundamental characteristic of the brain, and how architecture is bound to biological functions is still an open question. Some neuronal geometries seen in the retina or the cochlea are intriguing: information is processed in parallel by several entities like in "pooling" networks which have recently drawn the attention of signal processing scientists. These systems indeed exhibit the noise-enhanced processing effect, which is also actively discussed in the neuroscience community at the neuron scale. The aim of our project is to use in-vitro ordered neuron networks as living paradigms to test ideas coming from the computational science. The different technological bolts that have to be solved are enumerated and the first results are presented. A neuron is a polarised cell, with an excitatory axon and a receiving dendritic tree. We present how soma confinement and axon differentiation can be induced by surface functionalization techniques. The recording of large neuron networks, ordered or not, is also detailed and biological signals shown. The main difficulty to access neural noise in the case of weakly connected networks grown on micro electrode arrays is explained. This open the door to a new detection technology suitable for sub-cellular analysis and stimulation, whose development will constitute the next step of this project.

  13. Musical training enhances neural processing of binaural sounds.

    Science.gov (United States)

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

    2013-10-16

    While hearing in noise is a complex task, even in high levels of noise humans demonstrate remarkable hearing ability. Binaural hearing, which involves the integration and analysis of incoming sounds from both ears, is an important mechanism that promotes hearing in complex listening environments. Analyzing inter-ear differences helps differentiate between sound sources--a key mechanism that facilitates hearing in noise. Even when both ears receive the same input, known as diotic hearing, speech intelligibility in noise is improved. Although musicians have better speech-in-noise perception compared with non-musicians, we do not know to what extent binaural processing contributes to this advantage. Musicians often demonstrate enhanced neural responses to sound, however, which may undergird their speech-in-noise perceptual enhancements. Here, we recorded auditory brainstem responses in young adult musicians and non-musicians to a speech stimulus for which there was no musician advantage when presented monaurally. When presented diotically, musicians demonstrated faster neural timing and greater intertrial response consistency relative to non-musicians. Furthermore, musicians' enhancements to the diotically presented stimulus correlated with speech-in-noise perception. These data provide evidence for musical training's impact on biological processes and suggest binaural processing as a possible contributor to more proficient hearing in noise.

  14. The neural correlates of emotion processing in juvenile offenders.

    Science.gov (United States)

    Pincham, Hannah L; Bryce, Donna; Pasco Fearon, R M

    2015-11-01

    Individuals with severe antisocial behaviour often demonstrate abnormalities or difficulties in emotion processing. Antisocial behaviour typically onsets before adulthood and is reflected in antisocial individuals at the biological level. We therefore conducted a brain-based study of emotion processing in juvenile offenders. Male adolescent offenders and age-matched non-offenders passively viewed emotional images whilst their brain activity was recorded using electroencephalography. The early posterior negativity (EPN) and the late positive potential (LPP) components were used as indices of emotion processing. For both juvenile offenders and non-offenders, the EPN differentiated unpleasant images from other image types, suggesting that early perceptual processing was not impaired in the offender group. In line with normal emotion processing, the LPP was significantly enhanced following unpleasant images for non-offenders. However, for juvenile offenders, the LPP did not differ across image categories, indicative of deficient emotional processing. The findings indicated that this brain-based hypo-reactivity occurred during a late stage of cognitive processing and was not a consequence of atypical early visual attention or perception. This study is the first to show attenuated emotion processing in juvenile offenders at the neural level. Overall, these results have the potential to inform interventions for juvenile offending. © 2014 The Authors. Developmental Science Published by John Wiley & Sons Ltd.

  15. Genetic Algorithms vs. Artificial Neural Networks in Economic Forecasting Process

    Directory of Open Access Journals (Sweden)

    Nicolae Morariu

    2008-01-01

    Full Text Available This paper aims to describe the implementa-tion of a neural network and a genetic algorithm system in order to forecast certain economic indicators of a free market economy. In a free market economy forecasting process precedes the economic planning (a management function, providing important information for the result of the last process. Forecasting represents a starting point in setting of target for a firm, an organization or even a branch of the economy. Thus, the forecasting method used can influence in a significant mode the evolution of an entity. In the following we will describe the forecasting of an economic indicator using two intelligent systems. The difference between the results obtained by this two systems are described in chapter IV.

  16. Incomplete fuzzy data processing systems using artificial neural network

    Science.gov (United States)

    Patyra, Marek J.

    1992-01-01

    In this paper, the implementation of a fuzzy data processing system using an artificial neural network (ANN) is discussed. The binary representation of fuzzy data is assumed, where the universe of discourse is decartelized into n equal intervals. The value of a membership function is represented by a binary number. It is proposed that incomplete fuzzy data processing be performed in two stages. The first stage performs the 'retrieval' of incomplete fuzzy data, and the second stage performs the desired operation on the retrieval data. The method of incomplete fuzzy data retrieval is proposed based on the linear approximation of missing values of the membership function. The ANN implementation of the proposed system is presented. The system was computationally verified and showed a relatively small total error.

  17. Emotional sounds modulate early neural processing of emotional pictures

    Directory of Open Access Journals (Sweden)

    Antje B M Gerdes

    2013-10-01

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

  18. BOOK REVIEW: Theory of Neural Information Processing Systems

    Science.gov (United States)

    Galla, Tobias

    2006-04-01

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

  19. Neural Signaling of Food Healthiness Associated with Emotion Processing

    Science.gov (United States)

    Herwig, Uwe; Dhum, Matthias; Hittmeyer, Anna; Opialla, Sarah; Scherpiet, Sigrid; Keller, Carmen; Brühl, Annette B.; Siegrist, Michael

    2016-01-01

    The ability to differentiate healthy from unhealthy foods is important in order to promote good health. Food, however, may have an emotional connotation, which could be inversely related to healthiness. The neurobiological background of differentiating healthy and unhealthy food and its relations to emotion processing are not yet well understood. We addressed the neural activations, particularly considering the single subject level, when one evaluates a food item to be of a higher, compared to a lower grade of healthiness with a particular view on emotion processing brain regions. Thirty-seven healthy subjects underwent functional magnetic resonance imaging while evaluating the healthiness of food presented as photographs with a subsequent rating on a visual analog scale. We compared individual evaluations of high and low healthiness of food items and also considered gender differences. We found increased activation when food was evaluated to be healthy in the left dorsolateral prefrontal cortex and precuneus in whole brain analyses. In ROI analyses, perceived and rated higher healthiness was associated with lower amygdala activity and higher ventral striatal and orbitofrontal cortex activity. Females exerted a higher activation in midbrain areas when rating food items as being healthy. Our results underline the close relationship between food and emotion processing, which makes sense considering evolutionary aspects. Actively evaluating and deciding whether food is healthy is accompanied by neural signaling associated with reward and self-relevance, which could promote salutary nutrition behavior. The involved brain regions may be amenable to mechanisms of emotion regulation in the context of psychotherapeutic regulation of food intake. PMID:26903859

  20. Dynamic neural processing of linguistic cues related to death.

    Directory of Open Access Journals (Sweden)

    Xi Liu

    Full Text Available Behavioral studies suggest that humans evolve the capacity to cope with anxiety induced by the awareness of death's inevitability. However, the neurocognitive processes that underlie online death-related thoughts remain unclear. Our recent functional MRI study found that the processing of linguistic cues related to death was characterized by decreased neural activity in human insular cortex. The current study further investigated the time course of neural processing of death-related linguistic cues. We recorded event-related potentials (ERP to death-related, life-related, negative-valence, and neutral-valence words in a modified Stroop task that required color naming of words. We found that the amplitude of an early frontal/central negativity at 84-120 ms (N1 decreased to death-related words but increased to life-related words relative to neutral-valence words. The N1 effect associated with death-related and life-related words was correlated respectively with individuals' pessimistic and optimistic attitudes toward life. Death-related words also increased the amplitude of a frontal/central positivity at 124-300 ms (P2 and of a frontal/central positivity at 300-500 ms (P3. However, the P2 and P3 modulations were observed for both death-related and negative-valence words but not for life-related words. The ERP results suggest an early inverse coding of linguistic cues related to life and death, which is followed by negative emotional responses to death-related information.

  1. Neural Signaling of Food Healthiness Associated with Emotion Processing.

    Science.gov (United States)

    Herwig, Uwe; Dhum, Matthias; Hittmeyer, Anna; Opialla, Sarah; Scherpiet, Sigrid; Keller, Carmen; Brühl, Annette B; Siegrist, Michael

    2016-01-01

    The ability to differentiate healthy from unhealthy foods is important in order to promote good health. Food, however, may have an emotional connotation, which could be inversely related to healthiness. The neurobiological background of differentiating healthy and unhealthy food and its relations to emotion processing are not yet well understood. We addressed the neural activations, particularly considering the single subject level, when one evaluates a food item to be of a higher, compared to a lower grade of healthiness with a particular view on emotion processing brain regions. Thirty-seven healthy subjects underwent functional magnetic resonance imaging while evaluating the healthiness of food presented as photographs with a subsequent rating on a visual analog scale. We compared individual evaluations of high and low healthiness of food items and also considered gender differences. We found increased activation when food was evaluated to be healthy in the left dorsolateral prefrontal cortex and precuneus in whole brain analyses. In ROI analyses, perceived and rated higher healthiness was associated with lower amygdala activity and higher ventral striatal and orbitofrontal cortex activity. Females exerted a higher activation in midbrain areas when rating food items as being healthy. Our results underline the close relationship between food and emotion processing, which makes sense considering evolutionary aspects. Actively evaluating and deciding whether food is healthy is accompanied by neural signaling associated with reward and self-relevance, which could promote salutary nutrition behavior. The involved brain regions may be amenable to mechanisms of emotion regulation in the context of psychotherapeutic regulation of food intake.

  2. Neural signalling of food healthiness associated with emotion processing

    Directory of Open Access Journals (Sweden)

    Uwe eHerwig

    2016-02-01

    Full Text Available The ability to differentiate healthy from unhealthy foods is important in order to promote good health. Food, however, may have an emotional connotation, which could be inversely related to healthiness. The neurobiological background of differentiating healthy and unhealthy food and its relations to emotion processing are not yet well understood. We addressed the neural activations, particularly considering the single subject level, when one evaluates a food item to be of a higher, compared to a lower grade of healthiness with a particular view on emotion processing brain regionsThirty-seven healthy subjects underwent functional magnetic resonance imaging while evaluating the healthiness of food presented as photographs with a subsequent rating on a visual analogue scale. We compared individual evaluations of high and low healthiness of food items and also considered gender differences.We found increased activation when food was evaluated to be healthy in the left dorsolateral prefrontal cortex and precuneus in whole brain analyses. In ROI analyses, perceived and rated higher healthiness was associated with lower amygdala activity and higher ventral striatal and orbitofrontal cortex activity. Females exerted a higher activation in midbrain areas when rating food items as being healthy.Our results underline the close relationship between food and emotion processing, which makes sense considering evolutionary aspects. Actively evaluating and deciding whether food is healthy is accompanied by neural signalling associated with reward and self-relevance, which could promote salutary nutrition behaviour. The involved brain regions may be amenable to mechanisms of emotion regulation in the context of psychotherapeutic regulation of food intake.

  3. Assessing the Use of Tactical Clouds to Enhance Warfighter Effectiveness

    Science.gov (United States)

    2014-04-01

    Armoured Vehicle (LAV) – Within this report, a LAV is a generic term used to denote a mechanized infantry vehicle used to support warfighters in ground...are under considerable pressure to reduce IT expenditures. 10 The Defense Science Board, which... awarded to IBM, Lockheed Martin, HP, General Dynamics, Northrop Grumman, MicroTech and Criterion Systems. The contract consists of two parts. The first

  4. Development during adolescence of the neural processing of social emotion.

    Science.gov (United States)

    Burnett, Stephanie; Bird, Geoffrey; Moll, Jorge; Frith, Chris; Blakemore, Sarah-Jayne

    2009-09-01

    In this fMRI study, we investigated the development between adolescence and adulthood of the neural processing of social emotions. Unlike basic emotions (such as disgust and fear), social emotions (such as guilt and embarrassment) require the representation of another's mental states. Nineteen adolescents (10-18 years) and 10 adults (22-32 years) were scanned while thinking about scenarios featuring either social or basic emotions. In both age groups, the anterior rostral medial prefrontal cortex (MPFC) was activated during social versus basic emotion. However, adolescents activated a lateral part of the MPFC for social versus basic emotions, whereas adults did not. Relative to adolescents, adults showed higher activity in the left temporal pole for social versus basic emotions. These results show that, although the MPFC is activated during social emotion in both adults and adolescents, adolescents recruit anterior (MPFC) regions more than do adults, and adults recruit posterior (temporal) regions more than do adolescents.

  5. Unsupervised Neural Network Quantifies the Cost of Visual Information Processing.

    Science.gov (United States)

    Orbán, Levente L; Chartier, Sylvain

    2015-01-01

    Untrained, "flower-naïve" bumblebees display behavioural preferences when presented with visual properties such as colour, symmetry, spatial frequency and others. Two unsupervised neural networks were implemented to understand the extent to which these models capture elements of bumblebees' unlearned visual preferences towards flower-like visual properties. The computational models, which are variants of Independent Component Analysis and Feature-Extracting Bidirectional Associative Memory, use images of test-patterns that are identical to ones used in behavioural studies. Each model works by decomposing images of floral patterns into meaningful underlying factors. We reconstruct the original floral image using the components and compare the quality of the reconstructed image to the original image. Independent Component Analysis matches behavioural results substantially better across several visual properties. These results are interpreted to support a hypothesis that the temporal and energetic costs of information processing by pollinators served as a selective pressure on floral displays: flowers adapted to pollinators' cognitive constraints.

  6. Image processing and analysis using neural networks for optometry area

    Science.gov (United States)

    Netto, Antonio V.; Ferreira de Oliveira, Maria C.

    2002-11-01

    In this work we describe the framework of a functional system for processing and analyzing images of the human eye acquired by the Hartmann-Shack technique (HS), in order to extract information to formulate a diagnosis of eye refractive errors (astigmatism, hypermetropia and myopia). The analysis is to be carried out using an Artificial Intelligence system based on Neural Nets, Fuzzy Logic and Classifier Combination. The major goal is to establish the basis of a new technology to effectively measure ocular refractive errors that is based on methods alternative those adopted in current patented systems. Moreover, analysis of images acquired with the Hartmann-Shack technique may enable the extraction of additional information on the health of an eye under exam from the same image used to detect refraction errors.

  7. Reconstruction of an engine combustion process with a neural network

    Energy Technology Data Exchange (ETDEWEB)

    Jacob, P.J.; Gu, F.; Ball, A.D. [School of Engineering, University of Manchester, Manchester (United Kingdom)

    1997-12-31

    The cylinder pressure waveform in an internal combustion engine is one of the most important parameters in describing the engine combustion process. It is used for a range of diagnostic tasks such as identification of ignition faults or mechanical wear in the cylinders. However, it is very difficult to measure this parameter directly. Never-the-less, the cylinder pressure may be inferred from other more readily obtainable parameters. In this presentation it is shown how a Radial Basis Function network, which may be regarded as a form of neural network, may be used to model the cylinder pressure as a function of the instantaneous crankshaft velocity, recorded with a simple magnetic sensor. The application of the model is demonstrated on a four cylinder DI diesel engine with data from a wide range of speed and load settings. The prediction capabilities of the model once trained are validated against measured data. (orig.) 4 refs.

  8. Integrated Warfighter Biodefense Program (IWBP) - Next Phase

    Science.gov (United States)

    2011-11-10

    These include linear/non-linear regression, artificial neural networks (ANNs), and decision trees.8 Due to the lack of experimental data and differences...period, the existing Gryphon software architecture for infectious diseases was reviewed for generality and changes were made to the software...anomalous associations within network data that may be a precursor to a cyber threat. Other applications include the broader domain of anomalous

  9. Neural Generalized Predictive Control of a non-linear Process

    DEFF Research Database (Denmark)

    Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole

    1998-01-01

    The use of neural network in non-linear control is made difficult by the fact the stability and robustness is not guaranteed and that the implementation in real time is non-trivial. In this paper we introduce a predictive controller based on a neural network model which has promising stability...... detail and discuss the implementation difficulties. The neural generalized predictive controller is tested on a pneumatic servo sys-tem....

  10. Attention Modulates the Neural Processes Underlying Multisensory Integration of Emotion

    Directory of Open Access Journals (Sweden)

    Hao Tam Ho

    2011-10-01

    Full Text Available Integrating emotional information from multiple sensory modalities is generally assumed to be a pre-attentive process (de Gelder et al., 1999. This assumption, however, presupposes that the integrative process occurs independent of attention. Using event-potentials (ERP the present study investigated whether the neural processes underlying the integration of dynamic facial expression and emotional prosody is indeed unaffected by attentional manipulations. To this end, participants were presented with congruent and incongruent face-voice combinations (eg, an angry face combined with a neutral voice and performed different two-choice tasks in four consecutive blocks. Three of the tasks directed the participants' attention to emotion expressions in the face, the voice or both. The fourth task required participants to attend to the synchronicity between voice and lip movements. The results show divergent modulations of early ERP components by the different attentional manipulations. For example, when attention was directed to the face (or the voice, incongruent stimuli elicited a reduced N1 as compared to congruent stimuli. This effect was absent, when attention was diverted away from the emotionality in both face and voice suggesting that the detection of emotional incongruence already requires attention. Based on these findings, we question whether multisensory integration of emotion occurs indeed pre-attentively.

  11. Retinal vessel extraction using Lattice Neural Networks with Dendritic Processing.

    Science.gov (United States)

    Vega, Roberto; Sanchez-Ante, Gildardo; Falcon-Morales, Luis E; Sossa, Humberto; Guevara, Elizabeth

    2015-03-01

    Retinal images can be used to detect and follow up several important chronic diseases. The classification of retinal images requires an experienced ophthalmologist. This has been a bottleneck to implement routine screenings performed by general physicians. It has been proposed to create automated systems that can perform such task with little intervention from humans, with partial success. In this work, we report advances in such endeavor, by using a Lattice Neural Network with Dendritic Processing (LNNDP). We report results using several metrics, and compare against well known methods such as Support Vector Machines (SVM) and Multilayer Perceptrons (MLP). Our proposal shows better performance than other approaches reported in the literature. An additional advantage is that unlike those other tools, LNNDP requires no parameters, and it automatically constructs its structure to solve a particular problem. The proposed methodology requires four steps: (1) Pre-processing, (2) Feature computation, (3) Classification and (4) Post-processing. The Hotelling T(2) control chart was used to reduce the dimensionality of the feature vector, from 7 that were used before to 5 in this work. The experiments were run on images of DRIVE and STARE databases. The results show that on average, F1-Score is better in LNNDP, compared with SVM and MLP implementations. Same improvement is observed for MCC and the accuracy.

  12. Forward and Reverse Process Models for the Squeeze Casting Process Using Neural Network Based Approaches

    Directory of Open Access Journals (Sweden)

    Manjunath Patel Gowdru Chandrashekarappa

    2014-01-01

    Full Text Available The present research work is focussed to develop an intelligent system to establish the input-output relationship utilizing forward and reverse mappings of artificial neural networks. Forward mapping aims at predicting the density and secondary dendrite arm spacing (SDAS from the known set of squeeze cast process parameters such as time delay, pressure duration, squeezes pressure, pouring temperature, and die temperature. An attempt is also made to meet the industrial requirements of developing the reverse model to predict the recommended squeeze cast parameters for the desired density and SDAS. Two different neural network based approaches have been proposed to carry out the said task, namely, back propagation neural network (BPNN and genetic algorithm neural network (GA-NN. The batch mode of training is employed for both supervised learning networks and requires huge training data. The requirement of huge training data is generated artificially at random using regression equation derived through real experiments carried out earlier by the same authors. The performances of BPNN and GA-NN models are compared among themselves with those of regression for ten test cases. The results show that both models are capable of making better predictions and the models can be effectively used in shop floor in selection of most influential parameters for the desired outputs.

  13. Childhood social inequalities influences neural processes in young adult caregiving.

    Science.gov (United States)

    Kim, Pilyoung; Ho, Shaun S; Evans, Gary W; Liberzon, Israel; Swain, James E

    2015-12-01

    Childhood poverty is associated with harsh parenting with a risk of transmission to the next generation. This prospective study examined the relations between childhood poverty and non-parent adults' neural responses to infant cry sounds. While no main effects of poverty were revealed in contrasts of infant cry versus acoustically matched white noise, a gender by childhood poverty interaction emerged. In females, childhood poverty was associated with increased neural activations in the posterior insula, striatum, calcarine sulcus, hippocampus, and fusiform gyrus, while, in males, childhood poverty was associated with reduced levels of neural responses to infant cry in the same regions. Irrespective of gender, neural activation in these regions was associated with higher levels of annoyance with the cry sound and reduced desire to approach the crying infant. The findings suggest gender differences in neural and emotional responses to infant cry sounds among young adults growing up in poverty.

  14. Subcortical neural coding mechanisms for auditory temporal processing.

    Science.gov (United States)

    Frisina, R D

    2001-08-01

    Biologically relevant sounds such as speech, animal vocalizations and music have distinguishing temporal features that are utilized for effective auditory perception. Common temporal features include sound envelope fluctuations, often modeled in the laboratory by amplitude modulation (AM), and starts and stops in ongoing sounds, which are frequently approximated by hearing researchers as gaps between two sounds or are investigated in forward masking experiments. The auditory system has evolved many neural processing mechanisms for encoding important temporal features of sound. Due to rapid progress made in the field of auditory neuroscience in the past three decades, it is not possible to review all progress in this field in a single article. The goal of the present report is to focus on single-unit mechanisms in the mammalian brainstem auditory system for encoding AM and gaps as illustrative examples of how the system encodes key temporal features of sound. This report, following a systems analysis approach, starts with findings in the auditory nerve and proceeds centrally through the cochlear nucleus, superior olivary complex and inferior colliculus. Some general principles can be seen when reviewing this entire field. For example, as one ascends the central auditory system, a neural encoding shift occurs. An emphasis on synchronous responses for temporal coding exists in the auditory periphery, and more reliance on rate coding occurs as one moves centrally. In addition, for AM, modulation transfer functions become more bandpass as the sound level of the signal is raised, but become more lowpass in shape as background noise is added. In many cases, AM coding can actually increase in the presence of background noise. For gap processing or forward masking, coding for gaps changes from a decrease in spike firing rate for neurons of the peripheral auditory system that have sustained response patterns, to an increase in firing rate for more central neurons with

  15. Neural Processing of Auditory Signals and Modular Neural Control for Sound Tropism of Walking Machines

    Directory of Open Access Journals (Sweden)

    Hubert Roth

    2008-11-01

    Full Text Available The specialized hairs and slit sensillae of spiders (Cupiennius salei can sense the airflow and auditory signals in a low-frequency range. They provide the sensor information for reactive behavior, like e.g. capturing a prey. In analogy, in this paper a setup is described where two microphones and a neural preprocessing system together with a modular neural controller are used to generate a sound tropism of a four-legged walking machine. The neural preprocessing network is acting as a low-pass filter and it is followed by a network which discerns between signals coming from the left or the right. The parameters of these networks are optimized by an evolutionary algorithm. In addition, a simple modular neural controller then generates the desired different walking patterns such that the machine walks straight, then turns towards a switched-on sound source, and then stops near to it.

  16. Neural processing of auditory signals and modular neural control for sound tropism of walking machines

    DEFF Research Database (Denmark)

    Manoonpong, Poramate; Pasemann, Frank; Fischer, Joern

    2005-01-01

    The specialized hairs and slit sensillae of spiders (Cupiennius salei) can sense the airflow and auditory signals in a low-frequency range. They provide the sensor information for reactive behavior, like e.g. capturing a prey. In analogy, in this paper a setup is described where two microphones...... and a neural preprocessing system together with a modular neural controller are used to generate a sound tropism of a four-legged walking machine. The neural preprocessing network is acting as a low-pass filter and it is followed by a network which discerns between signals coming from the left or the right....... The parameters of these networks are optimized by an evolutionary algorithm. In addition, a simple modular neural controller then generates the desired different walking patterns such that the machine walks straight, then turns towards a switched-on sound source, and then stops near to it....

  17. 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.

  18. A novel neural-wavelet approach for process diagnostics and complex system modeling

    Science.gov (United States)

    Gao, Rong

    Neural networks have been effective in several engineering applications because of their learning abilities and robustness. However certain shortcomings, such as slow convergence and local minima, are always associated with neural networks, especially neural networks applied to highly nonlinear and non-stationary problems. These problems can be effectively alleviated by integrating a new powerful tool, wavelets, into conventional neural networks. The multi-resolution analysis and feature localization capabilities of the wavelet transform offer neural networks new possibilities for learning. A neural wavelet network approach developed in this thesis enjoys fast convergence rate with little possibility to be caught at a local minimum. It combines the localization properties of wavelets with the learning abilities of neural networks. Two different testbeds are used for testing the efficiency of the new approach. The first is magnetic flowmeter-based process diagnostics: here we extend previous work, which has demonstrated that wavelet groups contain process information, to more general process diagnostics. A loop at Applied Intelligent Systems Lab (AISL) is used for collecting and analyzing data through the neural-wavelet approach. The research is important for thermal-hydraulic processes in nuclear and other engineering fields. The neural-wavelet approach developed is also tested with data from the electric power grid. More specifically, the neural-wavelet approach is used for performing short-term and mid-term prediction of power load demand. In addition, the feasibility of determining the type of load using the proposed neural wavelet approach is also examined. The notion of cross scale product has been developed as an expedient yet reliable discriminator of loads. Theoretical issues involved in the integration of wavelets and neural networks are discussed and future work outlined.

  19. Unsupervised Neural Network Quantifies the Cost of Visual Information Processing.

    Directory of Open Access Journals (Sweden)

    Levente L Orbán

    Full Text Available Untrained, "flower-naïve" bumblebees display behavioural preferences when presented with visual properties such as colour, symmetry, spatial frequency and others. Two unsupervised neural networks were implemented to understand the extent to which these models capture elements of bumblebees' unlearned visual preferences towards flower-like visual properties. The computational models, which are variants of Independent Component Analysis and Feature-Extracting Bidirectional Associative Memory, use images of test-patterns that are identical to ones used in behavioural studies. Each model works by decomposing images of floral patterns into meaningful underlying factors. We reconstruct the original floral image using the components and compare the quality of the reconstructed image to the original image. Independent Component Analysis matches behavioural results substantially better across several visual properties. These results are interpreted to support a hypothesis that the temporal and energetic costs of information processing by pollinators served as a selective pressure on floral displays: flowers adapted to pollinators' cognitive constraints.

  20. Culture, gaze and the neural processing of fear expressions

    OpenAIRE

    2009-01-01

    The direction of others’ eye gaze has important influences on how we perceive their emotional expressions. Here, we examined differences in neural activation to direct- versus averted-gaze fear faces as a function of culture of the participant (Japanese versus US Caucasian), culture of the stimulus face (Japanese versus US Caucasian), and the relation between the two. We employed a previously validated paradigm to examine differences in neural activation in response to rapidly presented direc...

  1. Neural Networks as a Tool for Georadar Data Processing

    Directory of Open Access Journals (Sweden)

    Szymczyk Piotr

    2015-12-01

    Full Text Available In this article a new neural network based method for automatic classification of ground penetrating radar (GPR traces is proposed. The presented approach is based on a new representation of GPR signals by polynomials approximation. The coefficients of the polynomial (the feature vector are neural network inputs for automatic classification of a special kind of geologic structure—a sinkhole. The analysis and results show that the classifier can effectively distinguish sinkholes from other geologic structures.

  2. Neural network post-processing of grayscale optical correlator

    Science.gov (United States)

    Lu, Thomas T; Hughlett, Casey L.; Zhoua, Hanying; Chao, Tien-Hsin; Hanan, Jay C.

    2005-01-01

    In this paper we present the use of a radial basis function neural network (RBFNN) as a post-processor to assist the optical correlator to identify the objects and to reject false alarms. Image plane features near the correlation peaks are extracted and fed to the neural network for analysis. The approach is capable of handling large number of object variations and filter sets. Preliminary experimental results are presented and the performance is analyzed.

  3. Neural correlates of semantic ambiguity processing during context verification.

    Science.gov (United States)

    Hoenig, Klaus; Scheef, Lukas

    2009-04-15

    Understanding the relevant meaning of a word with different meanings (homonym) in a given context requires activation of the neural representations of the relevant meaning and inhibition of the irrelevant meaning. The cognitive demand of such disambiguation is highest when the dominant, yet contextually irrelevant meaning of a polar homonym must be suppressed. This central process (semantic inhibition) for lexico-semantic ambiguity resolution was monitored with fMRI during semantic context verifications. Twenty-two healthy volunteers decided whether congruent or incongruent target words fitted into the contexts established by preceding sentences. Half of the sentences ended with a homonym, thereby allowing to cross the factors ambiguity and semantic congruency. BOLD increases related to the inhibitory attentional control over non-selected meanings during ambiguity processing occurred in a brain network including left dorsolateral prefrontal cortex (DLPFC), bilateral angular gyrus (AG), bilateral anterior superior temporal gyrus (aSTG) as well as right ventromedial temporal lobe. In left DLPFC (BA 46/9) and left AG (BA 39) BOLD activity to target words of the incongruent-ambiguous condition correlated with the individual amount of semantic interference experienced by the subjects. BOLD increases of incongruent versus congruent semantic verifications occurred in bilateral inferior frontal gyrus. The results of the present study suggest a specific role of left DLPFC and AG in the resolution of semantic interference from contextually inappropriate homonym meanings. These fronto-parietal areas might exert inhibitory control over temporal regions in service of attentional selection between relevant and irrelevant homonym meanings, by creating a sufficient activation difference between their respective representations.

  4. Neural-networks-based feedback linearization versus model predictive control of continuous alcoholic fermentation process

    Energy Technology Data Exchange (ETDEWEB)

    Mjalli, F.S.; Al-Asheh, S. [Chemical Engineering Department, Qatar University, Doha (Qatar)

    2005-10-01

    In this work advanced nonlinear neural networks based control system design algorithms are adopted to control a mechanistic model for an ethanol fermentation process. The process model equations for such systems are highly nonlinear. A neural network strategy has been implemented in this work for capturing the dynamics of the mechanistic model for the fermentation process. The neural network achieved has been validated against the mechanistic model. Two neural network based nonlinear control strategies have also been adopted using the model identified. The performance of the feedback linearization technique was compared to neural network model predictive control in terms of stability and set point tracking capabilities. Under servo conditions, the feedback linearization algorithm gave comparable tracking and stability. The feedback linearization controller achieved the control target faster than the model predictive one but with vigorous and sudden controller moves. (Abstract Copyright [2005], Wiley Periodicals, Inc.)

  5. Double Glow Plasma Surface Alloying Process Modeling Using Artificial Neural Networks

    Institute of Scientific and Technical Information of China (English)

    Jiang XU; Xishan XIE; Zhong XU

    2003-01-01

    A model is developed for predicting the correlation between processing parameters and the technical target of double glowby applying artificial neural network (ANN). The input parameters of the neural network (NN) are source voltage, workpiecevoltage, working pressure and distance between source electrode and workpiece. The output of the NN model is three importanttechnical targets, namely the gross element content, the thickness of surface alloying layer and the absorption rate (the ratioof the mass loss of source materials to the increasing mass of workpiece) in the processing of double glow plasma surfacealloying. The processing parameters and technical target are then used as a training set for an artificial neural network. Themodel is based on multiplayer feedforward neural network. A very good performance of the neural network is achieved and thecalculated results are in good agreement with the experimental ones.

  6. Getting emotional with evolutionary simulations: the origin of affective processing in artificial neural networks

    NARCIS (Netherlands)

    B.T. Heerebout

    2011-01-01

    The main purpose of the present thesis was to investigate the evolutionary roots of basic affective processes and their underlying neural mechanisms. To this end, simulations were performed with agents that evolved artificial neural networks. Our general working hypothesis was that positive and nega

  7. Extending simulation-based acquisition (SBA) to the warfighter with the Air Force Joint Synthetic Battlespace (JSB-AF)

    Science.gov (United States)

    Faye, Phil; Andrew, Emily B.; Lee, Jayson

    2001-09-01

    The Air Force has vectored in a new direction to expand its investment in advanced simulation technologies to improve our readiness, lower costs, and dominate the battles of tomorrow. One of the critical initiatives in this direction is the Joint Synthetic Battlespace (JSB). The JSB will provide an integrated Modeling and Simulation (M&S) environment that brings together analysis, training, and simulations into a coherent whole. The ultimate goal is to develop a JSB simulation capability that will provide a new level of realism in synthetic mission and battlespace environments. This capability will be used to evaluate not only specific systems characteristics but also the associated tactics and procedures. The feature that distinguishes the JSB will be the its ability to realistically represent the real-world mission environment and provide the warfighter with real-time feedback on a system's expected performance. This unprecedented level of realism and response will enable the warfighter to evaluate mission effectiveness and conduct course of action analyses. At the same time, the JSB will increase the acquisition community's ability to build or modify systems to meet users' needs and expectations by making the warfighter the focus and direct participant in the acquisition process. As a detailed engineering and trades tool, the JSB will provide a completely scaleable environment for controlled executions of experiments to support analyses that require repeatability and controlled variations in the simulated environment. The government will also be able to ensure the configuration, control, and consistency of the JSB environment, while the users will develop their own plans and scenarios for their analyses. This will provide a standardized synthetic environment for acquisition programs that can be used as either a distributed or stand-alone application.

  8. Frontotemporal neural systems supporting semantic processing in Alzheimer's disease.

    Science.gov (United States)

    Peelle, Jonathan E; Powers, John; Cook, Philip A; Smith, Edward E; Grossman, Murray

    2014-03-01

    We hypothesized that semantic memory for object concepts involves both representations of visual feature knowledge in modality-specific association cortex and heteromodal regions that are important for integrating and organizing this semantic knowledge so that it can be used in a flexible, contextually appropriate manner. We examined this hypothesis in an fMRI study of mild Alzheimer's disease (AD). Participants were presented with pairs of printed words and asked whether the words matched on a given visual-perceptual feature (e.g., guitar, violin: SHAPE). The stimuli probed natural kinds and manufactured objects, and the judgments involved shape or color. We found activation of bilateral ventral temporal cortex and left dorsolateral prefrontal cortex during semantic judgments, with AD patients showing less activation of these regions than healthy seniors. Moreover, AD patients showed less ventral temporal activation than did healthy seniors for manufactured objects, but not for natural kinds. We also used diffusion-weighted MRI of white matter to examine fractional anisotropy (FA). Patients with AD showed significantly reduced FA in the superior longitudinal fasciculus and inferior frontal-occipital fasciculus, which carry projections linking temporal and frontal regions of this semantic network. Our results are consistent with the hypothesis that semantic memory is supported in part by a large-scale neural network involving modality-specific association cortex, heteromodal association cortex, and projections between these regions. The semantic deficit in AD thus arises from gray matter disease that affects the representation of feature knowledge and processing its content, as well as white matter disease that interrupts the integrated functioning of this large-scale network.

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

    Directory of Open Access Journals (Sweden)

    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.

  10. Data Process of Diagnose Expert System based on Neural Network

    Directory of Open Access Journals (Sweden)

    Shupeng Zhao

    2013-12-01

    Full Text Available Engine fault has a high rate in the car. Considering about the distinguishing feature of the engine, Engine Diagnosis Expert System was investigated based on Diagnosis Tree module, Fuzzy Neural Network module, and commix reasoning module. It was researched including Knowledge base and Reasoning machine, and so on. In Diagnosis Tree module, the origin problem was searched in right method. In which module distinguishing rate and low error and least cost was the aim. By means of synthesize judge and fuzzy relation reasoning to get fault origin from symptom, fuzzy synthesize reasoning diagnosis module was researched. Expert knowledge included failure symptom, engine system failure and engine part failure. In the system, Self-diagnosis method and general instruments method worked together, complex failure diagnosis became efficient. The system was intelligent, which was combined by fuzzy logic reasoning and the traditional neural network system. And it became more convenience for failure origin searching, because of utilizing the three methods. The system fuzzy neural networks were combined with fuzzy reasoning and traditional neural networks. Fuzzy neural network failure diagnosis module of system, as a important model was applied to engine diagnosis, with more advantages such as higher efficiency of searching and higher self-learning ability, which was compared with the traditional BP network

  11. Models of Innate Neural Attractors and Their Applications for Neural Information Processing.

    Science.gov (United States)

    Solovyeva, Ksenia P; Karandashev, Iakov M; Zhavoronkov, Alex; Dunin-Barkowski, Witali L

    2015-01-01

    In this work we reveal and explore a new class of attractor neural networks, based on inborn connections provided by model molecular markers, the molecular marker based attractor neural networks (MMBANN). Each set of markers has a metric, which is used to make connections between neurons containing the markers. We have explored conditions for the existence of attractor states, critical relations between their parameters and the spectrum of single neuron models, which can implement the MMBANN. Besides, we describe functional models (perceptron and SOM), which obtain significant advantages over the traditional implementation of these models, while using MMBANN. In particular, a perceptron, based on MMBANN, gets specificity gain in orders of error probabilities values, MMBANN SOM obtains real neurophysiological meaning, the number of possible grandma cells increases 1000-fold with MMBANN. MMBANN have sets of attractor states, which can serve as finite grids for representation of variables in computations. These grids may show dimensions of d = 0, 1, 2,…. We work with static and dynamic attractor neural networks of the dimensions d = 0 and 1. We also argue that the number of dimensions which can be represented by attractors of activities of neural networks with the number of elements N = 10(4) does not exceed 8.

  12. Research on the controller of an arc welding process based on a PID neural network

    Institute of Scientific and Technical Information of China (English)

    Kuanfang HE; Shisheng HUANG

    2008-01-01

    A controller based on a PID neural network(PIDNN)is proposed for an arc welding power source whose output characteristic in responding to a given value is quickly and intelligently controlled in the welding process.The new method syncretizes the PID control strategy and neural network to control the welding process intelligently,so it has the merit of PID control rules and the trait of better information disposal ability of the neural network.The results of simulation show that the controller has the properties of quick response,low overshoot quick convergence and good stable accuracy,which meet the requirements for control of the welding process.

  13. Techniques of Image Processing Based on Artificial Neural Networks

    Institute of Scientific and Technical Information of China (English)

    LI Wei-qing; WANG Qun; WANG Cheng-biao

    2006-01-01

    This paper presented an online quality inspection system based on artificial neural networks. Chromatism classification and edge detection are two difficult problems in glass steel surface quality inspection. Two artificial neural networks were made and the two problems were solved. The one solved chromatism classification. Hue,saturation and their probability of three colors, whose appearing probabilities were maximum in color histogram,were selected as input parameters, and the number of output node could be adjusted with the change of requirement. The other solved edge detection. In this neutral network, edge detection of gray scale image was able to be tested with trained neural networks for a binary image. It prevent the difficulty that the number of needed training samples was too large if gray scale images were directly regarded as training samples. This system is able to be applied to not only glass steel fault inspection but also other product online quality inspection and classification.

  14. Audience preferences are predicted by temporal reliability of neural processing.

    Science.gov (United States)

    Dmochowski, Jacek P; Bezdek, Matthew A; Abelson, Brian P; Johnson, John S; Schumacher, Eric H; Parra, Lucas C

    2014-07-29

    Naturalistic stimuli evoke highly reliable brain activity across viewers. Here we record neural activity from a group of naive individuals while viewing popular, previously-broadcast television content for which the broad audience response is characterized by social media activity and audience ratings. We find that the level of inter-subject correlation in the evoked encephalographic responses predicts the expressions of interest and preference among thousands. Surprisingly, ratings of the larger audience are predicted with greater accuracy than those of the individuals from whom the neural data is obtained. An additional functional magnetic resonance imaging study employing a separate sample of subjects shows that the level of neural reliability evoked by these stimuli covaries with the amount of blood-oxygenation-level-dependent (BOLD) activation in higher-order visual and auditory regions. Our findings suggest that stimuli which we judge favourably may be those to which our brains respond in a stereotypical manner shared by our peers.

  15. Correspondence between stimulus encoding- and maintenance-related neural processes underlies successful working memory.

    Science.gov (United States)

    Cohen, Jessica R; Sreenivasan, Kartik K; D'Esposito, Mark

    2014-03-01

    The ability to actively maintain information in working memory (WM) is vital for goal-directed behavior, but the mechanisms underlying this process remain elusive. We hypothesized that successful WM relies upon a correspondence between the neural processes associated with stimulus encoding and the neural processes associated with maintenance. Using functional magnetic resonance imaging, we identified regional activity and inter-regional connectivity during stimulus encoding and the maintenance of those stimuli when they were no longer present. We compared correspondence in these neural processes across encoding and maintenance epochs with WM performance. Critically, greater correspondence between encoding and maintenance in 1) regional activity in the lateral prefrontal cortex (PFC) and 2) connectivity between lateral PFC and extrastriate cortex was associated with increased performance. These findings suggest that the conservation of neural processes across encoding and maintenance supports the integrity of representations in WM.

  16. Batch Process Modelling and Optimal Control Based on Neural Network Models

    Institute of Scientific and Technical Information of China (English)

    Jie Zhang

    2005-01-01

    This paper presents several neural network based modelling, reliable optimal control, and iterative learning control methods for batch processes. In order to overcome the lack of robustness of a single neural network, bootstrap aggregated neural networks are used to build reliable data based empirical models. Apart from improving the model generalisation capability, a bootstrap aggregated neural network can also provide model prediction confidence bounds. A reliable optimal control method by incorporating model prediction confidence bounds into the optimisation objective function is presented. A neural network based iterative learning control strategy is presented to overcome the problem due to unknown disturbances and model-plant mismatches. The proposed methods are demonstrated on a simulated batch polymerisation process.

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

    Science.gov (United States)

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

    2015-08-01

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

  18. Neural Reward Processing Mediates the Relationship between Insomnia Symptoms and Depression in Adolescence

    National Research Council Canada - National Science Library

    Casement, Melynda D; Keenan, Kate E; Hipwell, Alison E; Guyer, Amanda E; Forbes, Erika E

    2016-01-01

    .... Adolescence may be a period during which such disruption is especially problematic given the rise in the incidence of insomnia and ongoing development of neural systems that support reward processing...

  19. Proceedings of the IEEE 2003 Neural Networks for Signal Processing Workshop

    DEFF Research Database (Denmark)

    Larsen, Jan

    This proceeding contains refereed papers presented at the thirteenth IEEE Workshop on Neural Networks for Signal Processing (NNSP’2003), held at the Atria-Mercure Conference Center, Toulouse, France, September 17-19, 2003. The Neural Networks for Signal Processing Technical Committee of the IEEE...... Signal Processing Society organized the workshop with sponsorship of the Signal Processing Society and the co-operation of the IEEE Neural Networks Society. The IEEE Press published the previous twelve volumes of the NNSP Workshop proceedings in a hardbound volume. This year, the bound volume...... is to be published by IEEE following the workshop, and we are pleased to inaugurate a new CDROM electronic format, which maintains the same standard as the printed version and facilitates the reading and searching of the papers. In recent years, the field of neural networks has matured considerably in both...

  20. Proceedings of the IEEE 2003 Neural Networks for Signal Processing Workshop

    DEFF Research Database (Denmark)

    Larsen, Jan

    This proceeding contains refereed papers presented at the thirteenth IEEE Workshop on Neural Networks for Signal Processing (NNSP’2003), held at the Atria-Mercure Conference Center, Toulouse, France, September 17-19, 2003. The Neural Networks for Signal Processing Technical Committee of the IEEE...... Signal Processing Society organized the workshop with sponsorship of the Signal Processing Society and the co-operation of the IEEE Neural Networks Society. The IEEE Press published the previous twelve volumes of the NNSP Workshop proceedings in a hardbound volume. This year, the bound volume...... is to be published by IEEE following the workshop, and we are pleased to inaugurate a new CDROM electronic format, which maintains the same standard as the printed version and facilitates the reading and searching of the papers. In recent years, the field of neural networks has matured considerably in both...

  1. Fast and Accurate CBR Defense for Homeland Security: Bringing HPC to the First Responder and Warfighter

    Science.gov (United States)

    2007-06-01

    Bringing HPC to the First Responder and Warfighter DISTRIBUTION: Approved for public release, distribution unlimited This paper is part of the following...thru ADP023803 UNCLASSIFIED Fast and Accurate CBR Defense for Homeland Security: Bringing HPC to the First Responder and Warfighter Gopal Patnaik and...Urban AerodynamicsE1 3, these models are now the fidelity and accuracy of CFD to the first responder or commonly applied to predict contaminant

  2. Cyber Electromagnetic Activities within the Mission Command Warfighting Function: Why is it Important and What is the Capability?

    Science.gov (United States)

    2013-12-13

    33 CEMA and the Mission Command Warfighting Function...Army Doctrine Publication ADRP Army Doctrine Reference Publication CEMA Cyber-Electromagnetic Activities DOD Department of Defense EMSO Electronic

  3. Ergonomic design considerations for an optical data link between a warfighter's head and body-worn technologies

    Science.gov (United States)

    Trew, Noel; Linn, Aaron; Nelson, Zac; Burnett, Greg; Sedillo, Mike

    2012-06-01

    Today, warfighters are burdened by a web of cables linking technologies that span the head and torso regions of the body. These cables help to provide interoperability between helmet-worn peripherals such as head mounted displays (HMDs), cameras, and communication equipment with chest-worn computers and radios. Although promoting enhanced capabilities, this cabling also poses snag hazards and makes it difficult for the warfighter to extricate himself from his kit when necessary. A newly developed wireless personal area network (WPAN), one that uses optical transceivers, may prove to be an acceptable alternative to traditional cabling. Researchers at the Air Force Research Laboratory's 711th Human Performance Wing are exploring how best to mount the WPAN transceivers to the body in order to facilitate unimpeded data transfer while also maintaining the operator's natural range of motion. This report describes the two-step research process used to identify the performance limitations and usability of a body-worn optical wireless system. Firstly, researchers characterized the field of view for the current generation of optical WPAN transceivers. Then, this field of view was compared with anthropometric data describing the range of motion of the cervical vertebrae to see if the data link would be lost at the extremes of an operator's head movement. Finally, this report includes an additional discussion of other possible military applications for an optical WPAN.

  4. Auditory processing disorders: relationship to cognitive processes and underlying auditory neural integrity.

    Science.gov (United States)

    Allen, Prudence; Allan, Chris

    2014-02-01

    Auditory processing disorder (APD) in children has been reported and discussed in the clinical and research literature for many years yet there remains poor agreement on diagnostic criteria, the relationship between APD and cognitive skills, and the importance of assessing underlying neural integrity. The present study used a repeated measures design to examine the relationship between a clinical APD diagnosis achieved with behavioral tests used in many clinics, cognitive abilities measured with standardized tests of intelligence, academic achievement, language, phonology, memory and attention and measures of auditory neural integrity as measured with acoustic reflex thresholds and auditory brainstem responses. Participants were 63 children, 7-17 years of age, who reported listening difficulties in spite of normal hearing thresholds. Parents/guardians completed surveys about the child's auditory and attention behavior while children completed an audiologic examination that included 5 behavioral tests of auditory processing ability. Standardized tests that examined intelligence, academic achievement, language, phonology, memory and attention, and objective tests auditory function included crossed and uncrossed acoustic reflex thresholds and auditory brainstem responses (ABR) were also administered to each child. Forty of the children received an APD diagnosis based on the 5 behavioral tests and 23 did not. The groups of children performed similarly on intelligence measures but the children with an APD diagnosis tended to perform more poorly on other cognitive measures. Auditory brainstem responses and acoustic reflex thresholds were often abnormal in both groups of children. Results of this study suggest that a purely behavioral test battery may be insufficient to accurately identify all children with auditory processing disorders. Physiologic test measures, including acoustic reflex and auditory brainstem response tests, are important indicators of auditory

  5. Parametric models to relate spike train and LFP dynamics with neural information processing.

    Science.gov (United States)

    Banerjee, Arpan; Dean, Heather L; Pesaran, Bijan

    2012-01-01

    Spike trains and local field potentials (LFPs) resulting from extracellular current flows provide a substrate for neural information processing. Understanding the neural code from simultaneous spike-field recordings and subsequent decoding of information processing events will have widespread applications. One way to demonstrate an understanding of the neural code, with particular advantages for the development of applications, is to formulate a parametric statistical model of neural activity and its covariates. Here, we propose a set of parametric spike-field models (unified models) that can be used with existing decoding algorithms to reveal the timing of task or stimulus specific processing. Our proposed unified modeling framework captures the effects of two important features of information processing: time-varying stimulus-driven inputs and ongoing background activity that occurs even in the absence of environmental inputs. We have applied this framework for decoding neural latencies in simulated and experimentally recorded spike-field sessions obtained from the lateral intraparietal area (LIP) of awake, behaving monkeys performing cued look-and-reach movements to spatial targets. Using both simulated and experimental data, we find that estimates of trial-by-trial parameters are not significantly affected by the presence of ongoing background activity. However, including background activity in the unified model improves goodness of fit for predicting individual spiking events. Uncovering the relationship between the model parameters and the timing of movements offers new ways to test hypotheses about the relationship between neural activity and behavior. We obtained significant spike-field onset time correlations from single trials using a previously published data set where significantly strong correlation was only obtained through trial averaging. We also found that unified models extracted a stronger relationship between neural response latency and trial

  6. Neural network modeling for dynamic pulsed GTAW process with wire filler based on MATLAB

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Double-sided weld pool shapes were determined by multiple welding parameters and wire feed parameters during pulsed GTAW with wire filler. Aiming at such a system with multiple inputs and outputs, an effective modeling method, consisting of the impulse signal design, model structure and parameter identification and verification, was developed based on MATLAB software. Then, dynamic neural network models, TDNNM (Topside dynamic neural network model) and BHDNNM (Backside width and topside height dynamic neural network model), were established to predict double-sided shape parameters of the weld pool. The characteristic relationship of the welding process was simulated and analyzed with the models.

  7. Neural Activations of Guided Imagery and Music in Negative Emotional Processing: A Functional MRI Study.

    Science.gov (United States)

    Lee, Sang Eun; Han, Yeji; Park, HyunWook

    2016-01-01

    The Bonny Method of Guided Imagery and Music uses music and imagery to access and explore personal emotions associated with episodic memories. Understanding the neural mechanism of guided imagery and music (GIM) as combined stimuli for emotional processing informs clinical application. We performed functional magnetic resonance imaging (fMRI) to demonstrate neural mechanisms of GIM for negative emotional processing when personal episodic memory is recalled and re-experienced through GIM processes. Twenty-four healthy volunteers participated in the study, which used classical music and verbal instruction stimuli to evoke negative emotions. To analyze the neural mechanism, activated regions associated with negative emotional and episodic memory processing were extracted by conducting volume analyses for the contrast between GIM and guided imagery (GI) or music (M). The GIM stimuli showed increased activation over the M-only stimuli in five neural regions associated with negative emotional and episodic memory processing, including the left amygdala, left anterior cingulate gyrus, left insula, bilateral culmen, and left angular gyrus (AG). Compared with GI alone, GIM showed increased activation in three regions associated with episodic memory processing in the emotional context, including the right posterior cingulate gyrus, bilateral parahippocampal gyrus, and AG. No neural regions related to negative emotional and episodic memory processing showed more activation for M and GI than for GIM. As a combined multimodal stimulus, GIM may increase neural activations related to negative emotions and episodic memory processing. Findings suggest a neural basis for GIM with personal episodic memories affecting cortical and subcortical structures and functions. © the American Music Therapy Association 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  8. A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects.

    Science.gov (United States)

    Truccolo, Wilson; Eden, Uri T; Fellows, Matthew R; Donoghue, John P; Brown, Emery N

    2005-02-01

    Multiple factors simultaneously affect the spiking activity of individual neurons. Determining the effects and relative importance of these factors is a challenging problem in neurophysiology. We propose a statistical framework based on the point process likelihood function to relate a neuron's spiking probability to three typical covariates: the neuron's own spiking history, concurrent ensemble activity, and extrinsic covariates such as stimuli or behavior. The framework uses parametric models of the conditional intensity function to define a neuron's spiking probability in terms of the covariates. The discrete time likelihood function for point processes is used to carry out model fitting and model analysis. We show that, by modeling the logarithm of the conditional intensity function as a linear combination of functions of the covariates, the discrete time point process likelihood function is readily analyzed in the generalized linear model (GLM) framework. We illustrate our approach for both GLM and non-GLM likelihood functions using simulated data and multivariate single-unit activity data simultaneously recorded from the motor cortex of a monkey performing a visuomotor pursuit-tracking task. The point process framework provides a flexible, computationally efficient approach for maximum likelihood estimation, goodness-of-fit assessment, residual analysis, model selection, and neural decoding. The framework thus allows for the formulation and analysis of point process models of neural spiking activity that readily capture the simultaneous effects of multiple covariates and enables the assessment of their relative importance.

  9. Characterization of neural stemness status through the neurogenesis process for bone marrow mesenchymal stem cells.

    Science.gov (United States)

    Mohammad, Maeda H; Al-Shammari, Ahmed M; Al-Juboory, Ahmad Adnan; Yaseen, Nahi Y

    2016-01-01

    The in vitro isolation, identification, differentiation, and neurogenesis characterization of the sources of mesenchymal stem cells (MSCs) were investigated to produce two types of cells in culture: neural cells and neural stem cells (NSCs). These types of stem cells were used as successful sources for the further treatment of central nervous system defects and injuries. The mouse bone marrow MSCs were used as the source of the stem cells in this study. β-Mercaptoethanol (BME) was used as the main inducer of the neurogenesis pathway to induce neural cells and to identify NSCs. Three types of neural markers were used: nestin as the immaturation stage marker, neurofilament light chain as the early neural marker, and microtubule-associated protein 2 as the maturation marker through different time intervals in the neurogenesis process starting from the MSCs, (as undifferentiated cells), NSCs, production stages, and toward neuron cells (as differentiated cells). The results of different exposure times to BME of the neural markers analysis done by immunocytochemistry and real time-polymerase chain reaction helped us to identify the exact timing for the neural stemness state. The results showed that the best exposure time that may be used for the production of NSCs was 6 hours. The best maintenance media for NSCs were also identified. Furthermore, we optimized exposure to BME with different times and concentrations, which could be an interesting way to modulate specific neuronal differentiation and obtain autologous neuronal phenotypes. This study was able to characterize NSCs in culture under differentiation for neurogenesis in the pathway of the neural differentiation process by studying the expressed neural genes and the ability to maintain these NSCs in culture for further differentiation in thousands of functional neurons for the treatment of brain and spinal cord injuries and defects.

  10. Breakout Prediction Based on BP Neural Network in Continuous Casting Process

    Directory of Open Access Journals (Sweden)

    Zhang Ben-guo

    2016-01-01

    Full Text Available An improved BP neural network model was presented by modifying the learning algorithm of the traditional BP neural network, based on the Levenberg-Marquardt algorithm, and was applied to the breakout prediction system in the continuous casting process. The results showed that the accuracy rate of the model for the temperature pattern of sticking breakout was 96.43%, and the quote rate was 100%, that verified the feasibility of the model.

  11. D-FNN Based Modeling and BP Neural Network Decoupling Control of PVC Stripping Process

    Directory of Open Access Journals (Sweden)

    Shu-zhi Gao

    2014-01-01

    Full Text Available PVC stripping process is a kind of complicated industrial process with characteristics of highly nonlinear and time varying. Aiming at the problem of establishing the accurate mathematics model due to the multivariable coupling and big time delay, the dynamic fuzzy neural network (D-FNN is adopted to establish the PVC stripping process model based on the actual process operation datum. Then, the PVC stripping process is decoupled by the distributed neural network decoupling module to obtain two single-input-single-output (SISO subsystems (slurry flow to top tower temperature and steam flow to bottom tower temperature. Finally, the PID controller based on BP neural networks is used to control the decoupled PVC stripper system. Simulation results show the effectiveness of the proposed integrated intelligent control method.

  12. Aberrant neural processing of moral violations in criminal psychopaths

    Science.gov (United States)

    Harenski, Carla L.; Harenski, Keith A.; Shane, Matthew S.; Kiehl, Kent A.

    2010-01-01

    A defining characteristic of psychopathy is the willingness to intentionally commit moral transgressions against others without guilt or remorse. Despite this ‘moral insensitivity’, the behavioral and neural correlates of moral decision-making in psychopathy have not been well studied. To address this issue, the authors used functional magnetic resonance imaging (fMRI) to record hemodynamic activity in 72 incarcerated male adults, stratified into psychopathic (N = 16) and nonpsychopathic (N = 16) groups based on scores from the Hare Psychopathy Checklist-Revised, while they made decisions regarding the ‘severity of moral violations’ of pictures that did or did not depict moral situations. Consistent with hypotheses, an analysis of brain activity during the evaluation of pictures depicting moral violations in psychopaths vs. nonpsychopaths showed atypical activity in several regions involved in moral decision-making. This included reduced moral/non-moral picture distinctions in the ventromedial prefrontal cortex and anterior temporal cortex in psychopaths relative to nonpsychopaths. In a separate analysis, the association between severity of moral violation ratings and brain activity across participants was compared in psychopaths versus nonpsychopaths. Results revealed a positive association between amygdala activity and severity ratings that was greater in nonpsychopaths than psychopaths, and a negative association between posterior temporal activity and severity ratings that was greater in psychopaths than nonpsychopaths. These results reveal potential neural underpinnings of moral insensitivity in psychopathy and are discussed with reference to neurobiological models of morality and psychopathy. PMID:21090881

  13. Nonoptimal component placement, but short processing paths, due to long-distance projections in neural systems.

    Directory of Open Access Journals (Sweden)

    Marcus Kaiser

    2006-07-01

    Full Text Available It has been suggested that neural systems across several scales of organization show optimal component placement, in which any spatial rearrangement of the components would lead to an increase of total wiring. Using extensive connectivity datasets for diverse neural networks combined with spatial coordinates for network nodes, we applied an optimization algorithm to the network layouts, in order to search for wire-saving component rearrangements. We found that optimized component rearrangements could substantially reduce total wiring length in all tested neural networks. Specifically, total wiring among 95 primate (Macaque cortical areas could be decreased by 32%, and wiring of neuronal networks in the nematode Caenorhabditis elegans could be reduced by 48% on the global level, and by 49% for neurons within frontal ganglia. Wiring length reductions were possible due to the existence of long-distance projections in neural networks. We explored the role of these projections by comparing the original networks with minimally rewired networks of the same size, which possessed only the shortest possible connections. In the minimally rewired networks, the number of processing steps along the shortest paths between components was significantly increased compared to the original networks. Additional benchmark comparisons also indicated that neural networks are more similar to network layouts that minimize the length of processing paths, rather than wiring length. These findings suggest that neural systems are not exclusively optimized for minimal global wiring, but for a variety of factors including the minimization of processing steps.

  14. Multi-Objective Optimization and Analysis Model of Sintering Process Based on BP Neural Network

    Institute of Scientific and Technical Information of China (English)

    ZHANG Jun-hong; XIE An-guo; SHEN Feng-man

    2007-01-01

    A multi-objective optimization and analysis model of the sintering process based on BP neural network is presented. Genetic algorithms are combined to simplify the BP neural network, which can reduce the learning time and increase the forecasting accuracy of the network model. This model has been experimented in the sintering process, and the production cost, the energy consumption, the quality (revolving intensity), and the output are considered at the same time. Moreover, the relation between some factors and the multi-objectives has been analyzed, and the results are consistent with the process. Different objectives are emphasized at different practical periods, and this can provide a theoretical basis for the manager.

  15. Neural processing of amplitude and formant rise time in dyslexia.

    Science.gov (United States)

    Peter, Varghese; Kalashnikova, Marina; Burnham, Denis

    2016-06-01

    This study aimed to investigate how children with dyslexia weight amplitude rise time (ART) and formant rise time (FRT) cues in phonetic discrimination. Passive mismatch responses (MMR) were recorded for a/ba/-/wa/contrast in a multiple deviant odd-ball paradigm to identify the neural response to cue weighting in 17 children with dyslexia and 17 age-matched control children. The deviant stimuli had either partial or full ART or FRT cues. The results showed that ART did not generate an MMR in either group, whereas both partial and full FRT cues generated MMR in control children while only full FRT cues generated MMR in children with dyslexia. These findings suggest that children, both controls and those with dyslexia, discriminate speech based on FRT cues and not ART cues. However, control children have greater sensitivity to FRT cues in speech compared to children with dyslexia. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  16. Neural processing of amplitude and formant rise time in dyslexia

    Directory of Open Access Journals (Sweden)

    Varghese Peter

    2016-06-01

    Full Text Available This study aimed to investigate how children with dyslexia weight amplitude rise time (ART and formant rise time (FRT cues in phonetic discrimination. Passive mismatch responses (MMR were recorded for a/ba/-/wa/contrast in a multiple deviant odd-ball paradigm to identify the neural response to cue weighting in 17 children with dyslexia and 17 age-matched control children. The deviant stimuli had either partial or full ART or FRT cues. The results showed that ART did not generate an MMR in either group, whereas both partial and full FRT cues generated MMR in control children while only full FRT cues generated MMR in children with dyslexia. These findings suggest that children, both controls and those with dyslexia, discriminate speech based on FRT cues and not ART cues. However, control children have greater sensitivity to FRT cues in speech compared to children with dyslexia.

  17. Computationally efficient locally-recurrent neural networks for online signal processing

    CERN Document Server

    Hussain, A; Shim, I

    1999-01-01

    A general class of computationally efficient locally recurrent networks (CERN) is described for real-time adaptive signal processing. The structure of the CERN is based on linear-in-the- parameters single-hidden-layered feedforward neural networks such as the radial basis function (RBF) network, the Volterra neural network (VNN) and the functionally expanded neural network (FENN), adapted to employ local output feedback. The corresponding learning algorithms are derived and key structural and computational complexity comparisons are made between the CERN and conventional recurrent neural networks. Two case studies are performed involving the real- time adaptive nonlinear prediction of real-world chaotic, highly non- stationary laser time series and an actual speech signal, which show that a recurrent FENN based adaptive CERN predictor can significantly outperform the corresponding feedforward FENN and conventionally employed linear adaptive filtering models. (13 refs).

  18. Neural Language Processing in Adolescent First-Language Learners: Longitudinal Case Studies in American Sign Language.

    Science.gov (United States)

    Ferjan Ramirez, Naja; Leonard, Matthew K; Davenport, Tristan S; Torres, Christina; Halgren, Eric; Mayberry, Rachel I

    2016-03-01

    One key question in neurolinguistics is the extent to which the neural processing system for language requires linguistic experience during early life to develop fully. We conducted a longitudinal anatomically constrained magnetoencephalography (aMEG) analysis of lexico-semantic processing in 2 deaf adolescents who had no sustained language input until 14 years of age, when they became fully immersed in American Sign Language. After 2 to 3 years of language, the adolescents' neural responses to signed words were highly atypical, localizing mainly to right dorsal frontoparietal regions and often responding more strongly to semantically primed words (Ferjan Ramirez N, Leonard MK, Torres C, Hatrak M, Halgren E, Mayberry RI. 2014. Neural language processing in adolescent first-language learners. Cereb Cortex. 24 (10): 2772-2783). Here, we show that after an additional 15 months of language experience, the adolescents' neural responses remained atypical in terms of polarity. While their responses to less familiar signed words still showed atypical localization patterns, the localization of responses to highly familiar signed words became more concentrated in the left perisylvian language network. Our findings suggest that the timing of language experience affects the organization of neural language processing; however, even in adolescence, language representation in the human brain continues to evolve with experience.

  19. Predicting Model forComplex Production Process Based on Dynamic Neural Network

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Based on the comparison of several methods of time series predicting, this paper points out that it is nec-essary to use dynamic neural network in modeling of complex production process. Because self-feedback and mutu-al-feedback are adopted among nodes at the same layer in Elman network, it has stronger ability of dynamic ap-proximation, and can describe any non-linear dynamic system. After the structure and mathematical description be-ing given, dynamic back-propagation (BP) algorithm of training weights of Elman neural network is deduced. Atlast, the network is used to predict ash content of black amber in jigging production process. The results show thatthis neural network is powerful in predicting and suitable for modeling, predicting, and controling of complex pro-duction process.

  20. Neural Tuning Size in a Model of Primate Visual Processing Accounts for Three Key Markers of Holistic Face Processing.

    Directory of Open Access Journals (Sweden)

    Cheston Tan

    Full Text Available Faces are an important and unique class of visual stimuli, and have been of interest to neuroscientists for many years. Faces are known to elicit certain characteristic behavioral markers, collectively labeled "holistic processing", while non-face objects are not processed holistically. However, little is known about the underlying neural mechanisms. The main aim of this computational simulation work is to investigate the neural mechanisms that make face processing holistic. Using a model of primate visual processing, we show that a single key factor, "neural tuning size", is able to account for three important markers of holistic face processing: the Composite Face Effect (CFE, Face Inversion Effect (FIE and Whole-Part Effect (WPE. Our proof-of-principle specifies the precise neurophysiological property that corresponds to the poorly-understood notion of holism, and shows that this one neural property controls three classic behavioral markers of holism. Our work is consistent with neurophysiological evidence, and makes further testable predictions. Overall, we provide a parsimonious account of holistic face processing, connecting computation, behavior and neurophysiology.

  1. The Neural Correlates of the Interaction between Semantic and Phonological Processing for Chinese Character Reading

    Science.gov (United States)

    Wang, Xiaojuan; Zhao, Rong; Zevin, Jason D.; Yang, Jianfeng

    2016-01-01

    Visual word recognition involves mappings among orthographic, phonological, and semantic codes. In alphabetic languages, it is hard to disentangle the effects of these codes, because orthographically well-formed words are typically pronounceable, confounding orthographic and phonological processes, and orthographic cues to meaning are rare, and where they occur are morphological, confounding orthographic and semantic processes. In Chinese character recognition, it is possible to explore orthography to phonology (O-P) and orthography to semantics (O-S) processes independently by taking advantage of the distinct phonetic and semantic components in Chinese phonograms. We analyzed data from an fMRI experiment using lexical decision for Chinese characters to explore the sensitivity of areas associated with character recognition to orthographic, phonological, and semantic processing. First, a correlation approach was used to identify regions associated with reaction time, frequency, consistency and visual complexity. Then, these ROIs were examined for their responses to stimuli with different types of information available. These results revealed two neural pathways, one for O-S processing relying on left middle temporal gyrus and angular gyrus, and the other for O-P processing relying on inferior frontal gyrus and insula. The two neural routes form a shared neural network both for real and pseudo-characters, and their cooperative division of labor reflects the neural basis for processing different types of characters. Results are broadly consistent with findings from alphabetic languages, as predicted by reading models that assume the same general architecture for logographic and alphabetic scripts. PMID:27445914

  2. Design of Experimentation, Artificial Neural Network Simulation and Optimization for Integrated Bamboo Processing Machine

    Directory of Open Access Journals (Sweden)

    P. G. Mehar

    2015-11-01

    Full Text Available In this research work experimentation on integrated bamboo processing machine for splitting and slicing of bamboo has been carried out. This paper presents the experimental investigation of some parameters of integrated bamboo processing machine. In this research paper simulation of experimental data using artificial neural network is carried out. An attempt of minimum-maximum principle has been made to optimize by range bound process for maximizing production rate of integrated bamboo processing machine.

  3. Neural Network Pruning Algorithm with Penalty OBS Process

    Institute of Scientific and Technical Information of China (English)

    MENG Jiang; WANG Yao-cai; LIU Tao

    2005-01-01

    Aimed at the great computing complexity of optimal brain surgeon (OBS) process, a pruning algorithm with penalty OBS process is presented. Compared with sensitive and regularized methods, the penalty OBS algorithm not only avoids time-consuming defect and low pruning efficiency in OBS process, but also keeps higher generalization and pruning accuracy than Levenberg-Marquardt method.

  4. Cue validity probability influences neural processing of targets.

    Science.gov (United States)

    Arjona, Antonio; Escudero, Miguel; Gómez, Carlos M

    2016-09-01

    The neural bases of the so-called Spatial Cueing Effect in a visuo-auditory version of the Central Cue Posneŕs Paradigm (CCPP) are analyzed by means of behavioral patterns (Reaction Times and Errors) and Event-Related Potentials (ERPs), namely the Contingent Negative Variation (CNV), N1, P2a, P2p, P3a, P3b and Negative Slow Wave (NSW). The present version consisted of three types of trial blocks with different validity/invalidity proportions: 50% valid - 50% invalid trials, 68% valid - 32% invalid trials and 86% valid - 14% invalid trials. Thus, ERPs can be analyzed as the proportion of valid trials per block increases. Behavioral (Reaction Times and Incorrect responses) and ERP (lateralized component of CNV, P2a, P3b and NSW) results showed a spatial cueing effect as the proportion of valid trials per block increased. Results suggest a brain activity modulation related to sensory-motor attention and working memory updating, in order to adapt to external unpredictable contingencies.

  5. Neural Information Processing in Cognition: We Start to Understand the Orchestra, but Where is the Conductor?

    Science.gov (United States)

    Palm, Günther

    2016-01-01

    Research in neural information processing has been successful in the past, providing useful approaches both to practical problems in computer science and to computational models in neuroscience. Recent developments in the area of cognitive neuroscience present new challenges for a computational or theoretical understanding asking for neural information processing models that fulfill criteria or constraints from cognitive psychology, neuroscience and computational efficiency. The most important of these criteria for the evaluation of present and future contributions to this new emerging field are listed at the end of this article.

  6. Neural information processing in cognition: we start to understand the orchestra, but where is the conductor?

    Directory of Open Access Journals (Sweden)

    Guenther ePalm

    2016-01-01

    Full Text Available Research in neural information processing has been successful in the past, providing useful approaches both to practical problems in computer science and to computational models in neuroscience. Recent developments in the area of cognitive neuroscience present new challenges for a computational or theoretical understanding asking for neural information processing models that fulfill criteria or constraints from cognitive psychology, neuroscience and computational efficiency. The most important of these criteria for the evaluation of present and future contributions to this new emerging field are listed at the end of this article.

  7. Current-mode implementation of processing modules in ART-based neural networks

    Science.gov (United States)

    Lopez-Alcantud, Jose-Alejandro; Hauer, Hans; Diaz-Madrid, Jose-Angel; Ruiz-Merino, Ramon

    2003-04-01

    This paper describes implementation of neural network processing layers using basic current-mode operating modules. The research work has been focused on the implementation of neural networks based on the Adaptive Resonance Theory, developed by S. Grossberg and G.A. Carpenter. The ART-based neural network whose operating modules have been choosen for development is the one called MART, proposed by F. Delgado, because of its complex architecture, auto--adaptive self-learning process, able to discard unmeaningful cathegories. Our presentation starts introducing the behaviour of MART with an analysis of its structure. The development described by this research work is focused on the monochannel block included in the main signal processing part of the MART neural network. The description of the computing algorithm of the layers inside a monochannel block are also provided in order to show what operational current-mode modules are needed (multiplier, divider, square-rooter, adder, substractor, absolute value, maximum and minimum evaluator...). Descriptions at schematic and layout levels of all the processing layers are given. All of them have been designed using AMS 0.35 micron technology with a supply voltage of 3.3 volts. The modules are designed to deal with input currents in the range of 20 to 50 microamps, showing a lineal behaviour and an output error of less than 10%, which is good enough for neural signal processing systems. The maximum frecuency of operation is around 200 kHz. Simulation results are included to show that the operation performed by the hardware designed matches the behaviour described by the MART neural network. For testing purposes we show the design of a monochannel block hardware implementation restricted to five inputs and three cathegories.

  8. Neural Network Approach to Predict Melt Temperature in Injection Molding Processes

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    Among the processing conditions of injection molding, temperature of the melt entering the mold plays a significant role in determining the quality of molded parts. In our previous research, a neural network was developed to predict, the melt temperature in the barrel during the plastication phase. In this paper, a neural network is proposed to predict the melt temperature at the nozzle exit during the injection phase. A typical two layer neural network with back propagation learning rules is used to model the relationship between input and output in the injection phase. The preliminary results show that the network works well and may be used for on-line optimization and control of injection molding processes.

  9. Neural interactions in unilateral colliculus and between bilateral colliculi modulate auditory signal processing

    Science.gov (United States)

    Mei, Hui-Xian; Cheng, Liang; Chen, Qi-Cai

    2013-01-01

    In the auditory pathway, the inferior colliculus (IC) is a major center for temporal and spectral integration of auditory information. There are widespread neural interactions in unilateral (one) IC and between bilateral (two) ICs that could modulate auditory signal processing such as the amplitude and frequency selectivity of IC neurons. These neural interactions are either inhibitory or excitatory, and are mostly mediated by γ-aminobutyric acid (GABA) and glutamate, respectively. However, the majority of interactions are inhibitory while excitatory interactions are in the minority. Such unbalanced properties between excitatory and inhibitory projections have an important role in the formation of unilateral auditory dominance and sound location, and the neural interaction in one IC and between two ICs provide an adjustable and plastic modulation pattern for auditory signal processing. PMID:23626523

  10. Neural interactions in unilateral colliculus and between bilateral colliculi modulate auditory signal processing.

    Science.gov (United States)

    Mei, Hui-Xian; Cheng, Liang; Chen, Qi-Cai

    2013-01-01

    In the auditory pathway, the inferior colliculus (IC) is a major center for temporal and spectral integration of auditory information. There are widespread neural interactions in unilateral (one) IC and between bilateral (two) ICs that could modulate auditory signal processing such as the amplitude and frequency selectivity of IC neurons. These neural interactions are either inhibitory or excitatory, and are mostly mediated by γ-aminobutyric acid (GABA) and glutamate, respectively. However, the majority of interactions are inhibitory while excitatory interactions are in the minority. Such unbalanced properties between excitatory and inhibitory projections have an important role in the formation of unilateral auditory dominance and sound location, and the neural interaction in one IC and between two ICs provide an adjustable and plastic modulation pattern for auditory signal processing.

  11. Neural correlates of language and non-language visuospatial processing in adolescents with reading disability.

    Science.gov (United States)

    Diehl, Joshua John; Frost, Stephen J; Sherman, Gordon; Mencl, W Einar; Kurian, Anish; Molfese, Peter; Landi, Nicole; Preston, Jonathan; Soldan, Anja; Fulbright, Robert K; Rueckl, Jay G; Seidenberg, Mark S; Hoeft, Fumiko; Pugh, Kenneth R

    2014-11-01

    Despite anecdotal evidence of relative visuospatial processing strengths in individuals with reading disability (RD), only a few studies have assessed the presence or the extent of these putative strengths. The current study examined the cognitive and neural bases of visuospatial processing abilities in adolescents with RD relative to typically developing (TD) peers. Using both cognitive tasks and functional magnetic resonance imaging (fMRI) we contrasted printed word recognition with non-language visuospatial processing tasks. Behaviorally, lower reading skill was related to a visuospatial processing advantage (shorter latencies and equivalent accuracy) on a geometric figure processing task, similar to findings shown in two published studies. FMRI analyses revealed key group by task interactions in patterns of cortical and subcortical activation, particularly in frontostriatal networks, and in the distributions of right and left hemisphere activation on the two tasks. The results are discussed in terms of a possible neural tradeoff in visuospatial processing in RD.

  12. Parametric models to relate spike train and LFP dynamics with neural information processing

    Directory of Open Access Journals (Sweden)

    Arpan eBanerjee

    2012-07-01

    Full Text Available Spike trains and local field potentials resulting from extracellular current flows provide a substrate for neural information processing. Understanding the neural code from simultaneous spike-field recordings and subsequent decoding of information processing events will have widespread applications. One way to demonstrate an understanding of the neural code, with particular advantages for the development of applications, is to formulate a parametric statistical model of neural activity and its covariates. Here, we propose a set of parametric spike-field models (unified models that can be used with existing decoding algorithms to reveal the timing of task or stimulus specific processing. Our proposed unified modeling framework captures the effects of two important features of information processing: time-varying stimulus driven inputs and ongoing background activity that occurs even in the absence of environmental inputs. We have applied this framework for decoding neural latencies in simulated and experimentally recorded spike-field sessions obtained from the lateral intraparietal area (LIP of awake, behaving monkeys performing cued look-and-reach movements to spatial targets. Using both simulated and experimental data, we find that estimates of trial-by-trial parameters are not significantly affected by the presence of ongoing background activity. However, including background activity in the unified model improves goodness of fit for predicting individual spiking events. Trial-by-trial spike-field correlation in visual response onset times are higher when the unified model is used, matching with corresponding values obtained using earlier trial-averaged measures on a previously published data set. Uncovering the relationship between the model parameters and the timing of movements offers new ways to test hypotheses about the relationship between neural activity and behavior.

  13. Does neural input or processing play a greater role in the magnitude of neuroimaging signals?

    Directory of Open Access Journals (Sweden)

    Sam Harris

    2010-08-01

    Full Text Available An important constraint on how hemodynamic neuroimaging signals such as fMRI can be interpreted in terms of the underlying evoked activity is an understanding of neurovascular coupling mechanisms that actually generate hemodynamic responses. The predominant view at present is that the hemodynamic response is most correlated with synaptic input and subsequent neural processing rather than spiking output. It is still not clear whether input or processing is more important in the generation of hemodynamics responses. In order to investigate this we measured the hemodynamic and neural responses to electrical whisker pad stimuli in rat whisker barrel somatosensory cortex both before and after the local cortical injections of the GABA-A agonist muscimol. Muscimol would not be expected to affect the thalamocortical input into the cortex but would inhibit subsequent intra-cortical processing. Pre-muscimol infusion whisker stimuli elicited the expected neural and accompanying hemodynamic responses to that reported previously. Following infusion of muscimol, although the temporal profile of neural responses to each pulse of the stimulus train was similar, the average response was reduced in magnitude by ~79% compared to that elicited pre-infusion. The whisker-evoked hemodynamic responses were reduced by a commensurate magnitude suggesting that, although the neurovascular coupling relationships were similar for synaptic input as well as for cortical processing, the magnitude of the overall response is dominated by processing rather than from that produced from the thalamocortical input alone.

  14. 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.

  15. Design of Experimentation, Artificial Neural Network Simulation and Optimization for Integrated Bamboo Processing Machine

    OpenAIRE

    P. G. Mehar; Dr.A.V.Vanalkar

    2015-01-01

    In this research work experimentation on integrated bamboo processing machine for splitting and slicing of bamboo has been carried out. This paper presents the experimental investigation of some parameters of integrated bamboo processing machine. In this research paper simulation of experimental data using artificial neural network is carried out. An attempt of minimum-maximum principle has been made to optimize by range bound process for maximizing production rate of integrated b...

  16. dNSP: a biologically inspired dynamic Neural network approach to Signal Processing.

    Science.gov (United States)

    Cano-Izquierdo, José Manuel; Ibarrola, Julio; Pinzolas, Miguel; Almonacid, Miguel

    2008-09-01

    The arriving order of data is one of the intrinsic properties of a signal. Therefore, techniques dealing with this temporal relation are required for identification and signal processing tasks. To perform a classification of the signal according with its temporal characteristics, it would be useful to find a feature vector in which the temporal attributes were embedded. The correlation and power density spectrum functions are suitable tools to manage this issue. These functions are usually defined with statistical formulation. On the other hand, in biology there can be found numerous processes in which signals are processed to give a feature vector; for example, the processing of sound by the auditory system. In this work, the dNSP (dynamic Neural Signal Processing) architecture is proposed. This architecture allows representing a time-varying signal by a spatial (thus statical) vector. Inspired by the aforementioned biological processes, the dNSP performs frequency decomposition using an analogical parallel algorithm carried out by simple processing units. The architecture has been developed under the paradigm of a multilayer neural network, where the different layers are composed by units whose activation functions have been extracted from the theory of Neural Dynamic [Grossberg, S. (1988). Nonlinear neural networks principles, mechanisms and architectures. Neural Networks, 1, 17-61]. A theoretical study of the behavior of the dynamic equations of the units and their relationship with some statistical functions allows establishing a parallelism between the unit activations and correlation and power density spectrum functions. To test the capabilities of the proposed approach, several testbeds have been employed, i.e. the frequencial study of mathematical functions. As a possible application of the architecture, a highly interesting problem in the field of automatic control is addressed: the recognition of a controlled DC motor operating state.

  17. Testing Multiple Psychological Processes for Common Neural Mechanisms Using EEG and Independent Component Analysis.

    Science.gov (United States)

    Wessel, Jan R

    2016-03-08

    Temporal independent component analysis (ICA) is applied to an electrophysiological signal mixture (such as an EEG recording) to disentangle the independent neural source signals-independent components-underlying said signal mixture. When applied to scalp EEG, ICA is most commonly used either as a pre-processing step (e.g., to isolate physiological processes from non-physiological artifacts), or as a data-reduction step (i.e., to focus on one specific neural process with increased signal-to-noise ratio). However, ICA can be used in an even more powerful way that fundamentally expands the inferential utility of scalp EEG. The core assumption of EEG-ICA-namely, that individual independent components represent separable neural processes-can be leveraged to derive the following inferential logic: If a specific independent component shows activity related to multiple psychological processes within the same dataset (e.g., elicited by different experimental events), it follows that those psychological processes involve a common, non-separable neural mechanism. As such, this logic allows testing a class of hypotheses that is beyond the reach of regular EEG analyses techniques, thereby crucially increasing the inferential utility of the EEG. In the current article, this logic will be referred to as the 'common independent process identification' (CIPI) approach. This article aims to provide a tutorial into the application of this powerful approach, targeted at researchers that have a basic understanding of standard EEG analysis. Furthermore, the article aims to exemplify the usage of CIPI by outlining recent studies that successfully applied this approach to test neural theories of mental functions.

  18. Application of Artificial Neural Networks and Chaos in Chemical Processes

    Science.gov (United States)

    Otawara, Kentaro

    1995-01-01

    An artificial neural network (ANN) and chaos, conceived and developed independently, are beginning to play essential roles in chemical engineering. Nonetheless, the ANN possesses an appreciable number of deficiencies that need be remedied, and the capability of the ANN to explore and tame chaos or an irregularly behaving system is yet to be fully realized. The present dissertation attempts to make substantial progress toward such ends. The problem of controlling the temperature of an industrial reactor carrying out semibatch polymerization has been solved by an innovative adaptive hybrid control system comprising an ANN and fuzzy expert system (FES) complemented by two supervisory ANN's. The system enhances the strength and compensates for the weaknesses of both the ANN and FES. The system, named dual ANN (DANN), has been proposed for characterizing the nonlinear nature of chaotic time -series data. Its capability to approximate the behavior of a chaotic system has been found to far exceed that of a conventional ANN. A novel approach has been devised for training an ANN through the modified interactive training (MIT) mode. This mode of training has been demonstrated to substantially outperform a conventional interactive training (CIT) mode. A method has been established for synchronizing chaos by resorting to an ANN. This method is capable of causing to be coherent the trajectories of systems whose deterministic governing equations are insufficiently known. This requires training the ANN with a time series and a common driving signal or signals. Examples are given for chaos generated by difference as well as differential equations. An alternative to the OGY method has been proposed for controlling chaos; it meticulously perturbs an accessible parameter of the chaotic system. A single, highly precise ANN suffices to render stable any of an infinite number of unstable periodic orbits embedded in a chaotic or strange attractor. A method for estimating sub

  19. Hippocampus: cognitive processes and neural representations that underlie declarative memory

    National Research Council Canada - National Science Library

    Eichenbaum, Howard

    2004-01-01

    .... Recent studies using functional brain imaging in humans and neuropsychological analyses of humans and animals with hippocampal damage have revealed some of the elemental cognitive processes mediated by the hippocampus...

  20. Process optimization of gravure printed light-emitting polymer layers by a neural network approach

    NARCIS (Netherlands)

    Michels, J.J.; Winter, S.H.P.M. de; Symonds, L.H.G.

    2009-01-01

    We demonstrate that artificial neural network modeling is a viable tool to predict the processing dependence of gravure printed light-emitting polymer layers for flexible OLED lighting applications. The (local) thickness of gravure printed light-emitting polymer (LEP) layers was analyzed using micro

  1. Neural Substrates for Processing Task-Irrelevant Sad Images in Adolescents

    Science.gov (United States)

    Wang, Lihong; Huettel, Scott; De Bellis, Michael D.

    2008-01-01

    Neural systems related to cognitive and emotional processing were examined in adolescents using event-related functional magnetic resonance imaging (fMRI). Ten healthy adolescents performed an emotional oddball task. Subjects detected infrequent circles (targets) within a continual stream of phase-scrambled images (standards). Sad and neutral…

  2. The Processing of Verbs and Nouns in Neural Networks: Insights from Synthetic Brain Imaging

    Science.gov (United States)

    Cangelosi, Angelo; Parisi, Domenico

    2004-01-01

    The paper presents a computational model of language in which linguistic abilities evolve in organisms that interact with an environment. Each individual's behavior is controlled by a neural network and we study the consequences in the network's internal functional organization of learning to process different classes of words. Agents are selected…

  3. Level of Processing Modulates the Neural Correlates of Emotional Memory Formation

    Science.gov (United States)

    Ritchey, Maureen; LaBar, Kevin S.; Cabeza, Roberto

    2011-01-01

    Emotion is known to influence multiple aspects of memory formation, including the initial encoding of the memory trace and its consolidation over time. However, the neural mechanisms whereby emotion impacts memory encoding remain largely unexplored. The present study used a levels-of-processing manipulation to characterize the impact of emotion on…

  4. A Comparison of Neural Networks and Fuzzy Logic Methods for Process Modeling

    Science.gov (United States)

    Cios, Krzysztof J.; Sala, Dorel M.; Berke, Laszlo

    1996-01-01

    The goal of this work was to analyze the potential of neural networks and fuzzy logic methods to develop approximate response surfaces as process modeling, that is for mapping of input into output. Structural response was chosen as an example. Each of the many methods surveyed are explained and the results are presented. Future research directions are also discussed.

  5. Process optimization of gravure printed light-emitting polymer layers by a neural network approach

    NARCIS (Netherlands)

    Michels, J.J.; Winter, S.H.P.M. de; Symonds, L.H.G.

    2009-01-01

    We demonstrate that artificial neural network modeling is a viable tool to predict the processing dependence of gravure printed light-emitting polymer layers for flexible OLED lighting applications. The (local) thickness of gravure printed light-emitting polymer (LEP) layers was analyzed using

  6. Optimally efficient neural systems for processing spoken language.

    Science.gov (United States)

    Zhuang, Jie; Tyler, Lorraine K; Randall, Billi; Stamatakis, Emmanuel A; Marslen-Wilson, William D

    2014-04-01

    Cognitive models claim that spoken words are recognized by an optimally efficient sequential analysis process. Evidence for this is the finding that nonwords are recognized as soon as they deviate from all real words (Marslen-Wilson 1984), reflecting continuous evaluation of speech inputs against lexical representations. Here, we investigate the brain mechanisms supporting this core aspect of word recognition and examine the processes of competition and selection among multiple word candidates. Based on new behavioral support for optimal efficiency in lexical access from speech, a functional magnetic resonance imaging study showed that words with later nonword points generated increased activation in the left superior and middle temporal gyrus (Brodmann area [BA] 21/22), implicating these regions in dynamic sound-meaning mapping. We investigated competition and selection by manipulating the number of initially activated word candidates (competition) and their later drop-out rate (selection). Increased lexical competition enhanced activity in bilateral ventral inferior frontal gyrus (BA 47/45), while increased lexical selection demands activated bilateral dorsal inferior frontal gyrus (BA 44/45). These findings indicate functional differentiation of the fronto-temporal systems for processing spoken language, with left middle temporal gyrus (MTG) and superior temporal gyrus (STG) involved in mapping sounds to meaning, bilateral ventral inferior frontal gyrus (IFG) engaged in less constrained early competition processing, and bilateral dorsal IFG engaged in later, more fine-grained selection processes.

  7. Neural correlates of audiovisual speech processing in a second language.

    Science.gov (United States)

    Barrós-Loscertales, Alfonso; Ventura-Campos, Noelia; Visser, Maya; Alsius, Agnès; Pallier, Christophe; Avila Rivera, César; Soto-Faraco, Salvador

    2013-09-01

    Neuroimaging studies of audiovisual speech processing have exclusively addressed listeners' native language (L1). Yet, several behavioural studies now show that AV processing plays an important role in non-native (L2) speech perception. The current fMRI study measured brain activity during auditory, visual, audiovisual congruent and audiovisual incongruent utterances in L1 and L2. BOLD responses to congruent AV speech in the pSTS were stronger than in either unimodal condition in both L1 and L2. Yet no differences in AV processing were expressed according to the language background in this area. Instead, the regions in the bilateral occipital lobe had a stronger congruency effect on the BOLD response (congruent higher than incongruent) in L2 as compared to L1. According to these results, language background differences are predominantly expressed in these unimodal regions, whereas the pSTS is similarly involved in AV integration regardless of language dominance.

  8. Hybrid Neural Network Model of an Industrial Ethanol Fermentation Process Considering the Effect of Temperature

    Science.gov (United States)

    Mantovanelli, Ivana C. C.; Rivera, Elmer Ccopa; da Costa, Aline C.; Filho, Rubens Maciel

    In this work a procedure for the development of a robust mathematical model for an industrial alcoholic fermentation process was evaluated. The proposed model is a hybrid neural model, which combines mass and energy balance equations with functional link networks to describe the kinetics. These networks have been shown to have a good nonlinear approximation capability, although the estimation of its weights is linear. The proposed model considers the effect of temperature on the kinetics and has the neural network weights reestimated always so that a change in operational conditions occurs. This allow to follow the system behavior when changes in operating conditions occur.

  9. Improved Marquardt Algorithm for Training Neural Networks for Chemical Process Modeling

    Institute of Scientific and Technical Information of China (English)

    吴建昱; 何小荣

    2002-01-01

    Back-propagation (BP) artificial neural networks have been widely used to model chemical processes. BP networks are often trained using the generalized delta-rule (GDR) algorithm but application of such networks is limited because of the low convergent speed of the algorithm. This paper presents a new algorithm incorporating the Marquardt algorithm into the BP algorithm for training feedforward BP neural networks. The new algorithm was tested with several case studies and used to model the Reid vapor pressure (RVP) of stabilizer gasoline. The new algorithm has faster convergence and is much more efficient than the GDR algorithm.

  10. Least square neural network model of the crude oil blending process.

    Science.gov (United States)

    Rubio, José de Jesús

    2016-06-01

    In this paper, the recursive least square algorithm is designed for the big data learning of a feedforward neural network. The proposed method as the combination of the recursive least square and feedforward neural network obtains four advantages over the alone algorithms: it requires less number of regressors, it is fast, it has the learning ability, and it is more compact. Stability, convergence, boundedness of parameters, and local minimum avoidance of the proposed technique are guaranteed. The introduced strategy is applied for the modeling of the crude oil blending process.

  11. Large scale neural circuit mapping data analysis accelerated with the graphical processing unit (GPU)

    Science.gov (United States)

    Shi, Yulin; Veidenbaum, Alexander V.; Nicolau, Alex; Xu, Xiangmin

    2014-01-01

    Background Modern neuroscience research demands computing power. Neural circuit mapping studies such as those using laser scanning photostimulation (LSPS) produce large amounts of data and require intensive computation for post-hoc processing and analysis. New Method Here we report on the design and implementation of a cost-effective desktop computer system for accelerated experimental data processing with recent GPU computing technology. A new version of Matlab software with GPU enabled functions is used to develop programs that run on Nvidia GPUs to harness their parallel computing power. Results We evaluated both the central processing unit (CPU) and GPU-enabled computational performance of our system in benchmark testing and practical applications. The experimental results show that the GPU-CPU co-processing of simulated data and actual LSPS experimental data clearly outperformed the multi-core CPU with up to a 22x speedup, depending on computational tasks. Further, we present a comparison of numerical accuracy between GPU and CPU computation to verify the precision of GPU computation. In addition, we show how GPUs can be effectively adapted to improve the performance of commercial image processing software such as Adobe Photoshop. Comparison with Existing Method(s) To our best knowledge, this is the first demonstration of GPU application in neural circuit mapping and electrophysiology-based data processing. Conclusions Together, GPU enabled computation enhances our ability to process large-scale data sets derived from neural circuit mapping studies, allowing for increased processing speeds while retaining data precision. PMID:25277633

  12. A Biologically-Inspired Neural Network Architecture for Image Processing

    Science.gov (United States)

    1990-12-01

    findings, in accord with other research cited here, were obtained from cortical measurements or, 15 adult cats and 12 kittens , all anesthetized (9...software models on a Cray computer. Furthermore, care should be taken to avoid exceeding machine memory capacity when running intensive processes

  13. Neural Correlates of Top-Down Letter Processing

    Science.gov (United States)

    Liu, Jiangang; Li, Jun; Zhang, Hongchuan; Rieth, Cory A.; Huber, David E.; Li, Wu; Lee, Kang; Tian, Jie

    2010-01-01

    This fMRI study investigated top-down letter processing with an illusory letter detection task. Participants responded whether one of a number of different possible letters was present in a very noisy image. After initial training that became increasingly difficult, they continued to detect letters even though the images consisted of pure noise,…

  14. CONTROL OF NONLINEAR PROCESS USING NEURAL NETWORK BASED MODEL PREDICTIVE CONTROL

    Directory of Open Access Journals (Sweden)

    Dr.A.TRIVEDI

    2011-04-01

    Full Text Available This paper presents a Neural Network based Model Predictive Control (NNMPC strategy to control nonlinear process. Multilayer Perceptron Neural Network (MLP is chosen to represent a Nonlinear Auto Regressive with eXogenous signal (NARX model of a nonlinear system. NARX dynamic model is based on feed-forward architecture and offers good approximation capabilities along with robustness and accuracy. Based on the identified neural model, a generalized predictive control (GPC algorithm is implemented to control the composition in acontinuous stirred tank reactor (CSTR, whose parameters are optimally determined by solving quadratic performance index using well known Levenberg-Marquardt and Quasi-Newton algorithm. NNMPC is tuned by selecting few horizon parameters and weighting factor. The tracking performance of the NNMPC is tested using different amplitude function as a reference signal on CSTR application. Also the robustness and performance is tested in the presence of disturbance on random reference signal.

  15. Musical experience limits the degradative effects of background noise on the neural processing of sound.

    Science.gov (United States)

    Parbery-Clark, Alexandra; Skoe, Erika; Kraus, Nina

    2009-11-11

    Musicians have lifelong experience parsing melodies from background harmonies, which can be considered a process analogous to speech perception in noise. To investigate the effect of musical experience on the neural representation of speech-in-noise, we compared subcortical neurophysiological responses to speech in quiet and noise in a group of highly trained musicians and nonmusician controls. Musicians were found to have a more robust subcortical representation of the acoustic stimulus in the presence of noise. Specifically, musicians demonstrated faster neural timing, enhanced representation of speech harmonics, and less degraded response morphology in noise. Neural measures were associated with better behavioral performance on the Hearing in Noise Test (HINT) for which musicians outperformed the nonmusician controls. These findings suggest that musical experience limits the negative effects of competing background noise, thereby providing the first biological evidence for musicians' perceptual advantage for speech-in-noise.

  16. Hybrid digital signal processing and neural networks for automated diagnostics using NDE methods

    Energy Technology Data Exchange (ETDEWEB)

    Upadhyaya, B.R.; Yan, W. [Tennessee Univ., Knoxville, TN (United States). Dept. of Nuclear Engineering

    1993-11-01

    The primary purpose of the current research was to develop an integrated approach by combining information compression methods and artificial neural networks for the monitoring of plant components using nondestructive examination data. Specifically, data from eddy current inspection of heat exchanger tubing were utilized to evaluate this technology. The focus of the research was to develop and test various data compression methods (for eddy current data) and the performance of different neural network paradigms for defect classification and defect parameter estimation. Feedforward, fully-connected neural networks, that use the back-propagation algorithm for network training, were implemented for defect classification and defect parameter estimation using a modular network architecture. A large eddy current tube inspection database was acquired from the Metals and Ceramics Division of ORNL. These data were used to study the performance of artificial neural networks for defect type classification and for estimating defect parameters. A PC-based data preprocessing and display program was also developed as part of an expert system for data management and decision making. The results of the analysis showed that for effective (low-error) defect classification and estimation of parameters, it is necessary to identify proper feature vectors using different data representation methods. The integration of data compression and artificial neural networks for information processing was established as an effective technique for automation of diagnostics using nondestructive examination methods.

  17. Neural correlates of three types of negative life events during angry face processing in adolescents.

    Science.gov (United States)

    Gollier-Briant, Fanny; Paillère-Martinot, Marie-Laure; Lemaitre, Hervé; Miranda, Ruben; Vulser, Hélène; Goodman, Robert; Penttilä, Jani; Struve, Maren; Fadai, Tahmine; Kappel, Viola; Poustka, Luise; Grimmer, Yvonne; Bromberg, Uli; Conrod, Patricia; Banaschewski, Tobias; Barker, Gareth J; Bokde, Arun L W; Büchel, Christian; Flor, Herta; Gallinat, Juergen; Garavan, Hugh; Heinz, Andreas; Lawrence, Claire; Mann, Karl; Nees, Frauke; Paus, Tomas; Pausova, Zdenka; Frouin, Vincent; Rietschel, Marcella; Robbins, Trevor W; Smolka, Michael N; Schumann, Gunter; Martinot, Jean-Luc; Artiges, Eric

    2016-12-01

    Negative life events (NLE) contribute to anxiety and depression disorders, but their relationship with brain functioning in adolescence has rarely been studied. We hypothesized that neural response to social threat would relate to NLE in the frontal-limbic emotional regions. Participants (N = 685) were drawn from the Imagen database of 14-year-old community adolescents recruited in schools. They underwent functional MRI while viewing angry and neutral faces, as a probe to neural response to social threat. Lifetime NLEs were assessed using the 'distress', 'family' and 'accident' subscales from a life event dimensional questionnaire. Relationships between NLE subscale scores and neural response were investigated. Links of NLE subscales scores with anxiety or depression outcomes at the age of 16 years were also investigated. Lifetime 'distress' positively correlated with ventral-lateral orbitofrontal and temporal cortex activations during angry face processing. 'Distress' scores correlated with the probabilities of meeting criteria for Generalized Anxiety Disorder or Major Depressive Disorder at the age of 16 years. Lifetime 'family' and 'accident' scores did not relate with neural response or follow-up conditions, however. Thus, different types of NLEs differentially predicted neural responses to threat during adolescence, and differentially predicted a de novo internalizing condition 2 years later. The deleterious effect of self-referential NLEs is suggested. © The Author (2016). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  18. Fault detection and diagnosis for complex multivariable processes using neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Weerasinghe, M

    1998-06-01

    Development of a reliable fault diagnosis method for large-scale industrial plants is laborious and often difficult to achieve due to the complexity of the targeted systems. The main objective of this thesis is to investigate the application of neural networks to the diagnosis of non-catastrophic faults in an industrial nuclear fuel processing plant. The proposed methods were initially developed by application to a simulated chemical process prior to further validation on real industrial data. The diagnosis of faults at a single operating point is first investigated. Statistical data conditioning methods of data scaling and principal component analysis are investigated to facilitate fault classification and reduce the complexity of neural networks. Successful fault diagnosis was achieved with significantly smaller networks than using all process variables as network inputs. Industrial processes often manufacture at various operating points, but demonstrated applications of neural networks for fault diagnosis usually only consider a single (primary) operating point. Developing a standard neural network scheme for fault diagnosis at all operating points would be usually impractical due to the unavailability of suitable training data for less frequently used (secondary) operating points. To overcome this problem, the application of a single neural network for the diagnosis of faults operating at different points is investigated. The data conditioning followed the same techniques as used for the fault diagnosis of a single operating point. The results showed that a single neural network could be successfully used to diagnose faults at operating points other than that it is trained for, and the data conditioning significantly improved the classification. Artificial neural networks have been shown to be an effective tool for process fault diagnosis. However, a main criticism is that details of the procedures taken to reach the fault diagnosis decisions are embedded in

  19. Adolescents' Neural Processing of Risky Decisions: Effects of Sex and Behavioral Disinhibition.

    Directory of Open Access Journals (Sweden)

    Thomas J Crowley

    Full Text Available Accidental injury and homicide, relatively common among adolescents, often follow risky behaviors; those are done more by boys and by adolescents with greater behavioral disinhibition (BD.Neural processing during adolescents' risky decision-making will differ in youths with greater BD severity, and in males vs. females, both before cautious behaviors and before risky behaviors.81 adolescents (PATIENTS with substance and conduct problems, and comparison youths (Comparisons, assessed in a 2 x 2 design (Comparisons x Male:Female repeatedly decided between doing a cautious behavior that earned 1 cent, or a risky one that either won 5 or lost 10 cents. Odds of winning after risky responses gradually decreased. Functional magnetic resonance imaging captured brain activity during 4-sec deliberation periods preceding responses. Most neural activation appeared in known decision-making structures. PATIENTS, who had more severe BD scores and clinical problems than Comparisons, also had extensive neural hypoactivity. Comparisons' greater activation before cautious responses included frontal pole, medial prefrontal cortex, striatum, and other regions; and before risky responses, insula, temporal, and parietal regions. Males made more risky and fewer cautious responses than females, but before cautious responses males activated numerous regions more than females. Before risky behaviors female-greater activation was more posterior, and male-greater more anterior.Neural processing differences during risky-cautious decision-making may underlie group differences in adolescents' substance-related and antisocial risk-taking. Patients reported harmful real-life decisions and showed extensive neural hypoactivity during risky-or-cautious decision-making. Males made more risky responses than females; apparently biased toward risky decisions, males (compared with females utilized many more neural resources to make and maintain cautious decisions, indicating an important

  20. Local active information storage as a tool to understand distributed neural information processing.

    Science.gov (United States)

    Wibral, Michael; Lizier, Joseph T; Vögler, Sebastian; Priesemann, Viola; Galuske, Ralf

    2014-01-01

    Every act of information processing can in principle be decomposed into the component operations of information storage, transfer, and modification. Yet, while this is easily done for today's digital computers, the application of these concepts to neural information processing was hampered by the lack of proper mathematical definitions of these operations on information. Recently, definitions were given for the dynamics of these information processing operations on a local scale in space and time in a distributed system, and the specific concept of local active information storage was successfully applied to the analysis and optimization of artificial neural systems. However, no attempt to measure the space-time dynamics of local active information storage in neural data has been made to date. Here we measure local active information storage on a local scale in time and space in voltage sensitive dye imaging data from area 18 of the cat. We show that storage reflects neural properties such as stimulus preferences and surprise upon unexpected stimulus change, and in area 18 reflects the abstract concept of an ongoing stimulus despite the locally random nature of this stimulus. We suggest that LAIS will be a useful quantity to test theories of cortical function, such as predictive coding.

  1. Effect of short-term escitalopram treatment on neural activation during emotional processing.

    Science.gov (United States)

    Maron, Eduard; Wall, Matt; Norbury, Ray; Godlewska, Beata; Terbeck, Sylvia; Cowen, Philip; Matthews, Paul; Nutt, David J

    2016-01-01

    Recent functional magnetic resonance (fMRI) imaging studies have revealed that subchronic medication with escitalopram leads to significant reduction in both amygdala and medial frontal gyrus reactivity during processing of emotional faces, suggesting that escitalopram may have a distinguishable modulatory effect on neural activation as compared with other serotonin-selective antidepressants. In this fMRI study we aimed to explore whether short-term medication with escitalopram in healthy volunteers is associated with reduced neural response to emotional processing, and whether this effect is predicted by drug plasma concentration. The neural response to fearful and happy faces was measured before and on day 7 of treatment with escitalopram (10mg) in 15 healthy volunteers and compared with those in a control unmedicated group (n=14). Significantly reduced activation to fearful, but not to happy facial expressions was observed in the bilateral amygdala, cingulate and right medial frontal gyrus following escitalopram medication. This effect was not correlated with plasma drug concentration. In accordance with previous data, we showed that escitalopram exerts its rapid direct effect on emotional processing via attenuation of neural activation in pathways involving medial frontal gyrus and amygdala, an effect that seems to be distinguishable from that of other SSRIs.

  2. Neural networks type MLP in the process of identification chosen varieties of maize

    Science.gov (United States)

    Boniecki, P.; Nowakowski, K.; Tomczak, R.

    2011-06-01

    During the adaptation process of the weights vector that occurs in the iterative presentation of the teaching vector, the the MLP type artificial neural network (MultiLayer Perceptron) attempts to learn the structure of the data. Such a network can learn to recognise aggregates of input data occurring in the input data set regardless of the assumed criteria of similarity and the quantity of the data explored. The MLP type neural network can be also used to detect regularities occurring in the obtained graphic empirical data. The neuronal image analysis is then a new field of digital processing of signals. It is possible to use it to identify chosen objects given in the form of bitmap. If at the network input, a new unknown case appears which the network is unable to recognise, it means that it is different from all the classes known previously. The MLP type artificial neural network taught in this way can serve as a detector signalling the appearance of a widely understood novelty. Such a network can also look for similarities between the known data and the noisy data. In this way, it is able to identify fragments of images presented in photographs of e.g. maze's grain. The purpose of the research was to use the MLP neural networks in the process of identification of chosen varieties of maize with the use of image analysis method. The neuronal classification shapes of grains was performed with the use of the Johan Gielis super formula.

  3. The role of automaticity and attention in neural processes underlying empathy for happiness, sadness, and anxiety.

    Science.gov (United States)

    Morelli, Sylvia A; Lieberman, Matthew D

    2013-01-01

    Although many studies have examined the neural basis of empathy, relatively little is known about how empathic processes are affected by different attentional conditions. Thus, we examined whether instructions to empathize might amplify responses in empathy-related regions and whether cognitive load would diminish the involvement of these regions. Thirty-two participants completed a functional magnetic resonance imaging session assessing empathic responses to individuals experiencing happy, sad, and anxious events. Stimuli were presented under three conditions: watching naturally, actively empathizing, and under cognitive load. Across analyses, we found evidence for a core set of neural regions that support empathic processes (dorsomedial prefrontal cortex, DMPFC; medial prefrontal cortex, MPFC; temporoparietal junction, TPJ; amygdala; ventral anterior insula, AI; and septal area, SA). Two key regions-the ventral AI and SA-were consistently active across all attentional conditions, suggesting that they are automatically engaged during empathy. In addition, watching vs. empathizing with targets was not markedly different and instead led to similar subjective and neural responses to others' emotional experiences. In contrast, cognitive load reduced the subjective experience of empathy and diminished neural responses in several regions related to empathy and social cognition (DMPFC, MPFC, TPJ, and amygdala). The results reveal how attention impacts empathic processes and provides insight into how empathy may unfold in everyday interactions.

  4. The role of automaticity and attention in neural processes underlying empathy for happiness, sadness, and anxiety

    Directory of Open Access Journals (Sweden)

    Sylvia A. Morelli

    2013-05-01

    Full Text Available Although many studies have examined the neural basis of experiencing empathy, relatively little is known about how empathic processes are affected by different attentional conditions. Thus, we examined whether instructions to empathize might amplify responses in empathy-related regions and whether cognitive load would diminish the involvement of these regions. 32 participants completed a functional magnetic resonance imaging session assessing empathic responses to individuals experiencing happy, sad, and anxious events. Stimuli were presented under three conditions: watching naturally, while instructed to empathize, and under cognitive load. Across analyses, we found evidence for a core set of neural regions that support empathic processes (dorsomedial prefrontal cortex, DMPFC; medial prefrontal cortex, MPFC; temporoparietal junction, TPJ; amygdala; ventral anterior insula, AI; septal area, SA. Two key regions – the ventral AI and SA – were consistently active across all attentional conditions, suggesting that they are automatically engaged during empathy. In addition, watching versus empathizing with targets was not markedly different and instead led to similar subjective and neural responses to others’ emotional experiences. In contrast, cognitive load reduced the subjective experience of empathy and diminished neural responses in several regions related to empathy (DMPFC, MPFC, TPJ, amygdala and social cognition. The current results reveal how attention impacts empathic processes and provides insight into how empathy may unfold in everyday interactions.

  5. Neural correlates of olfactory processing in congenital blindness

    DEFF Research Database (Denmark)

    Kupers, R; Beaulieu-Lefebvre, M; Schneider, F C

    2011-01-01

    Adaptive neuroplastic changes have been well documented in congenitally blind individuals for the processing of tactile and auditory information. By contrast, very few studies have investigated olfactory processing in the absence of vision. There is ample evidence that the olfactory system...... is highly plastic and that blind individuals rely more on their sense of smell than the sighted do. The olfactory system in the blind is therefore likely to be susceptible to cross-modal changes similar to those observed for the tactile and auditory modalities. To test this hypothesis, we used functional....... The stronger recruitment of the occipital cortex during odor detection demonstrates a preferential access of olfactory stimuli to this area when vision is lacking from birth. This finding expands current knowledge about the supramodal function of the visually deprived occipital cortex in congenital blindness...

  6. Neural Correlates of Feedback Processing in Decision Making under Risk

    Directory of Open Access Journals (Sweden)

    Beate eSchuermann

    2012-07-01

    Full Text Available Introduction. Event-related brain potentials (ERP provide important information about the sensitivity of the brain to process varying risks. The aim of the present study was to determine how different risk levels are reflected in decision-related ERPs, namely the feedback-related negativity (FRN and the P300. Material and Methods. 20 participants conducted a probabilistic two-choice gambling task while an electroencephalogram was recorded. Choices were provided between a low-risk option yielding low rewards and low losses and a high-risk option yielding high rewards and high losses. While options differed in expected risks, they were equal in expected values and in feedback probabilities. Results. At the behavioral level, participants were generally risk-averse but modulated their risk-taking behavior according to reward history. An early positivity (P200 was enhanced on negative feedbacks in high-risk compared to low-risk options. With regard to the FRN, there were significant amplitude differences between positive and negative feedbacks in high-risk options, but not in low-risk options. While the FRN on negative feedbacks did not vary with decision riskiness, reduced amplitudes were found for positive feedbacks in high-risk relative to low-risk choices. P300 amplitudes were larger in high-risk decisions, and in an additive way, after negative compared to positive feedback. Discussion. The present study revealed significant influences of risk and valence processing on ERPs. FRN findings suggest that the reward prediction error signal is increased after high-risk decisions. The increased P200 on negative feedback in risky decisions suggests that large negative prediction errors are processed as early as in the P200 time range. The later P300 amplitude is sensitive to feedback valence as well as to the risk of a decision. Thus, the P300 carries additional information for reward processing, mainly the enhanced motivational significance of risky

  7. Optimization of biopharmaceutical downstream processes supported by mechanistic models and artificial neural networks.

    Science.gov (United States)

    Pirrung, Silvia M; van der Wielen, Luuk A M; van Beckhoven, Ruud F W C; van de Sandt, Emile J A X; Eppink, Michel H M; Ottens, Marcel

    2017-01-05

    Downstream process development is a major area of importance within the field of bioengineering. During the design of such a downstream process, important decisions have to be made regarding the type of unit operations as well as their sequence and their operating conditions. Current computational approaches addressing these issues either show a high level of simplification or struggle with computational speed. Therefore, this article presents a new approach that combines detailed mechanistic models and speed-enhancing artificial neural networks. This approach was able to simultaneously optimize a process with three different chromatographic columns toward yield with a minimum purity of 99.9%. The addition of artificial neural networks greatly accelerated this optimization. Due to high computational speed, the approach is easily extendable to include more unit operations. Therefore, it can be of great help in the acceleration of downstream process development. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 2017.

  8. Residual neural processing of musical sound features in adult cochlear implant users

    DEFF Research Database (Denmark)

    Timm, Lydia; Vuust, Peter; Brattico, Evira

    2014-01-01

    neural skills for music processing even in CI users who have been implanted in adolescence or adulthood. HIGHLIGHTS: -Automatic brain responses to musical feature changes reflect the limitations of central auditory processing in adult Cochlear Implant users.-The brains of adult CI users automatically......Auditory processing in general and music perception in particular are hampered in adult cochlear implant (CI) users. To examine the residual music perception skills and their underlying neural correlates in CI users implanted in adolescence or adulthood, we conducted an electrophysiological...... setting lasting only 20 min. The presentation of stimuli did not require the participants' attention, allowing the study of the early automatic stage of feature processing in the auditory cortex. For the CI users, we obtained mismatch negativity (MMN) brain responses to five feature changes...

  9. Application of neural network method to process planning in ship pipe machining

    Institute of Scientific and Technical Information of China (English)

    ZHONG Yu-guang; QIU Chang-hua; SHI Dong-yan

    2004-01-01

    Based on artificial neural network for process planning decision in ship pipe manufacturing, a novel method is established by analyzing process characteristics of the ship pipe machining. The process knowledge of pipe machining is shifted from the expression of the external rules to the description of the internal net weight value in order for the net inferring engine to decide the process route of pipe machining rapidly and rightly. Simulation shows that the method can resolve problems of process decision, and overcome the drawbacks of "matching difficulty" and "combination explosion" in traditional intelligent CAPP based on symbol reasoning.

  10. Neural correlates of metaphor processing: the roles of figurativeness, familiarity and difficulty.

    Science.gov (United States)

    Schmidt, Gwenda L; Seger, Carol A

    2009-12-01

    There is currently much interest in investigating the neural substrates of metaphor processing. In particular, it has been suggested that the right hemisphere plays a special role in the comprehension of figurative (non-literal) language, and in particular metaphors. However, some studies find no evidence of right hemisphere involvement in metaphor comprehension (e.g. [Lee, S. S., & Dapretto, M. (2006). Metaphorical vs. literal word meanings: fMRI evidence against a selective role of the right hemisphere. NeuroImage, 29, 536-544; Rapp, A. M., Leube, D. T., Erb, M., Grodd, W., & Kircher, T. T. J. (2004). Neural correlates of metaphor processing. Cognitive Brain Research, 20, 395-402]). We suggest that lateralization differences between literal and metaphorical language may be due to factors such as differences in familiarity ([Schmidt, G. L., DeBuse, C. J., & Seger, C. A. (2007). Right hemisphere metaphor processing? Characterizing the lateralization of semantic processes. Brain and Language, 100, 127-141]), or difficulty ([Bookheimer, S. (2002). Functional MRI of language: New approaches to understanding the cortical organization of semantic processing. Annual Review of Neuroscience, 25, 151-188; Rapp, A. M., Leube, D. T., Erb, M., Grodd, W., & Kircher, T. T. J. (2004). Neural correlates of metaphor processing. Cognitive Brain Research, 20, 395-402]) in addition to figurativeness. The purpose of this study was to separate the effects of figurativeness, familiarity, and difficulty on the recruitment of neural systems involved in language, in particular right hemisphere mechanisms. This was achieved by comparing neural activation using functional magnetic resonance imaging (fMRI) between four conditions: literal sentences, familiar and easy to understand metaphors, unfamiliar and easy to understand metaphors, and unfamiliar and difficult to understand metaphors. Metaphors recruited the right insula, left temporal pole and right inferior frontal gyrus in comparison

  11. Neural differences in the processing of semantic relationships across cultures.

    Science.gov (United States)

    Gutchess, Angela H; Hedden, Trey; Ketay, Sarah; Aron, Arthur; Gabrieli, John D E

    2010-06-01

    The current study employed functional MRI to investigate the contribution of domain-general (e.g. executive functions) and domain-specific (e.g. semantic knowledge) processes to differences in semantic judgments across cultures. Previous behavioral experiments have identified cross-cultural differences in categorization, with East Asians preferring strategies involving thematic or functional relationships (e.g. cow-grass) and Americans preferring categorical relationships (e.g. cow-chicken). East Asians and American participants underwent functional imaging while alternating between categorical or thematic strategies to sort triads of words, as well as matching words on control trials. Many similarities were observed. However, across both category and relationship trials compared to match (control) trials, East Asians activated a frontal-parietal network implicated in controlled executive processes, whereas Americans engaged regions of the temporal lobes and the cingulate, possibly in response to conflict in the semantic content of information. The results suggest that cultures differ in the strategies employed to resolve conflict between competing semantic judgments.

  12. OPTIMIZATION OF OPERATING PARAMETERS FOR EDM PROCESS BASED ON THE TAGUCHI METHOD AND ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    A.Thillaivanan,

    2010-12-01

    Full Text Available In this paper the complexity of electrical discharge machining process which is very difficult to determine optimal cutting parameters for improving cutting performance has been reported. Optimization of operating parameters is an important step in machining, particularly for operating unconventional machiningprocedure like EDM. A suitable selection of machining parameters for the electrical discharge machining process relies heavily on the operators’ technologies and experience because of their numerous and diverse range. Machining parameters tables provided by the machine tool builder can not meet the operators’ requirements, since for anarbitrary desired machining time for a particular job, they do not provide the optimal machining conditions. An approach to determine parameters setting is proposed. Based on the Taguchi parameter design method and the analysis of variance, the significant factors affecting the machining performance such as total machining time, oversize and taper for a hole machined by EDM process, are determined.Artificial neural networks are highly flexible modeling tools with an ability to learn the mapping between input variables and output feature spaces. The superiority of using artificial neural networks inmodeling machining processes make easier to model the EDM process with dimensional input and output spaces. On the basis of the developed neural network model, for a required total machining time, oversize and taper the corresponding process parameters to be set in EDM by using the developed and trained ANN are determined.

  13. Dissociating neural mechanisms of temporal sequencing and processing phonemes.

    Science.gov (United States)

    Gelfand, Jenna R; Bookheimer, Susan Y

    2003-06-01

    Using fMRI, we sought to determine whether the posterior, superior portion of Broca's area performs operations on phoneme segments specifically or implements processes general to sequencing discrete units. Twelve healthy volunteers performed two sequence manipulation tasks and one matching task, using strings of syllables and hummed notes. The posterior portion of Broca's area responded specifically to the sequence manipulation tasks, independent of whether the stimuli were composed of phonemes or hummed notes. In contrast, the left supramarginal gyrus was somewhat more specific to sequencing phoneme segments. These results suggest a functional dissociation of the canonical left hemisphere language regions encompassing the "phonological loop," with the left posterior inferior frontal gyrus responding not to the sound structure of language but rather to sequential operations that may underlie the ability to form words out of dissociable elements.

  14. Modulated neural processing of Western harmony in folk musicians.

    Science.gov (United States)

    Brattico, Elvira; Tupala, Tiina; Glerean, Enrico; Tervaniemi, Mari

    2013-07-01

    A chord deviating from the conventions of Western tonal music elicits an early right anterior negativity (ERAN) in inferofrontal brain regions. Here, we tested whether the ERAN is modulated by expertise in more than one music culture, as typical of folk musicians. Finnish folk musicians and nonmusicians participated in electroencephalography recordings. The cadences consisted of seven chords. In incongruous cadences, the third, fifth, or seventh chord was a Neapolitan. The ERAN to the Neapolitans was enhanced in folk musicians compared to nonmusicians. Folk musicians showed an enhanced P3a for the ending Neapolitan. The Neapolitan at the fifth position was perceived differently and elicited a late enhanced ERAN in folk musicians. Hence, expertise in more than one music culture seems to modify chord processing by enhancing the ERAN to ambivalent chords and the P3a to incongruous chords, and by altering their perceptual attributes.

  15. What can psychiatric disorders tell us about neural processing of the self?

    Directory of Open Access Journals (Sweden)

    Weihua eZhao

    2013-08-01

    Full Text Available Many psychiatric disorders are associated with abnormal self-processing. While these disorders also have a wide-range of complex, and often heterogeneous sets of symptoms involving different cognitive, emotional and motor domains, an impaired sense of self can contribute to many of these. Research investigating self-processing in healthy subjects has facilitated identification of changes in specific neural circuits which may cause altered self-processing in psychiatric disorders. While there is evidence for altered self-processing in many psychiatric disorders, here we will focus on four of the most studied ones, schizophrenia, autism spectrum disorder (ASD, unipolar depression and borderline personality disorder (BPD. We review evidence for dysfunction in two different neural systems implicated in self-processing, namely the cortical midline system (CMS and the mirror-neuron system (MNS, as well as contributions from altered inter-hemispheric communication (IHC. We conclude that while abnormalities in frontal-parietal activity and/or connectivity in the CMS are common to all four disorders there is more disruption of integration between frontal and parietal regions resulting in a shift towards parietal control in schizophrenia and ASD which may contribute to the greater severity and delusional aspects of their symptoms. Abnormalities in the MNS and in IHC are also particularly evident in schizophrenia and ASD and may lead to disturbances in sense of agency and the physical self in these two disorders. A better future understanding of how changes in the neural systems sub-serving self-processing contribute to different aspects of symptom abnormality in psychiatric disorders will require that more studies carry out detailed individual assessments of altered self-processing in conjunction with measurements of neural functioning.

  16. Neural processing of speech in children is influenced by extent of bilingual experience.

    Science.gov (United States)

    Krizman, Jennifer; Slater, Jessica; Skoe, Erika; Marian, Viorica; Kraus, Nina

    2015-01-12

    Language experience fine-tunes how the auditory system processes sound. Bilinguals, relative to monolinguals, have more robust evoked responses to speech that manifest as stronger neural encoding of the fundamental frequency (F0) and greater across-trial consistency. However, it is unknown whether such enhancements increase with increasing second language experience. We predict that F0 amplitude and neural consistency scale with dual-language experience during childhood, such that more years of bilingual experience leads to more robust F0 encoding and greater neural consistency. To test this hypothesis, we recorded auditory brainstem responses to the synthesized syllables 'ba' and 'ga' in two groups of bilingual children who were matched for age at test (8.4 ± 0.67 years) but differed in their age of second language acquisition. One group learned English and Spanish simultaneously from birth (n=13), while the second group learned the two languages sequentially (n=15), spending on average their first four years as monolingual Spanish speakers. We find that simultaneous bilinguals have a larger F0 response to 'ba' and 'ga' and a more consistent response to 'ba' compared to sequential bilinguals and we demonstrate that these neural enhancements track with years of bilingual experience. These findings support the notion that bilingualism enhances subcortical auditory processing.

  17. Adaptive control of machining process based on extended entropy square error and wavelet neural network

    Institute of Scientific and Technical Information of China (English)

    LAI Xing-yu; YE Bang-yan; LI Wei-guang; YAN Chun-yan

    2007-01-01

    Combining information entropy and wavelet analysis with neural network, an adaptive control system and an adaptive control algorithm are presented for machining process based on extended entropy square error (EESE) and wavelet neural network (WNN). Extended entropy square error function is defined and its availability is proved theoretically. Replacing the mean square error criterion of BP algorithm with the EESE criterion, the proposed system is then applied to the on-line control of the cutting force with variable cutting parameters by searching adaptively wavelet base function and self adjusting scaling parameter, translating parameter of the wavelet and neural network weights. Simulation results show that the designed system is of fast response,non-overshoot and it is more effective than the conventional adaptive control of machining process based on the neural network. The suggested algorithm can adaptively adjust the feed rate on-line till achieving a constant cutting force approaching the reference force in varied cutting conditions, thus improving the machining efficiency and protecting the tool.

  18. Weak convergence of marked point processes generated by crossings of multivariate jump processes. Applications to neural network modeling

    Science.gov (United States)

    Tamborrino, Massimiliano; Sacerdote, Laura; Jacobsen, Martin

    2014-11-01

    We consider the multivariate point process determined by the crossing times of the components of a multivariate jump process through a multivariate boundary, assuming to reset each component to an initial value after its boundary crossing. We prove that this point process converges weakly to the point process determined by the crossing times of the limit process. This holds for both diffusion and deterministic limit processes. The almost sure convergence of the first passage times under the almost sure convergence of the processes is also proved. The particular case of a multivariate Stein process converging to a multivariate Ornstein-Uhlenbeck process is discussed as a guideline for applying diffusion limits for jump processes. We apply our theoretical findings to neural network modeling. The proposed model gives a mathematical foundation to the generalization of the class of Leaky Integrate-and-Fire models for single neural dynamics to the case of a firing network of neurons. This will help future study of dependent spike trains.

  19. Decisional Processes with Boolean Neural Network: the Emergence of Mental Schemes

    CERN Document Server

    Barnabei, Graziano; Conversano, Ciro; Lensi, Elena

    2010-01-01

    Human decisional processes result from the employment of selected quantities of relevant information, generally synthesized from environmental incoming data and stored memories. Their main goal is the production of an appropriate and adaptive response to a cognitive or behavioral task. Different strategies of response production can be adopted, among which haphazard trials, formation of mental schemes and heuristics. In this paper, we propose a model of Boolean neural network that incorporates these strategies by recurring to global optimization strategies during the learning session. The model characterizes as well the passage from an unstructured/chaotic attractor neural network typical of data-driven processes to a faster one, forward-only and representative of schema-driven processes. Moreover, a simplified version of the Iowa Gambling Task (IGT) is introduced in order to test the model. Our results match with experimental data and point out some relevant knowledge coming from psychological domain.

  20. Suprathreshold stochastic resonance in neural processing tuned by correlation

    Science.gov (United States)

    Durrant, Simon; Kang, Yanmei; Stocks, Nigel; Feng, Jianfeng

    2011-07-01

    Suprathreshold stochastic resonance (SSR) is examined in the context of integrate-and-fire neurons, with an emphasis on the role of correlation in the neuronal firing. We employed a model based on a network of spiking neurons which received synaptic inputs modeled by Poisson processes stimulated by a stepped input signal. The smoothed ensemble firing rate provided an output signal, and the mutual information between this signal and the input was calculated for networks with different noise levels and different numbers of neurons. It was found that an SSR effect was present in this context. We then examined a more biophysically plausible scenario where the noise was not controlled directly, but instead was tuned by the correlation between the inputs. The SSR effect remained present in this scenario with nonzero noise providing improved information transmission, and it was found that negative correlation between the inputs was optimal. Finally, an examination of SSR in the context of this model revealed its connection with more traditional stochastic resonance and showed a trade-off between supratheshold and subthreshold components. We discuss these results in the context of existing empirical evidence concerning correlations in neuronal firing.

  1. Endogenous testosterone levels are associated with neural activity in men with schizophrenia during facial emotion processing.

    Science.gov (United States)

    Ji, Ellen; Weickert, Cynthia Shannon; Lenroot, Rhoshel; Catts, Stanley V; Vercammen, Ans; White, Christopher; Gur, Raquel E; Weickert, Thomas W

    2015-06-01

    Growing evidence suggests that testosterone may play a role in the pathophysiology of schizophrenia given that testosterone has been linked to cognition and negative symptoms in schizophrenia. Here, we determine the extent to which serum testosterone levels are related to neural activity in affective processing circuitry in men with schizophrenia. Functional magnetic resonance imaging was used to measure blood-oxygen-level-dependent signal changes as 32 healthy controls and 26 people with schizophrenia performed a facial emotion identification task. Whole brain analyses were performed to determine regions of differential activity between groups during processing of angry versus non-threatening faces. A follow-up ROI analysis using a regression model in a subset of 16 healthy men and 16 men with schizophrenia was used to determine the extent to which serum testosterone levels were related to neural activity. Healthy controls displayed significantly greater activation than people with schizophrenia in the left inferior frontal gyrus (IFG). There was no significant difference in circulating testosterone levels between healthy men and men with schizophrenia. Regression analyses between activation in the IFG and circulating testosterone levels revealed a significant positive correlation in men with schizophrenia (r=.63, p=.01) and no significant relationship in healthy men. This study provides the first evidence that circulating serum testosterone levels are related to IFG activation during emotion face processing in men with schizophrenia but not in healthy men, which suggests that testosterone levels modulate neural processes relevant to facial emotion processing that may interfere with social functioning in men with schizophrenia.

  2. Eigenanalysis of a neural network for optic flow processing

    Science.gov (United States)

    Weber, F.; Eichner, H.; Cuntz, H.; Borst, A.

    2008-01-01

    Flies gain information about self-motion during free flight by processing images of the environment moving across their retina. The visual course control center in the brain of the blowfly contains, among others, a population of ten neurons, the so-called vertical system (VS) cells that are mainly sensitive to downward motion. VS cells are assumed to encode information about rotational optic flow induced by self-motion (Krapp and Hengstenberg 1996 Nature 384 463-6). Recent evidence supports a connectivity scheme between the VS cells where neurons with neighboring receptive fields are connected to each other by electrical synapses at the axonal terminals, whereas the boundary neurons in the network are reciprocally coupled via inhibitory synapses (Haag and Borst 2004 Nat. Neurosci. 7 628-34 Farrow et al 2005 J. Neurosci. 25 3985-93 Cuntz et al 2007 Proc. Natl Acad. Sci. USA). Here, we investigate the functional properties of the VS network and its connectivity scheme by reducing a biophysically realistic network to a simplified model, where each cell is represented by a dendritic and axonal compartment only. Eigenanalysis of this model reveals that the whole population of VS cells projects the synaptic input provided from local motion detectors on to its behaviorally relevant components. The two major eigenvectors consist of a horizontal and a slanted line representing the distribution of vertical motion components across the fly's azimuth. They are, thus, ideally suited for reliably encoding translational and rotational whole-field optic flow induced by respective flight maneuvers. The dimensionality reduction compensates for the contrast and texture dependence of the local motion detectors of the correlation-type, which becomes particularly pronounced when confronted with natural images and their highly inhomogeneous contrast distribution.

  3. Energy-efficient neural information processing in individual neurons and neuronal networks.

    Science.gov (United States)

    Yu, Lianchun; Yu, Yuguo

    2017-11-01

    Brains are composed of networks of an enormous number of neurons interconnected with synapses. Neural information is carried by the electrical signals within neurons and the chemical signals among neurons. Generating these electrical and chemical signals is metabolically expensive. The fundamental issue raised here is whether brains have evolved efficient ways of developing an energy-efficient neural code from the molecular level to the circuit level. Here, we summarize the factors and biophysical mechanisms that could contribute to the energy-efficient neural code for processing input signals. The factors range from ion channel kinetics, body temperature, axonal propagation of action potentials, low-probability release of synaptic neurotransmitters, optimal input and noise, the size of neurons and neuronal clusters, excitation/inhibition balance, coding strategy, cortical wiring, and the organization of functional connectivity. Both experimental and computational evidence suggests that neural systems may use these factors to maximize the efficiency of energy consumption in processing neural signals. Studies indicate that efficient energy utilization may be universal in neuronal systems as an evolutionary consequence of the pressure of limited energy. As a result, neuronal connections may be wired in a highly economical manner to lower energy costs and space. Individual neurons within a network may encode independent stimulus components to allow a minimal number of neurons to represent whole stimulus characteristics efficiently. This basic principle may fundamentally change our view of how billions of neurons organize themselves into complex circuits to operate and generate the most powerful intelligent cognition in nature. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  4. Altered neural reward and loss processing and prediction error signalling in depression.

    Science.gov (United States)

    Ubl, Bettina; Kuehner, Christine; Kirsch, Peter; Ruttorf, Michaela; Diener, Carsten; Flor, Herta

    2015-08-01

    Dysfunctional processing of reward and punishment may play an important role in depression. However, functional magnetic resonance imaging (fMRI) studies have shown heterogeneous results for reward processing in fronto-striatal regions. We examined neural responsivity associated with the processing of reward and loss during anticipation and receipt of incentives and related prediction error (PE) signalling in depressed individuals. Thirty medication-free depressed persons and 28 healthy controls performed an fMRI reward paradigm. Regions of interest analyses focused on neural responses during anticipation and receipt of gains and losses and related PE-signals. Additionally, we assessed the relationship between neural responsivity during gain/loss processing and hedonic capacity. When compared with healthy controls, depressed individuals showed reduced fronto-striatal activity during anticipation of gains and losses. The groups did not significantly differ in response to reward and loss outcomes. In depressed individuals, activity increases in the orbitofrontal cortex and nucleus accumbens during reward anticipation were associated with hedonic capacity. Depressed individuals showed an absence of reward-related PEs but encoded loss-related PEs in the ventral striatum. Depression seems to be linked to blunted responsivity in fronto-striatal regions associated with limited motivational responses for rewards and losses. Alterations in PE encoding might mirror blunted reward- and enhanced loss-related associative learning in depression. © The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  5. Forming Conditions and Neural Network Control of Continuously Directional Microstructure in Directional Solidification Continuous Casting Process

    Institute of Scientific and Technical Information of China (English)

    1999-01-01

    Directional solidification continuous casting (DSCC) process is a new manufacturing technology for metal- lic materials which combines advantages of both directional solidification technology and continuous casting technolo- gy. Unlimited long shaped metal with directionally solidifying microstructure can be produced by this process. It is experimentally shown that controlling condition of stable and continuous growth of single crystal structure means the precise control of the location of the S/L interface, which is affected and determined by seven process parameters. Moreover, these parameters are also interacted each other, so the disturbance of any parameters may cause the fail- ure of controlling of S/L interface. In this paper, on the basis of analyzing the forming conditions of continuously di- rectional microstructures in DSCC process, the control model of DSCC procedure by neural network control (NNC) method was proposed and discussed. Combining with the experiments, we first used the computer to simulate the effects of the solidification parameters on destination control variable (S/L interface) and the interactions among these parameters during DSCC procedure. Secondly many training samples necessary for neural network calculation can be obtained through the simulation. Moreover, these samples are inputted into neural network software (NNs) and trained, then the control model can be built up.

  6. Neural classifier in the estimation process of maturity of selected varieties of apples

    Science.gov (United States)

    Boniecki, P.; Piekarska-Boniecka, H.; Koszela, K.; Zaborowicz, M.; Przybył, K.; Wojcieszak, D.; Zbytek, Z.; Ludwiczak, A.; Przybylak, A.; Lewicki, A.

    2015-07-01

    This paper seeks to present methods of neural image analysis aimed at estimating the maturity state of selected varieties of apples which are popular in Poland. An identification of the degree of maturity of selected varieties of apples has been conducted on the basis of information encoded in graphical form, presented in the digital photos. The above process involves the application of the BBCH scale, used to determine the maturity of apples. The aforementioned scale is widely used in the EU and has been developed for many species of monocotyledonous plants and dicotyledonous plants. It is also worth noticing that the given scale enables detailed determinations of development stage of a given plant. The purpose of this work is to identify maturity level of selected varieties of apples, which is supported by the use of image analysis methods and classification techniques represented by artificial neural networks. The analysis of graphical representative features based on image analysis method enabled the assessment of the maturity of apples. For the utilitarian purpose the "JabVis 1.1" neural IT system was created, in accordance with requirements of the software engineering dedicated to support the decision-making processes occurring in broadly understood production process and processing of apples.

  7. Contextual Processing of Abstract Concepts Reveals Neural Representations of Non-Linguistic Semantic Content

    Science.gov (United States)

    Wilson-Mendenhall, Christine D.; Simmons, W. Kyle; Martin, Alex; Barsalou, Lawrence W.

    2014-01-01

    Concepts develop for many aspects of experience, including abstract internal states and abstract social activities that do not refer to concrete entities in the world. The current study assessed the hypothesis that, like concrete concepts, distributed neural patterns of relevant, non-linguistic semantic content represent the meanings of abstract concepts. In a novel neuroimaging paradigm, participants processed two abstract concepts (convince, arithmetic) and two concrete concepts (rolling, red) deeply and repeatedly during a concept-scene matching task that grounded each concept in typical contexts. Using a catch trial design, neural activity associated with each concept word was separated from neural activity associated with subsequent visual scenes to assess activations underlying the detailed semantics of each concept. We predicted that brain regions underlying mentalizing and social cognition (e.g., medial prefrontal cortex, superior temporal sulcus) would become active to represent semantic content central to convince, whereas brain regions underlying numerical cognition (e.g., bilateral intraparietal sulcus) would become active to represent semantic content central to arithmetic. The results supported these predictions, suggesting that the meanings of abstract concepts arise from distributed neural systems that represent concept-specific content. PMID:23363408

  8. Models of neural networks temporal aspects of coding and information processing in biological systems

    CERN Document Server

    Hemmen, J; Schulten, Klaus

    1994-01-01

    Since the appearance of Vol. 1 of Models of Neural Networks in 1991, the theory of neural nets has focused on two paradigms: information coding through coherent firing of the neurons and functional feedback. Information coding through coherent neuronal firing exploits time as a cardinal degree of freedom. This capacity of a neural network rests on the fact that the neuronal action potential is a short, say 1 ms, spike, localized in space and time. Spatial as well as temporal correlations of activity may represent different states of a network. In particular, temporal correlations of activity may express that neurons process the same "object" of, for example, a visual scene by spiking at the very same time. The traditional description of a neural network through a firing rate, the famous S-shaped curve, presupposes a wide time window of, say, at least 100 ms. It thus fails to exploit the capacity to "bind" sets of coherently firing neurons for the purpose of both scene segmentation and figure-ground segregatio...

  9. Erythropoietin modulates neural and cognitive processing of emotional information in biomarker models of antidepressant drug action in depressed patients

    DEFF Research Database (Denmark)

    Miskowiak, Kamilla W; Favaron, Elisa; Hafizi, Sepehr

    2010-01-01

    Erythropoietin (Epo) has neuroprotective and neurotrophic effects, and may be a novel therapeutic agent in the treatment of psychiatric disorders. We have demonstrated antidepressant-like effects of Epo on the neural and cognitive processing of facial expressions in healthy volunteers. The current...... study investigates the effects of Epo on the neural and cognitive response to emotional facial expressions in depressed patients....

  10. Neural Network Signal Processing Approach for Damage Assessment in Fiberoptic Smart Material Systems and Sructures①②

    Institute of Scientific and Technical Information of China (English)

    TUYaqing; HUANGShanglian

    1997-01-01

    An approach by using neural network signal processing in associate with embedded fiberoptic sensing array for the newly developed“smart material systems and structures” is discussed in this paper.The principle,structure of this approach and suitable neural network algorithms are described.The results of simulation experiments are also given.

  11. Recognition of Roasted Coffee Bean Levels using Image Processing and Neural Network

    Science.gov (United States)

    Nasution, T. H.; Andayani, U.

    2017-03-01

    The coffee beans roast levels have some characteristics. However, some people cannot recognize the coffee beans roast level. In this research, we propose to design a method to recognize the coffee beans roast level of images digital by processing the image and classifying with backpropagation neural network. The steps consist of how to collect the images data with image acquisition, pre-processing, feature extraction using Gray Level Co-occurrence Matrix (GLCM) method and finally normalization of data extraction using decimal scaling features. The values of decimal scaling features become an input of classifying in backpropagation neural network. We use the method of backpropagation to recognize the coffee beans roast levels. The results showed that the proposed method is able to identify the coffee roasts beans level with an accuracy of 97.5%.

  12. Optimization of magnetically driven directional solidification of silicon using artificial neural networks and Gaussian process models

    Science.gov (United States)

    Dropka, Natasha; Holena, Martin

    2017-08-01

    In directional solidification of silicon, the solid-liquid interface shape plays a crucial role for the quality of crystals. The interface shape can be influenced by forced convection using travelling magnetic fields. Up to now, there is no general and explicit methodology to identify the relation and the optimum combination of magnetic and growth parameters e.g., frequency, phase shift, current magnitude and interface deflection in a buoyancy regime. In the present study, 2D CFD modeling was used to generate data for the design and training of artificial neural networks and for Gaussian process modeling. The aim was to quickly assess the complex nonlinear dependences among the parameters and to optimize them for the interface flattening. The first encouraging results are presented and the pros and cons of artificial neural networks and Gaussian process modeling discussed.

  13. Development of a Neural Network-Based Renewable Energy Forecasting Framework for Process Industries

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Soobin; Ryu, Jun-Hyung; Hodge, Bri-Mathias; Lee, In-Beum

    2016-06-25

    This paper presents a neural network-based forecasting framework for photovoltaic power (PV) generation as a decision-supporting tool to employ renewable energies in the process industry. The applicability of the proposed framework is illustrated by comparing its performance against other methodologies such as linear and nonlinear time series modelling approaches. A case study of an actual PV power plant in South Korea is presented.

  14. Choice modulates the neural dynamics of prediction error processing during rewarded learning.

    Science.gov (United States)

    Peterson, David A; Lotz, Daniel T; Halgren, Eric; Sejnowski, Terrence J; Poizner, Howard

    2011-01-15

    Our ability to selectively engage with our environment enables us to guide our learning and to take advantage of its benefits. When facing multiple possible actions, our choices are a critical aspect of learning. In the case of learning from rewarding feedback, there has been substantial theoretical and empirical progress in elucidating the associated behavioral and neural processes, predominantly in terms of a reward prediction error, a measure of the discrepancy between actual versus expected reward. Nevertheless, the distinct influence of choice on prediction error processing and its neural dynamics remains relatively unexplored. In this study we used a novel paradigm to determine how choice influences prediction error processing and to examine whether there are correspondingly distinct neural dynamics. We recorded scalp electroencephalogram while healthy adults were administered a rewarded learning task in which choice trials were intermingled with control trials involving the same stimuli, motor responses, and probabilistic rewards. We used a temporal difference learning model of subjects' trial-by-trial choices to infer subjects' image valuations and corresponding prediction errors. As expected, choices were associated with lower overall prediction error magnitudes, most notably over the course of learning the stimulus-reward contingencies. Choices also induced a higher-amplitude relative positivity in the frontocentral event-related potential about 200 ms after reward signal onset that was negatively correlated with the differential effect of choice on the prediction error. Thus choice influences the neural dynamics associated with how reward signals are processed during learning. Behavioral, computational, and neurobiological models of rewarded learning should therefore accommodate a distinct influence for choice during rewarded learning.

  15. Temperament trait of sensory processing sensitivity moderates cultural differences in neural response

    OpenAIRE

    Aron, Arthur; Ketay, Sarah; Hedden, Trey; Aron, Elaine N; Rose Markus, Hazel; John D E Gabrieli

    2010-01-01

    This study focused on a possible temperament-by-culture interaction. Specifically, it explored whether a basic temperament/personality trait (sensory processing sensitivity; SPS), perhaps having a genetic component, might moderate a previously established cultural difference in neural responses when making context-dependent vs context-independent judgments of simple visual stimuli. SPS has been hypothesized to underlie what has been called inhibitedness or reactivity in infants, introversion ...

  16. Social anhedonia is associated with neural abnormalities during face emotion processing.

    Science.gov (United States)

    Germine, Laura T; Garrido, Lucia; Bruce, Lori; Hooker, Christine

    2011-10-01

    Human beings are social organisms with an intrinsic desire to seek and participate in social interactions. Social anhedonia is a personality trait characterized by a reduced desire for social affiliation and reduced pleasure derived from interpersonal interactions. Abnormally high levels of social anhedonia prospectively predict the development of schizophrenia and contribute to poorer outcomes for schizophrenia patients. Despite the strong association between social anhedonia and schizophrenia, the neural mechanisms that underlie individual differences in social anhedonia have not been studied and are thus poorly understood. Deficits in face emotion recognition are related to poorer social outcomes in schizophrenia, and it has been suggested that face emotion recognition deficits may be a behavioral marker for schizophrenia liability. In the current study, we used functional magnetic resonance imaging (fMRI) to see whether there are differences in the brain networks underlying basic face emotion processing in a community sample of individuals low vs. high in social anhedonia. We isolated the neural mechanisms related to face emotion processing by comparing face emotion discrimination with four other baseline conditions (identity discrimination of emotional faces, identity discrimination of neutral faces, object discrimination, and pattern discrimination). Results showed a group (high/low social anhedonia) × condition (emotion discrimination/control condition) interaction in the anterior portion of the rostral medial prefrontal cortex, right superior temporal gyrus, and left somatosensory cortex. As predicted, high (relative to low) social anhedonia participants showed less neural activity in face emotion processing regions during emotion discrimination as compared to each control condition. The findings suggest that social anhedonia is associated with abnormalities in networks responsible for basic processes associated with social cognition, and provide a

  17. The role of automaticity and attention in neural processes underlying empathy for happiness, sadness, and anxiety

    OpenAIRE

    Morelli, Sylvia A.; Lieberman, Matthew D.

    2013-01-01

    Although many studies have examined the neural basis of empathy, relatively little is known about how empathic processes are affected by different attentional conditions. Thus, we examined whether instructions to empathize might amplify responses in empathy-related regions and whether cognitive load would diminish the involvement of these regions. Thirty-two participants completed a functional magnetic resonance imaging session assessing empathic responses to individuals experiencing happy, s...

  18. Residual neural processing of musical sound features in adult cochlear implant users

    Directory of Open Access Journals (Sweden)

    Lydia eTimm

    2014-04-01

    Full Text Available AbstractAuditory processing in general and music perception in particular are hampered in adult Cochlear Implant (CI users. To examine the residual music perception skills and their underlying neural correlates in CI users implanted in adolescence or adulthood, we conducted an electrophysiological and behavioural study comparing adult CI users with normal-hearing age-matched controls (NH controls. We used a newly developed musical multi-feature paradigm, which makes it possible to test automatic auditory discrimination of six different types of sound feature changes inserted within a musical enriched setting lasting only 20 minutes. The presentation of stimuli did not require the participants’ attention, allowing the study of the early automatic stage of feature processing in the auditory cortex. For the CI users, we obtained mismatch negativity (MMN brain responses to five feature changes but not to changes of rhythm, whereas we obtained MMNs for all the feature changes in the NH controls. Furthermore, the MMNs to Deviants of pitch of CI users were reduced in amplitude and later than those of NH controls for changes of pitch and guitar timbre. No other group differences in MMN parameters were found to changes in intensity and saxophone timbre. Furthermore, the MMNs in CI users reflected the behavioral scores from a respective discrimination task and were correlated with patients’ age and speech intelligibility. Our results suggest that even though CI users are not performing at the same level as NH controls in neural discrimination of pitch-based features, they do possess potential neural abilities for music processing. However, CI users showed a disrupted ability to automatically discriminate rhythmic changes compared with controls. The current behavioural and MMN findings highlight the residual neural skills for music processing even in CI users who have been implanted in adolescence or adulthood.

  19. Artificial Neural Networks for Processing Graphs with Application to Image Understanding: A Survey

    Science.gov (United States)

    Bianchini, Monica; Scarselli, Franco

    In graphical pattern recognition, each data is represented as an arrangement of elements, that encodes both the properties of each element and the relations among them. Hence, patterns are modelled as labelled graphs where, in general, labels can be attached to both nodes and edges. Artificial neural networks able to process graphs are a powerful tool for addressing a great variety of real-world problems, where the information is naturally organized in entities and relationships among entities and, in fact, they have been widely used in computer vision, f.i. in logo recognition, in similarity retrieval, and for object detection. In this chapter, we propose a survey of neural network models able to process structured information, with a particular focus on those architectures tailored to address image understanding applications. Starting from the original recursive model (RNNs), we subsequently present different ways to represent images - by trees, forests of trees, multiresolution trees, directed acyclic graphs with labelled edges, general graphs - and, correspondingly, neural network architectures appropriate to process such structures.

  20. Thermomechanical processing optimization for 304 austenitic stainless steel using artificial neural network and genetic algorithm

    Science.gov (United States)

    Feng, Wen; Yang, Sen

    2016-12-01

    Thermomechanical processing has an important effect on the grain boundary character distribution. To obtain the optimal thermomechanical processing parameters is the key of grain boundary engineering. In this study, genetic algorithm (GA) based on artificial neural network model was proposed to optimize the thermomechanical processing parameters. In this model, a back-propagation neural network (BPNN) was established to map the relationship between thermomechanical processing parameters and the fraction of low-Σ CSL boundaries, and GA integrated with BPNN (BPNN/GA) was applied to optimize the thermomechanical processing parameters. The validation of the optimal thermomechanical processing parameters was verified by an experiment. Moreover, the microstructures and the intergranular corrosion resistance of the base material (BM) and the materials produced by the optimal thermomechanical processing parameters (termed as the GBEM) were studied. Compared to the BM specimen, the fraction of low-Σ CSL boundaries was increased from 56.8 to 77.9% and the random boundary network was interrupted by the low-Σ CSL boundaries, and the intergranular corrosion resistance was improved in the GBEM specimen. The results indicated that the BPNN/GA model was an effective and reliable means for the thermomechanical processing parameters optimization, which resulted in improving the intergranular corrosion resistance in 304 austenitic stainless steel.

  1. Error awareness and salience processing in the oddball task: Shared neural mechanisms.

    Directory of Open Access Journals (Sweden)

    Helga A Harsay

    2012-08-01

    Full Text Available A body of work suggests that there are similarities in the way we become aware of an error and process motivationally salient events. Yet, evidence for a shared neural mechanism has not been provided. A within-subject investigation of the brain regions involved in error awareness and salience processing has not been reported. While the neural response to motivationally salient events is classically studied during target detection after longer target-to-target intervals in an oddball task and engages a widespread insula-thalamo-cortical brain network, error awareness has recently been linked to, most prominently, anterior insula cortex. Here we explore whether the anterior insula activation for error awareness is related to salience processing, by testing for activation overlap in subjects undergoing two different task settings. Using a within-subjects design, we show activation overlap in six major brain areas during aware errors in an antisaccade task and during target detection (which were associated with longer target-to-target interval conditions in an oddball task: anterior insula, anterior cingulate, supplementary motor area, thalamus, brainstem and parietal lobe. Within subject analyses shows that the insula is engaged in both error awareness and the processing of salience, and that the anterior insula is more involved in both processes than the posterior insula. The results of a fine-grained spatial pattern overlap analysis between active clusters in the same subjects indicated that even if the anterior insula is activated for both error awareness and salience processing, the two types of processes might tend to activate non-identical neural ensembles on a finer-grained spatial level. Together, these outcomes suggest a similar functional phenomenon in the two different task settings. Error awareness and salience processing share a functional anatomy, with a tendency towards subregional dorsal and ventral specialization within the

  2. Neural and Behavioral Evidence for an Online Resetting Process in Visual Working Memory.

    Science.gov (United States)

    Balaban, Halely; Luria, Roy

    2017-02-01

    Visual working memory (VWM) guides behavior by holding a set of active representations and modifying them according to changes in the environment. This updating process relies on a unique mapping between each VWM representation and an actual object in the environment. Here, we destroyed this mapping by either presenting a coherent object but then breaking it into independent parts or presenting an object but then abruptly replacing it with a different object. This allowed us to introduce the neural marker and behavioral consequence of an online resetting process in humans' VWM. Across seven experiments, we demonstrate that this resetting process involves abandoning the old VWM contents because they no longer correspond to the objects in the environment. Then, VWM encodes the novel information and reestablishes the correspondence between the new representations and the objects. The resetting process was marked by a unique neural signature: a sharp drop in the amplitude of the electrophysiological index of VWM contents (the contralateral delay activity), presumably indicating the loss of the existent object-to-representation mappings. This marker was missing when an updating process occurred. Moreover, when tracking moving items, VWM failed to detect salient changes in the object's shape when these changes occurred during the resetting process. This happened despite the object being fully visible, presumably because the mapping between the object and a VWM representation was lost. Importantly, we show that resetting, its neural marker, and the behavioral cost it entails, are specific to situations that involve a destruction of the objects-to-representations correspondence. Visual working memory (VWM) maintains task-relevant information in an online state. Previous studies showed that VWM representations are accessed and modified after changes in the environment. Here, we show that this updating process critically depends on an ongoing mapping between the

  3. The design, fabrication, and test of a new VLSI hybrid analog-digital neural processing element

    Science.gov (United States)

    Deyong, Mark R.; Findley, Randall L.; Fields, Chris

    1992-01-01

    A hybrid analog-digital neural processing element with the time-dependent behavior of biological neurons has been developed. The hybrid processing element is designed for VLSI implementation and offers the best attributes of both analog and digital computation. Custom VLSI layout reduces the layout area of the processing element, which in turn increases the expected network density. The hybrid processing element operates at the nanosecond time scale, which enables it to produce real-time solutions to complex spatiotemporal problems found in high-speed signal processing applications. VLSI prototype chips have been designed, fabricated, and tested with encouraging results. Systems utilizing the time-dependent behavior of the hybrid processing element have been simulated and are currently in the fabrication process. Future applications are also discussed.

  4. The design, fabrication, and test of a new VLSI hybrid analog-digital neural processing element

    Science.gov (United States)

    Deyong, Mark R.; Findley, Randall L.; Fields, Chris

    1992-01-01

    A hybrid analog-digital neural processing element with the time-dependent behavior of biological neurons has been developed. The hybrid processing element is designed for VLSI implementation and offers the best attributes of both analog and digital computation. Custom VLSI layout reduces the layout area of the processing element, which in turn increases the expected network density. The hybrid processing element operates at the nanosecond time scale, which enables it to produce real-time solutions to complex spatiotemporal problems found in high-speed signal processing applications. VLSI prototype chips have been designed, fabricated, and tested with encouraging results. Systems utilizing the time-dependent behavior of the hybrid processing element have been simulated and are currently in the fabrication process. Future applications are also discussed.

  5. Modeling of batch processes using explicitly time-dependent artificial neural networks.

    Science.gov (United States)

    Ganesh, Botla; Kumar, Vadlagattu Varun; Rani, Kalipatnapu Yamuna

    2014-05-01

    A neural network architecture incorporating time dependency explicitly, proposed recently, for modeling nonlinear nonstationary dynamic systems is further developed in this paper, and three alternate configurations are proposed to represent the dynamics of batch chemical processes. The first configuration consists of L subnets, each having M inputs representing the past samples of process inputs and output; each subnet has a hidden layer with polynomial activation function; the outputs of the hidden layer are combined and acted upon by an explicitly time-dependent modulation function. The outputs of all the subnets are summed to obtain the output prediction. In the second configuration, additional weights are incorporated to obtain a more generalized model. In the third configuration, the subnets are eliminated by incorporating an additional hidden layer consisting of L nodes. Backpropagation learning algorithm is formulated for each of the proposed neural network configuration to determine the weights, the polynomial coefficients, and the modulation function parameters. The modeling capability of the proposed neural network configuration is evaluated by employing it to represent the dynamics of a batch reactor in which a consecutive reaction takes place. The results show that all the three time-varying neural networks configurations are able to represent the batch reactor dynamics accurately, and it is found that the third configuration is exhibiting comparable or better performance over the other two configurations while requiring much smaller number of parameters. The modeling ability of the third configuration is further validated by applying to modeling a semibatch polymerization reactor challenge problem. This paper illustrates that the proposed approach can be applied to represent dynamics of any batch/semibatch process.

  6. Neural Correlates of Hostile Jokes: Cognitive and Motivational Processes in Humor Appreciation

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    Yu-Chen Chan

    2016-10-01

    Full Text Available Hostile jokes provide aggressive catharsis and a feeling of superiority. Behavioral research has found that hostile jokes are perceived as funnier than non-hostile jokes. The purpose of the present study was to identify the neural correlates of the interaction between type and humor by comparing hostile jokes (HJs, non-hostile jokes (NJs, and their corresponding hostile sentences (HSs and non-hostile sentences (NSs. Hostile jokes primarily showed activation in the dorsomedial prefrontal cortex (dmPFC and midbrain compared with the corresponding hostile baseline. Conversely, non-hostile jokes primarily revealed activation in the ventromedial PFC (vmPFC, amygdala, midbrain, ventral anterior cingulate cortex, and nucleus accumbens (NAcc compared with the corresponding non-hostile baseline. These results support the critical role of the medial prefrontal cortex (mPFC for the neural correlates of social cognition and socio-emotional processing in response to different types of jokes. Moreover, the processing of hostile jokes showed increased activation in the dmPFC, which suggested cognitive operations of social motivation, whereas the processing of non-hostile jokes displayed increased activation in the vmPFC, which suggested social-affective engagement. Hostile jokes versus non-hostile jokes primarily showed increased activation in the dmPFC and midbrain, whereas non-hostile jokes versus hostile jokes primarily displayed greater activation in the amygdala and midbrain. The psychophysiological interaction (PPI analysis demonstrated functional coupling of the dmPFC-dlPFC and midbrain-dmPFC for hostile jokes and functional coupling of the vmPFC-midbrain and amygdala-midbrain-NAcc for non-hostile jokes. Surprisingly, the neural correlates of hostile jokes were not perceived as funnier than non-hostile jokes. Future studies could further investigate the neural correlates of potentially important traits of high-hostility tendencies in humor appreciation

  7. Neural processing associated with cognitive and affective Theory of Mind in adolescents and adults

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    Fontaine, Nathalie M. G.; Bird, Geoffrey; Blakemore, Sarah-Jayne; De Brito, Stephane A.; McCrory, Eamon J. P.; Viding, Essi

    2012-01-01

    Theory of Mind (ToM) is the ability to attribute thoughts, intentions and beliefs to others. This involves component processes, including cognitive perspective taking (cognitive ToM) and understanding emotions (affective ToM). This study assessed the distinction and overlap of neural processes involved in these respective components, and also investigated their development between adolescence and adulthood. While data suggest that ToM develops between adolescence and adulthood, these populations have not been compared on cognitive and affective ToM domains. Using fMRI with 15 adolescent (aged 11–16 years) and 15 adult (aged 24–40 years) males, we assessed neural responses during cartoon vignettes requiring cognitive ToM, affective ToM or physical causality comprehension (control). An additional aim was to explore relationships between fMRI data and self-reported empathy. Both cognitive and affective ToM conditions were associated with neural responses in the classic ToM network across both groups, although only affective ToM recruited medial/ventromedial PFC (mPFC/vmPFC). Adolescents additionally activated vmPFC more than did adults during affective ToM. The specificity of the mPFC/vmPFC response during affective ToM supports evidence from lesion studies suggesting that vmPFC may integrate affective information during ToM. Furthermore, the differential neural response in vmPFC between adult and adolescent groups indicates developmental changes in affective ToM processing. PMID:21467048

  8. Musical intervention enhances infants' neural processing of temporal structure in music and speech.

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    Zhao, T Christina; Kuhl, Patricia K

    2016-05-10

    Individuals with music training in early childhood show enhanced processing of musical sounds, an effect that generalizes to speech processing. However, the conclusions drawn from previous studies are limited due to the possible confounds of predisposition and other factors affecting musicians and nonmusicians. We used a randomized design to test the effects of a laboratory-controlled music intervention on young infants' neural processing of music and speech. Nine-month-old infants were randomly assigned to music (intervention) or play (control) activities for 12 sessions. The intervention targeted temporal structure learning using triple meter in music (e.g., waltz), which is difficult for infants, and it incorporated key characteristics of typical infant music classes to maximize learning (e.g., multimodal, social, and repetitive experiences). Controls had similar multimodal, social, repetitive play, but without music. Upon completion, infants' neural processing of temporal structure was tested in both music (tones in triple meter) and speech (foreign syllable structure). Infants' neural processing was quantified by the mismatch response (MMR) measured with a traditional oddball paradigm using magnetoencephalography (MEG). The intervention group exhibited significantly larger MMRs in response to music temporal structure violations in both auditory and prefrontal cortical regions. Identical results were obtained for temporal structure changes in speech. The intervention thus enhanced temporal structure processing not only in music, but also in speech, at 9 mo of age. We argue that the intervention enhanced infants' ability to extract temporal structure information and to predict future events in time, a skill affecting both music and speech processing.

  9. Intelligent sensors research using pulse-coupled neural networks for focal plane image processing

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    Tarr, Gregory L.; Carreras, Richard A.; DeHainaut, Christopher R.; Clastres, Xavier; Freyss, Laurent; Samuelides, Manuel

    1996-03-01

    An important difference between biological vision systems and their electronic counterparts is the large number of feedback signals controlling each aspect of the image collection process. For every forward path of information in the brain, from sensor to comprehension, there appears to be several neural bundles which send information back to the sensor to modify the way the information is collected. In this paper we will examine the role of such feedback signals and suggest algorithms for intelligent processing of images directly on the focal plane, using feedback. We consider first what form these signals might take and how they can be used to implement functions common to conventional image processing with the objective of moving the computation out of the digital domain and place much of its on the focal plane, or analog processing close to the focal plane. While this work falls under the general heading of artificial neural networks, it goes beyond the static processing of signals suggested by the McCulloch and Pitts model of the neuron and the Laplacian image processing suggested by Carver Mead by including the dynamics of temporal encoding in the analysis process.

  10. The neural processing of foreign-accented speech and its relationship to listener bias

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    Han-Gyol eYi

    2014-10-01

    Full Text Available Foreign-accented speech often presents a challenging listening condition. In addition to deviations from the target speech norms related to the inexperience of the nonnative speaker, listener characteristics may play a role in determining intelligibility levels. We have previously shown that an implicit visual bias for associating East Asian faces and foreignness predicts the listeners’ perceptual ability to process Korean-accented English audiovisual speech (Yi et al., 2013. Here, we examine the neural mechanism underlying the influence of listener bias to foreign faces on speech perception. In a functional magnetic resonance imaging (fMRI study, native English speakers listened to native- and Korean-accented English sentences, with or without faces. The participants’ Asian-foreign association was measured using an implicit association test (IAT, conducted outside the scanner. We found that foreign-accented speech evoked greater activity in the bilateral primary auditory cortices and the inferior frontal gyri, potentially reflecting greater computational demand. Higher IAT scores, indicating greater bias, were associated with increased BOLD response to foreign-accented speech with faces in the primary auditory cortex, the early node for spectrotemporal analysis. We conclude the following: (1 foreign-accented speech perception places greater demand on the neural systems underlying speech perception; (2 face of the talker can exaggerate the perceived foreignness of foreign-accented speech; (3 implicit Asian-foreign association is associated with decreased neural efficiency in early spectrotemporal processing.

  11. Neural dissociation of food- and money-related reward processing using an abstract incentive delay task.

    Science.gov (United States)

    Simon, Joe J; Skunde, Mandy; Wu, Mudan; Schnell, Knut; Herpertz, Sabine C; Bendszus, Martin; Herzog, Wolfgang; Friederich, Hans-Christoph

    2015-08-01

    Food is an innate reward stimulus related to energy homeostasis and survival, whereas money is considered a more general reward stimulus that gains a rewarding value through learning experiences. Although the underlying neural processing for both modalities of reward has been investigated independently from one another, a more detailed investigation of neural similarities and/or differences between food and monetary reward is still missing. Here, we investigated the neural processing of food compared with monetary-related rewards in 27 healthy, normal-weight women using functional magnetic resonance imaging. We developed a task distinguishing between the anticipation and the receipt of either abstract food or monetary reward. Both tasks activated the ventral striatum during the expectation of a reward. Compared with money, greater food-related activations were observed in prefrontal, parietal and central midline structures during the anticipation and lateral orbitofrontal cortex (lOFC) during the receipt of food reward. Furthermore, during the receipt of food reward, brain activation in the secondary taste cortex was positively related to the body mass index. These results indicate that food-dependent activations encompass to a greater extent brain regions involved in self-control and self-reflection during the anticipation and phylogenetically older parts of the lOFC during the receipt of reward.

  12. Neural Androgen Receptor Deletion Impairs the Temporal Processing of Objects and Hippocampal CA1-Dependent Mechanisms.

    Science.gov (United States)

    Picot, Marie; Billard, Jean-Marie; Dombret, Carlos; Albac, Christelle; Karameh, Nida; Daumas, Stéphanie; Hardin-Pouzet, Hélène; Mhaouty-Kodja, Sakina

    2016-01-01

    We studied the role of testosterone, mediated by the androgen receptor (AR), in modulating temporal order memory for visual objects. For this purpose, we used male mice lacking AR specifically in the nervous system. Control and mutant males were gonadectomized at adulthood and supplemented with equivalent amounts of testosterone in order to normalize their hormonal levels. We found that neural AR deletion selectively impaired the processing of temporal information for visual objects, without affecting classical object recognition or anxiety-like behavior and circulating corticosterone levels, which remained similar to those in control males. Thus, mutant males were unable to discriminate between the most recently seen object and previously seen objects, whereas their control littermates showed more interest in exploring previously seen objects. Because the hippocampal CA1 area has been associated with temporal memory for visual objects, we investigated whether neural AR deletion altered the functionality of this region. Electrophysiological analysis showed that neural AR deletion affected basal glutamate synaptic transmission and decreased the magnitude of N-methyl-D-aspartate receptor (NMDAR) activation and high-frequency stimulation-induced long-term potentiation. The impairment of NMDAR function was not due to changes in protein levels of receptor. These results provide the first evidence for the modulation of temporal processing of information for visual objects by androgens, via AR activation, possibly through regulation of NMDAR signaling in the CA1 area in male mice.

  13. Neural Androgen Receptor Deletion Impairs the Temporal Processing of Objects and Hippocampal CA1-Dependent Mechanisms.

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    Marie Picot

    Full Text Available We studied the role of testosterone, mediated by the androgen receptor (AR, in modulating temporal order memory for visual objects. For this purpose, we used male mice lacking AR specifically in the nervous system. Control and mutant males were gonadectomized at adulthood and supplemented with equivalent amounts of testosterone in order to normalize their hormonal levels. We found that neural AR deletion selectively impaired the processing of temporal information for visual objects, without affecting classical object recognition or anxiety-like behavior and circulating corticosterone levels, which remained similar to those in control males. Thus, mutant males were unable to discriminate between the most recently seen object and previously seen objects, whereas their control littermates showed more interest in exploring previously seen objects. Because the hippocampal CA1 area has been associated with temporal memory for visual objects, we investigated whether neural AR deletion altered the functionality of this region. Electrophysiological analysis showed that neural AR deletion affected basal glutamate synaptic transmission and decreased the magnitude of N-methyl-D-aspartate receptor (NMDAR activation and high-frequency stimulation-induced long-term potentiation. The impairment of NMDAR function was not due to changes in protein levels of receptor. These results provide the first evidence for the modulation of temporal processing of information for visual objects by androgens, via AR activation, possibly through regulation of NMDAR signaling in the CA1 area in male mice.

  14. Evidence for sex-specific shifting of neural processes underlying learning and memory following stress.

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    Beck, Kevin D; Luine, Victoria N

    2010-02-09

    Recent human research has been focused upon determining whether there is evidence that stress responses cause qualitative changes in neural activity such that people change their learning strategies from a spatial/contextual memory process through the hippocampus to a procedural stimulus-response process through the caudate nucleus. Moreover, interest has shifted to determining whether males and females exhibit the same type of stress-induced change in neural processing of associations. Presented is a select review of 2 different animal models that have examined how acute or chronic stressors change learning in a sex-specific manner. This is followed by a brief review of recent human studies documenting how learning and memory functions change following stressor exposure. In both cases, it is clear that ovarian hormones have a significant influence on how stress affects learning processes in females. We then examine the evidence for a role of acetylcholine, dopamine, norepinephrine, or serotonin in modulating this shifting of processing and how that may differ across sex. Conclusions drawn suggest that there may be evidence for sex-specific changes in amygdala and hippocampus neuromodulation; however, the behavioral data are still not conclusive as to whether this represents a common or sex-specific shift in how males and females process associations after stressor exposure.

  15. Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese

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    Sumit Goyal

    2012-04-01

    Full Text Available This paper presents the capability of Time–delay artificial neural network models for predicting shelf life of processed cheese. Datasets were divided into two subsets (30 for training and 6 for validation. Models with single and multi layers were developed and compared with each other. Mean Square Error, Root Mean Square Error, Coefficient of Determination and Nash -
    Sutcliffo Coefficient were used as performance evaluators, Time- delay model predicted the shelf life of processed cheese as 28.25 days, which is very close to experimental shelf life of 30 days.

  16. Image processing using pulse-coupled neural networks applications in Python

    CERN Document Server

    Lindblad, Thomas

    2013-01-01

    Image processing algorithms based on the mammalian visual cortex are powerful tools for extraction information and manipulating images. This book reviews the neural theory and translates them into digital models. Applications are given in areas of image recognition, foveation, image fusion and information extraction. The third edition reflects renewed international interest in pulse image processing with updated sections presenting several newly developed applications. This edition also introduces a suite of Python scripts that assist readers in replicating results presented in the text and to further develop their own applications.

  17. Artificial neural network approach to modeling of alcoholic fermentation of thick juice from sugar beet processing

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    Jokić Aleksandar I.

    2012-01-01

    Full Text Available In this paper the bioethanol production in batch culture by free Saccharomyces cerevisiae cells from thick juice as intermediate product of sugar beet processing was examined. The obtained results suggest that it is possible to decrease fermentation time for the cultivation medium based on thick juice with starting sugar content of 5-15 g kg-1. For the fermentation of cultivation medium based on thick juice with starting sugar content of 20 and 25 g kg-1 significant increase in ethanol content was attained during the whole fermentation process, resulting in 12.51 and 10.95 dm3 m-3 ethanol contents after 48 h, respectively. Other goals of this work were to investigate the possibilities for experimental results prediction using artificial neural networks (ANNs and to find its optimal topology. A feed-forward back-propagation artificial neural network was used to test the hypothesis. As input variables fermentation time and starting sugar content were used. Neural networks had one output value, ethanol content, yeast cell number or sugar content. There was one hidden layer and the optimal number of neurons was found to be nine for all selected network outputs. In this study transfer function was tansig and the selected learning rule was Levenberg-Marquardt. Results suggest that artificial neural networks are good prediction tool for selected network outputs. It was found that experimental results are in very good agreement with computed ones. The coefficient of determination (the R-squared was found to be 0.9997, 0.9997 and 0.9999 for ethanol content, yeast cell number and sugar content, respectively.

  18. Signal processing and neural network toolbox and its application to failure diagnosis and prognosis

    Science.gov (United States)

    Tu, Fang; Wen, Fang; Willett, Peter K.; Pattipati, Krishna R.; Jordan, Eric H.

    2001-07-01

    Many systems are comprised of components equipped with self-testing capability; however, if the system is complex involving feedback and the self-testing itself may occasionally be faulty, tracing faults to a single or multiple causes is difficult. Moreover, many sensors are incapable of reliable decision-making on their own. In such cases, a signal processing front-end that can match inference needs will be very helpful. The work is concerned with providing an object-oriented simulation environment for signal processing and neural network-based fault diagnosis and prognosis. In the toolbox, we implemented a wide range of spectral and statistical manipulation methods such as filters, harmonic analyzers, transient detectors, and multi-resolution decomposition to extract features for failure events from data collected by data sensors. Then we evaluated multiple learning paradigms for general classification, diagnosis and prognosis. The network models evaluated include Restricted Coulomb Energy (RCE) Neural Network, Learning Vector Quantization (LVQ), Decision Trees (C4.5), Fuzzy Adaptive Resonance Theory (FuzzyArtmap), Linear Discriminant Rule (LDR), Quadratic Discriminant Rule (QDR), Radial Basis Functions (RBF), Multiple Layer Perceptrons (MLP) and Single Layer Perceptrons (SLP). Validation techniques, such as N-fold cross-validation and bootstrap techniques, are employed for evaluating the robustness of network models. The trained networks are evaluated for their performance using test data on the basis of percent error rates obtained via cross-validation, time efficiency, generalization ability to unseen faults. Finally, the usage of neural networks for the prediction of residual life of turbine blades with thermal barrier coatings is described and the results are shown. The neural network toolbox has also been applied to fault diagnosis in mixed-signal circuits.

  19. Neural mechanisms of human perceptual learning: electrophysiological evidence for a two-stage process.

    Science.gov (United States)

    Hamamé, Carlos M; Cosmelli, Diego; Henriquez, Rodrigo; Aboitiz, Francisco

    2011-04-26

    Humans and other animals change the way they perceive the world due to experience. This process has been labeled as perceptual learning, and implies that adult nervous systems can adaptively modify the way in which they process sensory stimulation. However, the mechanisms by which the brain modifies this capacity have not been sufficiently analyzed. We studied the neural mechanisms of human perceptual learning by combining electroencephalographic (EEG) recordings of brain activity and the assessment of psychophysical performance during training in a visual search task. All participants improved their perceptual performance as reflected by an increase in sensitivity (d') and a decrease in reaction time. The EEG signal was acquired throughout the entire experiment revealing amplitude increments, specific and unspecific to the trained stimulus, in event-related potential (ERP) components N2pc and P3 respectively. P3 unspecific modification can be related to context or task-based learning, while N2pc may be reflecting a more specific attentional-related boosting of target detection. Moreover, bell and U-shaped profiles of oscillatory brain activity in gamma (30-60 Hz) and alpha (8-14 Hz) frequency bands may suggest the existence of two phases for learning acquisition, which can be understood as distinctive optimization mechanisms in stimulus processing. We conclude that there are reorganizations in several neural processes that contribute differently to perceptual learning in a visual search task. We propose an integrative model of neural activity reorganization, whereby perceptual learning takes place as a two-stage phenomenon including perceptual, attentional and contextual processes.

  20. Neural mechanisms of human perceptual learning: electrophysiological evidence for a two-stage process.

    Directory of Open Access Journals (Sweden)

    Carlos M Hamamé

    Full Text Available BACKGROUND: Humans and other animals change the way they perceive the world due to experience. This process has been labeled as perceptual learning, and implies that adult nervous systems can adaptively modify the way in which they process sensory stimulation. However, the mechanisms by which the brain modifies this capacity have not been sufficiently analyzed. METHODOLOGY/PRINCIPAL FINDINGS: We studied the neural mechanisms of human perceptual learning by combining electroencephalographic (EEG recordings of brain activity and the assessment of psychophysical performance during training in a visual search task. All participants improved their perceptual performance as reflected by an increase in sensitivity (d' and a decrease in reaction time. The EEG signal was acquired throughout the entire experiment revealing amplitude increments, specific and unspecific to the trained stimulus, in event-related potential (ERP components N2pc and P3 respectively. P3 unspecific modification can be related to context or task-based learning, while N2pc may be reflecting a more specific attentional-related boosting of target detection. Moreover, bell and U-shaped profiles of oscillatory brain activity in gamma (30-60 Hz and alpha (8-14 Hz frequency bands may suggest the existence of two phases for learning acquisition, which can be understood as distinctive optimization mechanisms in stimulus processing. CONCLUSIONS/SIGNIFICANCE: We conclude that there are reorganizations in several neural processes that contribute differently to perceptual learning in a visual search task. We propose an integrative model of neural activity reorganization, whereby perceptual learning takes place as a two-stage phenomenon including perceptual, attentional and contextual processes.

  1. Identifying temporal and causal contributions of neural processes underlying the Implicit Association Test (IAT

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    Chad Edward Forbes

    2012-11-01

    Full Text Available The Implicit Association Test (IAT is a popular behavioral measure that assesses the associative strength between outgroup members and stereotypical and counterstereotypical traits. Less is known, however, about the degree to which the IAT reflects automatic processing. Two studies examined automatic processing contributions to a gender-IAT using a data driven, social neuroscience approach. Performance on congruent (e.g., categorizing male names with synonyms of strength and incongruent (e.g., categorizing female names with synonyms of strength IAT blocks were separately analyzed using EEG (event-related potentials, or ERPs, and coherence; Study 1 and lesion (Study 2 methodologies. Compared to incongruent blocks, performance on congruent IAT blocks was associated with more positive ERPs that manifested in frontal and occipital regions at automatic processing speeds, occipital regions at more controlled processing speeds and was compromised by volume loss in the anterior temporal lobe, insula and medial PFC. Performance on incongruent blocks was associated with volume loss in supplementary motor areas, cingulate gyrus and a region in medial PFC similar to that found for congruent blocks. Greater coherence was found between frontal and occipital regions to the extent individuals exhibited more bias. This suggests there are separable neural contributions to congruent and incongruent blocks of the IAT but there is also a surprising amount of overlap. Given the temporal and regional neural distinctions, these results provide converging evidence that stereotypic associative strength assessed by the IAT indexes automatic processing to a degree.

  2. Prior perceptual processing enhances the effect of emotional arousal on the neural correlates of memory retrieval.

    Science.gov (United States)

    Dew, Ilana T Z; Ritchey, Maureen; LaBar, Kevin S; Cabeza, Roberto

    2014-07-01

    A fundamental idea in memory research is that items are more likely to be remembered if encoded with a semantic, rather than perceptual, processing strategy. Interestingly, this effect has been shown to reverse for emotionally arousing materials, such that perceptual processing enhances memory for emotional information or events. The current fMRI study investigated the neural mechanisms of this effect by testing how neural activations during emotional memory retrieval are influenced by the prior encoding strategy. Participants incidentally encoded emotional and neutral pictures under instructions to attend to either semantic or perceptual properties of each picture. Recognition memory was tested 2 days later. fMRI analyses yielded three main findings. First, right amygdalar activity associated with emotional memory strength was enhanced by prior perceptual processing. Second, prior perceptual processing of emotional pictures produced a stronger effect on recollection- than familiarity-related activations in the right amygdala and left hippocampus. Finally, prior perceptual processing enhanced amygdalar connectivity with regions strongly associated with retrieval success, including hippocampal/parahippocampal regions, visual cortex, and ventral parietal cortex. Taken together, the results specify how encoding orientations yield alterations in brain systems that retrieve emotional memories.

  3. Neural correlates of attentional and mnemonic processing in event-based prospective memory

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    Justin B Knight

    2010-02-01

    Full Text Available Prospective memory, or memory for realizing delayed intentions, was examined with an event-based paradigm while simultaneously measuring neural activity with high-density EEG recordings. Specifically, the neural substrates of monitoring for an event-based cue were examined, as well as those perhaps associated with the cognitive processes supporting detection of cues and fulfillment of intentions. Participants engaged in a baseline lexical decision task (LDT, followed by a LDT with an embedded prospective memory (PM component. Event-based cues were constituted by color and lexicality (red words. Behavioral data provided evidence that monitoring, or preparatory attentional processes, were used to detect cues. Analysis of the event-related potentials (ERP revealed visual attentional modulations at 140 and 220 ms post-stimulus associated with preparatory attentional processes. In addition, ERP components at 220, 350, and 400 ms post-stimulus were enhanced for intention-related items. Our results suggest preparatory attention may operate by selectively modulating processing of features related to a previously formed event-based intention, as well as provide further evidence for the proposal that dissociable component processes support the fulfillment of delayed intentions.

  4. Dissociated neural processing for decisions in managers and non-managers.

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    Svenja Caspers

    Full Text Available Functional neuroimaging studies of decision-making so far mainly focused on decisions under uncertainty or negotiation with other persons. Dual process theory assumes that, in such situations, decision making relies on either a rapid intuitive, automated or a slower rational processing system. However, it still remains elusive how personality factors or professional requirements might modulate the decision process and the underlying neural mechanisms. Since decision making is a key task of managers, we hypothesized that managers, facing higher pressure for frequent and rapid decisions than non-managers, prefer the heuristic, automated decision strategy in contrast to non-managers. Such different strategies may, in turn, rely on different neural systems. We tested managers and non-managers in a functional magnetic resonance imaging study using a forced-choice paradigm on word-pairs. Managers showed subcortical activation in the head of the caudate nucleus, and reduced hemodynamic response within the cortex. In contrast, non-managers revealed the opposite pattern. With the head of the caudate nucleus being an initiating component for process automation, these results supported the initial hypothesis, hinting at automation during decisions in managers. More generally, the findings reveal how different professional requirements might modulate cognitive decision processing.

  5. Unconscious neural processing differs with method used to render stimuli invisible

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    Sergey Victor Fogelson

    2014-06-01

    Full Text Available Visual stimuli can be kept from awareness using various methods. The extent of processing that a given stimulus receives in the absence of awareness is typically used to make claims about the role of consciousness more generally. The neural processing elicited by a stimulus, however, may also depend on the method used to keep it from awareness, and not only on whether the stimulus reaches awareness. Here we report that the method used to render an image invisible has a dramatic effect on how category information about the unseen stimulus is encoded across the human brain. We collected fMRI data while subjects viewed images of faces and tools, that were rendered invisible using either continuous flash suppression (CFS or chromatic flicker fusion (CFF. In a third condition, we presented the same images under normal fully visible viewing conditions. We found that category information about visible images could be extracted from patterns of fMRI responses throughout areas of neocortex known to be involved in face or tool processing. However, category information about stimuli kept from awareness using CFS could be recovered exclusively within occipital cortex, whereas information about stimuli kept from awareness using CFF was also decodable within temporal and frontal regions. We conclude that unconsciously presented objects are processed differently depending on how they are rendered subjectively invisible. Caution should therefore be used in making generalizations on the basis of any one method about the neural basis of consciousness or the extent of information processing without consciousness.

  6. Handedness is related to neural mechanisms underlying hemispheric lateralization of face processing

    Science.gov (United States)

    Frässle, Stefan; Krach, Sören; Paulus, Frieder Michel; Jansen, Andreas

    2016-06-01

    While the right-hemispheric lateralization of the face perception network is well established, recent evidence suggests that handedness affects the cerebral lateralization of face processing at the hierarchical level of the fusiform face area (FFA). However, the neural mechanisms underlying differential hemispheric lateralization of face perception in right- and left-handers are largely unknown. Using dynamic causal modeling (DCM) for fMRI, we aimed to unravel the putative processes that mediate handedness-related differences by investigating the effective connectivity in the bilateral core face perception network. Our results reveal an enhanced recruitment of the left FFA in left-handers compared to right-handers, as evidenced by more pronounced face-specific modulatory influences on both intra- and interhemispheric connections. As structural and physiological correlates of handedness-related differences in face processing, right- and left-handers varied with regard to their gray matter volume in the left fusiform gyrus and their pupil responses to face stimuli. Overall, these results describe how handedness is related to the lateralization of the core face perception network, and point to different neural mechanisms underlying face processing in right- and left-handers. In a wider context, this demonstrates the entanglement of structurally and functionally remote brain networks, suggesting a broader underlying process regulating brain lateralization.

  7. Sequential neural processes in abacus mental addition: an EEG and FMRI case study.

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    Yixuan Ku

    Full Text Available Abacus experts are able to mentally calculate multi-digit numbers rapidly. Some behavioral and neuroimaging studies have suggested a visuospatial and visuomotor strategy during abacus mental calculation. However, no study up to now has attempted to dissociate temporally the visuospatial neural process from the visuomotor neural process during abacus mental calculation. In the present study, an abacus expert performed the mental addition tasks (8-digit and 4-digit addends presented in visual or auditory modes swiftly and accurately. The 100% correct rates in this expert's task performance were significantly higher than those of ordinary subjects performing 1-digit and 2-digit addition tasks. ERPs, EEG source localizations, and fMRI results taken together suggested visuospatial and visuomotor processes were sequentially arranged during the abacus mental addition with visual addends and could be dissociated from each other temporally. The visuospatial transformation of the numbers, in which the superior parietal lobule was most likely involved, might occur first (around 380 ms after the onset of the stimuli. The visuomotor processing, in which the superior/middle frontal gyri were most likely involved, might occur later (around 440 ms. Meanwhile, fMRI results suggested that neural networks involved in the abacus mental addition with auditory stimuli were similar to those in the visual abacus mental addition. The most prominently activated brain areas in both conditions included the bilateral superior parietal lobules (BA 7 and bilateral middle frontal gyri (BA 6. These results suggest a supra-modal brain network in abacus mental addition, which may develop from normal mental calculation networks.

  8. Sequential neural processes in abacus mental addition: an EEG and FMRI case study.

    Science.gov (United States)

    Ku, Yixuan; Hong, Bo; Zhou, Wenjing; Bodner, Mark; Zhou, Yong-Di

    2012-01-01

    Abacus experts are able to mentally calculate multi-digit numbers rapidly. Some behavioral and neuroimaging studies have suggested a visuospatial and visuomotor strategy during abacus mental calculation. However, no study up to now has attempted to dissociate temporally the visuospatial neural process from the visuomotor neural process during abacus mental calculation. In the present study, an abacus expert performed the mental addition tasks (8-digit and 4-digit addends presented in visual or auditory modes) swiftly and accurately. The 100% correct rates in this expert's task performance were significantly higher than those of ordinary subjects performing 1-digit and 2-digit addition tasks. ERPs, EEG source localizations, and fMRI results taken together suggested visuospatial and visuomotor processes were sequentially arranged during the abacus mental addition with visual addends and could be dissociated from each other temporally. The visuospatial transformation of the numbers, in which the superior parietal lobule was most likely involved, might occur first (around 380 ms) after the onset of the stimuli. The visuomotor processing, in which the superior/middle frontal gyri were most likely involved, might occur later (around 440 ms). Meanwhile, fMRI results suggested that neural networks involved in the abacus mental addition with auditory stimuli were similar to those in the visual abacus mental addition. The most prominently activated brain areas in both conditions included the bilateral superior parietal lobules (BA 7) and bilateral middle frontal gyri (BA 6). These results suggest a supra-modal brain network in abacus mental addition, which may develop from normal mental calculation networks.

  9. Concepts in context: Processing mental state concepts with internal or external focus involves different neural systems.

    Science.gov (United States)

    Oosterwijk, Suzanne; Mackey, Scott; Wilson-Mendenhall, Christine; Winkielman, Piotr; Paulus, Martin P

    2015-01-01

    According to embodied cognition theories, concepts are contextually situated and grounded in neural systems that produce experiential states. This view predicts that processing mental state concepts recruits neural regions associated with different aspects of experience depending on the context in which people understand a concept. This neuroimaging study tested this prediction using a set of sentences that described emotional (e.g., fear, joy) and nonemotional (e.g., thinking, hunger) mental states with internal focus (i.e., focusing on bodily sensations and introspection) or external focus (i.e., focusing on expression and action). Consistent with our predictions, data suggested that the inferior frontal gyrus, a region associated with action representation, was engaged more by external than internal sentences. By contrast, the ventromedial prefrontal cortex, a region associated with the generation of internal states, was engaged more by internal emotion sentences than external sentence categories. Similar patterns emerged when we examined the relationship between neural activity and independent ratings of sentence focus. Furthermore, ratings of emotion were associated with activation in the medial prefrontal cortex, whereas ratings of activity were associated with activation in the inferior frontal gyrus. These results suggest that mental state concepts are represented in a dynamic way, using context-relevant interoceptive and sensorimotor resources.

  10. Power to punish norm violations affects the neural processes of fairness-related decision making

    Directory of Open Access Journals (Sweden)

    Xuemei eCheng

    2015-12-01

    Full Text Available Punishing norm violations is considered an important motive during rejection of unfair offers in the Ultimatum Game (UG. The present study investigates the impact of the power to punish norm violations on people’s responses to unfairness and associated neural correlates. In the UG condition participants had the power to punish norm violations, while an alternate condition, the Impunity Game (IG, was presented where participants had no power to punish norm violations since rejection only reduced the responder’s income to zero. Results showed that unfair offers were rejected more often in UG compared to IG. At the neural level, anterior insula and dorsal anterior cingulate cortex were more active when participants received and rejected unfair offers in both UG and IG. Moreover, greater dorsolateral prefrontal cortex activity was observed when participants rejected than accepted unfair offers in UG but not in IG. Ventromedial prefrontal cortex activation was higher in UG than IG when unfair offers were accepted as well as when rejecting unfair offers in IG as opposed to UG. Taken together, our results demonstrate that the power to punish norm violations affects not only people’s behavioral responses to unfairness but also the neural correlates of the fairness-related social decision-making process.

  11. Power to Punish Norm Violations Affects the Neural Processes of Fairness-Related Decision Making.

    Science.gov (United States)

    Cheng, Xuemei; Zheng, Li; Li, Lin; Guo, Xiuyan; Wang, Qianfeng; Lord, Anton; Hu, Zengxi; Yang, Guang

    2015-01-01

    Punishing norm violations is considered an important motive during rejection of unfair offers in the ultimatum game (UG). The present study investigates the impact of the power to punish norm violations on people's responses to unfairness and associated neural correlates. In the UG condition participants had the power to punish norm violations, while an alternate condition, the impunity game (IG), was presented where participants had no power to punish norm violations since rejection only reduced the responder's income to zero. Results showed that unfair offers were rejected more often in UG compared to IG. At the neural level, anterior insula and dorsal anterior cingulate cortex were more active when participants received and rejected unfair offers in both UG and IG. Moreover, greater dorsolateral prefrontal cortex activity was observed when participants rejected than accepted unfair offers in UG but not in IG. Ventromedial prefrontal cortex activation was higher in UG than IG when unfair offers were accepted as well as when rejecting unfair offers in IG as opposed to UG. Taken together, our results demonstrate that the power to punish norm violations affects not only people's behavioral responses to unfairness but also the neural correlates of the fairness-related social decision-making process.

  12. Degraded neural and behavioral processing of speech sounds in a rat model of Rett syndrome.

    Science.gov (United States)

    Engineer, Crystal T; Rahebi, Kimiya C; Borland, Michael S; Buell, Elizabeth P; Centanni, Tracy M; Fink, Melyssa K; Im, Kwok W; Wilson, Linda G; Kilgard, Michael P

    2015-11-01

    Individuals with Rett syndrome have greatly impaired speech and language abilities. Auditory brainstem responses to sounds are normal, but cortical responses are highly abnormal. In this study, we used the novel rat Mecp2 knockout model of Rett syndrome to document the neural and behavioral processing of speech sounds. We hypothesized that both speech discrimination ability and the neural response to speech sounds would be impaired in Mecp2 rats. We expected that extensive speech training would improve speech discrimination ability and the cortical response to speech sounds. Our results reveal that speech responses across all four auditory cortex fields of Mecp2 rats were hyperexcitable, responded slower, and were less able to follow rapidly presented sounds. While Mecp2 rats could accurately perform consonant and vowel discrimination tasks in quiet, they were significantly impaired at speech sound discrimination in background noise. Extensive speech training improved discrimination ability. Training shifted cortical responses in both Mecp2 and control rats to favor the onset of speech sounds. While training increased the response to low frequency sounds in control rats, the opposite occurred in Mecp2 rats. Although neural coding and plasticity are abnormal in the rat model of Rett syndrome, extensive therapy appears to be effective. These findings may help to explain some aspects of communication deficits in Rett syndrome and suggest that extensive rehabilitation therapy might prove beneficial.

  13. Artificial neural network associated to UV/Vis spectroscopy for monitoring bioreactions in biopharmaceutical processes.

    Science.gov (United States)

    Takahashi, Maria Beatriz; Leme, Jaci; Caricati, Celso Pereira; Tonso, Aldo; Fernández Núñez, Eutimio Gustavo; Rocha, José Celso

    2015-06-01

    Currently, mammalian cells are the most utilized hosts for biopharmaceutical production. The culture media for these cell lines include commonly in their composition a pH indicator. Spectroscopic techniques are used for biopharmaceutical process monitoring, among them, UV-Vis spectroscopy has found scarce applications. This work aimed to define artificial neural networks architecture and fit its parameters to predict some nutrients and metabolites, as well as viable cell concentration based on UV-Vis spectral data of mammalian cell bioprocess using phenol red in culture medium. The BHK-21 cell line was used as a mammalian cell model. Off-line spectra of supernatant samples taken from batches performed at different dissolved oxygen concentrations in two bioreactor configurations and with two pH control strategies were used to define two artificial neural networks. According to absolute errors, glutamine (0.13 ± 0.14 mM), glutamate (0.02 ± 0.02 mM), glucose (1.11 ± 1.70 mM), lactate (0.84 ± 0.68 mM) and viable cell concentrations (1.89 10(5) ± 1.90 10(5) cell/mL) were suitably predicted. The prediction error averages for monitored variables were lower than those previously reported using different spectroscopic techniques in combination with partial least squares or artificial neural network. The present work allows for UV-VIS sensor development, and decreases cost related to nutrients and metabolite quantifications.

  14. Lateral information processing by spiking neurons: a theoretical model of the neural correlate of consciousness.

    Science.gov (United States)

    Ebner, Marc; Hameroff, Stuart

    2011-01-01

    Cognitive brain functions, for example, sensory perception, motor control and learning, are understood as computation by axonal-dendritic chemical synapses in networks of integrate-and-fire neurons. Cognitive brain functions may occur either consciously or nonconsciously (on "autopilot"). Conscious cognition is marked by gamma synchrony EEG, mediated largely by dendritic-dendritic gap junctions, sideways connections in input/integration layers. Gap-junction-connected neurons define a sub-network within a larger neural network. A theoretical model (the "conscious pilot") suggests that as gap junctions open and close, a gamma-synchronized subnetwork, or zone moves through the brain as an executive agent, converting nonconscious "auto-pilot" cognition to consciousness, and enhancing computation by coherent processing and collective integration. In this study we implemented sideways "gap junctions" in a single-layer artificial neural network to perform figure/ground separation. The set of neurons connected through gap junctions form a reconfigurable resistive grid or sub-network zone. In the model, outgoing spikes are temporally integrated and spatially averaged using the fixed resistive grid set up by neurons of similar function which are connected through gap-junctions. This spatial average, essentially a feedback signal from the neuron's output, determines whether particular gap junctions between neurons will open or close. Neurons connected through open gap junctions synchronize their output spikes. We have tested our gap-junction-defined sub-network in a one-layer neural network on artificial retinal inputs using real-world images. Our system is able to perform figure/ground separation where the laterally connected sub-network of neurons represents a perceived object. Even though we only show results for visual stimuli, our approach should generalize to other modalities. The system demonstrates a moving sub-network zone of synchrony, within which the contents of

  15. Neural basis of first and second language processing of sentence-level linguistic prosody.

    Science.gov (United States)

    Gandour, Jackson; Tong, Yunxia; Talavage, Thomas; Wong, Donald; Dzemidzic, Mario; Xu, Yisheng; Li, Xiaojian; Lowe, Mark

    2007-02-01

    A fundamental question in multilingualism is whether the neural substrates are shared or segregated for the two or more languages spoken by polyglots. This study employs functional MRI to investigate the neural substrates underlying the perception of two sentence-level prosodic phenomena that occur in both Mandarin Chinese (L1) and English (L2): sentence focus (sentence-initial vs. -final position of contrastive stress) and sentence type (declarative vs. interrogative modality). Late-onset, medium proficiency Chinese-English bilinguals were asked to selectively attend to either sentence focus or sentence type in paired three-word sentences in both L1 and L2 and make speeded-response discrimination judgments. L1 and L2 elicited highly overlapping activations in frontal, temporal, and parietal lobes. Furthermore, region of interest analyses revealed that for both languages the sentence focus task elicited a leftward asymmetry in the supramarginal gyrus; both tasks elicited a rightward asymmetry in the mid-portion of the middle frontal gyrus. A direct comparison between L1 and L2 did not show any difference in brain activation in the sentence type task. In the sentence focus task, however, greater activation for L2 than L1 occurred in the bilateral anterior insula and superior frontal sulcus. The sentence focus task also elicited a leftward asymmetry in the posterior middle temporal gyrus for L1 only. Differential activation patterns are attributed primarily to disparities between L1 and L2 in the phonetic manifestation of sentence focus. Such phonetic divergences lead to increased computational demands for processing L2. These findings support the view that L1 and L2 are mediated by a unitary neural system despite late age of acquisition, although additional neural resources may be required in task-specific circumstances for unequal bilinguals.

  16. Neural processing of visual information under interocular suppression: A critical review

    Directory of Open Access Journals (Sweden)

    Philipp eSterzer

    2014-05-01

    Full Text Available When dissimilar stimuli are presented to the two eyes, only one stimulus dominates at a time while the other stimulus is invisible due to interocular suppression. When both stimuli are equally potent in competing for awareness, perception alternates spontaneously between the two stimuli, a phenomenon called binocular rivalry. However, when one stimulus is much stronger, e.g., due to higher contrast, the weaker stimulus can be suppressed for prolonged periods of time. A technique that has recently become very popular for the investigation of unconscious visual processing is continuous flash suppression (CFS: High-contrast dynamic patterns shown to one eye can render a low-contrast stimulus shown to the other eye invisible for up to minutes. Studies using CFS have produced new insights but also controversies regarding the types of visual information that can be processed unconsciously as well as the neural sites and the relevance of such unconscious processing. Here, we review the current state of knowledge in regard to neural processing of interocularly suppressed information. Focusing on recent neuroimaging findings, we discuss whether and to what degree such suppressed visual information is processed at early and more advanced levels of the visual processing hierarchy. We review controversial findings related to the influence of attention on early visual processing under interocular suppression, the putative differential roles of dorsal and ventral areas in unconscious object processing, and evidence suggesting privileged unconscious processing of emotional and other socially relevant information. On a more general note, we discuss methodological and conceptual issues, from practical issues of how unawareness of a stimulus is assessed to the overarching question of what constitutes an adequate operational definition of unawareness. Finally, we propose approaches for future research to resolve current controversies in this exciting research area.

  17. Neural processing of visual information under interocular suppression: a critical review.

    Science.gov (United States)

    Sterzer, Philipp; Stein, Timo; Ludwig, Karin; Rothkirch, Marcus; Hesselmann, Guido

    2014-01-01

    When dissimilar stimuli are presented to the two eyes, only one stimulus dominates at a time while the other stimulus is invisible due to interocular suppression. When both stimuli are equally potent in competing for awareness, perception alternates spontaneously between the two stimuli, a phenomenon called binocular rivalry. However, when one stimulus is much stronger, e.g., due to higher contrast, the weaker stimulus can be suppressed for prolonged periods of time. A technique that has recently become very popular for the investigation of unconscious visual processing is continuous flash suppression (CFS): High-contrast dynamic patterns shown to one eye can render a low-contrast stimulus shown to the other eye invisible for up to minutes. Studies using CFS have produced new insights but also controversies regarding the types of visual information that can be processed unconsciously as well as the neural sites and the relevance of such unconscious processing. Here, we review the current state of knowledge in regard to neural processing of interocularly suppressed information. Focusing on recent neuroimaging findings, we discuss whether and to what degree such suppressed visual information is processed at early and more advanced levels of the visual processing hierarchy. We review controversial findings related to the influence of attention on early visual processing under interocular suppression, the putative differential roles of dorsal and ventral areas in unconscious object processing, and evidence suggesting privileged unconscious processing of emotional and other socially relevant information. On a more general note, we discuss methodological and conceptual issues, from practical issues of how unawareness of a stimulus is assessed to the overarching question of what constitutes an adequate operational definition of unawareness. Finally, we propose approaches for future research to resolve current controversies in this exciting research area.

  18. Event segmentation in a visual language: neural bases of processing American Sign Language predicates.

    Science.gov (United States)

    Malaia, Evie; Ranaweera, Ruwan; Wilbur, Ronnie B; Talavage, Thomas M

    2012-02-15

    Motion capture studies show that American Sign Language (ASL) signers distinguish end-points in telic verb signs by means of marked hand articulator motion, which rapidly decelerates to a stop at the end of these signs, as compared to atelic signs (Malaia and Wilbur, in press). Non-signers also show sensitivity to velocity in deceleration cues for event segmentation in visual scenes (Zacks et al., 2010; Zacks et al., 2006), introducing the question of whether the neural regions used by ASL signers for sign language verb processing might be similar to those used by non-signers for event segmentation. The present study investigated the neural substrate of predicate perception and linguistic processing in ASL. Observed patterns of activation demonstrate that Deaf signers process telic verb signs as having higher phonological complexity as compared to atelic verb signs. These results, together with previous neuroimaging data on spoken and sign languages (Shetreet et al., 2010; Emmorey et al., 2009), illustrate a route for how a prominent perceptual-kinematic feature used for non-linguistic event segmentation might come to be processed as an abstract linguistic feature due to sign language exposure.

  19. Improved object segmentation using Markov random fields, artificial neural networks, and parallel processing techniques

    Science.gov (United States)

    Foulkes, Stephen B.; Booth, David M.

    1997-07-01

    Object segmentation is the process by which a mask is generated which identifies the area of an image which is occupied by an object. Many object recognition techniques depend on the quality of such masks for shape and underlying brightness information, however, segmentation remains notoriously unreliable. This paper considers how the image restoration technique of Geman and Geman can be applied to the improvement of object segmentations generated by a locally adaptive background subtraction technique. Also presented is how an artificial neural network hybrid, consisting of a single layer Kohonen network with each of its nodes connected to a different multi-layer perceptron, can be used to approximate the image restoration process. It is shown that the restoration techniques are very well suited for parallel processing and in particular the artificial neural network hybrid has the potential for near real time image processing. Results are presented for the detection of ships in SPOT panchromatic imagery and the detection of vehicles in infrared linescan images, these being a fair representation of the wider class of problem.

  20. The effect of stereotypical primes on the neural processing of racially ambiguous faces.

    Science.gov (United States)

    Dickter, Cheryl L; Kittel, Julie A

    2012-01-01

    Previous research has demonstrated that an early attentional component of the event-related potential (ERP), the P2, is sensitive to the distinction between the processing of racial outgroup and ingroup faces but may not be sensitive to the distinction between racially ambiguous and ingroup faces. Recent behavioral work, however, has suggested that contextual information may affect the processing of racially ambiguous faces. Thus, the first goal of this study was to examine whether the early neural processing of racially ambiguous faces would be affected by primed stereotypes. White college student participants (n = 29) completed a task in which they racially categorized monoracial Black and White faces and racially ambiguous Black-White morphs. These faces were preceded by positive and negative Black and White stereotypical primes. Results indicated that P2 amplitude to the racially ambiguous faces was moderated by the valence of the primes such that negative primes led to greater neural processing of the racially ambiguous faces than positive primes. Furthermore, the extent to which P2 amplitude was affected by prime valence was moderated by individual differences in preference for structure and categorical thinking, as well as comfort with ambiguity.

  1. Body posture and gender impact neural processing of power-related words.

    Science.gov (United States)

    Bailey, April H; Kelly, Spencer D

    2016-09-29

    Judging others' power facilitates successful social interaction. Both gender and body posture have been shown to influence judgments of another's power. However, little is known about how these two cues interact when they conflict or how they influence early processing. The present study investigated this question during very early processing of power-related words using event-related potentials (ERPs). Participants viewed images of women and men in dominant and submissive postures that were quickly followed by dominant or submissive words. Gender and posture both modulated neural responses in the N2 latency range to dominant words, but for submissive words they had little impact. Thus, in the context of dual-processing theories of person perception, information extracted from both behavior (i.e., posture) and from category membership (i.e., gender) are recruited side-by-side to impact word processing.

  2. Simulation of aging process of lead frame copper alloy by an artificial neural network

    Institute of Scientific and Technical Information of China (English)

    苏娟华; 董企铭; 刘平; 李贺军; 康布熙

    2003-01-01

    The aging hardening process makes it possible to get higher hardness and electrical conductivity of lead frame copper alloy.The process has only been studied empirically by trial-and-error method so far.The use of a supervised artificial neural network(ANN)was proposed to model the non-linear relationship between parameters of aging process with respect to hardness and conductivity properties of Cu-Cr-Zr alloy.The improved model was developed by the Levenberg-Marquardt training algorithm.A basic repository on the domain knowledge of aging process was established via sufficient data mining by the network.The results show that the ANN system is effective and successful for predicting and analyzing the properties of Cu-Cr-Zr alloy.

  3. Experience modulates the psychophysiological response of airborne warfighters during a tactical combat parachute jump.

    Science.gov (United States)

    Clemente-Suárez, Vicente Javier; de la Vega, Ricardo; Robles-Pérez, José Juan; Lautenschlaeger, Mario; Fernández-Lucas, Jesús

    2016-12-01

    We aimed to analyse the effect of experience level in the psychophysiological response and specific fine motor skills of novel and expert parachute warfighters during a tactical combat parachute jump. We analysed blood oxygen saturation, heart rate, salivary cortisol, blood glucose, lactate and creatinkinase, leg strength, isometric hand-grip strength, cortical arousal, specific fine motor skills and cognitive anxiety, somatic anxiety and self-confident before and after a tactical combat parachute jump in 40 warfighters divided in two group, novel (n=17) and expert group (n=23). Novels presented a higher heart rate, lactate, cognitive anxiety, somatic anxiety and a lower self-confident than experts during the jump. We concluded that experience level has a direct effect on the psychophysiological response since novel paratroopers presented a higher psychophysiological response than compared to the expert ones, however this result neither affected the specific fine motor skills nor the muscle structure after a tactical combat parachute jump.

  4. Sensitive periods for the functional specialization of the neural system for human face processing.

    Science.gov (United States)

    Röder, Brigitte; Ley, Pia; Shenoy, Bhamy H; Kekunnaya, Ramesh; Bottari, Davide

    2013-10-15

    The aim of the study was to identify possible sensitive phases in the development of the processing system for human faces. We tested the neural processing of faces in 11 humans who had been blind from birth and had undergone cataract surgery between 2 mo and 14 y of age. Pictures of faces and houses, scrambled versions of these pictures, and pictures of butterflies were presented while event-related potentials were recorded. Participants had to respond to the pictures of butterflies (targets) only. All participants, even those who had been blind from birth for several years, were able to categorize the pictures and to detect the targets. In healthy controls and in a group of visually impaired individuals with a history of developmental or incomplete congenital cataracts, the well-known enhancement of the N170 (negative peak around 170 ms) event-related potential to faces emerged, but a face-sensitive response was not observed in humans with a history of congenital dense cataracts. By contrast, this group showed a similar N170 response to all visual stimuli, which was indistinguishable from the N170 response to faces in the controls. The face-sensitive N170 response has been associated with the structural encoding of faces. Therefore, these data provide evidence for the hypothesis that the functional differentiation of category-specific neural representations in humans, presumably involving the elaboration of inhibitory circuits, is dependent on experience and linked to a sensitive period. Such functional specialization of neural systems seems necessary to archive high processing proficiency.

  5. Attenuated Neural Processing of Risk in Young Adults at Risk for Stimulant Dependence.

    Directory of Open Access Journals (Sweden)

    Martina Reske

    Full Text Available Approximately 10% of young adults report non-medical use of stimulants (cocaine, amphetamine, methylphenidate, which puts them at risk for the development of dependence. This fMRI study investigates whether subjects at early stages of stimulant use show altered decision making processing.158 occasional stimulants users (OSU and 50 comparison subjects (CS performed a "risky gains" decision making task during which they could select safe options (cash in 20 cents or gamble them for double or nothing in two consecutive gambles (win or lose 40 or 80 cents, "risky decisions". The primary analysis focused on risky versus safe decisions. Three secondary analyses were conducted: First, a robust regression examined the effect of lifetime exposure to stimulants and marijuana; second, subgroups of OSU with >1000 (n = 42, or <50 lifetime marijuana uses (n = 32, were compared to CS with <50 lifetime uses (n = 46 to examine potential marijuana effects; third, brain activation associated with behavioral adjustment following monetary losses was probed.There were no behavioral differences between groups. OSU showed attenuated activation across risky and safe decisions in prefrontal cortex, insula, and dorsal striatum, exhibited lower anterior cingulate cortex (ACC and dorsal striatum activation for risky decisions and greater inferior frontal gyrus activation for safe decisions. Those OSU with relatively more stimulant use showed greater dorsal ACC and posterior insula attenuation. In comparison, greater lifetime marijuana use was associated with less neural differentiation between risky and safe decisions. OSU who chose more safe responses after losses exhibited similarities with CS relative to those preferring risky options.Individuals at risk for the development of stimulant use disorders presented less differentiated neural processing of risky and safe options. Specifically, OSU show attenuated brain response in regions critical for performance monitoring

  6. Neural Reward Processing Mediates the Relationship between Insomnia Symptoms and Depression in Adolescence.

    Science.gov (United States)

    Casement, Melynda D; Keenan, Kate E; Hipwell, Alison E; Guyer, Amanda E; Forbes, Erika E

    2016-02-01

    Emerging evidence suggests that insomnia may disrupt reward-related brain function-a potentially important factor in the development of depressive disorder. Adolescence may be a period during which such disruption is especially problematic given the rise in the incidence of insomnia and ongoing development of neural systems that support reward processing. The present study uses longitudinal data to test the hypothesis that disruption of neural reward processing is a mechanism by which insomnia symptoms-including nocturnal insomnia symptoms (NIS) and nonrestorative sleep (NRS)-contribute to depressive symptoms in adolescent girls. Participants were 123 adolescent girls and their caregivers from an ongoing longitudinal study of precursors to depression across adolescent development. NIS and NRS were assessed annually from ages 9 to 13 years. Girls completed a monetary reward task during a functional MRI scan at age 16 years. Depressive symptoms were assessed at ages 16 and 17 years. Multivariable regression tested the prospective associations between NIS and NRS, neural response during reward anticipation, and the mean number of depressive symptoms (omitting sleep problems). NRS, but not NIS, during early adolescence was positively associated with late adolescent dorsal medial prefrontal cortex (dmPFC) response to reward anticipation and depressive symptoms. DMPFC response mediated the relationship between early adolescent NRS and late adolescent depressive symptoms. These results suggest that NRS may contribute to depression by disrupting reward processing via altered activity in a region of prefrontal cortex involved in affective control. The results also support the mechanistic differentiation of NIS and NRS. © 2016 Associated Professional Sleep Societies, LLC.

  7. Neural Correlates of Hostile Jokes: Cognitive and Motivational Processes in Humor Appreciation.

    Science.gov (United States)

    Chan, Yu-Chen; Liao, Yi-Jun; Tu, Cheng-Hao; Chen, Hsueh-Chih

    2016-01-01

    Hostile jokes (HJs) provide aggressive catharsis and a feeling of superiority. Behavioral research has found that HJs are perceived as funnier than non-hostile jokes (NJs). The purpose of the present study was to identify the neural correlates of the interaction between type and humor by comparing HJs, NJs, and their corresponding hostile sentences (HSs) and non-hostile sentences (NSs). HJs primarily showed activation in the dorsomedial prefrontal cortex (dmPFC) and midbrain compared with the corresponding hostile baseline. Conversely, NJs primarily revealed activation in the ventromedial PFC (vmPFC), amygdala, midbrain, ventral anterior cingulate cortex, and nucleus accumbens (NAcc) compared with the corresponding non-hostile baseline. These results support the critical role of the medial PFC (mPFC) for the neural correlates of social cognition and socio-emotional processing in response to different types of jokes. Moreover, the processing of HJs showed increased activation in the dmPFC, which suggested cognitive operations of social motivation, whereas the processing of NJs displayed increased activation in the vmPFC, which suggested social-affective engagement. HJs versus NJs primarily showed increased activation in the dmPFC and midbrain, whereas NJs versus HJs primarily displayed greater activation in the amygdala and midbrain. The psychophysiological interaction (PPI) analysis demonstrated functional coupling of the dmPFC-dlPFC and midbrain-dmPFC for HJs and functional coupling of the vmPFC-midbrain and amygdala-midbrain-NAcc for NJs. Surprisingly, HJs were not perceived as funnier than NJs. Future studies could further investigate the neural correlates of potentially important traits of high-hostility tendencies in humor appreciation based on the psychoanalytic and superiority theories of humor.

  8. Automatic detection of thermal damage in grinding process by artificial neural network

    Directory of Open Access Journals (Sweden)

    Fábio Romano Lofrano Dotto

    2003-12-01

    Full Text Available This work aims to develop an intelligent system for detecting the workpiece burn in the surface grinding process by utilizing a multi-perceptron neural network trained to generalize the process and, in turn, obtnaing the burning threshold. In general, the burning occurrence in grinding process can be detected by the DPO and FKS parameters. However, these ones were not efficient at the grinding conditions used in this work. Acoustic emission and electric power of the grinding wheel drive motor are the input variable and the output variable is the burning occurrence to the neural network. In the experimental work was employed one type of steel (ABNT-1045 annealed and one type of grinding wheel referred to as TARGA model ART 3TG80.3 NVHB.Esse trabalho tem por objetivo o desenvolvimento de um sistema inteligente para detecção da queima no processo de retificação tangencial plana através da utilização de uma rede neural perceptron multi camadas, treinada para generalizar o processo e, conseqüentemente, obter o limiar de queima. Em geral, a ocorrência da queima no processo de retificação pode ser detectada pelos parâmetros DPO e FKS. Porém esses parâmetros não são eficientes nas condições de usinagem usadas nesse trabalho. Os sinais de emissão acústica e potência elétrica do motor de acionamento do rebolo são variáveis de entrada e a variável de saída é a ocorrência da queima. No trabalho experimental, foram empregados um tipo de aço (ABNT 1045 temperado e um tipo de rebolo denominado TARGA, modelo ART 3TG80.3 NVHB.

  9. Symmetric sequence processing in a recurrent neural network model with a synchronous dynamics

    Energy Technology Data Exchange (ETDEWEB)

    Metz, F L; Theumann, W K [Instituto de Fisica, Universidade Federal do Rio Grande do Sul, Caixa Postal 15051, 91501-970 Porto Alegre (Brazil)], E-mail: fernando@itf.fys.kuleuven.be, E-mail: theumann@if.ufrgs.br

    2009-09-25

    The synchronous dynamics and the stationary states of a recurrent attractor neural network model with competing synapses between symmetric sequence processing and Hebbian pattern reconstruction are studied in this work allowing for the presence of a self-interaction for each unit. Phase diagrams of stationary states are obtained exhibiting phases of retrieval, symmetric and period-two cyclic states as well as correlated and frozen-in states, in the absence of noise. The frozen-in states are destabilized by synaptic noise and well-separated regions of correlated and cyclic states are obtained. Excitatory or inhibitory self-interactions yield enlarged phases of fixed-point or cyclic behaviour.

  10. Inside the brain of an elite athlete: the neural processes that support high achievement in sports.

    Science.gov (United States)

    Yarrow, Kielan; Brown, Peter; Krakauer, John W

    2009-08-01

    Events like the World Championships in athletics and the Olympic Games raise the public profile of competitive sports. They may also leave us wondering what sets the competitors in these events apart from those of us who simply watch. Here we attempt to link neural and cognitive processes that have been found to be important for elite performance with computational and physiological theories inspired by much simpler laboratory tasks. In this way we hope to inspire neuroscientists to consider how their basic research might help to explain sporting skill at the highest levels of performance.

  11. Detection of Common Warfighting Symbology (MIL-STD-2525) Air Symbols

    Science.gov (United States)

    2011-09-01

    UNCLASSIFIED Detection of Common Warfighting Symbology (MIL- STD -2525) Air Symbols Kingsley Fletcher, Ashley Arnold and Susan Cockshell...Standard Bravo version (MIL- STD -2525B) is a contractual requirement for a number of Australian defence projects. To evaluate how well MIL- STD -2525B...presence of each MIL- STD -2525B air affiliation symbol when symbol overlap and icon presence were manipulated. Detection efficiency and accuracy were

  12. Experience modulates the psychophysiological response of airborne warfighters during a tactical combat parachute jump

    OpenAIRE

    Clemente Suárez, Vicente Javier; Vega Marcos, Ricardo de la; Robles Pérez, José Juan; Lautenschlaeger, Mario; Fernández Lucas, Jesús

    2016-01-01

    We aimed to analyse the effect of experience level in the psychophysiological response and specific fine motor skills of novel and expert parachute warfighters during a tactical combat parachute jump. We analysed blood oxygen saturation, heart rate, salivary cortisol, blood glucose, lactate and creatinkinase, leg strength, isometric hand-grip strength, cortical arousal, specific fine motor skills and cognitive anxiety, somatic anxiety and self-confident before and after a tactical combat para...

  13. Real-time Monitoring of our Warfighters Health State: The Good, The Bad, and The Ugly

    Science.gov (United States)

    2008-04-05

    time Monitoring of our Warfighters Health State: The Good , The Bad , and The Ugly 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6...Status Physiological Monitor Concept 98 The GOOD Technologies & a solution framework have been greatly advanced. Health state monitoring is no...Medical Monitoring Telemetry System – In Action Results – Physiology, Real Time Display Th e GO OD The BAD Unique challenges make the creation and

  14. Sleep Logistics as a Force Multiplier: An Analysis of Reported Fatigue Factors From Southwest Asia Warfighters

    Science.gov (United States)

    2004-09-01

    adherence to sleep / rest plans. A second instrument, known as a Wrist Actigraphy Monitor8 (WAM), was used by a control group at NPS in order to test the...with operational missions, 8 Sometimes referred to as a Wrist Activity Monitor. 47 it was agreed that the study of warfighter sleep habits would be...the WAM to a computer and may be expressed numerically and graphically, aiding in sleep /wake history analysis. The activity measurements recorded

  15. Driving Innovation to Support the Warfighter: Additive Manufacturing Initiatives Within the Defense Logistics Agency

    Science.gov (United States)

    2016-12-01

    43 Defense AT&L: November-December 2016 Driving Innovation to Support the Warfighter Additive Manufacturing Initiatives Within the Defense...AM) in the commercial sector, but it is also intense for the Defense Logistics Agency (DLA), which hopes to capitalize on the promise of innovation ...to improve readiness and sup- port to the military. As it tracks AM through the Gartner Hype Cycle, which depicts phases that innovations move

  16. COMT Val108/158 Met Genotype Affects Neural but not Cognitive Processing in Healthy Individuals

    Science.gov (United States)

    Need, Anna C.; LaBar, Kevin S.; Waters-Metenier, Sheena; Cirulli, Elizabeth T.; Kragel, James; Goldstein, David B.; Cabeza, Roberto

    2010-01-01

    The relationship between cognition and a functional polymorphism in the catechol-O-methlytransferase (COMT) gene, val108/158met, is one of debate in the literature. Furthermore, based on the dopaminergic differences associated with the COMT val108/158met genotype, neural differences during cognition may be present, regardless of genotypic differences in cognitive performance. To investigate these issues the current study aimed to 1) examine the effects of COMT genotype using a large sample of healthy individuals (n = 496–1218) and multiple cognitive measures, and using a subset of the sample (n = 22), 2) examine whether COMT genotype effects medial temporal lobe (MTL) and frontal activity during successful relational memory processing, and 3) investigate group differences in functional connectivity associated with successful relational memory processing. Results revealed no significant group difference in cognitive performance between COMT genotypes in any of the 19 cognitive measures. However, in the subset sample, COMT val homozygotes exhibited significantly decreased MTL and increased prefrontal activity during both successful relational encoding and retrieval, and reduced connectivity between these regions compared with met homozygotes. Taken together, the results suggest that although the COMT val108/158met genotype has no effect on cognitive behavioral measures in healthy individuals, it is associated with differences in neural process underlying cognitive output. PMID:19641018

  17. Neural pathways in processing of sexual arousal: a dynamic causal modeling study.

    Science.gov (United States)

    Seok, J-W; Park, M-S; Sohn, J-H

    2016-09-01

    Three decades of research have investigated brain processing of visual sexual stimuli with neuroimaging methods. These researchers have found that sexual arousal stimuli elicit activity in a broad neural network of cortical and subcortical brain areas that are known to be associated with cognitive, emotional, motivational and physiological components. However, it is not completely understood how these neural systems integrate and modulated incoming information. Therefore, we identify cerebral areas whose activations were correlated with sexual arousal using event-related functional magnetic resonance imaging and used the dynamic causal modeling method for searching the effective connectivity about the sexual arousal processing network. Thirteen heterosexual males were scanned while they passively viewed alternating short trials of erotic and neutral pictures on a monitor. We created a subset of seven models based on our results and previous studies and selected a dominant connectivity model. Consequently, we suggest a dynamic causal model of the brain processes mediating the cognitive, emotional, motivational and physiological factors of human male sexual arousal. These findings are significant implications for the neuropsychology of male sexuality.

  18. Neural correlates of anticipation and processing of performance feedback in social anxiety.

    Science.gov (United States)

    Heitmann, Carina Y; Peterburs, Jutta; Mothes-Lasch, Martin; Hallfarth, Marlit C; Böhme, Stephanie; Miltner, Wolfgang H R; Straube, Thomas

    2014-12-01

    Fear of negative evaluation, such as negative social performance feedback, is the core symptom of social anxiety. The present study investigated the neural correlates of anticipation and perception of social performance feedback in social anxiety. High (HSA) and low (LSA) socially anxious individuals were asked to give a speech on a personally relevant topic and received standardized but appropriate expert performance feedback in a succeeding experimental session in which neural activity was measured during anticipation and presentation of negative and positive performance feedback concerning the speech performance, or a neutral feedback-unrelated control condition. HSA compared to LSA subjects reported greater anxiety during anticipation of negative feedback. Functional magnetic resonance imaging results showed deactivation of medial prefrontal brain areas during anticipation of negative feedback relative to the control and the positive condition, and medial prefrontal and insular hyperactivation during presentation of negative as well as positive feedback in HSA compared to LSA subjects. The results indicate distinct processes underlying feedback processing during anticipation and presentation of feedback in HSA as compared to LSA individuals. In line with the role of the medial prefrontal cortex in self-referential information processing and the insula in interoception, social anxiety seems to be associated with lower self-monitoring during feedback anticipation, and an increased self-focus and interoception during feedback presentation, regardless of feedback valence.

  19. Structure Data Processing and Damage Identification Based on Wavelet and Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Zhanfeng Gao

    2011-10-01

    Full Text Available Structural health monitoring is a multi-disciplinary integrated technology, mainly including signal processing and structural damage detection. The aim of the data processing is to obtain the useful information from large volumes of raw data containing noises. In order to obtain the useful information concerned, denoising method and feature extraction technique based on Wavelet analysis is studied. An improved wavelet thresholding algorithm to eliminate the noise for vibration signals is proposed. The results of analysis show that the method based on Wavelet is not only feasible to signal de-noising, but also valuable and effective to detect the health status of bridge structure. In order to detect the damage status of the structure, a multi-layer neural network models based on the BP algorithm is designed. The model is trained with the data from an engineering beam to filter different transfer function, train function and the unit number of hidden layer by contrast to determine the best network model for damage detection. At last, the model is used to detect the damage of cable-stayed bridge with an improved method of data pre-processing using the square rate of change in frequency as input date of network. The structural damage identification results show that the BP neural network model is easy to identify the damage by the changing of vibration modal frequency and effective to reflect the injury status of the existing structure.

  20. Functional Roles of Neural Preparatory Processes in a Cued Stroop Task Revealed by Linking Electrophysiology with Behavioral Performance.

    Directory of Open Access Journals (Sweden)

    Chao Wang

    Full Text Available It is well established that cuing facilitates behavioral performance and that different aspects of instructional cues evoke specific neural preparatory processes in cued task-switching paradigms. To deduce the functional role of these neural preparatory processes the majority of studies vary aspects of the experimental paradigm and describe how these variations alter markers of neural preparatory processes. Although these studies provide important insights, they also have notable limitations, particularly in terms of understanding the causal or functional relationship of neural markers to cognitive and behavioral processes. In this study, we sought to address these limitations and uncover the functional roles of neural processes by examining how variability in the amplitude of neural preparatory processes predicts behavioral performance to subsequent stimuli. To achieve this objective 16 young adults were recruited to perform a cued Stroop task while their brain activity was measured using high-density electroencephalography. Four temporally overlapping but functionally and topographically distinct cue-triggered event related potentials (ERPs were identified: 1 A left-frontotemporal negativity (250-700 ms that was positively associated with word-reading performance; 2 a midline-frontal negativity (450-800 ms that was positively associated with color-naming and incongruent performance; 3 a left-frontal negativity (450-800 ms that was positively associated with switch trial performance; and 4 a centroparietal positivity (450-800 ms that was positively associated with performance for almost all trial types. These results suggest that at least four dissociable cognitive processes are evoked by instructional cues in the present task, including: 1 domain-specific task facilitation; 2 switch-specific task-set reconfiguration; 3 preparation for response conflict; and 4 proactive attentional control. Examining the relationship between ERPs and behavioral

  1. Neural decoding reveals impaired face configural processing in the right fusiform face area of individuals with developmental prosopagnosia.

    Science.gov (United States)

    Zhang, Jiedong; Liu, Jia; Xu, Yaoda

    2015-01-28

    Most of human daily social interactions rely on the ability to successfully recognize faces. Yet ∼2% of the human population suffers from face blindness without any acquired brain damage [this is also known as developmental prosopagnosia (DP) or congenital prosopagnosia]). Despite the presence of severe behavioral face recognition deficits, surprisingly, a majority of DP individuals exhibit normal face selectivity in the right fusiform face area (FFA), a key brain region involved in face configural processing. This finding, together with evidence showing impairments downstream from the right FFA in DP individuals, has led some to argue that perhaps the right FFA is largely intact in DP individuals. Using fMRI multivoxel pattern analysis, here we report the discovery of a neural impairment in the right FFA of DP individuals that may play a critical role in mediating their face-processing deficits. In seven individuals with DP, we discovered that, despite the right FFA's preference for faces and it showing decoding for the different face parts, it exhibited impaired face configural decoding and did not contain distinct neural response patterns for the intact and the scrambled face configurations. This abnormality was not present throughout the ventral visual cortex, as normal neural decoding was found in an adjacent object-processing region. To our knowledge, this is the first direct neural evidence showing impaired face configural processing in the right FFA in individuals with DP. The discovery of this neural impairment provides a new clue to our understanding of the neural basis of DP.

  2. Neural network model for the on-line monitoring of a crystallization process

    Directory of Open Access Journals (Sweden)

    Guardani R.

    2001-01-01

    Full Text Available This paper presents the results of the application of a recently developed technique, based on Neural Networks (NN, in the recognition of angular distribution patterns of light scattered by particles in suspension, for the purpose of estimating concentration and crystal size distribution (CSD in a precipitation process based on the addition of antisolvent (a model system consisting of sodium chloride, water and ethanol. In the first step, in NN model was fitted, using particles with different size distributions and concentrations. Then the model was used to monitor the process, thus enabling a fast and reliable estimation of supersaturation and CSD. Such information, which is difficult to obtain by any other means, can be used in the study of fundamental aspects of crystallization and precipitation processes.

  3. Neural changes associated with semantic processing in healthy aging despite intact behavioral performance.

    Science.gov (United States)

    Lacombe, Jacinthe; Jolicoeur, Pierre; Grimault, Stephan; Pineault, Jessica; Joubert, Sven

    2015-10-01

    Semantic memory recruits an extensive neural network including the left inferior prefrontal cortex (IPC) and the left temporoparietal region, which are involved in semantic control processes, as well as the anterior temporal lobe region (ATL) which is considered to be involved in processing semantic information at a central level. However, little is known about the underlying neuronal integrity of the semantic network in normal aging. Young and older healthy adults carried out a semantic judgment task while their cortical activity was recorded using magnetoencephalography (MEG). Despite equivalent behavioral performance, young adults activated the left IPC to a greater extent than older adults, while the latter group recruited the temporoparietal region bilaterally and the left ATL to a greater extent than younger adults. Results indicate that significant neuronal changes occur in normal aging, mainly in regions underlying semantic control processes, despite an apparent stability in performance at the behavioral level.

  4. Common and dissociable neural correlates associated with component processes of inductive reasoning.

    Science.gov (United States)

    Jia, Xiuqin; Liang, Peipeng; Lu, Jie; Yang, Yanhui; Zhong, Ning; Li, Kuncheng

    2011-06-15

    The ability to draw numerical inductive reasoning requires two key cognitive processes, identification and extrapolation. This study aimed to identify the neural correlates of both component processes of numerical inductive reasoning using event-related fMRI. Three kinds of tasks: rule induction (RI), rule induction and application (RIA), and perceptual judgment (Jud) were solved by twenty right-handed adults. Our results found that the left superior parietal lobule (SPL) extending into the precuneus and left dorsolateral prefrontal cortex (DLPFC) were commonly recruited in the two components. It was also observed that the fronto-parietal network was more specific to identification, whereas the striatal-thalamic network was more specific to extrapolation. The findings suggest that numerical inductive reasoning is mediated by the coordination of multiple brain areas including the prefrontal, parietal, and subcortical regions, of which some are more specific to demands on only one of these two component processes, whereas others are sensitive to both.

  5. Fuzzy Neural Network Model of 4-CBA Concentration for Industrial Purified Terephthalic Acid Oxidation Process

    Institute of Scientific and Technical Information of China (English)

    刘瑞兰; 苏宏业; 牟盛静; 贾涛; 陈渭泉; 褚健

    2004-01-01

    A fuzzy neural network (FNN) model is developed to predict the 4-CBA concentration of the oxidation unit in purified terephthalic acid process. Several technologies are used to deal with the process data before modeling.First,a set of preliminary input variables is selected according to prior knowledge and experience. Secondly,a method based on the maximum correlation coefficient is proposed to detect the dead time between the process variables and response variables. Finally, the fuzzy curve method is used to reduce the unimportant input variables.The simulation results based on industrial data show that the relative error range of the FNN model is narrower than that of the American Oil Company (AMOCO) model. Furthermore, the FNN model can predict the trend of the 4-CBA concentration more accurately.

  6. Multidimensional analysis of the abnormal neural oscillations associated with lexical processing in schizophrenia.

    Science.gov (United States)

    Xu, Tingting; Stephane, Massoud; Parhi, Keshab K

    2013-04-01

    The neural mechanisms of language abnormalities, the core symptoms in schizophrenia, remain unclear. In this study, a new experimental paradigm, combining magnetoencephalography (MEG) techniques and machine intelligence methodologies, was designed to gain knowledge about the frequency, brain location, and time of occurrence of the neural oscillations that are associated with lexical processing in schizophrenia. The 248-channel MEG recordings were obtained from 12 patients with schizophrenia and 10 healthy controls, during a lexical processing task, where the patients discriminated correct from incorrect lexical stimuli that were visually presented. Event-related desynchronization/synchronization (ERD/ERS) was computed along the frequency, time, and space dimensions combined, that resulted in a large spectral-spatial-temporal ERD/ERS feature set. Machine intelligence techniques were then applied to select a small subset of oscillation patterns that are abnormal in patients with schizophrenia, according to their discriminating power in patient and control classification. Patients with schizophrenia showed abnormal ERD/ERS patterns during both lexical encoding and post-encoding periods. The top-ranked features were located at the occipital and left frontal-temporal areas, and covered a wide frequency range, including δ (1-4 Hz), α (8-12 Hz), β (12-32 Hz), and γ (32-48 Hz) bands. These top features could discriminate the patient group from the control group with 90.91% high accuracy, which demonstrates significant brain oscillation abnormalities in patients with schizophrenia at the specific frequency, time, and brain location indicated by these top features. As neural oscillation abnormality may be due to the mechanisms of the disease, the spectral, spatial, and temporal content of the discriminating features can offer useful information for helping understand the physiological basis of the language disorder in schizophrenia, as well as the pathology of the

  7. The neural processes underlying perceptual decision making in humans: recent progress and future directions.

    Science.gov (United States)

    Kelly, Simon P; O'Connell, Redmond G

    2015-01-01

    In the last two decades, animal neurophysiology research has made great strides towards explaining how the brain can enable adaptive action in the face of noisy sensory information. In particular, this work has identified neural signals that perform the role of a 'decision variable' which integrates sensory information in favor of a particular outcome up to an action-triggering threshold, consistent with long-standing predictions from mathematical psychology. This has provoked an intensive search for similar neural processes at work in the human brain. In this paper we review the progress that has been made in tracing the dynamics of perceptual decision formation in humans using functional imaging and electrophysiology. We highlight some of the limitations that non-invasive recording techniques place on our ability to make definitive judgments regarding the role that specific signals play in decision making. Finally, we provide an overview of our own work in this area which has focussed on two perceptual tasks - intensity change detection and motion discrimination - performed under continuous-monitoring conditions, and highlight the insights gained thus far. We show that through simple paradigm design features such as avoiding sudden intensity transients at evidence onset, a neural instantiation of the theoretical decision variable can be directly traced in the form of a centro-parietal positivity (CPP) in the standard event-related potential (ERP). We recapitulate evidence for the domain-general nature of the CPP process, being divorced from the sensory and motor requirements of the task, and re-plot data of both tasks highlighting this aspect as well as its relationship to decision outcome and reaction time. We discuss the implications of these findings for mechanistically principled research on normal and abnormal decision making in humans.

  8. Transcranial direct current stimulation effects on neural processing in post-stroke aphasia.

    Science.gov (United States)

    Darkow, Robert; Martin, Andrew; Würtz, Anna; Flöel, Agnes; Meinzer, Marcus

    2017-03-01

    Non-invasive transcranial direct current stimulation (tDCS) can enhance recovery after stroke. However, fundamental knowledge about how tDCS impacts neural processing in the lesioned human brain is currently lacking. In the present study, it was investigated how tDCS modulates brain function in patients with post-stroke language impairment (aphasia). In a cross-over, randomized trial, patients named pictures of common objects during functional magnetic resonance imaging (fMRI). Concurrently, excitatory (anodal-) or sham-tDCS (1 mA, 20 min, or 30 s, respectively) was administered to the left primary motor cortex, a montage with demonstrated potential to improve aphasic language. By choosing stimuli that could reliable be named by the patients, the authors aimed to derive a pure measure of stimulation effects that was independent of treatment or performance effects and to assess how tDCS interacts with the patients' residual language network. Univariate fMRI data analysis revealed reduced activity in domain-general regions mediating high-level cognitive control during anodal-tDCS. Independent component functional network analysis demonstrated selectively increased language network activity and an inter-correlated shift from higher to lower frequency bands, indicative of increased within-network communication. Compared with healthy controls, anodal-tDCS resulted in overall "normalization" of brain function in the patients. These results demonstrate for the first time how tDCS modulates neural processing in stroke patients. Such information is crucial to assure that behavioral treatments targeting specific neural circuits overlap with regions that are modulated by tDCS, thereby maximizing stimulation effects during therapy. Hum Brain Mapp 38:1518-1531, 2017. © 2016 Wiley Periodicals, Inc.

  9. A potential neural substrate for processing functional classes of complex acoustic signals.

    Directory of Open Access Journals (Sweden)

    Isabelle George

    Full Text Available Categorization is essential to all cognitive processes, but identifying the neural substrates underlying categorization processes is a real challenge. Among animals that have been shown to be able of categorization, songbirds are particularly interesting because they provide researchers with clear examples of categories of acoustic signals allowing different levels of recognition, and they possess a system of specialized brain structures found only in birds that learn to sing: the song system. Moreover, an avian brain nucleus that is analogous to the mammalian secondary auditory cortex (the caudo-medial nidopallium, or NCM has recently emerged as a plausible site for sensory representation of birdsong, and appears as a well positioned brain region for categorization of songs. Hence, we tested responses in this non-primary, associative area to clear and distinct classes of songs with different functions and social values, and for a possible correspondence between these responses and the functional aspects of songs, in a highly social songbird species: the European starling. Our results clearly show differential neuronal responses to the ethologically defined classes of songs, both in the number of neurons responding, and in the response magnitude of these neurons. Most importantly, these differential responses corresponded to the functional classes of songs, with increasing activation from non-specific to species-specific and from species-specific to individual-specific sounds. These data therefore suggest a potential neural substrate for sorting natural communication signals into categories, and for individual vocal recognition of same-species members. Given the many parallels that exist between birdsong and speech, these results may contribute to a better understanding of the neural bases of speech.

  10. Neural dynamics of morphological processing in spoken word comprehension: Laterality and automaticity

    Directory of Open Access Journals (Sweden)

    Caroline M. Whiting

    2013-11-01

    Full Text Available Rapid and automatic processing of grammatical complexity is argued to take place during speech comprehension, engaging a left-lateralised fronto-temporal language network. Here we address how neural activity in these regions is modulated by the grammatical properties of spoken words. We used combined magneto- and electroencephalography (MEG, EEG to delineate the spatiotemporal patterns of activity that support the recognition of morphologically complex words in English with inflectional (-s and derivational (-er affixes (e.g. bakes, baker. The mismatch negativity (MMN, an index of linguistic memory traces elicited in a passive listening paradigm, was used to examine the neural dynamics elicited by morphologically complex words. Results revealed an initial peak 130-180 ms after the deviation point with a major source in left superior temporal cortex. The localisation of this early activation showed a sensitivity to two grammatical properties of the stimuli: 1 the presence of morphological complexity, with affixed words showing increased left-laterality compared to non-affixed words; and 2 the grammatical category, with affixed verbs showing greater left-lateralisation in inferior frontal gyrus compared to affixed nouns (bakes vs. beaks. This automatic brain response was additionally sensitive to semantic coherence (the meaning of the stem vs. the meaning of the whole form in fronto-temporal regions. These results demonstrate that the spatiotemporal pattern of neural activity in spoken word processing is modulated by the presence of morphological structure, predominantly engaging the left-hemisphere’s fronto-temporal language network, and does not require focused attention on the linguistic input.

  11. Age differences in the neural correlates of novelty processing: The effects of item-relatedness.

    Science.gov (United States)

    Bowman, Caitlin R; Dennis, Nancy A

    2015-07-01

    Past research finds that age-related increases in false recognitions are a key contributor to age-related memory decline, suggesting that older adults have difficulty in correctly distinguishing between new and old information, particularly when new items at retrieval are semantically or perceptually related to items from encoding. However, little work has examined the neural mechanisms older adults engage to avoid false recognitions and successfully identify information as novel. In the present study, young and older adults were scanned during a retrieval task in which new items were exemplars from studied categories (related lures) or unstudied categories (unrelated lures) in order to detect age-related differences in the neural correlates of related and unrelated novelty processing. Results showed that, unlike young adults, older adults did not differentially recruit regions such as the anterior cingulate and bilateral middle/inferior temporal gyrus to capitalize on the salient categorical differences in unrelated items. Likewise, older adults did not differentially recruit regions of early visual cortex or anterior hippocampus, suggesting that older adults have difficulty using item-specific details to make successful related novelty decisions. Instead, older adults recruited bilateral ventrolateral prefrontal cortex differentially for successful novelty processing and particularly for related novelty processing. Overall, results suggest that age deficits in novelty processing may arise because older adults process related and unrelated lures similarly and do not capitalize on categorical or item-specific properties of novel items. Similar to aging patterns in memory retrieval, results also showed that older adults have the strongest novelty success activity in lateral PFC regions associated with control and monitoring processes. This article is part of a Special Issue entitled SI: Memory & Aging.

  12. "Thinking about not-thinking": neural correlates of conceptual processing during Zen meditation.

    Directory of Open Access Journals (Sweden)

    Giuseppe Pagnoni

    Full Text Available Recent neuroimaging studies have identified a set of brain regions that are metabolically active during wakeful rest and consistently deactivate in a variety the performance of demanding tasks. This "default network" has been functionally linked to the stream of thoughts occurring automatically in the absence of goal-directed activity and which constitutes an aspect of mental behavior specifically addressed by many meditative practices. Zen meditation, in particular, is traditionally associated with a mental state of full awareness but reduced conceptual content, to be attained via a disciplined regulation of attention and bodily posture. Using fMRI and a simplified meditative condition interspersed with a lexical decision task, we investigated the neural correlates of conceptual processing during meditation in regular Zen practitioners and matched control subjects. While behavioral performance did not differ between groups, Zen practitioners displayed a reduced duration of the neural response linked to conceptual processing in regions of the default network, suggesting that meditative training may foster the ability to control the automatic cascade of semantic associations triggered by a stimulus and, by extension, to voluntarily regulate the flow of spontaneous mentation.

  13. "Thinking about not-thinking": neural correlates of conceptual processing during Zen meditation.

    Science.gov (United States)

    Pagnoni, Giuseppe; Cekic, Milos; Guo, Ying

    2008-09-03

    Recent neuroimaging studies have identified a set of brain regions that are metabolically active during wakeful rest and consistently deactivate in a variety the performance of demanding tasks. This "default network" has been functionally linked to the stream of thoughts occurring automatically in the absence of goal-directed activity and which constitutes an aspect of mental behavior specifically addressed by many meditative practices. Zen meditation, in particular, is traditionally associated with a mental state of full awareness but reduced conceptual content, to be attained via a disciplined regulation of attention and bodily posture. Using fMRI and a simplified meditative condition interspersed with a lexical decision task, we investigated the neural correlates of conceptual processing during meditation in regular Zen practitioners and matched control subjects. While behavioral performance did not differ between groups, Zen practitioners displayed a reduced duration of the neural response linked to conceptual processing in regions of the default network, suggesting that meditative training may foster the ability to control the automatic cascade of semantic associations triggered by a stimulus and, by extension, to voluntarily regulate the flow of spontaneous mentation.

  14. A New Efficient Hybrid Intelligent Model for Biodegradation Process of DMP with Fuzzy Wavelet Neural Networks

    Science.gov (United States)

    Huang, Mingzhi; Zhang, Tao; Ruan, Jujun; Chen, Xiaohong

    2017-01-01

    A new efficient hybrid intelligent approach based on fuzzy wavelet neural network (FWNN) was proposed for effectively modeling and simulating biodegradation process of Dimethyl phthalate (DMP) in an anaerobic/anoxic/oxic (AAO) wastewater treatment process. With the self learning and memory abilities of neural networks (NN), handling uncertainty capacity of fuzzy logic (FL), analyzing local details superiority of wavelet transform (WT) and global search of genetic algorithm (GA), the proposed hybrid intelligent model can extract the dynamic behavior and complex interrelationships from various water quality variables. For finding the optimal values for parameters of the proposed FWNN, a hybrid learning algorithm integrating an improved genetic optimization and gradient descent algorithm is employed. The results show, compared with NN model (optimized by GA) and kinetic model, the proposed FWNN model have the quicker convergence speed, the higher prediction performance, and smaller RMSE (0.080), MSE (0.0064), MAPE (1.8158) and higher R2 (0.9851) values. which illustrates FWNN model simulates effluent DMP more accurately than the mechanism model.

  15. Individual differences in speech-in-noise perception parallel neural speech processing and attention in preschoolers.

    Science.gov (United States)

    Thompson, Elaine C; Woodruff Carr, Kali; White-Schwoch, Travis; Otto-Meyer, Sebastian; Kraus, Nina

    2017-02-01

    From bustling classrooms to unruly lunchrooms, school settings are noisy. To learn effectively in the unwelcome company of numerous distractions, children must clearly perceive speech in noise. In older children and adults, speech-in-noise perception is supported by sensory and cognitive processes, but the correlates underlying this critical listening skill in young children (3-5 year olds) remain undetermined. Employing a longitudinal design (two evaluations separated by ∼12 months), we followed a cohort of 59 preschoolers, ages 3.0-4.9, assessing word-in-noise perception, cognitive abilities (intelligence, short-term memory, attention), and neural responses to speech. Results reveal changes in word-in-noise perception parallel changes in processing of the fundamental frequency (F0), an acoustic cue known for playing a role central to speaker identification and auditory scene analysis. Four unique developmental trajectories (speech-in-noise perception groups) confirm this relationship, in that improvements and declines in word-in-noise perception couple with enhancements and diminishments of F0 encoding, respectively. Improvements in word-in-noise perception also pair with gains in attention. Word-in-noise perception does not relate to strength of neural harmonic representation or short-term memory. These findings reinforce previously-reported roles of F0 and attention in hearing speech in noise in older children and adults, and extend this relationship to preschool children. Copyright © 2016 Elsevier B.V. All rights reserved.

  16. Impaired neural reward processing in children and adolescents with reactive attachment disorder: A pilot study.

    Science.gov (United States)

    Mizuno, Kei; Takiguchi, Shinichiro; Yamazaki, Mika; Asano, Mizuki; Kato, Shiho; Kuriyama, Kikuko; Watanabe, Yasuyoshi; Sadato, Norihiro; Tomoda, Akemi

    2015-10-01

    Reactive attachment disorder (RAD) is characterized by markedly disturbed and developmentally inappropriate social relatedness due to parental maltreatment. RAD patients often display a high number of comorbid attention deficit/hyperactivity disorder (ADHD) symptoms, and certain RAD symptoms are difficult to discriminate from ADHD. One of the core characteristics of ADHD is a decrease in neural reward processing due to dopamine dysfunction. The aim of the present study was to determine whether the brain activity involved in reward processing in RAD patients is impaired in comparison with ADHD patients and typically developed controls. Five RAD patients, 17 typically developed (TD) controls and 17 ADHD patients aged 10-16 years performed tasks with high and low monetary reward while undergoing functional magnetic resonance imaging. ADHD patients were tested before and after 3 months treatment with osmotic release oral system-methylphenidate. Before treatment, ADHD patients showed that striatal and thalamus activities only in the tasks with low monetary reward were lower than TD controls. RAD patients showed decrease in activity of the caudate, putamen and thalamus during both the high and low monetary reward conditions in comparison with all the other groups. In RAD patients, the activity of the putamen was associated with the severity of posttraumatic stress and overt dissociation. Reward sensitivity was markedly decreased in children and adolescents with RAD, as evidenced by a diminished neural response during reward perception. This suggests that dopaminergic dysfunction exists in these patients, and may inform future dopaminergic treatment strategies for RAD.

  17. A New Efficient Hybrid Intelligent Model for Biodegradation Process of DMP with Fuzzy Wavelet Neural Networks

    Science.gov (United States)

    Huang, Mingzhi; Zhang, Tao; Ruan, Jujun; Chen, Xiaohong

    2017-01-01

    A new efficient hybrid intelligent approach based on fuzzy wavelet neural network (FWNN) was proposed for effectively modeling and simulating biodegradation process of Dimethyl phthalate (DMP) in an anaerobic/anoxic/oxic (AAO) wastewater treatment process. With the self learning and memory abilities of neural networks (NN), handling uncertainty capacity of fuzzy logic (FL), analyzing local details superiority of wavelet transform (WT) and global search of genetic algorithm (GA), the proposed hybrid intelligent model can extract the dynamic behavior and complex interrelationships from various water quality variables. For finding the optimal values for parameters of the proposed FWNN, a hybrid learning algorithm integrating an improved genetic optimization and gradient descent algorithm is employed. The results show, compared with NN model (optimized by GA) and kinetic model, the proposed FWNN model have the quicker convergence speed, the higher prediction performance, and smaller RMSE (0.080), MSE (0.0064), MAPE (1.8158) and higher R2 (0.9851) values. which illustrates FWNN model simulates effluent DMP more accurately than the mechanism model. PMID:28120889

  18. Linear-phase delay filters for ultra-low-power signal processing in neural recording implants.

    Science.gov (United States)

    Gosselin, Benoit; Sawan, Mohamad; Kerherve, Eric

    2010-06-01

    We present the design and implementation of linear-phase delay filters for ultra-low-power signal processing in neural recording implants. We use these filters as low-distortion delay elements along with an automatic biopotential detector to perform integral waveform extraction and efficient power management. The presented delay elements are realized employing continuous-time OTA-C filters featuring 9th-order equiripple transfer functions with constant group delay. Such analog delay enables processing neural waveforms with reduced overhead compared to a digital delay since it does not requires sampling and digitization. It uses an allpass transfer function for achieving wider constant-delay bandwidth than all-pole does. Two filters realizations are compared for implementing the delay element: the Cascaded structure and the Inverse follow-the-leader feedback filter. Their respective strengths and drawbacks are assessed by modeling parasitics and non-idealities of OTAs, and by transistor-level simulations. A budget of 200 nA is used in both filters. Experimental measurements with the chosen filter topology are presented and discussed.

  19. Textural identification of carbonate rocks by image processing and neural network: Methodology proposal and examples

    Science.gov (United States)

    Marmo, Roberto; Amodio, Sabrina; Tagliaferri, Roberto; Ferreri, Vittoria; Longo, Giuseppe

    2005-06-01

    Using more than 1000 thin section photos of ancient (Phanerozoic) carbonates from different marine environments (pelagic to shallow-water) a new numerical methodology, based on digitized images of thin sections, is proposed here. In accordance with the Dunham classification, it allows the user to automatically identify carbonate textures unaffected by post-depositional modifications (recrystallization, dolomitization, meteoric dissolution and so on). The methodology uses, as input, 256 grey-tone digital image and by image processing gives, as output, a set of 23 values of numerical features measured on the whole image including the "white areas" (calcite cement). A multi-layer perceptron neural network takes as input this features and gives, as output, the estimated class. We used 532 images of thin sections to train the neural network, whereas to test the methodology we used 268 images taken from the same photo collection and 215 images from San Lorenzello carbonate sequence (Matese Mountains, southern Italy), Early Cretaceous in age. This technique has shown 93.3% and 93.5% of accuracy to classify automatically textures of carbonate rocks using digitized images on the 268 and 215 test sets, respectively. Therefore, the proposed methodology is a further promising application to the geosciences allowing carbonate textures of many thin sections to be identified in a rapid and accurate way. A MATLAB-based computer code has been developed for the processing and display of images.

  20. Second language processing shows increased native-like neural responses after months of no exposure.

    Science.gov (United States)

    Morgan-Short, Kara; Finger, Ingrid; Grey, Sarah; Ullman, Michael T

    2012-01-01

    Although learning a second language (L2) as an adult is notoriously difficult, research has shown that adults can indeed attain native language-like brain processing and high proficiency levels. However, it is important to then retain what has been attained, even in the absence of continued exposure to the L2--particularly since periods of minimal or no L2 exposure are common. This event-related potential (ERP) study of an artificial language tested performance and neural processing following a substantial period of no exposure. Adults learned to speak and comprehend the artificial language to high proficiency with either explicit, classroom-like, or implicit, immersion-like training, and then underwent several months of no exposure to the language. Surprisingly, proficiency did not decrease during this delay. Instead, it remained unchanged, and there was an increase in native-like neural processing of syntax, as evidenced by several ERP changes--including earlier, more reliable, and more left-lateralized anterior negativities, and more robust P600s, in response to word-order violations. Moreover, both the explicitly and implicitly trained groups showed increased native-like ERP patterns over the delay, indicating that such changes can hold independently of L2 training type. The results demonstrate that substantial periods with no L2 exposure are not necessarily detrimental. Rather, benefits may ensue from such periods of time even when there is no L2 exposure. Interestingly, both before and after the delay the implicitly trained group showed more native-like processing than the explicitly trained group, indicating that type of training also affects the attainment of native-like processing in the brain. Overall, the findings may be largely explained by a combination of forgetting and consolidation in declarative and procedural memory, on which L2 grammar learning appears to depend. The study has a range of implications, and suggests a research program with

  1. Second language processing shows increased native-like neural responses after months of no exposure.

    Directory of Open Access Journals (Sweden)

    Kara Morgan-Short

    Full Text Available Although learning a second language (L2 as an adult is notoriously difficult, research has shown that adults can indeed attain native language-like brain processing and high proficiency levels. However, it is important to then retain what has been attained, even in the absence of continued exposure to the L2--particularly since periods of minimal or no L2 exposure are common. This event-related potential (ERP study of an artificial language tested performance and neural processing following a substantial period of no exposure. Adults learned to speak and comprehend the artificial language to high proficiency with either explicit, classroom-like, or implicit, immersion-like training, and then underwent several months of no exposure to the language. Surprisingly, proficiency did not decrease during this delay. Instead, it remained unchanged, and there was an increase in native-like neural processing of syntax, as evidenced by several ERP changes--including earlier, more reliable, and more left-lateralized anterior negativities, and more robust P600s, in response to word-order violations. Moreover, both the explicitly and implicitly trained groups showed increased native-like ERP patterns over the delay, indicating that such changes can hold independently of L2 training type. The results demonstrate that substantial periods with no L2 exposure are not necessarily detrimental. Rather, benefits may ensue from such periods of time even when there is no L2 exposure. Interestingly, both before and after the delay the implicitly trained group showed more native-like processing than the explicitly trained group, indicating that type of training also affects the attainment of native-like processing in the brain. Overall, the findings may be largely explained by a combination of forgetting and consolidation in declarative and procedural memory, on which L2 grammar learning appears to depend. The study has a range of implications, and suggests a research

  2. Deep architecture neural network-based real-time image processing for image-guided radiotherapy.

    Science.gov (United States)

    Mori, Shinichiro

    2017-08-01

    To develop real-time image processing for image-guided radiotherapy, we evaluated several neural network models for use with different imaging modalities, including X-ray fluoroscopic image denoising. Setup images of prostate cancer patients were acquired with two oblique X-ray fluoroscopic units. Two types of residual network were designed: a convolutional autoencoder (rCAE) and a convolutional neural network (rCNN). We changed the convolutional kernel size and number of convolutional layers for both networks, and the number of pooling and upsampling layers for rCAE. The ground-truth image was applied to the contrast-limited adaptive histogram equalization (CLAHE) method of image processing. Network models were trained to keep the quality of the output image close to that of the ground-truth image from the input image without image processing. For image denoising evaluation, noisy input images were used for the training. More than 6 convolutional layers with convolutional kernels >5×5 improved image quality. However, this did not allow real-time imaging. After applying a pair of pooling and upsampling layers to both networks, rCAEs with >3 convolutions each and rCNNs with >12 convolutions with a pair of pooling and upsampling layers achieved real-time processing at 30 frames per second (fps) with acceptable image quality. Use of our suggested network achieved real-time image processing for contrast enhancement and image denoising by the use of a conventional modern personal computer. Copyright © 2017 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

  3. The Timing of Vision – How Neural Processing Links to Different Temporal Dynamics

    Directory of Open Access Journals (Sweden)

    Timothée eMasquelier

    2011-06-01

    Full Text Available We review here our recent attempts to model the neural correlates of visual perception with biologically-inspired networks of spiking neurons, emphasizing the dynamical aspects. Experimental evidence suggests distinct processing modes depending on the type of task the visual system is engaged in. A first mode deals with rapidly extracting the glimpse of a visual scene in the first 100ms after its presentation. The promptness of this process points to mainly feedforward processing, which may be shaped by Spike Timing-Dependent Plasticity. Our simulations confirm the plausibility and efficiency of such a scheme. A second mode can be engaged whenever one needs to perform finer perceptual discrimination through evidence accumulation. Here, our simulations, together with theoretical considerations, show how predominantly local recurrent connections and long neural time-constants enable the integration and build-up of firing rates on this timescale. A third mode, involving additional top-down attentional signals, is relevant for more complex visual scene processing. In the model, as in the brain, these top-down attentional signals shape visual processing by biasing the competition between different neuron pools. The winning pools may not only have a higher firing rate, but also more synchronous oscillatory activity. This fourth mode, oscillatory activity, leads to faster reaction times and enhanced information transfers in the model. This has indeed been observed experimentally. Moreover, oscillatory activity can encode information in the spike phases with respect to the oscillatory cycle. This phenomenon is referred to as Phase-of-Firing Coding, and experimental evidence for it is accumulating in the visual system. Simulations show that this code can again be efficiently decoded by STDP. Future work should focus on continuous natural vision, bio-inspired hardware vision systems, and novel experimental paradigms to further distinguish current modeling

  4. Using artificial neural networks to model extrusion processes for the manufacturing of polymeric micro-tubes

    Science.gov (United States)

    Mekras, N.; Artemakis, I.

    2012-09-01

    In this paper a methodology and an application example are presented aiming to show how Artificial Neural Networks (ANNs) can be used to model manufacturing processes when mathematical models are missing or are not applicable e.g. due to the micro- & nano-scaling, due to non-conventional processes, etc. Besides the ANNs methodology, the results of a Software System developed will be presented, which was used to create ANNs models for micro & nano manufacturing processes. More specifically results of a specific application example will be presented, concerning the modeling of extrusion processes for polymeric micro-tubes. ANNs models are capable for modeling manufacturing processes as far as adequate experimental and/or historical data of processes' inputs & outputs are available for their training. The POLYTUBES ANNs models have been trained and tested with experimental data records of process' inputs and outputs concerning a micro-extrusion process of polymeric micro-tubes for several materials such as: COC, PC, PET, PETG, PP and PVDF. The main ANN model of the extrusion application example has 3 inputs and 9 outputs. The inputs are: tube's inner & outer diameters, and the material density. The model outputs are 9 process parameters, which correspond to the specific inputs e.g. process temperature, die inner & outer diameters, extrusion pressure, draw speed etc. The training of the ANN model was completed, when the errors for the model's outputs, which expressed the difference between the training target values and the ANNs outputs, were minimized to acceptable levels. After the training, the micro-extrusion ANN is capable to simulate the process and can be used to calculate model's outputs, which are the process parameters for any new set of inputs. By this way a satisfactory functional approximation of the whole process is achieved. This research work has been supported by the EU FP7 NMP project POLYTUBES.

  5. Risky decisions and their consequences: neural processing by boys with Antisocial Substance Disorder.

    Directory of Open Access Journals (Sweden)

    Thomas J Crowley

    Full Text Available BACKGROUND: Adolescents with conduct and substance problems ("Antisocial Substance Disorder" (ASD repeatedly engage in risky antisocial and drug-using behaviors. We hypothesized that, during processing of risky decisions and resulting rewards and punishments, brain activation would differ between abstinent ASD boys and comparison boys. METHODOLOGY/PRINCIPAL FINDINGS: We compared 20 abstinent adolescent male patients in treatment for ASD with 20 community controls, examining rapid event-related blood-oxygen-level-dependent (BOLD responses during functional magnetic resonance imaging. In 90 decision trials participants chose to make either a cautious response that earned one cent, or a risky response that would either gain 5 cents or lose 10 cents; odds of losing increased as the game progressed. We also examined those times when subjects experienced wins, or separately losses, from their risky choices. We contrasted decision trials against very similar comparison trials requiring no decisions, using whole-brain BOLD-response analyses of group differences, corrected for multiple comparisons. During decision-making ASD boys showed hypoactivation in numerous brain regions robustly activated by controls, including orbitofrontal and dorsolateral prefrontal cortices, anterior cingulate, basal ganglia, insula, amygdala, hippocampus, and cerebellum. While experiencing wins, ASD boys had significantly less activity than controls in anterior cingulate, temporal regions, and cerebellum, with more activity nowhere. During losses ASD boys had significantly more activity than controls in orbitofrontal cortex, dorsolateral prefrontal cortex, brain stem, and cerebellum, with less activity nowhere. CONCLUSIONS/SIGNIFICANCE: Adolescent boys with ASD had extensive neural hypoactivity during risky decision-making, coupled with decreased activity during reward and increased activity during loss. These neural patterns may underlie the dangerous, excessive, sustained

  6. Motor learning and cross-limb transfer rely upon distinct neural adaptation processes.

    Science.gov (United States)

    Stöckel, Tino; Carroll, Timothy J; Summers, Jeffery J; Hinder, Mark R

    2016-08-01

    Performance benefits conferred in the untrained limb after unilateral motor practice are termed cross-limb transfer. Although the effect is robust, the neural mechanisms remain incompletely understood. In this study we used noninvasive brain stimulation to reveal that the neural adaptations that mediate motor learning in the trained limb are distinct from those that underlie cross-limb transfer to the opposite limb. Thirty-six participants practiced a ballistic motor task with their right index finger (150 trials), followed by intermittent theta-burst stimulation (iTBS) applied to the trained (contralateral) primary motor cortex (cM1 group), the untrained (ipsilateral) M1 (iM1 group), or the vertex (sham group). After stimulation, another 150 training trials were undertaken. Motor performance and corticospinal excitability were assessed before motor training, pre- and post-iTBS, and after the second training bout. For all groups, training significantly increased performance and excitability of the trained hand, and performance, but not excitability, of the untrained hand, indicating transfer at the level of task performance. The typical facilitatory effect of iTBS on MEPs was reversed for cM1, suggesting homeostatic metaplasticity, and prior performance gains in the trained hand were degraded, suggesting that iTBS interfered with learning. In stark contrast, iM1 iTBS facilitated both performance and excitability for the untrained hand. Importantly, the effects of cM1 and iM1 iTBS on behavior were exclusive to the hand contralateral to stimulation, suggesting that adaptations within the untrained M1 contribute to cross-limb transfer. However, the neural processes that mediate learning in the trained hemisphere vs. transfer in the untrained hemisphere appear distinct.

  7. Disentangling cognitive processes from neural activation and psychic mechanisms: the example of empathy.

    Science.gov (United States)

    Guilé, Jean-Marc

    2010-12-01

    Empathy processes can be explored within a three-level model distinguishing neuronal, cognitive and intra-psychic operating levels. Cognitive and intra-psychic processes need not to be collapsed. Neural systems involved in empathy are described through neuroimaging and event-related potential (ERP) studies. On the cognitive level, empathy is threefold: procedural, semantic and biographical. Automatically activated since birth, procedural empathy processes are deeply enrooted in visuo-motor response capacities and responsible for automatic mimicry. These processes might rely on a prior sensori-motor contagion system. Semantic empathy parallels language development and expresses connexion between words, meaning and emotion. Biographical emerges later in life and corresponds to the interweaving of personal experience with feelings and words, together with a capacity to bridge with the others' experiences. On the intra-psychic level, defence mechanisms as well as identification processes, depicted from a subjective and interpersonal standpoint, are corresponding, without being similar, to empathetic processes described in cognitive neuroscience studies. Studies on semantic empathy need to control for the participants biographical information and concomitant memory activation. The interface between cognitive and intra-psychic processes needs to be further investigated.

  8. Cognitive function predicts neural activity associated with pre-attentive temporal processing.

    Science.gov (United States)

    Foster, Shannon M; Kisley, Michael A; Davis, Hasker P; Diede, Nathaniel T; Campbell, Alana M; Davalos, Deana B

    2013-01-01

    Temporal processing, or processing time-related information, appears to play a significant role in a variety of vital psychological functions. One of the main confounds to assessing the neural underpinnings and cognitive correlates of temporal processing is that behavioral measures of timing are generally confounded by other supporting cognitive processes, such as attention. Further, much theorizing in this field has relied on findings from clinical populations (e.g., individuals with schizophrenia) known to have temporal processing deficits. In this study, we attempted to avoid these difficulties by comparing temporal processing assessed by a pre-attentive event-related brain potential (ERP) waveform, the mismatch negativity (MMN) elicited by time-based stimulus features, to a number of cognitive functions within a non-clinical sample. We studied healthy older adults (without dementia), as this population inherently ensures more prominent variability in cognitive function than a younger adult sample, allowing for the detection of significant relationships between variables. Using hierarchical regression analyses, we found that verbal memory and executive functions (i.e., planning and conditional inhibition, but not set-shifting) uniquely predicted variance in temporal processing beyond that predicted by the demographic variables of age, gender, and hearing loss. These findings are consistent with a frontotemporal model of MMN waveform generation in response to changes in the temporal features of auditory stimuli.

  9. A probablistic neural network classification system for signal and image processing

    Energy Technology Data Exchange (ETDEWEB)

    Bowman, B. [Lawrence Livermore National Lab., CA (United States)

    1994-11-15

    The Acoustical Heart Valve Analysis Package is a system for signal and image processing and classification. It is being developed in both Matlab and C, to provide an interactive, interpreted environment, and has been optimized for large scale matrix operations. It has been used successfully to classify acoustic signals from implanted prosthetic heart valves in human patients, and will be integrated into a commercial Heart Valve Screening Center. The system uses several standard signal processing algorithms, as well as supervised learning techniques using the probabilistic neural network (PNN). Although currently used for the acoustic heart valve application, the algorithms and modular design allow it to be used for other applications, as well. We will describe the signal classification system, and show results from a set of test valves.

  10. A corticostriatal neural system enhances auditory perception through temporal context processing.

    Science.gov (United States)

    Geiser, Eveline; Notter, Michael; Gabrieli, John D E

    2012-05-02

    The temporal context of an acoustic signal can greatly influence its perception. The present study investigated the neural correlates underlying perceptual facilitation by regular temporal contexts in humans. Participants listened to temporally regular (periodic) or temporally irregular (nonperiodic) sequences of tones while performing an intensity discrimination task. Participants performed significantly better on intensity discrimination during periodic than nonperiodic tone sequences. There was greater activation in the putamen for periodic than nonperiodic sequences. Conversely, there was greater activation in bilateral primary and secondary auditory cortices (planum polare and planum temporale) for nonperiodic than periodic sequences. Across individuals, greater putamen activation correlated with lesser auditory cortical activation in both right and left hemispheres. These findings suggest that temporal regularity is detected in the putamen, and that such detection facilitates temporal-lobe cortical processing associated with superior auditory perception. Thus, this study reveals a corticostriatal system associated with contextual facilitation for auditory perception through temporal regularity processing.

  11. Subcellular Imaging of Voltage and Calcium Signals Reveals Neural Processing In Vivo.

    Science.gov (United States)

    Yang, Helen H; St-Pierre, François; Sun, Xulu; Ding, Xiaozhe; Lin, Michael Z; Clandinin, Thomas R

    2016-06-30

    A mechanistic understanding of neural computation requires determining how information is processed as it passes through neurons and across synapses. However, it has been challenging to measure membrane potential changes in axons and dendrites in vivo. We use in vivo, two-photon imaging of novel genetically encoded voltage indicators, as well as calcium imaging, to measure sensory stimulus-evoked signals in the Drosophila visual system with subcellular resolution. Across synapses, we find major transformations in the kinetics, amplitude, and sign of voltage responses to light. We also describe distinct relationships between voltage and calcium signals in different neuronal compartments, a substrate for local computation. Finally, we demonstrate that ON and OFF selectivity, a key feature of visual processing across species, emerges through the transformation of membrane potential into intracellular calcium concentration. By imaging voltage and calcium signals to map information flow with subcellular resolution, we illuminate where and how critical computations arise.

  12. Similarity and rules United: similarity- and rule-based processing in a single neural network.

    Science.gov (United States)

    Verguts, Tom; Fias, Wim

    2009-03-01

    A central controversy in cognitive science concerns the roles of rules versus similarity. To gain some leverage on this problem, we propose that rule- versus similarity-based processes can be characterized as extremes in a multidimensional space that is composed of at least two dimensions: the number of features (Pothos, 2005) and the physical presence of features. The transition of similarity- to rule-based processing is conceptualized as a transition in this space. To illustrate this, we show how a neural network model uses input features (and in this sense produces similarity-based responses) when it has a low learning rate or in the early phases of training, but it switches to using self-generated, more abstract features (and in this sense produces rule-based responses) when it has a higher learning rate or is in the later phases of training. Relations with categorization and the psychology of learning are pointed out.

  13. Potential of artificial neural network technology for predicting shelf life of processed cheese

    Directory of Open Access Journals (Sweden)

    Sumit Goyal

    Full Text Available Radial basis (fewer neurons artificial neural network (ANN models were developed for predicting the shelf life of processed cheese stored at 7-8o C. Mean square error, root mean square error, coefficient of determination and nash - sutcliffo coefficient were applied in order to compare the prediction ability of the developed models. Soluble nitrogen, pH; standard plate count, yeast & mouldcount, and spore count were the input parameters, while sensory score was output parameter for the developed model. The developed model showed very good correlation between actual data and predicted data with high coefficient of determination and nash - sutcliffo coefficient besides low root mean square error, suggesting that the developed model is quite efficient in predicting the shelf life of processed cheese.

  14. Dynamical Behavior of Delayed Reaction-Diffusion Hopfield Neural Networks Driven by Infinite Dimensional Wiener Processes.

    Science.gov (United States)

    Liang, Xiao; Wang, Linshan; Wang, Yangfan; Wang, Ruili

    2016-09-01

    In this paper, we focus on the long time behavior of the mild solution to delayed reaction-diffusion Hopfield neural networks (DRDHNNs) driven by infinite dimensional Wiener processes. We analyze the existence, uniqueness, and stability of this system under the local Lipschitz function by constructing an appropriate Lyapunov-Krasovskii function and utilizing the semigroup theory. Some easy-to-test criteria affecting the well-posedness and stability of the networks, such as infinite dimensional noise and diffusion effect, are obtained. The criteria can be used as theoretic guidance to stabilize DRDHNNs in practical applications when infinite dimensional noise is taken into consideration. Meanwhile, considering the fact that the standard Brownian motion is a special case of infinite dimensional Wiener process, we undertake an analysis of the local Lipschitz condition, which has a wider range than the global Lipschitz condition. Two samples are given to examine the availability of the results in this paper. Simulations are also given using the MATLAB.

  15. Dissociation of neural regions associated with anticipatory versus consummatory phases of incentive processing.

    Science.gov (United States)

    Dillon, Daniel G; Holmes, Avram J; Jahn, Allison L; Bogdan, Ryan; Wald, Lawrence L; Pizzagalli, Diego A

    2008-01-01

    Incentive delay tasks implicate the striatum and medial frontal cortex in reward processing. However, prior studies delivered more rewards than penalties, possibly leading to unwanted differences in signal-to-noise ratio. Also, whether particular brain regions are specifically involved in anticipation or consumption is unclear. We used a task featuring balanced incentive delivery and an analytic strategy designed to identify activity specific to anticipation or consumption. Reaction time data in two independent samples (n=13 and n=8) confirmed motivated responding. Functional magnetic resonance imaging revealed regions activated by anticipation (anterior cingulate) versus consumption (orbital and medial frontal cortex). Ventral striatum was active during reward anticipation but not significantly more so than during consumption. Although the study features several methodological improvements and helps clarify the neural basis of incentive processing, replications in larger samples are needed.

  16. Dissociation of neural regions associated with anticipatory versus consummatory phases of incentive processing

    Science.gov (United States)

    Dillon, Daniel G.; Holmes, Avram J.; Jahn, Allison L.; Bogdan, Ryan; Wald, Lawrence L.; Pizzagalli, Diego A.

    2007-01-01

    Incentive delay tasks implicate the striatum and medial frontal cortex in reward processing. However, prior studies delivered more rewards than penalties, possibly leading to unwanted differences in signal-to-noise ratio. Also, whether particular brain regions are specifically involved in anticipation or consumption is unclear. We used a task featuring balanced incentive delivery and an analytic strategy designed to identify activity specific to anticipation or consumption. RT data in two independent samples (n=13 and n=8) confirmed motivated responding. FMRI revealed regions activated by anticipation (anterior cingulate) vs. consumption (orbital and medial frontal cortex). Ventral striatum was active during reward anticipation but not significantly more so than during consumption. While the study features several methodological improvements and helps clarify the neural basis of incentive processing, replications in larger samples are needed. PMID:17850241

  17. Neural processing of facial identity and emotion in infants at high risk for autism spectrum disorders

    Directory of Open Access Journals (Sweden)

    Sharon Elizabeth Fox

    2013-04-01

    Full Text Available Deficits in face processing and social impairment are core characteristics of autism spectrum disorder. The present work examined 7 month-old infants at high risk for developing autism and typically developing controls at low risk, using a face perception task designed to differentiate between the effects of face identity and facial emotions on neural response using functional Near Infrared Spectroscopy (fNIRS. In addition, we employed independent component analysis (ICA, as well as a novel method of condition-related component selection and classification to identify group differences in hemodynamic waveforms and response distributions associated with face and emotion processing. The results indicate similarities of waveforms, but differences in the magnitude, spatial distribution, and timing of responses between groups. These early differences in local cortical regions and the hemodynamic response may, in turn, contribute to differences in patterns of functional connectivity.

  18. Democratic organization of the thalamocortical neural ensembles in nociceptive signal processing

    Institute of Scientific and Technical Information of China (English)

    LUO Fei; WANG Jin-Yan

    2008-01-01

    Acute pain is a warning protective sensation for any impending harm. However, chronic pain syndromes are often resistant diseases that may consume large amount of health care costs. It has been suggested by recent studies that pain perception may be formed in central neural networks via large-scale coding processes, which involves sensory, affective, and cognitive dimensions. Many central areas are involved in these processes, including structures from the spinal cord, the brain stem, the limbic system, to the cortices. Thus, chronic painful diseases may be the result of some abnormal coding within this network. A thorough investigation of coding mechanism of pain within the central neuromatrix will bring us great insight into the mechanisms responsible for the development of chronic pain, hence leading to novel therapeutic interventions for pain management.

  19. Do you have the nerves to regenerate? The importance of neural signalling in the regeneration process.

    Science.gov (United States)

    Pirotte, Nicky; Leynen, Nathalie; Artois, Tom; Smeets, Karen

    2016-01-01

    The importance of nerve-derived signalling for correct regeneration has been the topic of research for more than a hundred years, but we are just beginning to identify the underlying molecular pathways of this process. Within the current review, we attempt to provide an extensive overview of the neural influences during early and late phases of both vertebrate and invertebrate regeneration. In general, denervation impairs limb regeneration, but the presence of nerves is not essential for the regeneration of aneurogenic extremities. This observation led to the "neurotrophic factor(s) hypothesis", which states that certain trophic factors produced by the nerves are necessary for proper regeneration. Possible neuron-derived factors which regulate regeneration as well as the denervation-affected processes are discussed. Copyright © 2015. Published by Elsevier Inc.

  20. Two distinct neural mechanisms in early visual cortex determine subsequent visual processing.

    Science.gov (United States)

    Jacobs, Christianne; de Graaf, Tom A; Sack, Alexander T

    2014-10-01

    Neuroscience research has conventionally focused on how the brain processes sensory information, after the information has been received. Recently, increased interest focuses on how the state of the brain upon receiving inputs determines and biases their subsequent processing and interpretation. Here, we investigated such 'pre-stimulus' brain mechanisms and their relevance for objective and subjective visual processing. Using non-invasive focal brain stimulation [transcranial magnetic stimulation (TMS)] we disrupted spontaneous brain state activity within early visual cortex (EVC) before onset of visual stimulation, at two different pre-stimulus-onset-asynchronies (pSOAs). We found that TMS pulses applied to EVC at either 20 msec or 50 msec before onset of a simple orientation stimulus both prevented this stimulus from reaching visual awareness. Interestingly, only the TMS-induced visual suppression following TMS at a pSOA of ?20 msec was retinotopically specific, while TMS at a pSOA of ?50 msec was not. In a second experiment, we used more complex symbolic arrow stimuli, and found TMS-induced suppression only when disrupting EVC at a pSOA of ? ?60 msec, which, in line with Experiment 1, was not retinotopically specific. Despite this topographic unspecificity of the ?50 msec effect, the additional control measurements as well as tracking and removal of eye blinks, suggested that also this effect was not the result of an unspecific artifact, and thus neural in origin. We therefore obtained evidence of two distinct neural mechanisms taking place in EVC, both determining whether or not subsequent visual inputs are successfully processed by the human visual system.

  1. Impact of load-related neural processes on feature binding in visuospatial working memory.

    Directory of Open Access Journals (Sweden)

    Nicole A Kochan

    Full Text Available BACKGROUND: The capacity of visual working memory (WM is substantially limited and only a fraction of what we see is maintained as a temporary trace. The process of binding visual features has been proposed as an adaptive means of minimising information demands on WM. However the neural mechanisms underlying this process, and its modulation by task and load effects, are not well understood. OBJECTIVE: To investigate the neural correlates of feature binding and its modulation by WM load during the sequential phases of encoding, maintenance and retrieval. METHODS AND FINDINGS: 18 young healthy participants performed a visuospatial WM task with independent factors of load and feature conjunction (object identity and position in an event-related functional MRI study. During stimulus encoding, load-invariant conjunction-related activity was observed in left prefrontal cortex and left hippocampus. During maintenance, greater activity for task demands of feature conjunction versus single features, and for increased load was observed in left-sided regions of the superior occipital cortex, precuneus and superior frontal cortex. Where these effects were expressed in overlapping cortical regions, their combined effect was additive. During retrieval, however, an interaction of load and feature conjunction was observed. This modulation of feature conjunction activity under increased load was expressed through greater deactivation in medial structures identified as part of the default mode network. CONCLUSIONS AND SIGNIFICANCE: The relationship between memory load and feature binding qualitatively differed through each phase of the WM task. Of particular interest was the interaction of these factors observed within regions of the default mode network during retrieval which we interpret as suggesting that at low loads, binding processes may be 'automatic' but at higher loads it becomes a resource-intensive process leading to disengagement of activity in this

  2. Early neural disruption and auditory processing outcomes in rodent models: Implications for developmental language disability

    Directory of Open Access Journals (Sweden)

    Roslyn Holly Fitch

    2013-10-01

    Full Text Available Most researchers in the field of neural plasticity are familiar with the Kennard Principle," which purports a positive relationship between age at brain injury and severity of subsequent deficits (plateauing in adulthood. As an example, a child with left hemispherectomy can recover seemingly normal language, while an adult with focal injury to sub-regions of left temporal and/or frontal cortex can suffer dramatic and permanent language loss. Here we present data regarding the impact of early brain injury in rat models as a function of type and timing, measuring long-term behavioral outcomes via auditory discrimination tasks varying in temporal demand. These tasks were created to model (in rodents aspects of human sensory processing that may correlate – both developmentally and functionally – with typical and atypical language. We found that bilateral focal lesions to the cortical plate in rats during active neuronal migration led to worse auditory outcomes than comparable lesions induced after cortical migration was complete. Conversely, unilateral hypoxic-ischemic injuries (similar to those seen in premature infants and term infants with birth complications led to permanent auditory processing deficits when induced at a neurodevelopmental point comparable to human "term," but only transient deficits (undetectable in adulthood when induced in a "preterm" window. Convergent evidence suggests that regardless of when or how disruption of early neural development occurs, the consequences may be particularly deleterious to rapid auditory processing outcomes when they trigger developmental alterations that extend into subcortical structures (i.e., lower sensory processing stations. Collective findings hold implications for the study of behavioral outcomes following early brain injury as well as genetic/environmental disruption, and are relevant to our understanding of the neurologic risk factors underlying developmental language disability in

  3. Prediction of Emergency Department Hospital Admission Based on Natural Language Processing and Neural Networks.

    Science.gov (United States)

    Zhang, Xingyu; Kim, Joyce; Patzer, Rachel E; Pitts, Stephen R; Patzer, Aaron; Schrager, Justin D

    2017-08-16

    To describe and compare logistic regression and neural network modeling strategies to predict hospital admission or transfer following initial presentation to Emergency Department (ED) triage with and without the addition of natural language processing elements. Using data from the National Hospital Ambulatory Medical Care Survey (NHAMCS), a cross-sectional probability sample of United States EDs from 2012 and 2013 survey years, we developed several predictive models with the outcome being admission to the hospital or transfer vs. discharge home. We included patient characteristics immediately available after the patient has presented to the ED and undergone a triage process. We used this information to construct logistic regression (LR) and multilayer neural network models (MLNN) which included natural language processing (NLP) and principal component analysis from the patient's reason for visit. Ten-fold cross validation was used to test the predictive capacity of each model and receiver operating curves (AUC) were then calculated for each model. Of the 47,200 ED visits from 642 hospitals, 6,335 (13.42%) resulted in hospital admission (or transfer). A total of 48 principal components were extracted by NLP from the reason for visit fields, which explained 75% of the overall variance for hospitalization. In the model including only structured variables, the AUC was 0.824 (95% CI 0.818-0.830) for logistic regression and 0.823 (95% CI 0.817-0.829) for MLNN. Models including only free-text information generated AUC of 0.742 (95% CI 0.731- 0.753) for logistic regression and 0.753 (95% CI 0.742-0.764) for MLNN. When both structured variables and free text variables were included, the AUC reached 0.846 (95% CI 0.839-0.853) for logistic regression and 0.844 (95% CI 0.836-0.852) for MLNN. The predictive accuracy of hospital admission or transfer for patients who presented to ED triage overall was good, and was improved with the inclusion of free text data from a patient

  4. Lateral Information Processing by Spiking Neurons: A Theoretical Model of the Neural Correlate of Consciousness

    Directory of Open Access Journals (Sweden)

    Marc Ebner

    2011-01-01

    Full Text Available Cognitive brain functions, for example, sensory perception, motor control and learning, are understood as computation by axonal-dendritic chemical synapses in networks of integrate-and-fire neurons. Cognitive brain functions may occur either consciously or nonconsciously (on “autopilot”. Conscious cognition is marked by gamma synchrony EEG, mediated largely by dendritic-dendritic gap junctions, sideways connections in input/integration layers. Gap-junction-connected neurons define a sub-network within a larger neural network. A theoretical model (the “conscious pilot” suggests that as gap junctions open and close, a gamma-synchronized subnetwork, or zone moves through the brain as an executive agent, converting nonconscious “auto-pilot” cognition to consciousness, and enhancing computation by coherent processing and collective integration. In this study we implemented sideways “gap junctions” in a single-layer artificial neural network to perform figure/ground separation. The set of neurons connected through gap junctions form a reconfigurable resistive grid or sub-network zone. In the model, outgoing spikes are temporally integrated and spatially averaged using the fixed resistive grid set up by neurons of similar function which are connected through gap-junctions. This spatial average, essentially a feedback signal from the neuron's output, determines whether particular gap junctions between neurons will open or close. Neurons connected through open gap junctions synchronize their output spikes. We have tested our gap-junction-defined sub-network in a one-layer neural network on artificial retinal inputs using real-world images. Our system is able to perform figure/ground separation where the laterally connected sub-network of neurons represents a perceived object. Even though we only show results for visual stimuli, our approach should generalize to other modalities. The system demonstrates a moving sub-network zone of

  5. Artificial neural network prediction of the aluminum extraction from bauxite in the Bayer process

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    Đurić Isidora

    2012-01-01

    Full Text Available This paper presents the results of statistical modeling of the bauxite leaching process, as part of Bayer technology for an alumina production. Based on the data, collected during the period between 2008 - 2009 (659 days from the industrial production in the alumina factory Birač, Zvornik (Bosnia and Herzegovina, the statistical modeling of the above mentioned process was performed. The dependant variable, which was the main target of the modeling procedure, was the degree of Al2O3 recovery from boehmite bauxite during the leaching process. The statistical model was developed as an attempt to define the dependence of the Al2O3 degree of recovery as a function of input variables of the leaching process: composition of bauxite, composition of the sodium aluminate solution and the caustic module of the solution before and after the leaching process. As the statistical modeling tools, Multiple Linear Regression Analysis (MLRA and Artificial Neural Networks (ANNs were used. The fitting level, obtained by using the MLRA, was R2 = 0.463, while ANN resulted with the value of R2 = 0.723. This way, the model, defined by using the ANN methodology, can be used for the efficient prediction of the Al2O3 degree of recovery as a function of the process inputs, under the industrial conditions of the alumina factory Birač, Zvornik. The proposed model also has got a universal character and, as such, is applicable in other factories practicing the Bayer technology for alumina production.

  6. Intermodal auditory, visual, and tactile attention modulates early stages of neural processing.

    Science.gov (United States)

    Karns, Christina M; Knight, Robert T

    2009-04-01

    We used event-related potentials (ERPs) and gamma band oscillatory responses (GBRs) to examine whether intermodal attention operates early in the auditory, visual, and tactile modalities. To control for the effects of spatial attention, we spatially coregistered all stimuli and varied the attended modality across counterbalanced blocks in an intermodal selection task. In each block, participants selectively responded to either auditory, visual, or vibrotactile stimuli from the stream of intermodal events. Auditory and visual ERPs were modulated at the latencies of early cortical processing, but attention manifested later for tactile ERPs. For ERPs, auditory processing was modulated at the latency of the Na (29 msec), which indexes early cortical or thalamocortical processing and the subsequent P1 (90 msec) ERP components. Visual processing was modulated at the latency of the early phase of the C1 (62-72 msec) thought to be generated in the primary visual cortex and the subsequent P1 and N1 (176 msec). Tactile processing was modulated at the latency of the N160 (165 msec) likely generated in the secondary association cortex. Intermodal attention enhanced early sensory GBRs for all three modalities: auditory (onset 57 msec), visual (onset 47 msec), and tactile (onset 27 msec). Together, these results suggest that intermodal attention enhances neural processing relatively early in the sensory stream independent from differential effects of spatial and intramodal selective attention.

  7. Higher-order neural processing tunes motion neurons to visual ecology in three species of hawkmoths.

    Science.gov (United States)

    Stöckl, A L; O'Carroll, D; Warrant, E J

    2017-06-28

    To sample information optimally, sensory systems must adapt to the ecological demands of each animal species. These adaptations can occur peripherally, in the anatomical structures of sensory organs and their receptors; and centrally, as higher-order neural processing in the brain. While a rich body of investigations has focused on peripheral adaptations, our understanding is sparse when it comes to central mechanisms. We quantified how peripheral adaptations in the eyes, and central adaptations in the wide-field motion vision system, set the trade-off between resolution and sensitivity in three species of hawkmoths active at very different light levels: nocturnal Deilephila elpenor, crepuscular Manduca sexta, and diurnal Macroglossum stellatarum. Using optical measurements and physiological recordings from the photoreceptors and wide-field motion neurons in the lobula complex, we demonstrate that all three species use spatial and temporal summation to improve visual performance in dim light. The diurnal Macroglossum relies least on summation, but can only see at brighter intensities. Manduca, with large sensitive eyes, relies less on neural summation than the smaller eyed Deilephila, but both species attain similar visual performance at nocturnal light levels. Our results reveal how the visual systems of these three hawkmoth species are intimately matched to their visual ecologies. © 2017 The Author(s).

  8. Adaptive neural reward processing during anticipation and receipt of monetary rewards in mindfulness meditators.

    Science.gov (United States)

    Kirk, Ulrich; Brown, Kirk Warren; Downar, Jonathan

    2015-05-01

    Reward seeking is ubiquitous and adaptive in humans. But excessive reward seeking behavior, such as chasing monetary rewards, may lead to diminished subjective well-being. This study examined whether individuals trained in mindfulness meditation show neural evidence of lower susceptibility to monetary rewards. Seventy-eight participants (34 meditators, 44 matched controls) completed the monetary incentive delay task while undergoing functional magnetic resonance imaging. The groups performed equally on the task, but meditators showed lower neural activations in the caudate nucleus during reward anticipation, and elevated bilateral posterior insula activation during reward anticipation. Meditators also evidenced reduced activations in the ventromedial prefrontal cortex during reward receipt compared with controls. Connectivity parameters between the right caudate and bilateral anterior insula were attenuated in meditators during incentive anticipation. In summary, brain regions involved in reward processing-both during reward anticipation and receipt of reward-responded differently in mindfulness meditators than in nonmeditators, indicating that the former are less susceptible to monetary incentives. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  9. Organization of neural systems for aversive information processing: pain, error, and punishment

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    Shunsuke eKobayashi

    2012-09-01

    Full Text Available The avoidance of aversive events is critically important for the survival of organisms. It has been proposed that the medial pain system, including the amygdala, periaqueductal gray (PAG, and anterior cingulate cortex (ACC, contains the neural circuitry that signals pain affect and negative value. This system appears to have multiple defense mechanisms, such as rapid stereotyped escape, aversive association learning, and cognitive adaptation. These defense mechanisms vary in speed and flexibility, reflecting different strategies of self-protection. Over the course of evolution, the medial pain system appears to have developed primitive, associative, and cognitive solutions for aversive avoidance. There may be a functional grading along the caudal-rostral axis, such that the amygdala-PAG system underlies automatic and autonomic responses, the amygdala-orbitofrontal system contributes to associative learning, and the ACC controls cognitive processes in cooperation with the lateral prefrontal cortex. A review of behavioral and physiological studies on the aversive system is presented, and a conceptual framework for understanding the neural organization of the aversive avoidance system is proposed.

  10. Age associations with neural processing of reward anticipation in adolescents with bipolar disorders

    Directory of Open Access Journals (Sweden)

    Snežana Urošević

    2016-01-01

    Full Text Available Reward/behavioral approach system hypersensitivity is implicated in bipolar disorders (BD and in normative development during adolescence. Pediatric onset of BD is associated with a more severe illness course. However, little is known about neural processing of rewards in adolescents with BD or developmental (i.e., age associations with activation of these neural systems. The present study aims to address this knowledge gap. The present sample included 21 adolescents with BD and 26 healthy adolescents, ages 13 to 19. Participants completed a functional magnetic resonance imaging (fMRI protocol using the Monetary Incentive Delay (MID task. Behavioral performance was similar between groups. Group differences in BOLD activation during target anticipation and feedback anticipation periods of the task were examined using whole-brain analyses, as were group differences in age effects. During both target anticipation and feedback anticipation, adolescents with BD, compared to adolescents without psychopathology, exhibited decreased engagement of frontal regions involved in cognitive control (i.e., dorsolateral prefrontal cortex. Healthy adolescents exhibited age-related decreases, while adolescents with BD exhibited age-related increases, in activity of other cognitive control frontal areas (i.e., right inferior frontal gyrus, suggesting altered development in the BD group. Longitudinal research is needed to examine potentially abnormal development of cognitive control during reward pursuit in adolescent BD and whether early therapeutic interventions can prevent these potential deviations from normative development.

  11. Neural substrates in color processing: a comparison between painting majors and non-majors.

    Science.gov (United States)

    Long, Zhiying; Peng, Danling; Chen, Kewei; Jin, Zhen; Yao, Li

    2011-01-07

    Although several studies provide evidence of differences in the neural mechanisms of art professionals and non-professionals, little is known about the neural mechanism differences between painting professionals/majors and non-professionals/non-majors during color processing. For the first time, we compared functional activation patterns, functional connectivity during both color naming and passive color viewing, and gray-matter density in 12 painting majors and 12 controls through both functional and structural magnetic resonance imaging techniques. Inter-group comparisons revealed that the painting majors showed more activation in the color selective areas and increased correlation between left V4 and the left ventral lateral prefrontal cortex during color naming. In contrast, the controls exhibited stronger activity in the Broca's area during color naming. Moreover, increased gray matter density in the left V4 complex was found when the painting majors were compared to the controls. This study demonstrates that the left V4 complex shows both functional and structural differences between painting majors and non-majors. In addition, the results suggest the reorganization of the brain circuit underlying lexical retrieval during color naming in the anterior regions of the painting major group.

  12. Age associations with neural processing of reward anticipation in adolescents with bipolar disorders.

    Science.gov (United States)

    Urošević, Snežana; Luciana, Monica; Jensen, Jonathan B; Youngstrom, Eric A; Thomas, Kathleen M

    2016-01-01

    Reward/behavioral approach system hypersensitivity is implicated in bipolar disorders (BD) and in normative development during adolescence. Pediatric onset of BD is associated with a more severe illness course. However, little is known about neural processing of rewards in adolescents with BD or developmental (i.e., age) associations with activation of these neural systems. The present study aims to address this knowledge gap. The present sample included 21 adolescents with BD and 26 healthy adolescents, ages 13 to 19. Participants completed a functional magnetic resonance imaging (fMRI) protocol using the Monetary Incentive Delay (MID) task. Behavioral performance was similar between groups. Group differences in BOLD activation during target anticipation and feedback anticipation periods of the task were examined using whole-brain analyses, as were group differences in age effects. During both target anticipation and feedback anticipation, adolescents with BD, compared to adolescents without psychopathology, exhibited decreased engagement of frontal regions involved in cognitive control (i.e., dorsolateral prefrontal cortex). Healthy adolescents exhibited age-related decreases, while adolescents with BD exhibited age-related increases, in activity of other cognitive control frontal areas (i.e., right inferior frontal gyrus), suggesting altered development in the BD group. Longitudinal research is needed to examine potentially abnormal development of cognitive control during reward pursuit in adolescent BD and whether early therapeutic interventions can prevent these potential deviations from normative development.

  13. Adolescents' risky decision-making activates neural networks related to social cognition and cognitive control processes.

    Science.gov (United States)

    Rodrigo, María José; Padrón, Iván; de Vega, Manuel; Ferstl, Evelyn C

    2014-01-01

    This study examines by means of functional magnetic resonance imaging the neural mechanisms underlying adolescents' risk decision-making in social contexts. We hypothesize that the social context could engage brain regions associated with social cognition processes and developmental changes are also expected. Sixty participants (adolescents: 17-18, and young adults: 21-22 years old) read narratives describing typical situations of decision-making in the presence of peers. They were asked to make choices in risky situations (e.g., taking or refusing a drug) or ambiguous situations (e.g., eating a hamburger or a hotdog). Risky as compared to ambiguous scenarios activated bilateral temporoparietal junction (TPJ), bilateral middle temporal gyrus (MTG), right medial prefrontal cortex, and the precuneus bilaterally; i.e., brain regions related to social cognition processes, such as self-reflection and theory of mind (ToM). In addition, brain structures related to cognitive control were active [right anterior cingulate cortex (ACC), bilateral dorsolateral prefrontal cortex (DLPFC), bilateral orbitofrontal cortex], whereas no significant clusters were obtained in the reward system (ventral striatum). Choosing the dangerous option involved a further activation of control areas (ACC) and emotional and social cognition areas (temporal pole). Adolescents employed more neural resources than young adults in the right DLPFC and the right TPJ in risk situations. When choosing the dangerous option, young adults showed a further engagement in ToM related regions (bilateral MTG) and in motor control regions related to the planning of actions (pre-supplementary motor area). Finally, the right insula and the right superior temporal gyrus were more activated in women than in men, suggesting more emotional involvement and more intensive modeling of the others' perspective in the risky conditions. These findings call for more comprehensive developmental accounts of decision-making in

  14. Adolescents’ risky decision-making activates neural networks related to social cognition and cognitive control processes

    Directory of Open Access Journals (Sweden)

    María José eRodrigo

    2014-02-01

    Full Text Available This study examines by means of fMRI the neural mechanisms underlying adolescents’ risk decision-making in social contexts. We hypothesize that the social context could engage brain regions associated with social cognition processes and developmental changes are also expected. Sixty participants (adolescents: 17-18, and young adults: 21-22 years old read narratives describing typical situations of decision-making in the presence of peers. They were asked to make choices in risky situations (e.g., taking or refusing a drug or ambiguous situations (e.g., eating a hamburger or a hotdog. Risky as compared to ambiguous scenarios activated bilateral temporoparietal junction (TPJ, bilateral middle temporal gyrus (MTG, right medial prefrontal cortex (mPFC, and the precuneus bilaterally; i.e., brain regions related to social cognition processes, such as self-reflection and theory of mind. In addition, brain structures related to cognitive control were active (right ACC, bilateral DLPFC, bilateral OFC, whereas no significant clusters were obtained in the reward system (VS. Choosing the dangerous option involved a further activation of control areas (ACC and emotional and social cognition areas (temporal pole. Adolescents employed more neural resources than young adults in the right DLPFC and the right TPJ in risk situations. When choosing the dangerous option, young adults showed a further engagement in theory of mind related regions (bilateral middle temporal gyrus and in motor control regions related to the planning of actions (pre-supplementary motor area. Finally, the right insula and the right superior temporal gyrus were more activated in women than in men, suggesting more emotional involvement and more intensive modeling of the others’ perspective in the risky conditions. These findings call for more comprehensive developmental accounts of decision-making in social contexts that incorporate the role of emotional and social cognition processes.

  15. Interaction matters: A perceived social partner alters the neural processing of human speech.

    Science.gov (United States)

    Rice, Katherine; Redcay, Elizabeth

    2016-04-01

    Mounting evidence suggests that social interaction changes how communicative behaviors (e.g., spoken language, gaze) are processed, but the precise neural bases by which social-interactive context may alter communication remain unknown. Various perspectives suggest that live interactions are more rewarding, more attention-grabbing, or require increased mentalizing-thinking about the thoughts of others. Dissociating between these possibilities is difficult because most extant neuroimaging paradigms examining social interaction have not directly compared live paradigms to conventional "offline" (or recorded) paradigms. We developed a novel fMRI paradigm to assess whether and how an interactive context changes the processing of speech matched in content and vocal characteristics. Participants listened to short vignettes--which contained no reference to people or mental states--believing that some vignettes were prerecorded and that others were presented over a real-time audio-feed by a live social partner. In actuality, all speech was prerecorded. Simply believing that speech was live increased activation in each participant's own mentalizing regions, defined using a functional localizer. Contrasting live to recorded speech did not reveal significant differences in attention or reward regions. Further, higher levels of autistic-like traits were associated with altered neural specialization for live interaction. These results suggest that humans engage in ongoing mentalizing about social partners, even when such mentalizing is not explicitly required, illustrating how social context shapes social cognition. Understanding communication in social context has important implications for typical and atypical social processing, especially for disorders like autism where social difficulties are more acute in live interaction. Copyright © 2015 Elsevier Inc. All rights reserved.

  16. Classification of biological and non-biological fluvial particles using image processing and artificial neural network

    Science.gov (United States)

    Shrestha, Bim Prasad; Shrestha, Nabin Kumar; Poudel, Laxman

    2009-04-01

    Particles flowing along with water largely affect safe drinking water, irrigation, aquatic life preservation and hydropower generation. This research describes activities that lead to development of fluvial particle characterization that includes detection of biological and non-biological particles and shape characterization using Image Processing and Artificial Neural Network (ANN). Fluvial particles are characterized based on multi spectral images processing using ANN. Images of wavelength of 630nm and 670nm are taken as most distinctive characterizing properties of biological and non-biological particles found in Bagmati River of Nepal. The samples were collected at pre-monsoon, monsoon and post-monsoon seasons. Random samples were selected and multi spectral images are processed using MATLAB 6.5. Thirty matrices were built from each sample. The obtained data of 42 rows and 60columns were taken as input training with an output matrix of 42 rows and 2 columns. Neural Network of Perceptron model was created using a transfer function. The system was first validated and later on tested at 18 different strategic locations of Bagmati River of Kathmandu Valley, Nepal. This network classified biological and non biological particles. Development of new non-destructive technique to characterize biological and non-biological particles from fluvial sample in a real time has a significance breakthrough. This applied research method and outcome is an attractive model for real time monitoring of particles and has many applications that can throw a significant outlet to many researches and for effective utilization of water resources. It opened a new horizon of opportunities for basic and applied research at Kathmandu University in Nepal.

  17. Fluid Intelligence and Automatic Neural Processes in Facial Expression Perception: An Event-Related Potential Study.

    Directory of Open Access Journals (Sweden)

    Tongran Liu

    Full Text Available The relationship between human fluid intelligence and social-emotional abilities has been a topic of considerable interest. The current study investigated whether adolescents with different intellectual levels had different automatic neural processing of facial expressions. Two groups of adolescent males were enrolled: a high IQ group and an average IQ group. Age and parental socioeconomic status were matched between the two groups. Participants counted the numbers of the central cross changes while paired facial expressions were presented bilaterally in an oddball paradigm. There were two experimental conditions: a happy condition, in which neutral expressions were standard stimuli (p = 0.8 and happy expressions were deviant stimuli (p = 0.2, and a fearful condition, in which neutral expressions were standard stimuli (p = 0.8 and fearful expressions were deviant stimuli (p = 0.2. Participants were required to concentrate on the primary task of counting the central cross changes and to ignore the expressions to ensure that facial expression processing was automatic. Event-related potentials (ERPs were obtained during the tasks. The visual mismatch negativity (vMMN components were analyzed to index the automatic neural processing of facial expressions. For the early vMMN (50-130 ms, the high IQ group showed more negative vMMN amplitudes than the average IQ group in the happy condition. For the late vMMN (320-450 ms, the high IQ group had greater vMMN responses than the average IQ group over frontal and occipito-temporal areas in the fearful condition, and the average IQ group evoked larger vMMN amplitudes than the high IQ group over occipito-temporal areas in the happy condition. The present study elucidated the close relationships between fluid intelligence and pre-attentive change detection on social-emotional information.

  18. Fluid Intelligence and Automatic Neural Processes in Facial Expression Perception: An Event-Related Potential Study.

    Science.gov (United States)

    Liu, Tongran; Xiao, Tong; Li, Xiaoyan; Shi, Jiannong

    2015-01-01

    The relationship between human fluid intelligence and social-emotional abilities has been a topic of considerable interest. The current study investigated whether adolescents with different intellectual levels had different automatic neural processing of facial expressions. Two groups of adolescent males were enrolled: a high IQ group and an average IQ group. Age and parental socioeconomic status were matched between the two groups. Participants counted the numbers of the central cross changes while paired facial expressions were presented bilaterally in an oddball paradigm. There were two experimental conditions: a happy condition, in which neutral expressions were standard stimuli (p = 0.8) and happy expressions were deviant stimuli (p = 0.2), and a fearful condition, in which neutral expressions were standard stimuli (p = 0.8) and fearful expressions were deviant stimuli (p = 0.2). Participants were required to concentrate on the primary task of counting the central cross changes and to ignore the expressions to ensure that facial expression processing was automatic. Event-related potentials (ERPs) were obtained during the tasks. The visual mismatch negativity (vMMN) components were analyzed to index the automatic neural processing of facial expressions. For the early vMMN (50-130 ms), the high IQ group showed more negative vMMN amplitudes than the average IQ group in the happy condition. For the late vMMN (320-450 ms), the high IQ group had greater vMMN responses than the average IQ group over frontal and occipito-temporal areas in the fearful condition, and the average IQ group evoked larger vMMN amplitudes than the high IQ group over occipito-temporal areas in the happy condition. The present study elucidated the close relationships between fluid intelligence and pre-attentive change detection on social-emotional information.

  19. Group Influences on Young Adult Warfighters Risk-Taking

    Science.gov (United States)

    2015-10-01

    Cognitive Regulation. 10 EXPERIMENT 2 AIM (COMPLETED): To determine whether the inclusion of one older adult (ages 25-30) within the foursome of males...impact of peers on adolescents’ and adults ’ neural response to reward. Developmental Cognitive Neuroscience, 11, 75–82. doi:10.1016/ j.dcn.2014.08.010...brain development and risk taking from a social- developmental perspective. Brain and Cognition , 89, 70–78. doi:10.1016/j.bandc.2013.09.008 10 SILVA

  20. A VLSI field-programmable mixed-signal array to perform neural signal processing and neural modeling in a prosthetic system.

    Science.gov (United States)

    Bamford, Simeon A; Hogri, Roni; Giovannucci, Andrea; Taub, Aryeh H; Herreros, Ivan; Verschure, Paul F M J; Mintz, Matti; Del Giudice, Paolo

    2012-07-01

    A very-large-scale integration field-programmable mixed-signal array specialized for neural signal processing and neural modeling has been designed. This has been fabricated as a core on a chip prototype intended for use in an implantable closed-loop prosthetic system aimed at rehabilitation of the learning of a discrete motor response. The chosen experimental context is cerebellar classical conditioning of the eye-blink response. The programmable system is based on the intimate mixing of switched capacitor analog techniques with low speed digital computation; power saving innovations within this framework are presented. The utility of the system is demonstrated by the implementation of a motor classical conditioning model applied to eye-blink conditioning in real time with associated neural signal processing. Paired conditioned and unconditioned stimuli were repeatedly presented to an anesthetized rat and recordings were taken simultaneously from two precerebellar nuclei. These paired stimuli were detected in real time from this multichannel data. This resulted in the acquisition of a trigger for a well-timed conditioned eye-blink response, and repetition of unpaired trials constructed from the same data led to the extinction of the conditioned response trigger, compatible with natural cerebellar learning in awake animals.

  1. Neural correlates of British sign language comprehension: spatial processing demands of topographic language.

    Science.gov (United States)

    MacSweeney, Mairéad; Woll, Bencie; Campbell, Ruth; Calvert, Gemma A; McGuire, Philip K; David, Anthony S; Simmons, Andrew; Brammer, Michael J

    2002-10-01

    In all signed languages used by deaf people, signs are executed in "sign space" in front of the body. Some signed sentences use this space to map detailed "real-world" spatial relationships directly. Such sentences can be considered to exploit sign space "topographically." Using functional magnetic resonance imaging, we explored the extent to which increasing the topographic processing demands of signed sentences was reflected in the differential recruitment of brain regions in deaf and hearing native signers of the British Sign Language. When BSL signers performed a sentence anomaly judgement task, the occipito-temporal junction was activated bilaterally to a greater extent for topographic than nontopographic processing. The differential role of movement in the processing of the two sentence types may account for this finding. In addition, enhanced activation was observed in the left inferior and superior parietal lobules during processing of topographic BSL sentences. We argue that the left parietal lobe is specifically involved in processing the precise configuration and location of hands in space to represent objects, agents, and actions. Importantly, no differences in these regions were observed when hearing people heard and saw English translations of these sentences. Despite the high degree of similarity in the neural systems underlying signed and spoken languages, exploring the linguistic features which are unique to each of these broadens our understanding of the systems involved in language comprehension.

  2. Optimization of LPDC Process Parameters Using the Combination of Artificial Neural Network and Genetic Algorithm Method

    Science.gov (United States)

    Zhang, Liqiang; Li, Luoxing; Wang, Shiuping; Zhu, Biwu

    2012-04-01

    In this article, the low-pressure die-cast (LPDC) process parameters of aluminum alloy thin-walled component with permanent mold are optimized using a combining artificial neural network and genetic algorithm (ANN/GA) method. In this method, an ANN model combining learning vector quantization (LVQ) and back-propagation (BP) algorithm is proposed to map the complex relationship between process conditions and quality indexes of LPDC. The genetic algorithm is employed to optimize the process parameters with the fitness function based on the trained ANN model. Then, by applying the optimized parameters, a thin-walled component with 300 mm in length, 100 mm in width, and 1.5 mm in thickness is successfully prepared and no obvious defects such as shrinkage, gas porosity, distortion, and crack were found in the component. The results indicate that the combining ANN/GA method is an effective tool for the process optimization of LPDC, and they also provide valuable reference on choosing the right process parameters for LPDC thin-walled aluminum alloy casting.

  3. Musical experience facilitates lexical tone processing among Mandarin speakers: Behavioral and neural evidence.

    Science.gov (United States)

    Tang, Wei; Xiong, Wen; Zhang, Yu-Xuan; Dong, Qi; Nan, Yun

    2016-10-01

    Music and speech share many sound attributes. Pitch, as the percept of fundamental frequency, often occupies the center of researchers' attention in studies on the relationship between music and speech. One widely held assumption is that music experience may confer an advantage in speech tone processing. The cross-domain effects of musical training on non-tonal language speakers' linguistic pitch processing have been relatively well established. However, it remains unclear whether musical experience improves the processing of lexical tone for native tone language speakers who actually use lexical tones in their daily communication. Using a passive oddball paradigm, the present study revealed that among Mandarin speakers, musicians demonstrated enlarged electrical responses to lexical tone changes as reflected by the increased mismatch negativity (MMN) amplitudes, as well as faster behavioral discrimination performance compared with age- and IQ-matched nonmusicians. The current results suggest that in spite of the preexisting long-term experience with lexical tones in both musicians and nonmusicians, musical experience can still modulate the cortical plasticity of linguistic tone processing and is associated with enhanced neural processing of speech tones. Our current results thus provide the first electrophysiological evidence supporting the notion that pitch expertise in the music domain may indeed be transferable to the speech domain even for native tone language speakers. Copyright © 2016 Elsevier Ltd. All rights reserved.

  4. Modeling of CVI process in fabrication of carbon/carbon composites by an artificial neural network

    Institute of Scientific and Technical Information of China (English)

    李爱军; 李贺军; 李克智; 顾正彬

    2003-01-01

    The chemical vapor infiltration(CVI) process in fabrication of carbon-carbon composites is very complex and highly inefficient, which adds considerably to the cost of fabrication and limits the application of the material. This paper tries to use a supervised artificial neural network(ANN) to model the nonlinear relationship between parameters of isothermal CVI(ICVI) processes and physical properties of C/C composites. A model for preprocessing dataset and selecting its topology is developed using the Levenberg-Marquardt training algorithm and trained with comprehensive dataset of tubal C/C components collected from experimental data and abundant simulated data obtained by the finite element method. A basic repository on the domain knowledge of CVI processes is established via sufficient data mining by the network. With the help of the repository stored in the trained network, not only the time-dependent effects of parameters in CVI processes but also their coupling effects can be analyzed and predicted. The results show that the ANN system is effective and successful for optimizing CVI processes in fabrication of C/C composites.

  5. ECG processing techniques based on neural networks and bidirectional associative memories.

    Science.gov (United States)

    Maglaveras, N; Stamkopoulos, T; Pappas, C; Strintzis, M

    1998-01-01

    Two ECG processing techniques are described for the classification of QRSs, PVCs and normal and ischaemic beats. The techniques use neural network (NN) technology in two ways. The first technique, uses nonlinear ECG mapping preprocessing and subsequently for classification uses a shrinking algorithm based on NNs. This technique is applied to the QRS/PVC problem with good result. The second technique is based on the Bidirectional Associative Memory (BAM) NN and is used to distinguish normal from ischaemic beats. In this technique the ECG beat is treated as a digitized image which is then transformed into a bipolar vector suitable for input in the BAM. The results show that this method, if properly calibrated, can result in a fast and reliable ischaemic beat detection algorithm.

  6. Double hidden layer RBF process neural network based online prediction of steam turbine exhaust enthalpy

    Institute of Scientific and Technical Information of China (English)

    GONG Huanchun

    2014-01-01

    In order to diagnose the unit economic performance online,the radial basis function (RBF) process neural network with two hidden layers was introduced to online prediction of steam turbine exhaust enthalpy.Thus,the model reflecting complicated relationship between the steam turbine exhaust enthalpy and the relative operation parameters was established.Moreover,the enthalpy of final stage extraction steam and exhaust from a 300 MW unit turbine was taken as the example to perform the online calculation. The results show that,the average relative error of this method is less than 1%,so the accuracy of this al-gorithm is higher than that of the BP neutral network.Furthermore,this method has advantages of high convergence rate,simple structure and high accuracy.

  7. Optimization of Force and Surface Roughness for Carbonized Steel in Turning Process through Neural Network

    Directory of Open Access Journals (Sweden)

    Anita Jha

    2016-06-01

    Full Text Available These days one of the most important machining processes in industries is turning. Turning is affected by many factors such as the cutting velocity, feed rate, depth of cut and geometry of cutting tool etc., which are input parameters in this paper work. The desired product of dimensional accuracy and less surface roughness is influenced by cutting force and tool vibration which are the responses and the functions of these input parameters. In this paper work we determine the optimal setting of cutting parameters cutting speed, depth of cut, feed and of the tool by using artificial neural network to get a maximum cutting force an minimum surface roughness. This study highlights the use of modern optimization technique to optimize the multi response in turning operation.

  8. Gender effects and sexual-orientation impact on androstadienone-evoked behavior and neural processing

    Directory of Open Access Journals (Sweden)

    Jacqueline eKrajnik

    2014-07-01

    Full Text Available In humans, the most established and investigated substance acting as a chemosignal, i.e., a substance that is excreted from the body, is 4,16-androstadien-3-one (AND. AND, which is found in sweat and saliva, is known to be responsible for influencing several variables, such as psychophysiological status, behavior, as well as cortical processing. The aim of the present review is to give insight into the variety of AND effects, with special regard to specific cross-sexual characteristics of this putative human chemosignal, emphasizing the neural activation patterns and factors such as contextual conditions. This review highlights the importance of including those contributing factors into the analysis of behavioral as well as brain-related studies.

  9. Speech Sound Processing Deficits and Training-Induced Neural Plasticity in Rats with Dyslexia Gene Knockdown

    Science.gov (United States)

    Centanni, Tracy M.; Chen, Fuyi; Booker, Anne M.; Engineer, Crystal T.; Sloan, Andrew M.; Rennaker, Robert L.; LoTurco, Joseph J.; Kilgard, Michael P.

    2014-01-01

    In utero RNAi of the dyslexia-associated gene Kiaa0319 in rats (KIA-) degrades cortical responses to speech sounds and increases trial-by-trial variability in onset latency. We tested the hypothesis that KIA- rats would be impaired at speech sound discrimination. KIA- rats needed twice as much training in quiet conditions to perform at control levels and remained impaired at several speech tasks. Focused training using truncated speech sounds was able to normalize speech discrimination in quiet and background noise conditions. Training also normalized trial-by-trial neural variability and temporal phase locking. Cortical activity from speech trained KIA- rats was sufficient to accurately discriminate between similar consonant sounds. These results provide the first direct evidence that assumed reduced expression of the dyslexia-associated gene KIAA0319 can cause phoneme processing impairments similar to those seen in dyslexia and that intensive behavioral therapy can eliminate these impairments. PMID:24871331

  10. Recursive neural networks for processing graphs with labelled edges: theory and applications.

    Science.gov (United States)

    Bianchini, M; Maggini, M; Sarti, L; Scarselli, F

    2005-10-01

    In this paper, we introduce a new recursive neural network model able to process directed acyclic graphs with labelled edges. The model uses a state transition function which considers the edge labels and is independent both from the number and the order of the children of each node. The computational capabilities of the new recursive architecture are assessed. Moreover, in order to test the proposed architecture on a practical challenging application, the problem of object detection in images is also addressed. In fact, the localization of target objects is a preliminary step in any recognition system. The proposed technique is general and can be applied in different detection systems, since it does not exploit any a priori knowledge on the particular problem. Some experiments on face detection, carried out on scenes acquired by an indoor camera, are reported, showing very promising results.

  11. Neural image analysis in the process of quality assessment: domestic pig oocytes

    Science.gov (United States)

    Boniecki, P.; Przybył, J.; Kuzimska, T.; Mueller, W.; Raba, B.; Lewicki, A.; Przybył, K.; Zaborowicz, M.; Koszela, K.

    2014-04-01

    The questions related to quality classification of animal oocytes are explored by numerous scientific and research centres. This research is important, particularly in the context of improving the breeding value of farm animals. The methods leading to the stimulation of normal development of a larger number of fertilised animal oocytes in extracorporeal conditions are of special importance. Growing interest in the techniques of supported reproduction resulted in searching for new, increasingly effective methods for quality assessment of mammalian gametes and embryos. Progress in the production of in vitro animal embryos in fact depends on proper classification of obtained oocytes. The aim of this paper was the development of an original method for quality assessment of oocytes, performed on the basis of their graphical presentation in the form of microscopic digital images. The classification process was implemented on the basis of the information coded in the form of microphotographic pictures of the oocytes of domestic pig, using the modern methods of neural image analysis.

  12. Estimation of Apple Volume and Its Shape Indentation Using Image Processing Technique and Neural Network

    Directory of Open Access Journals (Sweden)

    M Jafarlou

    2014-04-01

    Full Text Available Physical properties of agricultural products such as volume are the most important parameters influencing grading and packaging systems. They should be measured accurately as they are considered for any good system design. Image processing and neural network techniques are both non-destructive and useful methods which are recently used for such purpose. In this study, the images of apples were captured from a constant distance and then were processed in MATLAB software and the edges of apple images were extracted. The interior area of apple image was divided into some thin trapezoidal elements perpendicular to longitudinal axis. Total volume of apple was estimated by the summation of incremental volumes of these elements revolved around the apple’s longitudinal axis. The picture of half cut apple was also captured in order to obtain the apple shape’s indentation volume, which was subtracted from the previously estimated total volume of apple. The real volume of apples was measured using water displacement method and the relation between the real volume and estimated volume was obtained. The t-test and Bland-Altman indicated that the difference between the real volume and the estimated volume was not significantly different (p>0.05 i.e. the mean difference was 1.52 cm3 and the accuracy of measurement was 92%. Utilizing neural network with input variables of dimension and mass has increased the accuracy up to 97% and the difference between the mean of volumes decreased to 0.7 cm3.

  13. The Effect of Age on Neural Processing of Pleasant Soft Touch Stimuli

    Directory of Open Access Journals (Sweden)

    April C May

    2014-02-01

    Full Text Available Tactile interactions with our environment stimulate afferent fibers within the skin, which deliver information about sensations of pain, texture, itch and other feelings to the brain as a comprehensive sense of self. These tactile interactions can stimulate brain regions involved in interoception and reward processing. This study examined subjective, behavioral, and neural processing as a function of age during stimulation of A-beta (Aβ and C tactile (CT afferents using a soft brush stroke task. 16 adolescents (ages 15-17, 22 young adults (ages 20-28, and 20 mature adults (ages 29-55 underwent a simple continuous performance task while periodically anticipating and experiencing a soft touch to the palm or forearm, during functional magnetic resonance imaging (fMRI. fMRI results showed that adolescents displayed greater bilateral posterior insula activation than young and mature adults across all conditions and stimulus types. Adolescents also demonstrated greater bilateral posterior insula activation than young and mature adults specifically in response to the soft touch condition. Adolescents also exhibited greater activation than mature adults in bilateral inferior frontal gyrus and striatum during the soft touch condition. However, mature adults showed greater striatum activation than adolescents and young adults during anticipation. In the left anterior cingulate cortex, mature adults exhibited greater activation than adolescents and young adults when anticipating the upcoming touch. These results support the hypothesis that adolescents show an exaggerated neural response to pleasant stimulation of afferents, which may have profound effects on how they approach or avoid social and risky situations. In particular, heightened interoceptive reactivity to pleasant stimuli might cause adolescents to seek experiences that are associated with pleasant stimulation.

  14. Using an Artificial Neural Network Approach for Supplier Evaluation Process and a Sectoral Application

    Directory of Open Access Journals (Sweden)

    A. Yeşim Yayla

    2011-02-01

    Full Text Available In this study, a-three layered feed-forward backpropagation Artificial Neural Network (ANN model is developed for the supplier firms in ceramic sector on the bases of user effectiveness for using concurrent engineering method. The developed model is also questioned for its usability in the supplier evaluation process. The network's independent variables of the developed model are considered as input variables of the network and dependent variables are used as output variables. The values of these variables are determined with factor analysis. For obtaining the date set to be used in the analysis, a questionnaire form with 34 questions explaining the network's input and output variables are prepared and sent out to 52 firms active in related sector. For obtaining more accurate results from the network, the questions having factor load below 0,6 are eliminated from the analysis. With the elimination of the questions from the analysis, the answers given for 22 questions explaining 8 input variables are used for the evaluation the network's inputs, the answers given for 3 questions explaining output variables are used for the evaluation the network's outputs. The data set of the network's are divided into four equal groups with k-fold method in order to get four different alternative network structures. As a conclusion, the forecasted firm scores giving the minimum error from the network test simulation and real firm scores are found to be very close to each other, thus, it is concluded that the developed artificial neural network model can be used effectively in the supplier evaluation process.

  15. Exploring the Role of Spatial Frequency Information during Neural Emotion Processing in Human Infants

    Directory of Open Access Journals (Sweden)

    Sarah Jessen

    2017-10-01

    Full Text Available Enhanced attention to fear expressions in adults is primarily driven by information from low as opposed to high spatial frequencies contained in faces. However, little is known about the role of spatial frequency information in emotion processing during infancy. In the present study, we examined the role of low compared to high spatial frequencies in the processing of happy and fearful facial expressions by using filtered face stimuli and measuring event-related brain potentials (ERPs in 7-month-old infants (N = 26. Our results revealed that infants’ brains discriminated between emotional facial expressions containing high but not between expressions containing low spatial frequencies. Specifically, happy faces containing high spatial frequencies elicited a smaller Nc amplitude than fearful faces containing high spatial frequencies and happy and fearful faces containing low spatial frequencies. Our results demonstrate that already in infancy spatial frequency content influences the processing of facial emotions. Furthermore, we observed that fearful facial expressions elicited a comparable Nc response for high and low spatial frequencies, suggesting a robust detection of fearful faces irrespective of spatial frequency content, whereas the detection of happy facial expressions was contingent upon frequency content. In summary, these data provide new insights into the neural processing of facial emotions in early development by highlighting the differential role played by spatial frequencies in the detection of fear and happiness.

  16. Emotionally anesthetized: media violence induces neural changes during emotional face processing.

    Science.gov (United States)

    Stockdale, Laura A; Morrison, Robert G; Kmiecik, Matthew J; Garbarino, James; Silton, Rebecca L

    2015-10-01

    Media violence exposure causes increased aggression and decreased prosocial behavior, suggesting that media violence desensitizes people to the emotional experience of others. Alterations in emotional face processing following exposure to media violence may result in desensitization to others' emotional states. This study used scalp electroencephalography methods to examine the link between exposure to violence and neural changes associated with emotional face processing. Twenty-five participants were shown a violent or nonviolent film clip and then completed a gender discrimination stop-signal task using emotional faces. Media violence did not affect the early visual P100 component; however, decreased amplitude was observed in the N170 and P200 event-related potentials following the violent film, indicating that exposure to film violence leads to suppression of holistic face processing and implicit emotional processing. Participants who had just seen a violent film showed increased frontal N200/P300 amplitude. These results suggest that media violence exposure may desensitize people to emotional stimuli and thereby require fewer cognitive resources to inhibit behavior. © The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  17. Second Language Processing Shows Increased Native-Like Neural Responses after Months of No Exposure

    Science.gov (United States)

    Morgan-Short, Kara; Finger, Ingrid; Grey, Sarah; Ullman, Michael T.

    2012-01-01

    Although learning a second language (L2) as an adult is notoriously difficult, research has shown that adults can indeed attain native language-like brain processing and high proficiency levels. However, it is important to then retain what has been attained, even in the absence of continued exposure to the L2—particularly since periods of minimal or no L2 exposure are common. This event-related potential (ERP) study of an artificial language tested performance and neural processing following a substantial period of no exposure. Adults learned to speak and comprehend the artificial language to high proficiency with either explicit, classroom-like, or implicit, immersion-like training, and then underwent several months of no exposure to the language. Surprisingly, proficiency did not decrease during this delay. Instead, it remained unchanged, and there was an increase in native-like neural processing of syntax, as evidenced by several ERP changes—including earlier, more reliable, and more left-lateralized anterior negativities, and more robust P600s, in response to word-order violations. Moreover, both the explicitly and implicitly trained groups showed increased native-like ERP patterns over the delay, indicating that such changes can hold independently of L2 training type. The results demonstrate that substantial periods with no L2 exposure are not necessarily detrimental. Rather, benefits may ensue from such periods of time even when there is no L2 exposure. Interestingly, both before and after the delay the implicitly trained group showed more native-like processing than the explicitly trained group, indicating that type of training also affects the attainment of native-like processing in the brain. Overall, the findings may be largely explained by a combination of forgetting and consolidation in declarative and procedural memory, on which L2 grammar learning appears to depend. The study has a range of implications, and suggests a research program with

  18. Temperament trait of sensory processing sensitivity moderates cultural differences in neural response.

    Science.gov (United States)

    Aron, Arthur; Ketay, Sarah; Hedden, Trey; Aron, Elaine N; Rose Markus, Hazel; Gabrieli, John D E

    2010-06-01

    This study focused on a possible temperament-by-culture interaction. Specifically, it explored whether a basic temperament/personality trait (sensory processing sensitivity; SPS), perhaps having a genetic component, might moderate a previously established cultural difference in neural responses when making context-dependent vs context-independent judgments of simple visual stimuli. SPS has been hypothesized to underlie what has been called inhibitedness or reactivity in infants, introversion in adults, and reactivity or responsivness in diverse animal species. Some biologists view the trait as one of two innate strategies-observing carefully before acting vs being first to act. Thus the central characteristic of SPS is hypothesized to be a deep processing of information. Here, 10 European-Americans and 10 East Asians underwent functional magnetic resonance imaging while performing simple visuospatial tasks emphasizing judgments that were either context independent (typically easier for Americans) or context dependent (typically easier for Asians). As reported elsewhere, each group exhibited greater activation for the culturally non-preferred task in frontal and parietal regions associated with greater effort in attention and working memory. However, further analyses, reported here for the first time, provided preliminary support for moderation by SPS. Consistent with the careful-processing theory, high-SPS individuals showed little cultural difference; low-SPS, strong culture differences.

  19. Neural organization and visual processing in the anterior optic tubercle of the honeybee brain.

    Science.gov (United States)

    Mota, Theo; Yamagata, Nobuhiro; Giurfa, Martin; Gronenberg, Wulfila; Sandoz, Jean-Christophe

    2011-08-10

    The honeybee Apis mellifera represents a valuable model for studying the neural segregation and integration of visual information. Vision in honeybees has been extensively studied at the behavioral level and, to a lesser degree, at the physiological level using intracellular electrophysiological recordings of single neurons. However, our knowledge of visual processing in honeybees is still limited by the lack of functional studies of visual processing at the circuit level. Here we contribute to filling this gap by providing a neuroanatomical and neurophysiological characterization at the circuit level of a practically unstudied visual area of the bee brain, the anterior optic tubercle (AOTu). First, we analyzed the internal organization and neuronal connections of the AOTu. Second, we established a novel protocol for performing optophysiological recordings of visual circuit activity in the honeybee brain and studied the responses of AOTu interneurons during stimulation of distinct eye regions. Our neuroanatomical data show an intricate compartmentalization and connectivity of the AOTu, revealing a dorsoventral segregation of the visual input to the AOTu. Light stimuli presented in different parts of the visual field (dorsal, lateral, or ventral) induce distinct patterns of activation in AOTu output interneurons, retaining to some extent the dorsoventral input segregation revealed by our neuroanatomical data. In particular, activity patterns evoked by dorsal and ventral eye stimulation are clearly segregated into distinct AOTu subunits. Our results therefore suggest an involvement of the AOTu in the processing of dorsoventrally segregated visual information in the honeybee brain.

  20. Neural mechanisms of reward processing associated with depression-related personality traits.

    Science.gov (United States)

    Umemoto, Akina; Holroyd, Clay B

    2017-07-01

    Although impaired reward processing in depression has been well-documented, the exact nature of that deficit remains poorly understood. To investigate the link between depression and the neural mechanisms of reward processing, we examined individual differences in personality. We recorded the electroencephalogram from healthy college students engaged in a probabilistic reinforcement learning task. Participants also completed several personality questionnaires that assessed traits related to reward sensitivity, motivation, and depression. We examined whether behavioral measures of reward learning and event-related potential components related to outcome processing and reward anticipation-namely, the cue and feedback-related reward positivity (RewP) and the stimulus preceding negativity (SPN)-would link these personality traits to depression. Participants who scored high in reward sensitivity produced a relatively larger feedback-RewP. By contrast, participants who scored high in depression learned the contingencies for infrequently rewarded cue-response combinations relatively poorly, exhibited a larger SPN, and produced a smaller feedback-RewP, especially to outcomes following cue-response combinations that were frequently rewarded. These results point to a primary deficit in reward valuation in individuals who score high in depression, with secondary consequences that impact reward learning and anticipation. Despite recent evidence arguing for an anticipatory deficit in depression, impaired reward valuation as a primary deficit should be further examined in clinical samples. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

  1. Neural Correlates of Indicators of Sound Change in Cantonese: Evidence from Cortical and Subcortical Processes

    Science.gov (United States)

    Maggu, Akshay R.; Liu, Fang; Antoniou, Mark; Wong, Patrick C. M.

    2016-01-01

    Across time, languages undergo changes in phonetic, syntactic, and semantic dimensions. Social, cognitive, and cultural factors contribute to sound change, a phenomenon in which the phonetics of a language undergo changes over time. Individuals who misperceive and produce speech in a slightly divergent manner (called innovators) contribute to variability in the society, eventually leading to sound change. However, the cause of variability in these individuals is still unknown. In this study, we examined whether such misperceptions are represented in neural processes of the auditory system. We investigated behavioral, subcortical (via FFR), and cortical (via P300) manifestations of sound change processing in Cantonese, a Chinese language in which several lexical tones are merging. Across the merging categories, we observed a similar gradation of speech perception abilities in both behavior and the brain (subcortical and cortical processes). Further, we also found that behavioral evidence of tone merging correlated with subjects' encoding at the subcortical and cortical levels. These findings indicate that tone-merger categories, that are indicators of sound change in Cantonese, are represented neurophysiologically with high fidelity. Using our results, we speculate that innovators encode speech in a slightly deviant neurophysiological manner, and thus produce speech divergently that eventually spreads across the community and contributes to sound change. PMID:28066218

  2. Behavioral and Neural Correlates of Executive Function: Interplay between Inhibition and Updating Processes

    Directory of Open Access Journals (Sweden)

    Na Young Kim

    2017-06-01

    Full Text Available This study investigated the interaction between two executive function processes, inhibition and updating, through analyses of behavioral, neurophysiological, and effective connectivity metrics. Although, many studies have focused on behavioral effects of executive function processes individually, few studies have examined the dynamic causal interactions between these two functions. A total of twenty participants from a local university performed a dual task combing flanker and n-back experimental paradigms, and completed the Operation Span Task designed to measure working memory capacity. We found that both behavioral (accuracy and reaction time and neurophysiological (P300 amplitude and alpha band power metrics on the inhibition task (i.e., flanker task were influenced by the updating load (n-back level and modulated by working memory capacity. Using independent component analysis, source localization (DIPFIT, and Granger Causality analysis of the EEG time-series data, the present study demonstrated that manipulation of cognitive demand in a dual executive function task influenced the causal neural network. We compared connectivity across three updating loads (n-back levels and found that experimental manipulation of working memory load enhanced causal connectivity of a large-scale neurocognitive network. This network contains the prefrontal and parietal cortices, which are associated with inhibition and updating executive function processes. This study has potential applications in human performance modeling and assessment of mental workload, such as the design of training materials and interfaces for those performing complex multitasking under stress.

  3. Statistical learning problem of artificial neural network to control roofing process

    Directory of Open Access Journals (Sweden)

    Lapidus Azariy

    2017-01-01

    Full Text Available Now software developed on the basis of artificial neural networks (ANN has been actively implemented in construction companies to support decision-making in organization and management of construction processes. ANN learning is the main stage of its development. A key question for supervised learning is how many number of training examples we need to approximate the true relationship between network inputs and output with the desired accuracy. Also designing of ANN architecture is related to learning problem known as “curse of dimensionality”. This problem is important for the study of construction process management because of the difficulty to get training data from construction sites. In previous studies the authors have designed a 4-layer feedforward ANN with a unit model of 12-5-4-1 to approximate estimation and prediction of roofing process. This paper presented the statistical learning side of created ANN with simple-error-minimization algorithm. The sample size to efficient training and the confidence interval of network outputs defined. In conclusion the authors predicted successful ANN learning in a large construction business company within a short space of time.

  4. Cultural influences on the neural correlate of moral decision making processes.

    Science.gov (United States)

    Han, Hyemin; Glover, Gary H; Jeong, Changwoo

    2014-02-01

    This study compares the neural substrate of moral decision making processes between Korean and American participants. By comparison with Americans, Korean participants showed increased activity in the right putamen associated with socio-intuitive processes and right superior frontal gyrus associated with cognitive control processes under a moral-personal condition, and in the right postcentral sulcus associated with mental calculation in familiar contexts under a moral-impersonal condition. On the other hand, American participants showed a significantly higher degree of activity in the bilateral anterior cingulate cortex (ACC) associated with conflict resolution under the moral-personal condition, and in the right medial frontal gyrus (MFG) associated with simple cognitive branching in non-familiar contexts under the moral-impersonal condition when a more lenient threshold was applied, than Korean participants. These findings support the ideas of the interactions between the cultural background, education, and brain development, proposed in the field of cultural psychology and educational psychology. The study introduces educational implications relevant to moral psychologists and educators.

  5. Neural, psychophysiological, and behavioral markers of fear processing in PTSD: a review of the literature.

    Science.gov (United States)

    Shvil, Erel; Rusch, Heather L; Sullivan, Gregory M; Neria, Yuval

    2013-05-01

    As presently defined, post-traumatic stress disorder (PTSD) is an amalgam of symptoms falling into: re-experiencing of the trauma, avoidance of reminders of it, emotional numbing and hyperarousal. PTSD has a well-known proximate cause, commonly occurring after a life-threatening event that induces a response of intense fear, horror, and helplessness. Much of the advancement in understanding of the neurobiology of PTSD has emerged from conceptualizing the disorder as one that involves substantial dysfunction in fear processing. This article reviews recent knowledge of fear processing markers in PTSD. A systematic search was performed of reports within the specific three-year publication time period of January 2010 to December 2012. We identified a total of 31 studies reporting fear processing markers in PTSD. We further categorized them according to the following classification: (1) neural-activation markers (n=10), (2) psychophysiological markers (n=14), and (3) behavioral markers (n=7). Across most studies reviewed here, significant differences between individuals with PTSD and healthy controls were shown. Methodological, theoretical and clinical implications were discussed.

  6. Information processing in micro and meso-scale neural circuits during normal and disease states

    Science.gov (United States)

    Luongo, Francisco

    Neural computation can occur at multiple spatial and temporal timescales. The sum total of all of these processes is to guide optimal behaviors within the context of the constraints imposed by the physical world. How the circuits of the brain achieves this goal represents a central question in systems neuroscience. Here I explore the many ways in which the circuits of the brain can process information at both the micro and meso scale. Understanding the way information is represented and processed in the brain could shed light on the neuropathology underlying complex neuropsychiatric diseases such as autism and schizophrenia. Chapter 2 establishes an experimental paradigm for assaying patterns of microcircuit activity and examines the role of dopaminergic modulation on prefrontal microcircuits. We find that dopamine type 2 (D2) receptor activation results in an increase in spontaneous activity while dopamine type 1 (D1) activation does not. Chapter 3 of this dissertation presents a study that illustrates how cholingergic activation normally produces what has been suggested as a neural substrate of attention; pairwise decorrelation in microcircuit activity. This study also shows that in two etiologicall distinct mouse models of autism, FMR1 knockout mice and Valproic Acid exposed mice, this ability to decorrelate in the presence of cholinergic activation is lost. This represents a putative microcircuit level biomarker of autism. Chapter 4 examines the structure/function relationship within the prefrontal microcircuit. Spontaneous activity in prefrontal microcircuits is shown to be organized according to a small world architecture. Interestingly, this architecture is important for one concrete function of neuronal microcircuits; the ability to produce temporally stereotyped patterns of activation. In the final chapter, we identify subnetworks in chronic intracranial electrocorticographic (ECoG) recordings using pairwise electrode coherence and dimensionality reduction

  7. Point process modeling and estimation: Advances in the analysis of dynamic neural spiking data

    Science.gov (United States)

    Deng, Xinyi

    A common interest of scientists in many fields is to understand the relationship between the dynamics of a physical system and the occurrences of discrete events within such physical system. Seismologists study the connection between mechanical vibrations of the Earth and the occurrences of earthquakes so that future earthquakes can be better predicted. Astrophysicists study the association between the oscillating energy of celestial regions and the emission of photons to learn the Universe's various objects and their interactions. Neuroscientists study the link between behavior and the millisecond-timescale spike patterns of neurons to understand higher brain functions. Such relationships can often be formulated within the framework of state-space models with point process observations. The basic idea is that the dynamics of the physical systems are driven by the dynamics of some stochastic state variables and the discrete events we observe in an interval are noisy observations with distributions determined by the state variables. This thesis proposes several new methodological developments that advance the framework of state-space models with point process observations at the intersection of statistics and neuroscience. In particular, we develop new methods 1) to characterize the rhythmic spiking activity using history-dependent structure, 2) to model population spike activity using marked point process models, 3) to allow for real-time decision making, and 4) to take into account the need for dimensionality reduction for high-dimensional state and observation processes. We applied these methods to a novel problem of tracking rhythmic dynamics in the spiking of neurons in the subthalamic nucleus of Parkinson's patients with the goal of optimizing placement of deep brain stimulation electrodes. We developed a decoding algorithm that can make decision in real-time (for example, to stimulate the neurons or not) based on various sources of information present in

  8. Imaging a cognitive model of apraxia: the neural substrate of gesture-specific cognitive processes.

    Science.gov (United States)

    Peigneux, Philippe; Van der Linden, Martial; Garraux, Gaetan; Laureys, Steven; Degueldre, Christian; Aerts, Joel; Del Fiore, Guy; Moonen, Gustave; Luxen, Andre; Salmon, Eric

    2004-03-01

    The present study aimed to ascertain the neuroanatomical basis of an influential neuropsychological model for upper limb apraxia [Rothi LJ, et al. The Neuropsychology of Action. 1997. Hove, UK: Psychology Press]. Regional cerebral blood flow was measured in healthy volunteers using H2 15O PET during performance of four tasks commonly used for testing upper limb apraxia, i.e., pantomime of familiar gestures on verbal command, imitation of familiar gestures, imitation of novel gestures, and an action-semantic task that consisted in matching objects for functional use. We also re-analysed data from a previous PET study in which we investigated the neural basis of the visual analysis of gestures. First, we found that two sets of discrete brain areas are predominantly engaged in the imitation of familiar and novel gestures, respectively. Segregated brain activation for novel gesture imitation concur with neuropsychological reports to support the hypothesis that knowledge about the organization of the human body mediates the transition from visual perception to motor execution when imitating novel gestures [Goldenberg Neuropsychologia 1995;33:63-72]. Second, conjunction analyses revealed distinctive neural bases for most of the gesture-specific cognitive processes proposed in this cognitive model of upper limb apraxia. However, a functional analysis of brain imaging data suggested that one single memory store may be used for "to-be-perceived" and "to-be-produced" gestural representations, departing from Rothi et al.'s proposal. Based on the above considerations, we suggest and discuss a revised model for upper limb apraxia that might best account for both brain imaging findings and neuropsychological dissociations reported in the apraxia literature. Copyright 2004 Wiley-Liss, Inc.

  9. Neural activation during processing of aversive faces predicts treatment outcome in alcoholism.

    Science.gov (United States)

    Charlet, Katrin; Schlagenhauf, Florian; Richter, Anne; Naundorf, Karina; Dornhof, Lina; Weinfurtner, Christopher E J; König, Friederike; Walaszek, Bernadeta; Schubert, Florian; Müller, Christian A; Gutwinski, Stefan; Seissinger, Annette; Schmitz, Lioba; Walter, Henrik; Beck, Anne; Gallinat, Jürgen; Kiefer, Falk; Heinz, Andreas

    2014-05-01

    Neuropsychological studies reported decoding deficits of emotional facial expressions in alcohol-dependent patients, and imaging studies revealed reduced prefrontal and limbic activation during emotional face processing. However, it remains unclear whether this reduced neural activation is mediated by alcohol-associated volume reductions and whether it interacts with treatment outcome. We combined analyses of neural activation during an aversive face-cue-comparison task and local gray matter volumes (GM) using Biological Parametric Mapping in 33 detoxified alcohol-dependent patients and 33 matched healthy controls. Alcoholics displayed reduced activation toward aversive faces-neutral shapes in bilateral fusiform gyrus [FG; Brodmann areas (BA) 18/19], right middle frontal gyrus (BA46/47), right inferior parietal gyrus (BA7) and left cerebellum compared with controls, which were explained by GM differences (except for cerebellum). Enhanced functional activation in patients versus controls was found in left rostral anterior cingulate cortex (ACC) and medial frontal gyrus (BA10/11), even after GM reduction control. Increased ACC activation correlated significantly with less (previous) lifetime alcohol intake [Lifetime Drinking History (LDH)], longer abstinence and less subsequent binge drinking in patients. High LDH appear to impair treatment outcome via its neurotoxicity on ACC integrity. Thus, high activation of the rostral ACC elicited by affective faces appears to be a resilience factor predicting better treatment outcome. Although no group differences were found, increased FG activation correlated with patients' higher LDH. Because high LDH correlated with worse task performance for facial stimuli in patients, elevated activation in the fusiform 'face' area may reflect inefficient compensatory activation. Therapeutic interventions (e.g. emotion evaluation training) may enable patients to cope with social stress and to decrease relapses after detoxification.

  10. Erythropoietin modulates neural and cognitive processing of emotional information in biomarker models of antidepressant drug action in depressed patients

    DEFF Research Database (Denmark)

    Miskowiak, Kamilla W; Favaron, Elisa; Hafizi, Sepehr

    2010-01-01

    Erythropoietin (Epo) has neuroprotective and neurotrophic effects, and may be a novel therapeutic agent in the treatment of psychiatric disorders. We have demonstrated antidepressant-like effects of Epo on the neural and cognitive processing of facial expressions in healthy volunteers. The curren...

  11. Review: “Implementation of Feedforward and Feedback Neural Network for Signal Processing Using Analog VLSI Technology”

    Directory of Open Access Journals (Sweden)

    Miss. Rachana R. Patil

    2015-01-01

    Full Text Available Main focus of project is on implementation of Neural Network Architecture (NNA with on chip learning on Analog VLSI Technology for signal processing application. In the proposed paper the analog components like Gilbert Cell Multiplier (GCM, Neuron Activation Function (NAF are used to implement artificial NNA. Analog components used comprises of multiplier, adder and tan sigmoidal function circuit using MOS transistor. This Neural Architecture is trained using Back Propagation (BP Algorithm in analog domain with new techniques of weight storage. Layout design and verification of above design is carried out using VLSI Backend Microwind 3.1 software Tool. The technology used to design layout is 32 nm CMOS Technology

  12. Prediction of Pitting Corrosion Mass Loss for 304 Stainless Steel by Image Processing and BP Neural Network

    Institute of Scientific and Technical Information of China (English)

    ZHANG Wei; LIANG Cheng-hao

    2005-01-01

    Image processing technique was employed to analyze pitting corrosion morphologies of 304 stainless steel exposed to FeCl3 environments. BP neural network models were developed for the prediction of pitting corrosion mass loss using the obtained data of the total and the average pit areas which were extracted from pitting binary image. The results showed that the predicted results obtained by the 2-5-1 type BP neural network model are in good agreement with the experimental data of pitting corrosion mass loss. The maximum relative error of prediction is 6.78%.

  13. Difficulty-related changes in inter-regional neural synchrony are dissociated between target and non-target processing.

    Science.gov (United States)

    Choi, Jeong Woo; Cha, Kwang Su; Choi, Jong Doo; Jung, Ki-Young; Kim, Kyung Hwan

    2015-04-01

    The major purpose of this study was to explore the changes in the local/global gamma-band neural synchronies during target/non-target processing due to task difficulty under an auditory three-stimulus oddball paradigm. Multichannel event-related potentials (ERPs) were recorded from fifteen healthy participants during the oddball task. In addition to the conventional ERP analysis, we investigated the modulations in gamma-band activity (GBA) and inter-regional gamma-band phase synchrony (GBPS) for infrequent target and non-target processing due to task difficulty. The most notable finding was that the difficulty-related changes in inter-regional GBPS (33-35 Hz) at P300 epoch (350-600 ms) completely differed for target and non-target processing. As task difficulty increased, the GBPS significantly reduced for target processing but increased for non-target processing. This result contrasts with the local neural synchrony in gamma-bands, which was not affected by task difficulty. Another major finding was that the spatial patterns of functional connectivity were dissociated for target and non-target processing with regard to the difficult task. The spatial pattern for target processing was compatible with the top-down attention network, whereas that for the non-target corresponded to the bottom-up attention network. Overall, we found that the inter-regional gamma-band neural synchronies during target/non-target processing change significantly with task difficulty and that this change is dissociated between target and non-target processing. Our results indicate that large-scale neural synchrony is more relevant for the difference in information processing between target and non-target stimuli.

  14. Estimating the Number of Test Workers Necessary for a Software Testing Process Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Alaa F. Sheta

    2014-08-01

    Full Text Available On time and within budget software project development represents a challenge for software project managers. Software management activities include but are not limited to: estimation of project cost, development of schedules and budgets, meeting user requirements and complying with standards. Recruiting development team members is a sophisticated problem for a software project manager. Since the utmost cost in software development effort is manpower, software project effort and is associated cost estimation models are used in estimating the effort required to complete a project. This effort estimate can then be converted into dollars based on the proper labor rates. An initial development team needs to be selected not only at the beginning of the project but also during the development process. It is important to allocate the necessary team to a project and efficiently distribute their effort during the development life cycle. In this paper, we provide our initial idea of developing a prediction model for defining the estimated required number of test workers of a software project during the software testing process. The developed models utilize the test instance and the number of observed faults as input to the proposed models. Artificial Neural Networks (ANNs successfully build the dynamic relationships between the inputs and output and produce and accurate predication estimates.

  15. Temperature and relative humidity estimation and prediction in the tobacco drying process using Artificial Neural Networks.

    Science.gov (United States)

    Martínez-Martínez, Víctor; Baladrón, Carlos; Gomez-Gil, Jaime; Ruiz-Ruiz, Gonzalo; Navas-Gracia, Luis M; Aguiar, Javier M; Carro, Belén

    2012-10-17

    This paper presents a system based on an Artificial Neural Network (ANN) for estimating and predicting environmental variables related to tobacco drying processes. This system has been validated with temperature and relative humidity data obtained from a real tobacco dryer with a Wireless Sensor Network (WSN). A fitting ANN was used to estimate temperature and relative humidity in different locations inside the tobacco dryer and to predict them with different time horizons. An error under 2% can be achieved when estimating temperature as a function of temperature and relative humidity in other locations. Moreover, an error around 1.5 times lower than that obtained with an interpolation method can be achieved when predicting the temperature inside the tobacco mass as a function of its present and past values with time horizons over 150 minutes. These results show that the tobacco drying process can be improved taking into account the predicted future value of the monitored variables and the estimated actual value of other variables using a fitting ANN as proposed.

  16. Temperature and Relative Humidity Estimation and Prediction in the Tobacco Drying Process Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Belén Carro

    2012-10-01

    Full Text Available This paper presents a system based on an Artificial Neural Network (ANN for estimating and predicting environmental variables related to tobacco drying processes. This system has been validated with temperature and relative humidity data obtained from a real tobacco dryer with a Wireless Sensor Network (WSN. A fitting ANN was used to estimate temperature and relative humidity in different locations inside the tobacco dryer and to predict them with different time horizons. An error under 2% can be achieved when estimating temperature as a function of temperature and relative humidity in other locations. Moreover, an error around 1.5 times lower than that obtained with an interpolation method can be achieved when predicting the temperature inside the tobacco mass as a function of its present and past values with time horizons over 150 minutes. These results show that the tobacco drying process can be improved taking into account the predicted future value of the monitored variables and the estimated actual value of other variables using a fitting ANN as proposed.

  17. Processing Narratives Concerning Protected Values: A Cross-Cultural Investigation of Neural Correlates.

    Science.gov (United States)

    Kaplan, Jonas T; Gimbel, Sarah I; Dehghani, Morteza; Immordino-Yang, Mary Helen; Sagae, Kenji; Wong, Jennifer D; Tipper, Christine M; Damasio, Hanna; Gordon, Andrew S; Damasio, Antonio

    2017-02-01

    Narratives are an important component of culture and play a central role in transmitting social values. Little is known, however, about how the brain of a listener/reader processes narratives. A receiver's response to narration is influenced by the narrator's framing and appeal to values. Narratives that appeal to "protected values," including core personal, national, or religious values, may be particularly effective at influencing receivers. Protected values resist compromise and are tied with identity, affective value, moral decision-making, and other aspects of social cognition. Here, we investigated the neural mechanisms underlying reactions to protected values in narratives. During fMRI scanning, we presented 78 American, Chinese, and Iranian participants with real-life stories distilled from a corpus of over 20 million weblogs. Reading these stories engaged the posterior medial, medial prefrontal, and temporo-parietal cortices. When participants believed that the protagonist was appealing to a protected value, signal in these regions was increased compared with when no protected value was perceived, possibly reflecting the intensive and iterative search required to process this material. The effect strength also varied across groups, potentially reflecting cultural differences in the degree of concern for protected values.

  18. Impact of Visual Corticostriatal Loop Disruption on Neural Processing within the Parahippocampal Place Area.

    Science.gov (United States)

    Nasr, Shahin; Rosas, Herminia D

    2016-10-05

    The caudate nucleus is a part of the visual corticostriatal loop (VCSL), receiving input from different visual areas and projecting back to the same cortical areas via globus pallidus, substantia nigra, and thalamus. Despite perceptual and navigation impairments in patients with VCSL disruption due to caudate atrophy (e.g., Huntington's disease, HD), the relevance of the caudate nucleus and VCSL on cortical visual processing is not fully understood. In a series of fMRI experiments, we found that the caudate showed a stronger functional connection to parahippocampal place area (PPA) compared with adjacent regions (e.g., fusiform face area, FFA) within the temporal visual cortex. Consistent with this functional link, the caudate showed a higher response to scenes compared with faces, similar to the PPA. Testing the impact of VCSL disruption on neural processes within PPA, HD patients showed reduced scene-selective activity within PPA compared with healthy matched controls. In contrast, the level of selective activity in adjacent cortical and subcortical face-selective areas (i.e., FFA and amygdala) remained intact. These results show some of the first evidence for the direct impact and potential clinical significance of VCSL on the generation of "selective" activity within PPA.

  19. The relaxation time of processes in a FitzHugh-Nagumo neural system with time delay

    Energy Technology Data Exchange (ETDEWEB)

    Gong Ailing; Zeng Chunhua [Faculty of Science, Kunming University of Science and Technology, Kunming 650093 (China); Wang Hua, E-mail: zchh2009@126.com [Province Engineering Research Center of Industrial Energy Conservation and New Technology, Kunming University of Science and Technology, Kunming, Yunnan 650093 (China)

    2011-08-01

    In this paper, we study the relaxation time (RT) of the steady-state correlation function in a FitzHugh-Nagumo neural system under the presence of multiplicative and additive white noises and time delay. The noise correlation parameter {lambda} can produce a critical behavior in the RT as functions of the multiplicative noise intensity D, the additive noise intensity Q and the time delay {tau}. That is, the RT decreases as the noise intensities D and Q increase, and increases as the time delay {tau} increases below the critical value of {lambda}. However, above the critical value, the RT first increases, reaches a maximum, and then decreases as D, Q and {tau} increase, i.e. a noise intensity D or Q and a time delay {tau} exist, at which the time scales of the relaxation process are at their largest. In addition, the additive noise intensity Q can also produce a critical behavior in the RT as a function of {lambda}. The noise correlation parameter {lambda} first increases the RT of processes, then decreases it below the critical value of Q. Above the critical value, {lambda} increases it.

  20. Neural responses during emotional processing before and after cognitive trauma therapy for battered women.

    Science.gov (United States)

    Aupperle, Robin L; Allard, Carolyn B; Simmons, Alan N; Flagan, Taru; Thorp, Steven R; Norman, Sonya B; Paulus, Martin P; Stein, Murray B

    2013-10-30

    Therapy for combat and accident-related posttraumatic stress disorder (PTSD) has been reported to influence amygdala and anterior cingulate cortex (ACC) response during emotional processing. It is not yet understood how therapy influences different phases of emotional processing, and whether previous findings generalize to other PTSD populations. We hypothesized that cognitive trauma therapy for battered women (CTT-BW) would alter insula, amygdala, and cingulate responses during anticipation and presentation of emotional images. Fourteen female patients with PTSD related to domestic violence completed the Clinician Administered PTSD Scale (CAPS) and functional magnetic resonance imaging (fMRI) before and after CTT-BW. The fMRI task involved cued anticipation followed by presentation of positive versus negative affective images. CTT-BW was associated with decreases in CAPS score, enhanced ACC and decreased anterior insula activation during anticipation, and decreased dorsolateral prefrontal cortex and amygdala response during image presentation (negative-positive). Pre-treatment ACC activation during anticipation and image presentation exhibited positive and negative relationships to treatment response, respectively. Results suggest that CTT-BW enhanced efficiency of neural responses during preparation for upcoming emotional events in a way that reduced the need to recruit prefrontal-amygdala responses during the occurrence of the event. Results also suggest that enhancing ACC function during anticipation may be beneficial for PTSD treatment.

  1. The Neural Correlates of the Body-Object Interaction Effect in Semantic Processing

    Directory of Open Access Journals (Sweden)

    Ian Scott Hargreaves

    2012-02-01

    Full Text Available The semantic richness dimension referred to as body-object interaction (BOI measures perceptions of the ease with which people can physically interact with words’ referents. Previous studies have shown facilitated lexical and semantic processing for words rated high in BOI (e.g., belt than for words rated low in BOI (e.g., sun (e.g., Siakaluk, Pexman, Sears, Wilson, Locheed, & Owen, 2008b. These BOI effects have been taken as evidence that embodied information is relevant to word recognition. However, to date there is no evidence linking BOI manipulations to differences in the utilization of perceptual or sensorimotor areas of the brain. The current study used event-related fMRI to examine the neural correlates of BOI in a semantic categorization task (SCT. Sixteen healthy adults participated. Results showed that high BOI words were associated with activation in the left inferior parietal lobule (supramarginal gyrus, BA 40, a sensory association area involved in kinesthetic memory. These results provide evidence that the BOI dimension captures sensorimotor information, and that this contributes to semantic processing.

  2. Neural evidence for the subliminal processing of facial trustworthiness in infancy.

    Science.gov (United States)

    Jessen, Sarah; Grossmann, Tobias

    2017-04-22

    Face evaluation is thought to play a vital role in human social interactions. One prominent aspect is the evaluation of facial signs of trustworthiness, which has been shown to occur reliably, rapidly, and without conscious awareness in adults. Recent developmental work indicates that the sensitivity to facial trustworthiness has early ontogenetic origins as it can already be observed in infancy. However, it is unclear whether infants' sensitivity to facial signs of trustworthiness relies upon conscious processing of a face or, similar to adults, occurs also in response to subliminal faces. To investigate this question, we conducted an event-related brain potential (ERP) study, in which we presented 7-month-old infants with faces varying in trustworthiness. Facial stimuli were presented subliminally (below infants' face visibility threshold) for only 50ms and then masked by presenting a scrambled face image. Our data revealed that infants' ERP responses to subliminally presented faces differed as a function of trustworthiness. Specifically, untrustworthy faces elicited an enhanced negative slow wave (800-1000ms) at frontal and central electrodes. The current findings critically extend prior work by showing that, similar to adults, infants' neural detection of facial signs of trustworthiness occurs also in response to subliminal face. This supports the view that detecting facial trustworthiness is an early developing and automatic process in humans. Copyright © 2017 Elsevier Ltd. All rights reserved.

  3. Ageing differentially affects neural processing of different conflict types – an fMRI study

    Directory of Open Access Journals (Sweden)

    Margarethe eKorsch

    2014-04-01

    Full Text Available Interference control and conflict resolution is affected by ageing. There is increasing evidence that ageing does not compromise interference control in general but rather shows distinctive effects on different components of interference control. Different conflict types, (e.g. stimulus-stimulus (S-S or stimulus-response (S-R conflicts trigger different cognitive processes and thus activate different neural networks. In the present functional magnetic resonance imaging (fMRI study, we used a combined Flanker and Stimulus Response Conflict (SRC task to investigate the effect of ageing on S-S and S-R conflicts. Behavioral data analysis revealed larger SRC effects in elderly. fMRI Results show that both age groups recruited similar regions (caudate nucleus, cingulate gyrus and middle occipital gyrus during Flanker conflict processing. Furthermore, elderly show an additional activation pattern in parietal and frontal areas. In contrast, no common activation of both age groups was found in response to the SRC. These data suggest that ageing has distinctive effects on S-S and S-R conflicts.

  4. Parameters Optimization of Plasma Hardening Process Using Genetic Algorithm and Neural Network

    Institute of Scientific and Technical Information of China (English)

    LIU Gu; WANG Liu-ying; CHEN Gui-ming; HUA Shao-chun

    2011-01-01

    Plasma surface hardening process was performed to improve the performance of the AISI 1045 carbon steel.Experiments were carried out to characterize the hardening qualities.A predicting and optimizing model using genetic algorithm-back propagation neural network(GA-BP) was developed based on the experimental results.The non-linear relationship between properties of hardening layers and process parameters was established.The results show that the GA-BP predicting model is reliable since prediction results are in rather good agreement with measured results.The optimal properties of the hardened layer were deduced from GA.And through multi optimizations,the optimum comprehensive performances of the hardened layer were as follows:plasma arc current is 90 A,hardening speed is 2.2 m/min,plasma gas flow rate is 6.0 L/min and hardening distance is 4.3 mm.It concludes that GA-BP mode developed in this study provides a promising method for plasma hardening parameters prediction and optimization.

  5. A point-process response model for spike trains from single neurons in neural circuits under optogenetic stimulation.

    Science.gov (United States)

    Luo, X; Gee, S; Sohal, V; Small, D

    2016-02-10

    Optogenetics is a new tool to study neuronal circuits that have been genetically modified to allow stimulation by flashes of light. We study recordings from single neurons within neural circuits under optogenetic stimulation. The data from these experiments present a statistical challenge of modeling a high-frequency point process (neuronal spikes) while the input is another high-frequency point process (light flashes). We further develop a generalized linear model approach to model the relationships between two point processes, employing additive point-process response functions. The resulting model, point-process responses for optogenetics (PRO), provides explicit nonlinear transformations to link the input point process with the output one. Such response functions may provide important and interpretable scientific insights into the properties of the biophysical process that governs neural spiking in response to optogenetic stimulation. We validate and compare the PRO model using a real dataset and simulations, and our model yields a superior area-under-the-curve value as high as 93% for predicting every future spike. For our experiment on the recurrent layer V circuit in the prefrontal cortex, the PRO model provides evidence that neurons integrate their inputs in a sophisticated manner. Another use of the model is that it enables understanding how neural circuits are altered under various disease conditions and/or experimental conditions by comparing the PRO parameters. Copyright © 2015 John Wiley & Sons, Ltd.

  6. Supporting the joint warfighter by development, training, and fielding of man-portable UGVs

    Science.gov (United States)

    Ebert, Kenneth A.; Stratton, Benjamin V.

    2005-05-01

    The Robotic Systems Pool (RSP), sponsored by the Joint Robotics Program (JRP), is an inventory of small robotic systems, payloads, and components intended to expedite the development and integration of technology into effective, supportable, fielded robotic assets. The RSP loans systems to multiple users including the military, first-responders, research organizations, and academia. These users provide feedback in their specific domain, accelerating research and development improvements of robotic systems, which in turn allow the joint warfighter to benefit from such changes more quickly than from traditional acquisition cycles. Over the past year, RSP assets have been used extensively for pre-deployment operator and field training of joint Explosive Ordnance Disposal (EOD) teams, and for the training of Navy Reservist repair technicians. These Reservists are part of the Robotic Systems Combat Support Platoon (RSCSP), attached to Space and Naval Warfare Systems Center, San Diego. The RSCSP maintains and repairs RSP assets and provides deployable technical support for users of robotic systems. Currently, a small team from the RSCSP is deployed at Camp Victory repairing and maintaining man-portable unmanned ground vehicles (UGVs) used by joint EOD teams in Operation Iraqi Freedom. The focus of this paper is to elaborate on the RSP and RSCSP and their role as invaluable resources for spiral development in the robotics community by gaining first-hand technical feedback from the warfighter and other users.

  7. Dissociated emergent-response system and fine-processing system in human neural network and a heuristic neural architecture for autonomous humanoid robots.

    Science.gov (United States)

    Yan, Xiaodan

    2010-01-01

    The current study investigated the functional connectivity of the primary sensory system with resting state fMRI and applied such knowledge into the design of the neural architecture of autonomous humanoid robots. Correlation and Granger causality analyses were utilized to reveal the functional connectivity patterns. Dissociation was within the primary sensory system, in that the olfactory cortex and the somatosensory cortex were strongly connected to the amygdala whereas the visual cortex and the auditory cortex were strongly connected with the frontal cortex. The posterior cingulate cortex (PCC) and the anterior cingulate cortex (ACC) were found to maintain constant communication with the primary sensory system, the frontal cortex, and the amygdala. Such neural architecture inspired the design of dissociated emergent-response system and fine-processing system in autonomous humanoid robots, with separate processing units and another consolidation center to coordinate the two systems. Such design can help autonomous robots to detect and respond quickly to danger, so as to maintain their sustainability and independence.

  8. Dissociated Emergent-Response System and Fine-Processing System in Human Neural Network and a Heuristic Neural Architecture for Autonomous Humanoid Robots

    Directory of Open Access Journals (Sweden)

    Xiaodan Yan

    2010-01-01

    Full Text Available The current study investigated the functional connectivity of the primary sensory system with resting state fMRI and applied such knowledge into the design of the neural architecture of autonomous humanoid robots. Correlation and Granger causality analyses were utilized to reveal the functional connectivity patterns. Dissociation was within the primary sensory system, in that the olfactory cortex and the somatosensory cortex were strongly connected to the amygdala whereas the visual cortex and the auditory cortex were strongly connected with the frontal cortex. The posterior cingulate cortex (PCC and the anterior cingulate cortex (ACC were found to maintain constant communication with the primary sensory system, the frontal cortex, and the amygdala. Such neural architecture inspired the design of dissociated emergent-response system and fine-processing system in autonomous humanoid robots, with separate processing units and another consolidation center to coordinate the two systems. Such design can help autonomous robots to detect and respond quickly to danger, so as to maintain their sustainability and independence.

  9. Neural systems underlying British Sign Language and audio-visual English processing in native users.

    Science.gov (United States)

    MacSweeney, Mairéad; Woll, Bencie; Campbell, Ruth; McGuire, Philip K; David, Anthony S; Williams, Steven C R; Suckling, John; Calvert, Gemma A; Brammer, Michael J

    2002-07-01

    In order to understand the evolution of human language, it is necessary to explore the neural systems that support language processing in its many forms. In particular, it is informative to separate those mechanisms that may have evolved for sensory processing (hearing) from those that have evolved to represent events and actions symbolically (language). To what extent are the brain systems that support language processing shaped by auditory experience and to what extent by exposure to language, which may not necessarily be acoustically structured? In this first neuroimaging study of the perception of British Sign Language (BSL), we explored these questions by measuring brain activation using functional MRI in nine hearing and nine congenitally deaf native users of BSL while they performed a BSL sentence-acceptability task. Eight hearing, non-signing subjects performed an analogous task that involved audio-visual English sentences. The data support the argument that there are both modality-independent and modality-dependent language localization patterns in native users. In relation to modality-independent patterns, regions activated by both BSL in deaf signers and by spoken English in hearing non-signers included inferior prefrontal regions bilaterally (including Broca's area) and superior temporal regions bilaterally (including Wernicke's area). Lateralization patterns were similar for the two languages. There was no evidence of enhanced right-hemisphere recruitment for BSL processing in comparison with audio-visual English. In relation to modality-specific patterns, audio-visual speech in hearing subjects generated greater activation in the primary and secondary auditory cortices than BSL in deaf signers, whereas BSL generated enhanced activation in the posterior occipito-temporal regions (V5), reflecting the greater movement component of BSL. The influence of hearing status on the recruitment of sign language processing systems was explored by comparing deaf

  10. Implicit and explicit second language training recruit common neural mechanisms for syntactic processing.

    Science.gov (United States)

    Batterink, Laura; Neville, Helen

    2013-06-01

    In contrast to native language acquisition, adult second-language (L2) acquisition occurs under highly variable learning conditions. Although most adults acquire their L2 at least partially through explicit instruction, as in a classroom setting, many others acquire their L2 primarily through implicit exposure, as is typical of an immersion environment. Whether these differences in acquisition environment play a role in determining the neural mechanisms that are ultimately recruited to process L2 grammar has not been well characterized. This study investigated this issue by comparing the ERP response to novel L2 syntactic rules acquired under conditions of implicit exposure and explicit instruction, using a novel laboratory language-learning paradigm. Native speakers tested on these stimuli showed a biphasic response to syntactic violations, consisting of an earlier negativity followed by a later P600 effect. After merely an hour of training, both implicitly and explicitly trained learners who were capable of detecting grammatical violations also elicited P600 effects. In contrast, learners who were unable to discriminate between grammatically correct and incorrect sentences did not show significant P600 effects. The magnitude of the P600 effect was found to correlate with learners' behavioral proficiency. Behavioral measures revealed that successful learners from both the implicit and explicit groups gained explicit, verbalizable knowledge about the L2 grammar rules. Taken together, these results indicate that late, controlled mechanisms indexed by the P600 play a crucial role in processing a late-learned L2 grammar, regardless of training condition. These findings underscore the remarkable plasticity of later, attention-dependent processes and their importance in lifelong learning.

  11. Feed Forward Artificial Neural Network Model to Estimate the TPH Removal Efficiency in Soil Washing Process

    Directory of Open Access Journals (Sweden)

    Hossein Jafari Mansoorian

    2017-01-01

    Full Text Available Background & Aims of the Study: A feed forward artificial neural network (FFANN was developed to predict the efficiency of total petroleum hydrocarbon (TPH removal from a contaminated soil, using soil washing process with Tween 80. The main objective of this study was to assess the performance of developed FFANN model for the estimation of   TPH removal. Materials and Methods: Several independent repressors including pH, shaking speed, surfactant concentration and contact time were used to describe the removal of TPH as a dependent variable in a FFANN model. 85% of data set observations were used for training the model and remaining 15% were used for model testing, approximately. The performance of the model was compared with linear regression and assessed, using Root of Mean Square Error (RMSE as goodness-of-fit measure Results: For the prediction of TPH removal efficiency, a FANN model with a three-hidden-layer structure of 4-3-1 and a learning rate of 0.01 showed the best predictive results. The RMSE and R2 for the training and testing steps of the model were obtained to be 2.596, 0.966, 10.70 and 0.78, respectively. Conclusion: For about 80% of the TPH removal efficiency can be described by the assessed regressors the developed model. Thus, focusing on the optimization of soil washing process regarding to shaking speed, contact time, surfactant concentration and pH can improve the TPH removal performance from polluted soils. The results of this study could be the basis for the application of FANN for the assessment of soil washing process and the control of petroleum hydrocarbon emission into the environments.

  12. Neural populations in human posteromedial cortex display opposing responses during memory and numerical processing

    Science.gov (United States)

    Foster, Brett L.; Dastjerdi, Mohammad; Parvizi, Josef

    2012-01-01

    Our understanding of the human default mode network derives primarily from neuroimaging data but its electrophysiological correlates remain largely unexplored. To address this limitation, we recorded intracranially from the human posteromedial cortex (PMC), a core structure of the default mode network, during various conditions of internally directed (e.g., autobiographical memory) as opposed to externally directed focus (e.g., arithmetic calculation). We observed late-onset (>400 ms) increases in broad high γ-power (70–180 Hz) within PMC subregions during memory retrieval. High γ-power was significantly reduced or absent when subjects retrieved self-referential semantic memories or responded to self-judgment statements, respectively. Conversely, a significant deactivation of high γ-power was observed during arithmetic calculation, the duration of which correlated with reaction time at the signal-trial level. Strikingly, at each recording site, the magnitude of activation during episodic autobiographical memory retrieval predicted the degree of suppression during arithmetic calculation. These findings provide important anatomical and temporal details—at the neural population level—of PMC engagement during autobiographical memory retrieval and address how the same populations are actively suppressed during tasks, such as numerical processing, which require externally directed attention. PMID:22949666

  13. Repetition-related reductions in neural activity reveal component processes of mental simulation.

    Science.gov (United States)

    Szpunar, Karl K; St Jacques, Peggy L; Robbins, Clifford A; Wig, Gagan S; Schacter, Daniel L

    2014-05-01

    In everyday life, people adaptively prepare for the future by simulating dynamic events about impending interactions with people, objects and locations. Previous research has consistently demonstrated that a distributed network of frontal-parietal-temporal brain regions supports this ubiquitous mental activity. Nonetheless, little is known about the manner in which specific regions of this network contribute to component features of future simulation. In two experiments, we used a functional magnetic resonance (fMR)-repetition suppression paradigm to demonstrate that distinct frontal-parietal-temporal regions are sensitive to processing the scenarios or what participants imagined was happening in an event (e.g., medial prefrontal, posterior cingulate, temporal-parietal and middle temporal cortices are sensitive to the scenarios associated with future social events), people (medial prefrontal cortex), objects (inferior frontal and premotor cortices) and locations (posterior cingulate/retrosplenial, parahippocampal and posterior parietal cortices) that typically constitute simulations of personal future events. This pattern of results demonstrates that the neural substrates of these component features of event simulations can be reliably identified in the context of a task that requires participants to simulate complex, everyday future experiences.

  14. The neural correlates of priming emotion and reward systems for conflict processing in alcoholics.

    Science.gov (United States)

    Schulte, T; Jung, Y-C; Sullivan, E V; Pfefferbaum, A; Serventi, M; Müller-Oehring, E M

    2016-11-04

    Emotional dysregulation in alcoholism (ALC) may result from disturbed inhibitory mechanisms. We therefore tested emotion and alcohol cue reactivity and inhibitory processes using negative priming. To test the neural correlates of cue reactivity and negative priming, 26 ALC and 26 age-matched controls underwent functional MRI performing a Stroop color match-to-sample task. In cue reactivity trials, task-irrelevant emotion and alcohol-related pictures were interspersed between color samples and color words. In negative priming trials, pictures primed the semantic content of an alcohol or emotion Stroop word. Behaviorally, both groups showed response facilitation to picture cue trials and response inhibition to primed trials. For cue reactivity to emotion and alcohol pictures, ALC showed midbrain-limbic activation. By contrast, controls activated frontoparietal executive control regions. Greater midbrain-hippocampal activation in ALC correlated with higher amounts of lifetime alcohol consumption and higher anxiety. With negative priming, ALC exhibited frontal cortical but not midbrain-hippocampal activation, similar to the pattern observed in controls. Higher frontal activation to alcohol-priming correlated with less craving and to emotion-priming with fewer depressive symptoms. The findings suggest that neurofunctional systems in ALC can be primed to deal with upcoming emotion- and alcohol-related conflict and can overcome the prepotent midbrain-limbic cue reactivity response.

  15. dFasArt: dynamic neural processing in FasArt model.

    Science.gov (United States)

    Cano-Izquierdo, Jose-Manuel; Almonacid, Miguel; Pinzolas, Miguel; Ibarrola, Julio

    2009-05-01

    The temporal character of the input is, generally, not taken into account in the neural models. This paper presents an extension of the FasArt model focused on the treatment of temporal signals. FasArt model is proposed as an integration of the characteristic elements of the Fuzzy System Theory in an ART architecture. A duality between the activation concept and membership function is established. FasArt maintains the structure of the Fuzzy ARTMAP architecture, implying a static character since the dynamic response of the input is not considered. The proposed novel model, dynamic FasArt (dFasArt), uses dynamic equations for the processing stages of FasArt: activation, matching and learning. The new formulation of dFasArt includes time as another characteristic of the input. This allows the activation of the units to have a history-dependent character instead of being only a function of the last input value. Therefore, dFasArt model is robust to spurious values and noisy inputs. As experimental work, some cases have been used to check the robustness of dFasArt. A possible application has been proposed for the detection of variations in the system dynamics.

  16. Dimethylsulfoniopropionate Promotes Process Outgrowth in Neural Cells and Exerts Protective Effects against Tropodithietic Acid

    Directory of Open Access Journals (Sweden)

    Heidi Wichmann

    2016-05-01

    Full Text Available The marine environment harbors a plethora of bioactive substances, including drug candidates of potential value in the field of neuroscience. The present study was undertaken to investigate the effects of dimethylsulfoniopropionate (DMSP, produced by several algae, corals and higher plants, on cells of the mammalian nervous system, i.e., neuronal N2a and OLN-93 cells as model system for nerve cells and glia, respectively. Additionally, the protective capabilities of DMSP were assessed in cells treated with tropodithietic acid (TDA, a marine metabolite produced by several Roseobacter clade bacteria. Both cell lines, N2a and OLN-93, have previously been shown to be a sensitive target for the action of TDA, and cytotoxic effects of TDA have been connected to the induction of oxidative stress. Our data shows that DMSP promotes process outgrowth and microtubule reorganization and bundling, accompanied by an increase in alpha-tubulin acetylation. Furthermore, DMSP was able to prevent the cytotoxic effects exerted by TDA, including the breakdown of the mitochondrial membrane potential, upregulation of heat shock protein Hsp32 and activation of the extracellular signal-regulated kinases 1/2 (ERK1/2. Our study points to the conclusion that DMSP provides an antioxidant defense, not only in algae but also in mammalian neural cells.

  17. Neural correlates of encoding processes predicting subsequent cued recall and source memory.

    Science.gov (United States)

    Angel, Lucie; Isingrini, Michel; Bouazzaoui, Badiâa; Fay, Séverine

    2013-03-06

    In this experiment, event-related potentials were used to examine whether the neural correlates of encoding processes predicting subsequent successful recall differed from those predicting successful source memory retrieval. During encoding, participants studied lists of words and were instructed to memorize each word and the list in which it occurred. At test, they had to complete stems (the first four letters) with a studied word and then make a judgment of the initial temporal context (i.e. list). Event-related potentials recorded during encoding were segregated according to subsequent memory performance to examine subsequent memory effects (SMEs) reflecting successful cued recall (cued recall SME) and successful source retrieval (source memory SME). Data showed a cued recall SME on parietal electrode sites from 400 to 1200 ms and a late inversed cued recall SME on frontal sites in the 1200-1400 ms period. Moreover, a source memory SME was reported from 400 to 1400 ms on frontal areas. These findings indicate that patterns of encoding-related activity predicting successful recall and source memory are clearly dissociated.

  18. Experience Shapes the Development of Neural Substrates of Face Processing in Human Ventral Temporal Cortex.

    Science.gov (United States)

    Golarai, Golijeh; Liberman, Alina; Grill-Spector, Kalanit

    2017-02-01

    In adult humans, the ventral temporal cortex (VTC) represents faces in a reproducible topology. However, it is unknown what role visual experience plays in the development of this topology. Using functional magnetic resonance imaging in children and adults, we found a sequential development, in which the topology of face-selective activations across the VTC was matured by age 7, but the spatial extent and degree of face selectivity continued to develop past age 7 into adulthood. Importantly, own- and other-age faces were differentially represented, both in the distributed multivoxel patterns across the VTC, and also in the magnitude of responses of face-selective regions. These results provide strong evidence that experience shapes cortical representations of faces during development from childhood to adulthood. Our findings have important implications for the role of experience and age in shaping the neural substrates of face processing in the human VTC. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  19. Experimental and Computational Studies of Cortical Neural Network Properties Through Signal Processing

    Science.gov (United States)

    Clawson, Wesley Patrick

    Previous studies, both theoretical and experimental, of network level dynamics in the cerebral cortex show evidence for a statistical phenomenon called criticality; a phenomenon originally studied in the context of phase transitions in physical systems and that is associated with favorable information processing in the context of the brain. The focus of this thesis is to expand upon past results with new experimentation and modeling to show a relationship between criticality and the ability to detect and discriminate sensory input. A line of theoretical work predicts maximal sensory discrimination as a functional benefit of criticality, which can then be characterized using mutual information between sensory input, visual stimulus, and neural response,. The primary finding of our experiments in the visual cortex in turtles and neuronal network modeling confirms this theoretical prediction. We show that sensory discrimination is maximized when visual cortex operates near criticality. In addition to presenting this primary finding in detail, this thesis will also address our preliminary results on change-point-detection in experimentally measured cortical dynamics.

  20. Estimation of snow covered area for an urban catchment using image processing and neural networks.

    Science.gov (United States)

    Matheussen, B V; Thorolfsson, S T

    2003-01-01

    This paper presents a method to estimate the snow covered area (SCA) for small urban catchments. The method uses images taken with a digital camera positioned on top of a tall building. The camera is stationary and takes overview images of the same area every fifteen minutes throughout the winter season. The images were read into an image-processing program and a three-layered feed-forward perceptron artificial neural network (ANN) was used to calculate fractional snow cover within three different land cover types (road, park and roofs). The SCA was estimated from the number of pixels with snow cover relative to the total number of pixels. The method was tested for a small urban catchment, Risvollan in Trondheim, Norway. A time series of images taken during spring of 2001 and the 2001-2002 winter season was used to generate a time series of SCA. Snow covered area was also estimated from aerial photos. The results showed a strong correlation between SCA estimated from the digital camera and the aerial photos. The time series of SCA can be used for verification of urban snowmelt models.

  1. Implementation of Biography Based Neural Clustering (BBNC with Genetic Processing for tumor detection from medical images

    Directory of Open Access Journals (Sweden)

    Kaur Chandanpreet

    2016-01-01

    Full Text Available Segmentation is a best method to divide the required region from the medical images. This research is based on segmentation of medical images (MRI, CT scans based on the previous method known as pre-operative and post-recurrence tumor registration (PORTR and proposed method biography based neural clustering (BBNC with genetic processing for tumor segmentation. By using the new technique the extracted part can be view in 3D model and also can get the actual segmented tumor region. This new method will be helpful for diagnostics to find the tumor area as well as pixel difference in segmented part to define the tumor area accurately. While in the previous approach all the parameters have been used likewise, in which the registration method is used to transform the different sets of data into one coordinate system for segmentation of medical images. Registration basically is used to improve the signals to reduce the noise from the images. These techniques are better to find the tumor area from the MRI and CT scans, but after comparing them better results have been obtained in proposed technique. The proposed technique (BBNC reduces the extracted region again into required and actual region of tumor with accuracy of area, time and pixel difference.

  2. The effect of opioid receptor blockade on the neural processing of thermal stimuli.

    Directory of Open Access Journals (Sweden)

    Eszter D Schoell

    Full Text Available The endogenous opioid system represents one of the principal systems in the modulation of pain. This has been demonstrated in studies of placebo analgesia and stress-induced analgesia, where anti-nociceptive activity triggered by pain itself or by cognitive states is blocked by opioid antagonists. The aim of this study was to characterize the effect of opioid receptor blockade on the physiological processing of painful thermal stimulation in the absence of cognitive manipulation. We therefore measured BOLD (blood oxygen level dependent signal responses and intensity ratings to non-painful and painful thermal stimuli in a double-blind, cross-over design using the opioid receptor antagonist naloxone. On the behavioral level, we observed an increase in intensity ratings under naloxone due mainly to a difference in the non-painful stimuli. On the neural level, painful thermal stimulation was associated with a negative BOLD signal within the pregenual anterior cingulate cortex, and this deactivation was abolished by naloxone.

  3. Using a Large-scale Neural Model of Cortical Object Processing to Investigate the Neural Substrate for Managing Multiple Items in Short-term Memory.

    Science.gov (United States)

    Liu, Qin; Ulloa, Antonio; Horwitz, Barry

    2017-07-07

    Many cognitive and computational models have been proposed to help understand working memory. In this article, we present a simulation study of cortical processing of visual objects during several working memory tasks using an extended version of a previously constructed large-scale neural model [Tagamets, M. A., & Horwitz, B. Integrating electrophysiological and anatomical experimental data to create a large-scale model that simulates a delayed match-to-sample human brain imaging study. Cerebral Cortex, 8, 310-320, 1998]. The original model consisted of arrays of Wilson-Cowan type of neuronal populations representing primary and secondary visual cortices, inferotemporal (IT) cortex, and pFC. We added a module representing entorhinal cortex, which functions as a gating module. We successfully implemented multiple working memory tasks using the same model and produced neuronal patterns in visual cortex, IT cortex, and pFC that match experimental findings. These working memory tasks can include distractor stimuli or can require that multiple items be retained in mind during a delay period (Sternberg's task). Besides electrophysiology data and behavioral data, we also generated fMRI BOLD time series from our simulation. Our results support the involvement of IT cortex in working memory maintenance and suggest the cortical architecture underlying the neural mechanisms mediating particular working memory tasks. Furthermore, we noticed that, during simulations of memorizing a list of objects, the first and last items in the sequence were recalled best, which may implicate the neural mechanism behind this important psychological effect (i.e., the primacy and recency effect).

  4. Adults with high social anhedonia have altered neural connectivity with ventral lateral prefrontal cortex when processing positive social signals

    Directory of Open Access Journals (Sweden)

    Hong eYin

    2015-08-01

    Full Text Available Social anhedonia (SA is a debilitating characteristic of schizophrenia and a vulnerability for developing schizophrenia among people at risk. Prior work (Hooker et al, 2014 has revealed neural deficits in ventral lateral prefrontal cortex (VLPFC during processing of positive emotion in a community sample of people with high social anhedonia. Deficits in VLPFC neural activity are related to worse self-reported schizophrenia-spectrum symptoms and worse mood and behavior after social stress. In the current study, psychophysiological interaction (PPI analysis was applied to investigate the neural mechanisms mediated by VLPFC during emotion processing. PPI analysis revealed that, compared to low SA controls, participants with high SA displayed reduced VLPFC integration, specifically reduced connectivity between VLPFC and premotor cortex, inferior parietal and posterior temporal regions when viewing positive relative to neutral emotion. Across all participants, connectivity between VLPFC and inferior parietal region when viewing positive (versus neutral emotion was significantly correlated with measures of emotion management and attentional control. Additionally connectivity between VLPFC and superior temporal sulcus was related to reward and pleasure anticipation, and connectivity between VLPFC and inferior temporal sulcus correlated with attentional control measure. Our results suggest that impairments to VLPFC mediated neural circuitry underlie the cognitive and emotional deficits.

  5. The use of artificial neural network (ANN) for modeling of COD removal from antibiotic aqueous solution by the Fenton process.

    Science.gov (United States)

    Elmolla, Emad S; Chaudhuri, Malay; Eltoukhy, Mohamed Meselhy

    2010-07-15

    The study examined the implementation of artificial neural network (ANN) for the prediction and simulation of antibiotic degradation in aqueous solution by the Fenton process. A three-layer backpropagation neural network was optimized to predict and simulate the degradation of amoxicillin, ampicillin and cloxacillin in aqueous solution in terms of COD removal. The configuration of the backpropagation neural network giving the smallest mean square error (MSE) was three-layer ANN with tangent sigmoid transfer function (tansig) at hidden layer with 14 neurons, linear transfer function (purelin) at output layer and Levenberg-Marquardt backpropagation training algorithm (LMA). ANN predicted results are very close to the experimental results with correlation coefficient (R(2)) of 0.997 and MSE 0.000376. The sensitivity analysis showed that all studied variables (reaction time, H(2)O(2)/COD molar ratio, H(2)O(2)/Fe(2+) molar ratio, pH and antibiotics concentration) have strong effect on antibiotic degradation in terms of COD removal. In addition, H(2)O(2)/Fe(2+) molar ratio is the most influential parameter with relative importance of 25.8%. The results showed that neural network modeling could effectively predict and simulate the behavior of the Fenton process. 2010 Elsevier B.V. All rights reserved.

  6. The method of educational assessment affects children's neural processing and performance: behavioural and fMRI Evidence

    Science.gov (United States)

    Howard, Steven J.; Burianová, Hana; Calleia, Alysha; Fynes-Clinton, Samuel; Kervin, Lisa; Bokosmaty, Sahar

    2017-08-01

    Standardised educational assessments are now widespread, yet their development has given comparatively more consideration to what to assess than how to optimally assess students' competencies. Existing evidence from behavioural studies with children and neuroscience studies with adults suggest that the method of assessment may affect neural processing and performance, but current evidence remains limited. To investigate the impact of assessment methods on neural processing and performance in young children, we used functional magnetic resonance imaging to identify and quantify the neural correlates during performance across a range of current approaches to standardised spelling assessment. Results indicated that children's test performance declined as the cognitive load of assessment method increased. Activation of neural nodes associated with working memory further suggests that this performance decline may be a consequence of a higher cognitive load, rather than the complexity of the content. These findings provide insights into principles of assessment (re)design, to ensure assessment results are an accurate reflection of students' true levels of competency.

  7. Optimization of thermomechanical processes in Cu-Cr-Zr lead frame alloy using neural networks and genetic algorithms

    Institute of Scientific and Technical Information of China (English)

    SU; Juanhua; LIU; Ping; DONG; Qiming; LI; Hejun

    2005-01-01

    The thermomechanical treatment process is effective in enhancing the properties of the lead frame copper alloy. In this study, an optimal pattern of the thermomechanical processes for Cu-Cr-Zr was investegated using an intelligent control technique consisting of neural networks and genetic algorithms. The input parameters of the artificial neural network (ANN) are the reduction ratio of cold rolling, aging temperature and aging time. The outputs of the ANN model are the two most important properties of hardness and conductivity. Based on the successfully trained ANN model,genetic algorithms (GA) are used to optimize the input parameters of the model and select perfect combinations of thermomechanical processing parameters and properties.The good generalization performance and optimized results of the integrated model are achieved.

  8. Differential neural network approach in information process for prediction of roadside air pollution by peat fire

    Science.gov (United States)

    Lozhkin, V.; Tarkhov, D.; Timofeev, V.; Lozhkina, O.; Vasilyev, A.

    2016-11-01

    The paper presents a novel differential neural network model estimating the dispersion of CO emissions from a peat fire near a highway. We have developed approaches for the optimization of the model on the base of simulated and experimental measurements of CO concentrations in the area of dispersion of the smoke cloud. The numerical solutions of the problem are presented in the form of neural network approximations by the Gaussian model and in the form of neural network approximate solutions of partial differential equations. The trained neural network model can be used for the prediction of emergency when wind speed and direction and other fire parameters are changing. The method is also recommended for the development of air quality monitoring and predicting information systems.

  9. Fluvial particle characterization using artificial neural network and spectral image processing

    Science.gov (United States)

    Shrestha, Bim Prasad; Gautam, Bijaya; Nagata, Masateru

    2008-03-01

    Sand, chemical waste, microbes and other solid materials flowing with the water bodies are of great significance to us as they cause substantial impact to different sectors including drinking water management, hydropower generation, irrigation, aquatic life preservation and various other socio-ecological factors. Such particles can't completely be avoided due to the high cost of construction and maintenance of the waste-treatment methods. A detailed understanding of solid particles in surface water system can have benefit in effective, economic, environmental and social management of water resources. This paper describes an automated system of fluvial particle characterization based on spectral image processing that lead to the development of devices for monitoring flowing particles in river. Previous research in coherent field has shown that it is possible to automatically classify shapes and sizes of solid particles ranging from 300-400 μm using artificial neural networks (ANN) and image processing. Computer facilitated with hyper spectral and multi spectral images using ANN can further classify fluvial materials into organic, inorganic, biodegradable, bio non degradable and microbes. This makes the method attractive for real time monitoring of particles, sand and microorganism in water bodies at strategic locations. Continuous monitoring can be used to determine the effect of socio-economic activities in upstream rivers, or to monitor solid waste disposal from treatment plants and industries or to monitor erosive characteristic of sand and its contribution to degradation of efficiency of hydropower plant or to identify microorganism, calculate their population and study the impact of their presence. Such system can also be used to characterize fluvial particles for planning effective utilization of water resources in micro-mega hydropower plant, irrigation, aquatic life preservation etc.

  10. Separate Perceptual and Neural Processing of Velocity- and Disparity-Based 3D Motion Signals.

    Science.gov (United States)

    Joo, Sung Jun; Czuba, Thaddeus B; Cormack, Lawrence K; Huk, Alexander C

    2016-10-19

    Although the visual system uses both velocity- and disparity-based binocular information for computing 3D motion, it is unknown whether (and how) these two signals interact. We found that these two binocular signals are processed distinctly at the levels of both cortical activity in human MT and perception. In human MT, adaptation to both velocity-based and disparity-based 3D motions demonstrated direction-selective neuroimaging responses. However, when adaptation to one cue was probed using the other cue, there was no evidence of interaction between them (i.e., there was no "cross-cue" adaptation). Analogous psychophysical measurements yielded correspondingly weak cross-cue motion aftereffects (MAEs) in the face of very strong within-cue adaptation. In a direct test of perceptual independence, adapting to opposite 3D directions generated by different binocular cues resulted in simultaneous, superimposed, opposite-direction MAEs. These findings suggest that velocity- and disparity-based 3D motion signals may both flow through area MT but constitute distinct signals and pathways. Recent human neuroimaging and monkey electrophysiology have revealed 3D motion selectivity in area MT, which is driven by both velocity-based and disparity-based 3D motion signals. However, to elucidate the neural mechanisms by which the brain extracts 3D motion given these binocular signals, it is essential to understand how-or indeed if-these two binocular cues interact. We show that velocity-based and disparity-based signals are mostly separate at the levels of both fMRI responses in area MT and perception. Our findings suggest that the two binocular cues for 3D motion might be processed by separate specialized mechanisms. Copyright © 2016 the authors 0270-6474/16/3610791-12$15.00/0.

  11. Neural interaction between logical reasoning and pragmatic processing in narrative discourse.

    Science.gov (United States)

    Prado, Jérôme; Spotorno, Nicola; Koun, Eric; Hewitt, Emily; Van der Henst, Jean-Baptiste; Sperber, Dan; Noveck, Ira A

    2015-04-01

    Logical connectives (e.g., or, if, and not) are central to everyday conversation, and the inferences they generate are made with little effort in pragmatically sound situations. In contrast, the neural substrates of logical inference-making have been studied exclusively in abstract tasks where pragmatic concerns are minimal. Here, we used fMRI in an innovative design that employed narratives to investigate the interaction between logical reasoning and pragmatic processing in natural discourse. Each narrative contained three premises followed by a statement. In Fully-deductive stories, the statement confirmed a conclusion that followed from two steps of disjunction-elimination (e.g., Xavier considers Thursday, Friday, or Saturday for inviting his girlfriend out; he removes Thursday before he rejects Saturday and declares "I will invite her out for Friday"). In Implicated-premise stories, an otherwise identical narrative included three premises that twice removed a single option from consideration (i.e., Xavier rejects Thursday for two different reasons). The conclusion therefore necessarily prompts an implication (i.e., Xavier must have removed Saturday from consideration as well). We report two main findings. First, conclusions of Implicated-premise stories are associated with more activity than conclusions of Fully-deductive stories in a bilateral frontoparietal system, suggesting that these regions play a role in inferring an implicated premise. Second, brain connectivity between these regions increases with pragmatic abilities when reading conclusions in Implicated-premise stories. These findings suggest that pragmatic processing interacts with logical inference-making when understanding arguments in narrative discourse.

  12. Functionally integrated neural processing of linguistic and talker information: An event-related fMRI and ERP study.

    Science.gov (United States)

    Zhang, Caicai; Pugh, Kenneth R; Mencl, W Einar; Molfese, Peter J; Frost, Stephen J; Magnuson, James S; Peng, Gang; Wang, William S-Y

    2016-01-01

    Speech signals contain information of both linguistic content and a talker's voice. Conventionally, linguistic and talker processing are thought to be mediated by distinct neural systems in the left and right hemispheres respectively, but there is growing evidence that linguistic and talker processing interact in many ways. Previous studies suggest that talker-related vocal tract changes are processed integrally with phonetic changes in the bilateral posterior superior temporal gyrus/superior temporal sulcus (STG/STS), because the vocal tract parameter influences the perception of phonetic information. It is yet unclear whether the bilateral STG is also activated by the integral processing of another parameter - pitch, which influences the perception of lexical tone information and is related to talker differences in tone languages. In this study, we conducted separate functional magnetic resonance imaging (fMRI) and event-related potential (ERP) experiments to examine the spatial and temporal loci of interactions of lexical tone and talker-related pitch processing in Cantonese. We found that the STG was activated bilaterally during the processing of talker changes when listeners attended to lexical tone changes in the stimuli and during the processing of lexical tone changes when listeners attended to talker changes, suggesting that lexical tone and talker processing are functionally integrated in the bilateral STG. It extends the previous study, providing evidence for a general neural mechanism of integral phonetic and talker processing in the bilateral STG. The ERP results show interactions of lexical tone and talker processing 500-800ms after auditory word onset (a simultaneous posterior P3b and a frontal negativity). Moreover, there is some asymmetry in the interaction, such that unattended talker changes affect linguistic processing more than vice versa, which may be related to the ambiguity that talker changes cause in speech perception and/or attention bias

  13. A novel hybrid-maximum neural network in stereo-matching process.

    Science.gov (United States)

    Laskowski, Lukasz

    2013-01-01

    In the present paper, the completely innovative architecture of artificial neural network based on Hopfield structure for solving a stereo-matching problem-hybrid neural network, consisting of the classical analog Hopfield neural network and the Maximum Neural Network-is described. The application of this kind of structure as a part of assistive device for visually impaired individuals is considered. The role of the analog Hopfield network is to find the attraction area of the global minimum, whereas Maximum Neural Network is finding accurate location of this minimum. The network presented here is characterized by an extremely high rate of work performance with the same accuracy as a classical Hopfield-like network, which makes it possible to use this kind of structure as a part of systems working in real time. The network considered here underwent experimental tests with the use of real stereo pictures as well as simulated stereo images. This enables error calculation and direct comparison with the classic analog Hopfield neural network as well as other networks proposed in the literature.

  14. Neural processing of race during imitation: self-similarity versus social status.

    Science.gov (United States)

    Losin, Elizabeth A Reynolds; Cross, Katy A; Iacoboni, Marco; Dapretto, Mirella

    2014-04-01

    People preferentially imitate others who are similar to them or have high social status. Such imitative biases are thought to have evolved because they increase the efficiency of cultural acquisition. Here we focused on distinguishing between self-similarity and social status as two candidate mechanisms underlying neural responses to a person's race during imitation. We used fMRI to measure neural responses when 20 African American (AA) and 20 European American (EA) young adults imitated AA, EA and Chinese American (CA) models and also passively observed their gestures and faces. We found that both AA and EA participants exhibited more activity in lateral frontoparietal and visual regions when imitating AAs compared with EAs or CAs. These results suggest that racial self-similarity is not likely to modulate neural responses to race during imitation, in contrast with findings from previous neuroimaging studies of face perception and action observation. Furthermore, AA and EA participants associated AAs with lower social status than EAs or CAs, suggesting that the social status associated with different racial groups may instead modulate neural activity during imitation of individuals from those groups. Taken together, these findings suggest that neural responses to race during imitation are driven by socially learned associations rather than self-similarity. This may reflect the adaptive role of imitation in social learning, where learning from higher status models can be more beneficial. This study provides neural evidence consistent with evolutionary theories of cultural acquisition.

  15. The Effect of an Enrichment Reading Program on the Cognitive Processes and Neural Structures of Children Having Reading Difficulties

    Directory of Open Access Journals (Sweden)

    Hayriye Gül KURUYER

    2017-06-01

    Full Text Available The main purpose of the current study is to explain the effect of an enrichment reading program on the cognitive processes and neural structures of children experiencing reading difficulties. The current study was carried out in line with a single-subject research method and the between-subjects multiple probe design belonging to this method. This research focuses on a group of eight students with reading difficulties. Within the context of the study, memory capacities, attention spans, reading-related activation and white matter pathways of the students were determined before and after the application of the enrichment reading program. This determination process was carried out in two stages. Neuro-imaging was performed in the first stage and in the second stage the students’ cognitive processes and neural structures were investigated in terms of focusing attention and memory capacities by using the following tools: Stroop Test TBAG Form, Auditory Verbal Digit Span Test-Form B, Cancellation Test and Number Order Learning Test. The results obtained show that the enrichment reading program resulted in an improvement in the reading profiles of the students having reading difficulties in terms of their cognitive processes and neural structures.

  16. Preproduction display R&D facility: a framework for developing flexible display systems reducing the war-fighters' load

    Science.gov (United States)

    Zenhausern, Frederick; Raupp, Gregory B.

    2004-09-01

    There is an ever increasing need for lightweight, flexible, inexpensive integrated systems encompassing displays, sensors, computers, and other electronics to provide unprecedented information capability to a broad range of war-fighters. During the next few years, a team of experts will be engaged in an intensive development program pursuing a two-pronged goal: (1) to integrate and fabricate reflective and emissive systems such as organic light emitting devices on flexible substrates including plastics, and (2) to develop materials and structural platforms that allow flexible backplane electronics to be integrated with ancillaries and display components, as well as to be mass-produced economically. An underlying theme of this effort continues to be leveraging emerging processing techniques, for example a-Si and poly-Si thin film transistor (TFT) technologies, but also advanced micro-contact pattern transfer techniques for producing low cost product with molecular structures for combined communication and electronic appliances. The initial technology integration target is a 4" diagonal active matrix QVGA display on conformal plastic substrates. These advanced developments will be realized through a unique collaborative effort between the U.S. Army, Arizona State University (ASU) in close collaboration with its academic partners, and industry partners, who are united in our shared commitment to optimize the necessary production technologies for large area/large scale, low cost, cutting-edge display products and state-of-the-art manufacturing capabilities. The newly formed Flexible Display Center (FDC) at Arizona State University provides a one-of-a-kind environment fully dedicated to fulfill the major technical challenges not addressed by display manufacturers producing glass-based flat panel displays.

  17. Army Aviation and the Mission Command Warfighting Function

    Science.gov (United States)

    2017-06-09

    first lieutenant is likely full of energy and optimism , but void of the knowledge and experience necessary to plan an aviation operation. Based upon...information systems, processes and procedures, optimize facilities and equipment, and build understanding of the networks that link the headquarters...Trainer,” accessed April 14, 2017, http://asc.army.mil/web/ portfolio -item/peo-stri-aviation-combined-arms-tactical-trainer-avcatt/. 85 Randy Jones

  18. Warfighter decision making performance analysis as an investment priority driver

    Science.gov (United States)

    Thornley, David J.; Dean, David F.; Kirk, James C.

    2010-04-01

    Estimating the relative value of alternative tactics, techniques and procedures (TTP) and information systems requires measures of the costs and benefits of each, and methods for combining and comparing those measures. The NATO Code of Best Practice for Command and Control Assessment explains that decision making quality would ideally be best assessed on outcomes. Lessons learned in practice can be assessed statistically to support this, but experimentation with alternate measures in live conflict is undesirable. To this end, the development of practical experimentation to parameterize effective constructive simulation and analytic modelling for system utility prediction is desirable. The Land Battlespace Systems Department of Dstl has modeled human development of situational awareness to support constructive simulation by empirically discovering how evidence is weighed according to circumstance, personality, training and briefing. The human decision maker (DM) provides the backbone of the information processing activity associated with military engagements because of inherent uncertainty associated with combat operations. To develop methods for representing the process in order to assess equipment and non-technological interventions such as training and TTPs we are developing componentized or modularized timed analytic stochastic model components and instruments as part of a framework to support quantitative assessment of intelligence production and consumption methods in a human decision maker-centric mission space. In this paper, we formulate an abstraction of the human intelligence fusion process from the Defence Science and Technology Laboratory's (Dstl's) INCIDER model to include in our framework, and synthesize relevant cost and benefit characteristics.

  19. Improved Neural Processing Efficiency in a Chronic Aphasia Patient following Melodic Intonation Therapy: A Neuropsychological and Functional MRI Study

    Directory of Open Access Journals (Sweden)

    Ken-Ichi Tabei

    2016-09-01

    Full Text Available Melodic intonation therapy (MIT is a treatment program for the rehabilitation of aphasic patients with speech production disorders. We report a case of severe chronic non-fluent aphasia unresponsive to several years of conventional therapy that showed a marked improvement following intensive nine-day training on the Japanese version of MIT (MIT-J. The purposes of this study were to verify the efficacy of MIT-J by functional assessment and examine associated changes in neural processing by functional magnetic resonance imaging. MIT improved language output and auditory comprehension, and decreased the response time for picture naming. Following MIT-J, an area of the right hemisphere was less activated on correct naming trials than compared to before training but similarly activated on incorrect trials. These results suggest that the aphasic symptoms of our patient were improved by increased neural processing efficiency and a concomitant decrease in cognitive load.

  20. Neural and Hybrid Modeling: An Alternative Route to Efficiently Predict the Behavior of Biotechnological Processes Aimed at Biofuels Obtainment

    Science.gov (United States)

    Saraceno, Alessandra; Calabrò, Vincenza; Iorio, Gabriele

    2014-01-01

    The present paper was aimed at showing that advanced modeling techniques, based either on artificial neural networks or on hybrid systems, might efficiently predict the behavior of two biotechnological processes designed for the obtainment of second-generation biofuels from waste biomasses. In particular, the enzymatic transesterification of waste-oil glycerides, the key step for the obtainment of biodiesel, and the anaerobic digestion of agroindustry wastes to produce biogas were modeled. It was proved that the proposed modeling approaches provided very accurate predictions of systems behavior. Both neural network and hybrid modeling definitely represented a valid alternative to traditional theoretical models, especially when comprehensive knowledge of the metabolic pathways, of the true kinetic mechanisms, and of the transport phenomena involved in biotechnological processes was difficult to be achieved. PMID:24516363

  1. Neural and hybrid modeling: an alternative route to efficiently predict the behavior of biotechnological processes aimed at biofuels obtainment.

    Science.gov (United States)

    Curcio, Stefano; Saraceno, Alessandra; Calabrò, Vincenza; Iorio, Gabriele

    2014-01-01

    The present paper was aimed at showing that advanced modeling techniques, based either on artificial neural networks or on hybrid systems, might efficiently predict the behavior of two biotechnological processes designed for the obtainment of second-generation biofuels from waste biomasses. In particular, the enzymatic transesterification of waste-oil glycerides, the key step for the obtainment of biodiesel, and the anaerobic digestion of agroindustry wastes to produce biogas were modeled. It was proved that the proposed modeling approaches provided very accurate predictions of systems behavior. Both neural network and hybrid modeling definitely represented a valid alternative to traditional theoretical models, especially when comprehensive knowledge of the metabolic pathways, of the true kinetic mechanisms, and of the transport phenomena involved in biotechnological processes was difficult to be achieved.

  2. Optimal iterative learning control for end-point product qualities in semi-batch process based on neural network model

    Institute of Scientific and Technical Information of China (English)

    XIONG ZhiHua; DONG Jin; ZHANG Jie

    2009-01-01

    An optimal iterative learning control (ILC) strategy of improving endpoint products in semi-batch processes is presented by combining a neural network model. Control affine feed-forward neural network (CAFNN) is proposed to build a model of semi-batch process. The main advantage of CAFNN is to obtain analytically its gradient of endpoint products with respect to input. Therefore, an optimal ILC law with direct error feedback is obtained explicitly, and the convergence of tracking error can be analyzed theoretically. It has been proved that the tracking errors may converge to small values. The proposed modeling and control strategy is illustrated on a simulated isothermal semi-batch reactor, and the results show that the endpoint products can be improved gradually from batch to batch.

  3. The D2 antagonist sulpiride modulates the neural processing of both rewarding and aversive stimuli in healthy volunteers

    OpenAIRE

    2011-01-01

    Rationale Animal studies indicate that dopamine pathways in the ventral striatum code for the motivational salience of both rewarding and aversive stimuli, but evidence for this mechanism in humans is less established. We have developed a functional magnetic resonance imaging (fMRI) model which permits examination of the neural processing of both rewarding and aversive stimuli. Objectives The aim of the study was to determine the effect of the dopamine receptor antagonist, sulpiride, on the n...

  4. Computational Models of Cognitive Processes: Proceedings of the 13th Neural Computation and Psychology Workshop (Ncpw13)

    OpenAIRE

    Mayor, Julien; Gomez, Pablo

    2013-01-01

    Computational Models of Cognitive Processes collects refereed versions of papers presented at the 13th Neural Computation and Psychology Workshop (NCPW13) that took place in July 2012, in San Sebastian (Spain). This workshop series is a well-established and unique forum that brings together researchers from such diverse disciplines as artificial intelligence, cognitive science, computer science, neurobiology, philosophy and psychology to discuss their latest work on models of cognitive proces...

  5. Neurocomputational Models of Cognitive Development and Processing: Proceedings of the 14th Neural Computation and Psychology Workshop

    OpenAIRE

    2016-01-01

    This volume presents peer-reviewed versions of papers presented at the 14th Neural Computation and Psychology Workshop (NCPW14), which took place in July 2014 at Lancaster University, UK. The workshop draws international attendees from the cutting edge of interdisciplinary research in psychology, computational modeling, artificial intelligence and psychology, and aims to drive forward our understanding of the mechanisms underlying a range of cognitive processes.

  6. Neural Dynamics of Feedforward and Feedback Processing in Figure-Ground Segregation

    Directory of Open Access Journals (Sweden)

    Oliver W. Layton

    2014-09-01

    Full Text Available Determining whether a region belongs to the interior or exterior of a shape (figure-ground segregation is a core competency of the primate brain, yet the underlying mechanisms are not well understood. Many models assume that figure-ground segregation occurs by assembling progressively more complex representations through feedforward connections, with feedback playing only a modulatory role. We present a dynamical model of figure-ground segregation in the primate ventral stream wherein feedback plays a crucial role in disambiguating a figure’s interior and exterior. We introduce a processing strategy whereby jitter in RF center locations and variation in RF sizes is exploited to enhance and suppress neural activity inside and outside of figures, respectively. Feedforward projections emanate from units that model cells in V4 known to respond to the curvature of boundary contours (curved contour cells, and feedback projections from units predicted to exist in IT that strategically group neurons with different RF sizes and RF center locations (teardrop cells. Neurons (convex cells that preferentially respond when centered on a figure dynamically balance feedforward (bottom-up information and feedback from higher visual areas. The activation is enhanced when an interior portion of a figure is in the RF via feedback from units that detect closure in the boundary contours of a figure. Our model produces maximal activity along the medial axis of well-known figures with and without concavities, and inside algorithmically generated shapes. Our results suggest that the dynamic balancing of feedforward signals with the specific feedback mechanisms proposed by the model is crucial for figure-ground segregation.

  7. Dissociating the Neural Basis of Conceptual Self-Awareness from Perceptual Awareness and Unaware Self-Processing.

    Science.gov (United States)

    Tacikowski, Pawel; Berger, Christopher C; Ehrsson, H Henrik

    2017-01-23

    Conceptual self-awareness is a mental state in which the content of one's consciousness refers to a particular aspect of semantic knowledge about oneself. This form of consciousness plays a crucial role in shaping human behavior; however, little is known about its neural basis. Here, we use functional magnetic resonance imaging (fMRI) and a visual masked priming paradigm to dissociate the neural responses related to the awareness of semantic autobiographical information (one's own name, surname, etc.) from the awareness of information related to any visual stimulus (perceptual awareness), as well as from the unaware processing of self-relevant stimuli. To detect brain activity that is highly selective for self-relevant information, we used the blood-oxygen-level-dependent (BOLD) adaptation approach, which goes beyond the spatial limitations of conventional fMRI. We found that self-awareness was associated with BOLD adaptation in the medial frontopolar-retrosplenial areas, whereas perceptual awareness and unaware self-processing were associated with BOLD adaptation in the lateral fronto-parietal areas and the inferior temporal cortex, respectively. Thus, using a direct manipulation of conscious awareness we demonstrate for the first time that the neural basis of conceptual self-awareness is neuroanatomically distinct from the network mediating perceptual awareness of the sensory environment or unaware processing of self-related stimuli. © The Author 2017. Published by Oxford University Press.

  8. What can the monetary incentive delay task tell us about the neural processing of reward and punishment?

    Directory of Open Access Journals (Sweden)

    Lutz K

    2014-04-01

    Full Text Available Kai Lutz,1–3 Mario Widmer1,2,41Department of Neurology, University Hospital Zürich, Zürich, 2Cereneo, Center for Neurology and Rehabilitation, Vitznau, 3Division of Neuropsychology, Institute of Psychology, University of Zürich, Zürich, 4Neural Control of Movement Lab, ETH Zürich, Zürich, SwitzerlandAbstract: Since its introduction in 2000, the monetary incentive delay (MID task has been used extensively to investigate changes in neural activity in response to the processing of reward and punishment in healthy, but also in clinical populations. Typically, the MID task requires an individual to react to a target stimulus presented after an incentive cue to win or to avoid losing the indicated reward. In doing so, this paradigm allows the detailed examination of different stages of reward processing like reward prediction, anticipation, outcome processing, and consumption as well as the processing of tasks under different reward conditions. This review gives an overview of different utilizations of the MID task by outlining the neuronal processes involved in distinct aspects of human reward processing, such as anticipation versus consumption, reward versus punishment, and, with a special focus, reward-based learning processes. Furthermore, literature on specific influences on reward processing like behavioral, clinical and developmental influences, is reviewed, describing current findings and possible future directions.Keywords: reward, punishment, dopamine, reward system

  9. Different neural processes accompany self-recognition in photographs across the lifespan: an ERP study using dizygotic twins.

    Directory of Open Access Journals (Sweden)

    David L Butler

    Full Text Available Our appearance changes over time, yet we can recognize ourselves in photographs from across the lifespan. Researchers have extensively studied self-recognition in photographs and have proposed that specific neural correlates are involved, but few studies have examined self-recognition using images from different periods of life. Here we compared ERP responses to photographs of participants when they were 5-15, 16-25, and 26-45 years old. We found marked differences between the responses to photographs from these time periods in terms of the neural markers generally assumed to reflect (i the configural processing of faces (i.e., the N170, (ii the matching of the currently perceived face to a representation already stored in memory (i.e., the P250, and (iii the retrieval of information about the person being recognized (i.e., the N400. There was no uniform neural signature of visual self-recognition. To test whether there was anything specific to self-recognition in these brain responses, we also asked participants to identify photographs of their dizygotic twins taken from the same time periods. Critically, this allowed us to minimize the confounding effects of exposure, for it is likely that participants have been similarly exposed to each other's faces over the lifespan. The same pattern of neural response emerged with only one exception: the neural marker reflecting the retrieval of mnemonic information (N400 differed across the lifespan for self but not for twin. These results, as well as our novel approach using twins and photographs from across the lifespan, have wide-ranging consequences for the study of self-recognition and the nature of our personal identity through time.

  10. Application of fuzzy neural network to the nuclear power plant in process fault diagnosis

    Institute of Scientific and Technical Information of China (English)

    LIU Yong-kuo; XIA Hong; XIE Chun-li

    2005-01-01

    The fuzzy logic and neural networks are combined in this paper,setting up the fuzzy neural network (FNN); meanwhile, the distinct differences and connections between the fuzzy logic and neural network are compared. Furthermore, the algorithm and structure of the FNN are introduced. In order to diagnose the faults of nuclear power plant, the FNN is applied to the nuclear power plant, and the intelligence fault diagnostic system of the nuclear power plant is built based on the FNN . The fault symptoms and the possibility of the inverted U-tube break accident of steam generator are discussed. In order to test the system's validity, the inverted U-tube break accident of steam generator is used as an example and many simulation experiments are performed. The test result shows that the FNN can identify the fault.

  11. Development of VIPER: a simulator for assessing vision performance of warfighters

    Science.gov (United States)

    Familoni, Jide; Thompson, Roger; Moyer, Steve; Mueller, Gregory; Williams, Tim; Nguyen, Hung-Quang; Espinola, Richard L.; Sia, Rose K.; Ryan, Denise S.; Rivers, Bruce A.

    2016-05-01

    Background: When evaluating vision, it is important to assess not just the ability to read letters on a vision chart, but also how well one sees in real life scenarios. As part of the Warfighter Refractive Eye Surgery Program (WRESP), visual outcomes are assessed before and after refractive surgery. A Warfighter's ability to read signs and detect and identify objects is crucial, not only when deployed in a military setting, but also in their civilian lives. Objective: VIPER, a VIsion PERformance simulator was envisioned as actual video-based simulated driving to test warfighters' functional vision under realistic conditions. Designed to use interactive video image controlled environments at daytime, dusk, night, and with thermal imaging vision, it simulates the experience of viewing and identifying road signs and other objects while driving. We hypothesize that VIPER will facilitate efficient and quantifiable assessment of changes in vision and measurement of functional military performance. Study Design: Video images were recorded on an isolated 1.1 mile stretch of road with separate target sets of six simulated road signs and six objects of military interest, separately. The video footage were integrated with customdesigned C++ based software that presented the simulated drive to an observer on a computer monitor at 10, 20 or 30 miles/hour. VIPER permits the observer to indicate when a target is seen and when it is identified. Distances at which the observer recognizes and identifies targets are automatically logged. Errors in recognition and identification are also recorded. This first report describes VIPER's development and a preliminary study to establish a baseline for its performance. In the study, nine soldiers viewed simulations at 10 miles/hour and 30 miles/hour, run in randomized order for each participant seated at 36 inches from the monitor. Relevance: Ultimately, patients are interested in how their vision will affect their ability to perform daily

  12. Quantifying warfighter performance in a target acquisition and aiming task using wireless inertial sensors.

    Science.gov (United States)

    Davidson, Steven P; Cain, Stephen M; McGinnis, Ryan S; Vitali, Rachel R; Perkins, Noel C; McLean, Scott G

    2016-09-01

    An array of inertial measurement units (IMUS) was experimentally employed to analyze warfighter performance on a target acquisition task pre/post fatigue. Eleven participants (5M/6F) repeated an exercise circuit carrying 20 kg of equipment until fatigued. IMUs secured to the sacrum, sternum, and a rifle quantified peak angular velocity magnitude (PAVM) and turn time (TT) on a target acquisition task (three aiming events with two 180° turns) within the exercise circuit. Turning performance of two turns was evaluated pre/post fatigue. Turning performance decreased with fatigue. PAVMs decreased during both turns for the sternum (p performance after fatigue. Similar methodologies can be applied to many movement tasks, including quantifying movement performance for load, fatigue, and equipment conditions.

  13. EEG frequency tagging dissociates between neural processing of motion synchrony and human quality of multiple point-light dancers

    Science.gov (United States)

    Alp, Nihan; Nikolaev, Andrey R.; Wagemans, Johan; Kogo, Naoki

    2017-01-01

    Do we perceive a group of dancers moving in synchrony differently from a group of drones flying in-sync? The brain has dedicated networks for perception of coherent motion and interacting human bodies. However, it is unclear to what extent the underlying neural mechanisms overlap. Here we delineate these mechanisms by independently manipulating the degree of motion synchrony and the humanoid quality of multiple point-light displays (PLDs). Four PLDs moving within a group were changing contrast in cycles of fixed frequencies, which permits the identification of the neural processes that are tagged by these frequencies. In the frequency spectrum of the steady-state EEG we found two emergent frequency components, which signified distinct levels of interactions between PLDs. The first component was associated with motion synchrony, the second with the human quality of the moving items. These findings indicate that visual processing of synchronously moving dancers involves two distinct neural mechanisms: one for the perception of a group of items moving in synchrony and one for the perception of a group of moving items with human quality. We propose that these mechanisms underlie high-level perception of social interactions. PMID:28272421

  14. The use of artificial neural network for modeling the decolourization of acid orange 7 solution of industrial by ozonation process

    Science.gov (United States)

    Fatimah, S.; Wiharto, W.

    2017-02-01

    Acid Orange 7 (AO7) is one of the synthetic dye in the dyeing process in the textile industry. The use of this dye can produce wastewater which will be endangered if not treated well. Ozonation method is one technique to solve this problem. Ozonation is a waste processing techniques using ozone as an oxidizing agent. Variables used in this research is the ozone concentration, the initial concentration of AO7, temperature, and pH. Based on the experimental result that the optimum value decolourization percentage is 80% when the ozone concentration is 560 mg/L, the initial concentration AO7 is 14 mg/L, the temperature is 390 °C, and pH is 7,6. Decolourization efficiency of experimental results and predictions successfully modelled by the neural network architecture. The data used to construct a neural network architecture quasi newton one step secant as many as 31 data. A comparison between the predicted results of the designed ANN models and experiment was conducted. From the modeling results obtained MAPE value of 0.7763%. From the results of this artificial neural network architecture obtained the optimum value decolourization percentage in 80,64% when the concentration of ozone is 550 mg/L, the initial concentration AO7 is 11 mg/L, the temperature is 41 °C, and the pH is 7.9.

  15. Neural Systems Underlying Emotional and Non-emotional Interference Processing: An ALE Meta-Analysis of Functional Neuroimaging Studies

    Science.gov (United States)

    Xu, Min; Xu, Guiping; Yang, Yang

    2016-01-01

    Understanding how the nature of interference might influence the recruitments of the neural systems is considered as the key to understanding cognitive control. Although, interference processing in the emotional domain has recently attracted great interest, the question of whether there are separable neural patterns for emotional and non-emotional interference processing remains open. Here, we performed an activation likelihood estimation meta-analysis of 78 neuroimaging experiments, and examined common and distinct neural systems for emotional and non-emotional interference processing. We examined brain activation in three domains of interference processing: emotional verbal interference in the face-word conflict task, non-emotional verbal interference in the color-word Stroop task, and non-emotional spatial interference in the Simon, SRC and Flanker tasks. Our results show that the dorsal anterior cingulate cortex (ACC) was recruited for both emotional and non-emotional interference. In addition, the right anterior insula, presupplementary motor area (pre-SMA), and right inferior frontal gyrus (IFG) were activated by interference processing across both emotional and non-emotional domains. In light of these results, we propose that the anterior insular cortex may serve to integrate information from different dimensions and work together with the dorsal ACC to detect and monitor conflicts, whereas pre-SMA and right IFG may be recruited to inhibit inappropriate responses. In contrast, the dorsolateral prefrontal cortex (DLPFC) and posterior parietal cortex (PPC) showed different degrees of activation and distinct lateralization patterns for different processing domains, which suggests that these regions may implement cognitive control based on the specific task requirements. PMID:27895564

  16. Neural correlates of inhibition and contextual cue processing related to treatment response in PTSD

    NARCIS (Netherlands)

    van Rooij, Sanne J H; Geuze, Elbert; Kennis, Mitzy; Rademaker, Arthur R; Vink, Matthijs

    2015-01-01

    Thirty to fifty percent of posttraumatic stress disorder (PTSD) patients do not respond to treatment. Understanding the neural mechanisms underlying treatment response could contribute to improve response rates. PTSD is often associated with decreased inhibition of fear responses in a safe environme

  17. Neural Differences in Bilingual Children's Arithmetic Processing Depending on Language of Instruction

    NARCIS (Netherlands)

    Mondt, K.; Struys, E.; Noort, M.W.M.L. van den; Balériaux, D.; Metens, T.; Paquier, P.; Craen, P. van de; Bosch, M.P.C.; Denolin, V.

    2011-01-01

    Many children in bilingual regions follow lessons in a language at school (school-language) that they hardly ever speak at home or in other informal settings. What are the neural effects of this phenomenon? This functional magnetic resonance imaging (fMRI) study investigates the effects of using sch

  18. Neural Correlates of Explicit versus Implicit Facial Emotion Processing in ASD

    Science.gov (United States)

    Luckhardt, Christina; Kröger, Anne; Cholemkery, Hannah; Bender, Stephan; Freitag, Christine M.

    2017-01-01

    The underlying neural mechanisms of implicit and explicit facial emotion recognition (FER) were studied in children and adolescents with autism spectrum disorder (ASD) compared to matched typically developing controls (TDC). EEG was obtained from N = 21 ASD and N = 16 TDC. Task performance, visual (P100, N170) and cognitive (late positive…

  19. Neural Processing of Spoken Words in Specific Language Impairment and Dyslexia

    Science.gov (United States)

    Helenius, Paivi; Parviainen, Tiina; Paetau, Ritva; Salmelin, Riitta

    2009-01-01

    Young adults with a history of specific language impairment (SLI) differ from reading-impaired (dyslexic) individuals in terms of limited vocabulary and poor verbal short-term memory. Phonological short-term memory has been shown to play a significant role in learning new words. We investigated the neural signatures of auditory word recognition…

  20. Artificial neural network modeling of DDGS flowability with varying process and storage parameters

    Science.gov (United States)

    Neural Network (NN) modeling techniques were used to predict flowability behavior in distillers dried grains with solubles (DDGS) prepared with varying CDS (10, 15, and 20%, wb), drying temperature (100, 200, and 300°C), cooling temperature (-12, 0, and 35°C) and cooling time (0 and 1 month) levels....

  1. Neural Dissociation of Number from Letter Recognition and Its Relationship to Parietal Numerical Processing

    Science.gov (United States)

    Park, Joonkoo; Hebrank, Andrew; Polk, Thad A.; Park, Denise C.

    2012-01-01

    The visual recognition of letters dissociates from the recognition of numbers at both the behavioral and neural level. In this article, using fMRI, we investigate whether the visual recognition of numbers dissociates from letters, thereby establishing a double dissociation. In Experiment 1, participants viewed strings of consonants and Arabic…

  2. MODELS OF INNATE NEURAL ATTRACTORS AND THEIR APPLICATIONS FOR NEURALINFORMATION PROCESSING

    Directory of Open Access Journals (Sweden)

    Ksenia P. Solovyeva

    2016-01-01

    Full Text Available In this work we reveal and explore a new class of attractor neural networks, based on inborn connections provided by model molecular markers, the molecular marker based attractor neural networks (MMBANN. Each set of markers has a metric, which is used to make connections between neurons containing the markers. We have explored conditions for the existence of attractor states, critical relations between their parameters and the spectrum of single neuron models, which can implement the MMBANN. Besides, we describe functional models (perceptron and SOM, which obtain significant advantages over the traditional implementation of these models, while using MMBANN. In particular, a perceptron, based on MMBANN, gets specificity gain in orders of error probabilities values, MMBANN SOM obtains real neurophysiological meaning, the number of possible grandma cells increases 1000-fold with MMBANN. MMBANN have sets of attractor states, which can serve as finite grids for representation of variables in computations. These grids may show dimensions of d = 0, 1, 2, ... We work with static and dynamic attractor neural networks of the dimensions d = 0 and d = 1. We also argue that the number of dimensions which can be represented by attractors of activities of neural networks with the number of elements N=104 does not exceed 8.

  3. Neural Signatures of Number Processing in Human Infants: Evidence for Two Core Systems Underlying Numerical Cognition

    Science.gov (United States)

    Hyde, Daniel C.; Spelke, Elizabeth S.

    2011-01-01

    Behavioral research suggests that two cognitive systems are at the foundations of numerical thinking: one for representing 1-3 objects in parallel and one for representing and comparing large, approximate numerical magnitudes. We tested for dissociable neural signatures of these systems in preverbal infants by recording event-related potentials…

  4. Use of Artificial Neural Network for Testing Effectiveness of Intelligent Computing Models for Predicting Shelf Life of Processed Cheese

    Directory of Open Access Journals (Sweden)

    GOYAL Kumar Gyanendra

    2012-10-01

    Full Text Available This paper presents the suitability of artificial neural network (ANN models for predicting the shelf life of processed cheese stored at 7-8ºC. Soluble nitrogen, pH; standard plate count, yeast & mould count, and spore count were input variables, and sensory score was output variable. Mean square error, root mean square error, coefficient of determination and Nash - sutcliffo coefficient were used in order to test the effectiveness of the developed ANN models. Excellent agreement was found between experimental results and these mathematical parameters, thus confirming that ANN models are very effective in predicting the shelf life of processed cheese.

  5. Spintronic characteristics of self-assembled neurotransmitter acetylcholine molecular complexes enable quantum information processing in neural networks and brain

    Science.gov (United States)

    Tamulis, Arvydas; Majauskaite, Kristina; Kairys, Visvaldas; Zborowski, Krzysztof; Adhikari, Kapil; Krisciukaitis, Sarunas

    2016-09-01

    Implementation of liquid state quantum information processing based on spatially localized electronic spin in the neurotransmitter stable acetylcholine (ACh) neutral molecular radical is discussed. Using DFT quantum calculations we proved that this molecule possesses stable localized electron spin, which may represent a qubit in quantum information processing. The necessary operating conditions for ACh molecule are formulated in self-assembled dimer and more complex systems. The main quantum mechanical research result of this paper is that the neurotransmitter ACh systems, which were proposed, include the use of quantum molecular spintronics arrays to control the neurotransmission in neural networks.

  6. Rapid regulation of sialidase activity in response to neural activity and sialic acid removal during memory processing in rat hippocampus.

    Science.gov (United States)

    Minami, Akira; Meguro, Yuko; Ishibashi, Sayaka; Ishii, Ami; Shiratori, Mako; Sai, Saki; Horii, Yuuki; Shimizu, Hirotaka; Fukumoto, Hokuto; Shimba, Sumika; Taguchi, Risa; Takahashi, Tadanobu; Otsubo, Tadamune; Ikeda, Kiyoshi; Suzuki, Takashi

    2017-04-07

    Sialidase cleaves sialic acids on the extracellular cell surface as well as inside the cell and is necessary for normal long-term potentiation (LTP) at mossy fiber-CA3 pyramidal cell synapses and for hippocampus-dependent spatial memory. Here, we investigated in detail the role of sialidase in memory processing. Sialidase activity measured with 4-methylumbelliferyl-α-d-N-acetylneuraminic acid (4MU-Neu5Ac) or 5-bromo-4-chloroindol-3-yl-α-d-N-acetylneuraminic acid (X-Neu5Ac) and Fast Red Violet LB was increased by high-K(+)-induced membrane depolarization. Sialidase activity was also increased by chemical LTP induction with forskolin and activation of BDNF signaling, non-NMDA receptors, or NMDA receptors. The increase in sialidase activity with neural excitation appears to be caused not by secreted sialidase or by an increase in sialidase expression but by a change in the subcellular localization of sialidase. Astrocytes as well as neurons are also involved in the neural activity-dependent increase in sialidase activity. Sialidase activity visualized with a benzothiazolylphenol-based sialic acid derivative (BTP3-Neu5Ac), a highly sensitive histochemical imaging probe for sialidase activity, at the CA3 stratum lucidum of rat acute hippocampal slices was immediately increased in response to LTP-inducible high-frequency stimulation on a time scale of seconds. To obtain direct evidence for sialic acid removal on the extracellular cell surface during neural excitation, the extracellular free sialic acid level in the hippocampus was monitored using in vivo microdialysis. The free sialic acid level was increased by high-K(+)-induced membrane depolarization. Desialylation also occurred during hippocampus-dependent memory formation in a contextual fear-conditioning paradigm. Our results show that neural activity-dependent desialylation by sialidase may be involved in hippocampal memory processing. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

  7. Neural network based control of an absorption column in the process of bioethanol production

    Directory of Open Access Journals (Sweden)

    Eduardo Eyng

    2009-08-01

    Full Text Available Gaseous ethanol may be recovered from the effluent gas mixture of the sugar cane fermentation process using a staged absorption column. In the present work, the development of a nonlinear controller, based on a neural network inverse model (ANN controller, was proposed and tested to manipulate the absorbent flow rate in order to control the residual ethanol concentration in the effluent gas phase. Simulation studies were carried out, in which a noise was applied to the ethanol concentration signals from the rigorous model. The ANN controller outperformed the dynamic matrix control (DMC when step disturbances were imposed to the gas mixture composition. A security device, based on a conventional feedback algorithm, and a digital filter were added to the proposed strategy to improve the system robustness when unforeseen operating and environmental conditions occured. The results demonstrated that ANN controller was a robust and reliable tool to control the absorption column.Deseja-se recuperar o etanol perdido por evaporação durante o processo de fermentação da cana-de-açúcar. Para tanto, faz-se uso de uma coluna de absorção. O controle da concentração de etanol no efluente gasoso da coluna é realizado pela manipulação da vazão de solvente, sendo esta determinada pelo controlador não linear proposto, baseado em um modelo inverso de redes neurais (controlador ANN. Foram feitas simulações adicionando-se um sinal de ruído a medida de concentração de etanol na fase gasosa. Quando perturbações degrau foram inseridas na mistura gasosa afluente, o controlador ANN demonstrou desempenho superior ao controle por matriz dinâmica (DMC. Um dispositivo de segurança, baseado em um controlador feedback convencional, e um filtro digital foram implementados à estratégia de controle proposta para agregar robustez no tratamento de distúrbios ocorridos no ambiente operacional. Os resultados demonstraram que o controlador ANN é uma

  8. Soft-Sensor Modeling of PVC Polymerizing Process Based on F-GMDH-Type Neural Network Algorithm

    Directory of Open Access Journals (Sweden)

    Wei-zhen Sun

    2017-01-01

    Full Text Available For predicting the conversion velocity of the vinyl chloride monomer (VCM in the polymerization process of polyvinylchloride (PVC, an improved Group Method of Data Handling- (GMDH- type neural network soft-sensor model is proposed. After analyzing the technique of PVC manufacturing process, the auxiliary variables for setting up the soft-sensor model are selected and the experimental data are normalized. Because the internal standard of the original GMDH-type neural cannot solve the problem of multiple-collinearity problem and the useful variables tend to be prematurely eliminated in the modeling process, a hybrid method combining the regression analysis method and the least squares method is proposed to solve the multiple-collinearity problem. On the same time, by adopting some optimization experiences in genetic algorithm (GA, the generational crossover combination variables method is proposed to solve the shortcoming of useful variable being eliminated prematurely. The simulation results show that the proposed soft-sensor model can significantly improve the prediction accuracy of economic and technical indicators in the PVC polymerization process and can meet the real time control requirements of polymerization reactor production process.

  9. Prediction of Increasing Production Activities using Combination of Query Aggregation on Complex Events Processing and Neural Network

    Directory of Open Access Journals (Sweden)

    Achmad Arwan

    2016-07-01

    Full Text Available AbstrakProduksi, order, penjualan, dan pengiriman adalah serangkaian event yang saling terkait dalam industri manufaktur. Selanjutnya hasil dari event tersebut dicatat dalam event log. Complex Event Processing adalah metode yang digunakan untuk menganalisis apakah terdapat pola kombinasi peristiwa tertentu (peluang/ancaman yang terjadi pada sebuah sistem, sehingga dapat ditangani secara cepat dan tepat. Jaringan saraf tiruan adalah metode yang digunakan untuk mengklasifikasi data peningkatan proses produksi. Hasil pencatatan rangkaian proses yang menyebabkan peningkatan produksi digunakan sebagai data latih untuk mendapatkan fungsi aktivasi dari jaringan saraf tiruan. Penjumlahan hasil catatan event log dimasukkan ke input jaringan saraf tiruan untuk perhitungan nilai aktivasi. Ketika nilai aktivasi lebih dari batas yang ditentukan, maka sistem mengeluarkan sinyal untuk meningkatkan produksi, jika tidak, sistem tetap memantau kejadian. Hasil percobaan menunjukkan bahwa akurasi dari metode ini adalah 77% dari 39 rangkaian aliran event.Kata kunci: complex event processing, event, jaringan saraf tiruan, prediksi peningkatan produksi, proses. AbstractProductions, orders, sales, and shipments are series of interrelated events within manufacturing industry. Further these events were recorded in the event log. Complex event processing is a method that used to analyze whether there are patterns of combinations of certain events (opportunities / threats that occur in a system, so it can be addressed quickly and appropriately. Artificial neural network is a method that we used to classify production increase activities. The series of events that cause the increase of the production used as a dataset to train the weight of neural network which result activation value. An aggregate stream of events inserted into the neural network input to compute the value of activation. When the value is over a certain threshold (the activation value results

  10. Monocular denervation of visual nuclei modulates APP processing and sAPPα production: A possible role on neural plasticity.

    Science.gov (United States)

    Vasques, Juliana Ferreira; Heringer, Pedro Vinícius Bastos; Gonçalves, Renata Guedes de Jesus; Campello-Costa, Paula; Serfaty, Claudio Alberto; Faria-Melibeu, Adriana da Cunha

    2017-08-01

    Amyloid precursor protein (APP) is essential to physiological processes such as synapse formation and neural plasticity. Sequential proteolysis of APP by beta- and gamma-secretases generates amyloid-beta peptide (Aβ), the main component of senile plaques in Alzheimer Disease. Alternative APP cleavage by alpha-secretase occurs within Aβ domain, releasing soluble α-APP (sAPPα), a neurotrophic fragment. Among other functions, sAPPα is important to synaptogenesis, neural survival and axonal growth. APP and sAPPα levels are increased in models of neuroplasticity, which suggests an important role for APP and its metabolites, especially sAPPα, in the rearranging brain. In this work we analyzed the effects of monocular enucleation (ME), a classical model of lesion-induced plasticity, upon APP content, processing and also in secretases levels. Besides, we addressed whether α-secretase activity is crucial for retinotectal remodeling after ME. Our results showed that ME induced a transient reduction in total APP content. We also detected an increase in α-secretase expression and in sAPP production concomitant with a reduction in Aβ and β-secretase contents. These data suggest that ME facilitates APP processing by the non-amyloidogenic pathway, increasing sAPPα levels. Indeed, the pharmacological inhibition of α-secretase activity reduced the axonal sprouting of ipsilateral retinocollicular projections from the intact eye after ME, suggesting that sAPPα is necessary for synaptic structural rearrangement. Understanding how APP processing is regulated under lesion conditions may provide new insights into APP physiological role on neural plasticity. Copyright © 2017 ISDN. Published by Elsevier Ltd. All rights reserved.

  11. Towards Pro-active Embodied Agents: On the Importance of Neural Mechanisms Suitable to Process Time Information

    Science.gov (United States)

    de Croon, G.; Nolfi, S.; Postma, E. O.

    In Embodied Cognitive Science, many studies have focused on reactive agents, i.e. agents that have no internal state and always respond in the same way to the same stimulus. However, this particular focus is not due to a rejection of the importance of internal states. Rather, it is due to the difficulty of developing pro-active embodied and situated agents, that is agents able to: (a) extract internal states by integrating sensorymotor information through time and, (b) later use these internal states to modulate their motor behaviour according to the current environmental circumstances. In this chapter we will focus on how pro-active agents can be developed and, more specifically, on which are the neural mechanisms that might favour the development of pro-active agents. By comparing the results of five sets of evolutionary experiments in which simulated robots are provided with different types of recurrent neural networks, we gain insight into the relation between the robots` capabilities and the characteristics of their neural controllers. We show how special mechanisms for processing information in time facilitate the exploitation of internal states.

  12. In our own image? Emotional and neural processing differences when observing human-human vs human-robot interactions.

    Science.gov (United States)

    Wang, Yin; Quadflieg, Susanne

    2015-11-01

    Notwithstanding the significant role that human-robot interactions (HRI) will play in the near future, limited research has explored the neural correlates of feeling eerie in response to social robots. To address this empirical lacuna, the current investigation examined brain activity using functional magnetic resonance imaging while a group of participants (n = 26) viewed a series of human-human interactions (HHI) and HRI. Although brain sites constituting the mentalizing network were found to respond to both types of interactions, systematic neural variation across sites signaled diverging social-cognitive strategies during HHI and HRI processing. Specifically, HHI elicited increased activity in the left temporal-parietal junction indicative of situation-specific mental state attributions, whereas HRI recruited the precuneus and the ventromedial prefrontal cortex (VMPFC) suggestive of script-based social reasoning. Activity in the VMPFC also tracked feelings of eeriness towards HRI in a parametric manner, revealing a potential neural correlate for a phenomenon known as the uncanny valley. By demonstrating how understanding social interactions depends on the kind of agents involved, this study highlights pivotal sub-routes of impression formation and identifies prominent challenges in the use of humanoid robots. © The Author (2015). Published by Oxford University Press.

  13. Neural changes associated to procedural learning and automatization process in Developmental Coordination Disorder and/or Developmental Dyslexia.

    Science.gov (United States)

    Biotteau, Maëlle; Péran, Patrice; Vayssière, Nathalie; Tallet, Jessica; Albaret, Jean-Michel; Chaix, Yves

    2017-03-01

    Recent theories hypothesize that procedural learning may support the frequent overlap between neurodevelopmental disorders. The neural circuitry supporting procedural learning includes, among others, cortico-cerebellar and cortico-striatal loops. Alteration of these loops may account for the frequent comorbidity between Developmental Coordination Disorder (DCD) and Developmental Dyslexia (DD). The aim of our study was to investigate cerebral changes due to the learning and automatization of a sequence learning task in children with DD, or DCD, or both disorders. fMRI on 48 children (aged 8-12) with DD, DCD or DD + DCD was used to explore their brain activity during procedural tasks, performed either after two weeks of training or in the early stage of learning. Firstly, our results indicate that all children were able to perform the task with the same level of automaticity, but recruit different brain processes to achieve the same performance. Secondly, our fMRI results do not appear to confirm Nicolson and Fawcett's model. The neural correlates recruited for procedural learning by the DD and the comorbid groups are very close, while the DCD group presents distinct characteristics. This provide a promising direction on the neural mechanisms associated with procedural learning in neurodevelopmental disorders and for understanding comorbidity. Published by Elsevier Ltd.

  14. Resolving the neural dynamics of visual and auditory scene processing in the human brain: a methodological approach

    Science.gov (United States)

    Teng, Santani

    2017-01-01

    In natural environments, visual and auditory stimulation elicit responses across a large set of brain regions in a fraction of a second, yielding representations of the multimodal scene and its properties. The rapid and complex neural dynamics underlying visual and auditory information processing pose major challenges to human cognitive neuroscience. Brain signals measured non-invasively are inherently noisy, the format of neural representations is unknown, and transformations between representations are complex and often nonlinear. Further, no single non-invasive brain measurement technique provides a spatio-temporally integrated view. In this opinion piece, we argue that progress can be made by a concerted effort based on three pillars of recent methodological development: (i) sensitive analysis techniques such as decoding and cross-classification, (ii) complex computational modelling using models such as deep neural networks, and (iii) integration across imaging methods (magnetoencephalography/electroencephalography, functional magnetic resonance imaging) and models, e.g. using representational similarity analysis. We showcase two recent efforts that have been undertaken in this spirit and provide novel results about visual and auditory scene analysis. Finally, we discuss the limits of this perspective and sketch a concrete roadmap for future research. This article is part of the themed issue ‘Auditory and visual scene analysis’. PMID:28044019

  15. Particle swarm optimization of a neural network model in a machining process

    Indian Academy of Sciences (India)

    Saurabh Garg; Karali Patra; Surjya K Pal

    2014-06-01

    This paper presents a particle swarm optimization (PSO) technique to train an artificial neural network (ANN) for prediction of flank wear in drilling, and compares the network performance with that of the back propagation neural network (BPNN). This analysis is carried out following a series of experiments employing high speed steel (HSS) drills for drilling on mild steel workpieces, under different sets of cutting conditions and noting the root mean square (RMS) value of spindle motor current as well as the average flank wear in each case. The results show that the PSO trained ANN not only gives better prediction results and reduced computational times compared to the BPNN, it is also a more robust model, being free of getting trapped in local optimum solutions unlike the latter. Besides, it offers the advantages of a straight-forward logic, simple realization and underlying intelligence.

  16. Reciprocal inhibitory connections within a neural network for rotational optic-flow processing

    Directory of Open Access Journals (Sweden)

    Juergen Haag

    2007-10-01

    Full Text Available Neurons in the visual system of the blowfly have large receptive fields that are selective for specific optic flow fields. Here, we studied the neural mechanisms underlying flow-field selectivity in proximal Vertical System (VS-cells, a particular subset of tangential cells in the fly. These cells have local preferred directions that are distributed such as to match the flow field occurring during a rotation of the fly. However, the neural circuitry leading to this selectivity is not fully understood. Through dual intracellular recordings from proximal VS cells and other tangential cells, we characterized the specific wiring between VS cells themselves and between proximal VS cells and horizontal sensitive tangential cells. We discovered a spiking neuron (Vi involved in this circuitry that has not been described before. This neuron turned out to be connected to proximal VS cells via gap junctions and, in addition, it was found to be inhibitory onto VS1.

  17. Neural networks involved in adolescent reward processing: An activation likelihood estimation meta-analysis of functional neuroimaging studies.

    Science.gov (United States)

    Silverman, Merav H; Jedd, Kelly; Luciana, Monica

    2015-11-15

    Behavioral responses to, and the neural processing of, rewards change dramatically during adolescence and may contribute to observed increases in risk-taking during this developmental period. Functional MRI (fMRI) studies suggest differences between adolescents and adults in neural activation during reward processing, but findings are contradictory, and effects have been found in non-predicted directions. The current study uses an activation likelihood estimation (ALE) approach for quantitative meta-analysis of functional neuroimaging studies to: (1) confirm the network of brain regions involved in adolescents' reward processing, (2) identify regions involved in specific stages (anticipation, outcome) and valence (positive, negative) of reward processing, and (3) identify differences in activation likelihood between adolescent and adult reward-related brain activation. Results reveal a subcortical network of brain regions involved in adolescent reward processing similar to that found in adults with major hubs including the ventral and dorsal striatum, insula, and posterior cingulate cortex (PCC). Contrast analyses find that adolescents exhibit greater likelihood of activation in the insula while processing anticipation relative to outcome and greater likelihood of activation in the putamen and amygdala during outcome relative to anticipation. While processing positive compared to negative valence, adolescents show increased likelihood for activation in the posterior cingulate cortex (PCC) and ventral striatum. Contrasting adolescent reward processing with the existing ALE of adult reward processing reveals increased likelihood for activation in limbic, frontolimbic, and striatal regions in adolescents compared with adults. Unlike adolescents, adults also activate executive control regions of the frontal and parietal lobes. These findings support hypothesized elevations in motivated activity during adolescence.

  18. The neural time course of art perception: an ERP study on the processing of style versus content in art.

    Science.gov (United States)

    Augustin, M Dorothee; Defranceschi, Birgit; Fuchs, Helene K; Carbon, Claus-Christian; Hutzler, Florian

    2011-06-01

    A central prerequisite to understand the phenomenon of art in psychological terms is to investigate the nature of the underlying perceptual and cognitive processes. Building on a study by Augustin, Leder, Hutzler, and Carbon (2008) the current ERP study examined the neural time course of two central aspects of representational art, one of which is closely related to object- and scene perception, the other of which is art-specific: content and style. We adapted a paradigm that has repeatedly been employed in psycholinguistics and that allows one to examine the neural time course of two processes in terms of when sufficient information is available to allow successful classification. Twenty-two participants viewed pictures that systematically varied in style and content and conducted a combined go/nogo dual choice task. The dependent variables of interest were the Lateralised Readiness Potential (LRP) and the N200 effect. Analyses of both measures support the notion that in the processing of art style follows content, with style-related information being available at around 224 ms or between 40 and 94 ms later than content-related information. The paradigm used here offers a promising approach to further explore the time course of art perception, thus helping to unravel the perceptual and cognitive processes that underlie the phenomenon of art and the fascination it exerts.

  19. The effect of age of acquisition, socioeducational status, and proficiency on the neural processing of second language speech sounds.

    Science.gov (United States)

    Archila-Suerte, Pilar; Zevin, Jason; Hernandez, Arturo E

    2015-02-01

    This study investigates the role of age of acquisition (AoA), socioeducational status (SES), and second language (L2) proficiency on the neural processing of L2 speech sounds. In a task of pre-attentive listening and passive viewing, Spanish-English bilinguals and a control group of English monolinguals listened to English syllables while watching a film of natural scenery. Eight regions of interest were selected from brain areas involved in speech perception and executive processes. The regions of interest were examined in 2 separate two-way ANOVA (AoA×SES; AoA×L2 proficiency). The results showed that AoA was the main variable affecting the neural response in L2 speech processing. Direct comparisons between AoA groups of equivalent SES and proficiency level enhanced the intensity and magnitude of the results. These results suggest that AoA, more than SES and proficiency level, determines which brain regions are recruited for the processing of second language speech sounds. Copyright © 2014 Elsevier Inc. All rights reserved.

  20. Neural hyporesponsiveness and hyperresponsiveness during immediate and delayed reward processing in adult attention-deficit/hyperactivity disorder.

    Science.gov (United States)

    Plichta, Michael M; Vasic, Nenad; Wolf, Robert Christian; Lesch, Klaus-Peter; Brummer, Dagmar; Jacob, Christian; Fallgatter, Andreas J; Grön, Georg

    2009-01-01

    Dysfunctional reward processing, accompanied by a limited ability to tolerate reward delays, has been proposed as an important feature in attention-deficit/hyperactivity disorder (ADHD). Using functional magnetic resonance imaging (fMRI), brain activation in adult patients with ADHD (n=14) and healthy control subjects (n=12) was examined during a series of choices between two monetary reward options that varied by delay to delivery. Compared with healthy control subjects, hyporesponsiveness of the ventral-striatal reward system was replicated in patients with ADHD and was evident for both immediate and delayed rewards. In contrast, delayed rewards evoked hyperactivation in dorsal caudate nucleus and amygdala of ADHD patients. In both structures, neural activity toward delayed rewards was significantly correlated with self-rated ADHD symptom severity. The finding of ventral-striatal hyporesponsiveness during immediate and delayed reward processing in patients with ADHD further strengthens the concept of a diminished neural processing of rewards in ADHD. Hyperactivation during delayed reward processing, gradually increasing along the ventral-to-dorsal extension of the caudate nucleus, and especially the concomitant hyperactivation of the amygdala are in accordance with predictions of the delay aversion hypothesis.

  1. Neural Features of Processing the Enforcement Phrases Used during Occupational Health and Safety Inspections: An ERP Study

    Science.gov (United States)

    Ma, Qingguo; Shi, Liping; Hu, Linfeng; Liu, Qiang; Yang, Zheng; Wang, Qiuzhen

    2016-01-01

    The appropriate enforcement phrases used during occupational health and safety (OHS) inspection activities is a crucial factor to guarantee the compliance with OHS regulations in enterprises. However, few researchers have empirically investigated the issue of how enforcement phrases are processed. The present study explored the neural features of processing two types of enforcement phrases (severe-and-deterrent vs. mild-and-polite phrases) used during OHS inspections by applying event-related potentials (ERP) method. Electroencephalogram data were recorded while the participants distinguished between severe-and-deterrent phrases and mild-and-polite phrases depicted in written Chinese words. The ERP results showed that severe-and-deterrent phrases elicited significantly augmented P300 amplitude with a central-parietal scalp distribution compared with mild-and-polite phrases, indicating the allocation of more attention resources to and elaborate processing of the severe-and-deterrent phrases. It reveals that humans may consider the severe-and-deterrent phrases as more motivationally significant and elaborately process the severity and deterrence information contained in the enforcement phrases for the adaptive protection. The current study provides an objective and supplementary way to measure the efficiency of different enforcement phrases at neural level, which may help generate appropriate enforcement phrases and improve the performance of OHS inspections. PMID:27807404

  2. Prediction of Crack for Drilling Process on Alumina Using Neural Network and Taguchi Method

    Directory of Open Access Journals (Sweden)

    Kingsun Lee

    2015-01-01

    Full Text Available This study analyzes a variety of significant drilling conditions on aluminum oxide (with L18 orthogonal array using a diamond drill. The drilling parameters evaluated are spindle speed, feed rate, depth of cut, and diamond abrasive size. An orthogonal array, signal-to-noise (S/N ratio, and analysis of variance (ANOVA are employed to analyze the effects of these drilling parameters. The results were confirmed by experiments, which indicated that the selected drilling parameters effectively reduce the crack. The neural network is applied to establish a model based on the relationship between input parameters (spindle speed, feed rate, depth of cut, and diamond abrasive size and output parameter (cracking area percentage. The neural network can predict individual crack in terms of input parameters, which provides faster and more automated model synthesis. Accurate prediction of crack ensures that poor drilling parameters are not suitable for machining products, avoiding the fabrication of poor-quality products. Confirmation experiments showed that neural network precisely predicted the cracking area percentage in drilling of alumina.

  3. DO DYNAMIC NEURAL NETWORKS STAND A BETTER CHANCE IN FRACTIONALLY INTEGRATED PROCESS FORECASTING?

    Directory of Open Access Journals (Sweden)

    Majid Delavari

    2013-04-01

    Full Text Available The main purpose of the present study was to investigate the capabilities of two generations of models such as those based on dynamic neural network (e.g., Nonlinear Neural network Auto Regressive or NNAR model and a regressive (Auto Regressive Fractionally Integrated Moving Average model which is based on Fractional Integration Approach in forecasting daily data related to the return index of Tehran Stock Exchange (TSE. In order to compare these models under similar conditions, Mean Square Error (MSE and also Root Mean Square Error (RMSE were selected as criteria for the models’ simulated out-of-sample forecasting performance. Besides, fractal markets hypothesis was examined and according to the findings, fractal structure was confirmed to exist in the time series under investigation. Another finding of the study was that dynamic artificial neural network model had the best performance in out-of-sample forecasting based on the criteria introduced for calculating forecasting error in comparison with the ARFIMA model.

  4. Real-time process optimization based on grey-box neural models

    Directory of Open Access Journals (Sweden)

    F. A. Cubillos

    2007-09-01

    Full Text Available This paper investigates the feasibility of using grey-box neural models (GNM in Real Time Optimization (RTO. These models are based on a suitable combination of fundamental conservation laws and neural networks, being used in at least two different ways: to complement available phenomenological knowledge with empirical information, or to reduce dimensionality of complex rigorous physical models. We have observed that the benefits of using these simple adaptable models are counteracted by some difficulties associated with the solution of the optimization problem. Nonlinear Programming (NLP algorithms failed in finding the global optimum due to the fact that neural networks can introduce multimodal objective functions. One alternative considered to solve this problem was the use of some kind of evolutionary algorithms, like Genetic Algorithms (GA. Although these algorithms produced better results in terms of finding the appropriate region, they took long periods of time to reach the global optimum. It was found that a combination of genetic and nonlinear programming algorithms can be use to fast obtain the optimum solution. The proposed approach was applied to the Williams-Otto reactor, considering three different GNM models of increasing complexity. Results demonstrated that the use of GNM models and mixed GA/NLP optimization algorithms is a promissory approach for solving dynamic RTO problems.

  5. Neural processing of high and low spatial frequency information in faces changes across development: qualitative changes in face processing during adolescence.

    Science.gov (United States)

    Peters, Judith C; Vlamings, Petra; Kemner, Chantal

    2013-05-01

    Face perception in adults depends on skilled processing of interattribute distances ('configural' processing), which is disrupted for faces presented in inverted orientation (face inversion effect or FIE). Children are not proficient in configural processing, and this might relate to an underlying immaturity to use facial information in low spatial frequency (SF) ranges, which capture the coarse information needed for configural processing. We hypothesized that during adolescence a shift from use of high to low SF information takes place. Therefore, we studied the influence of SF content on neural face processing in groups of children (9-10 years), adolescents (14-15 years) and young adults (21-29 years) by measuring event-related potentials (ERPs) to upright and inverted faces which varied in SF content. Results revealed that children show a neural FIE in early processing stages (i.e. P1; generated in early visual areas), suggesting a superficial, global facial analysis. In contrast, ERPs of adults revealed an FIE at later processing stages (i.e. N170; generated in face-selective, higher visual areas). Interestingly, adolescents showed FIEs in both processing stages, suggesting a hybrid developmental stage. Furthermore, adolescents and adults showed FIEs for stimuli containing low SF information, whereas such effects were driven by both low and high SF information in children. These results indicate that face processing has a protracted maturational course into adolescence, and is dependent on changes in SF processing. During adolescence, sensitivity to configu