WorldWideScience

Sample records for neurally inspired computer

  1. A Case Study on Neural Inspired Dynamic Memory Management Strategies for High Performance Computing.

    Energy Technology Data Exchange (ETDEWEB)

    Vineyard, Craig Michael [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Verzi, Stephen Joseph [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-09-01

    As high performance computing architectures pursue more computational power there is a need for increased memory capacity and bandwidth as well. A multi-level memory (MLM) architecture addresses this need by combining multiple memory types with different characteristics as varying levels of the same architecture. How to efficiently utilize this memory infrastructure is an unknown challenge, and in this research we sought to investigate whether neural inspired approaches can meaningfully help with memory management. In particular we explored neurogenesis inspired re- source allocation, and were able to show a neural inspired mixed controller policy can beneficially impact how MLM architectures utilize memory.

  2. Low-cost autonomous perceptron neural network inspired by quantum computation

    Science.gov (United States)

    Zidan, Mohammed; Abdel-Aty, Abdel-Haleem; El-Sadek, Alaa; Zanaty, E. A.; Abdel-Aty, Mahmoud

    2017-11-01

    Achieving low cost learning with reliable accuracy is one of the important goals to achieve intelligent machines to save time, energy and perform learning process over limited computational resources machines. In this paper, we propose an efficient algorithm for a perceptron neural network inspired by quantum computing composite from a single neuron to classify inspirable linear applications after a single training iteration O(1). The algorithm is applied over a real world data set and the results are outer performs the other state-of-the art algorithms.

  3. Cellular computational platform and neurally inspired elements thereof

    Energy Technology Data Exchange (ETDEWEB)

    Okandan, Murat

    2016-11-22

    A cellular computational platform is disclosed that includes a multiplicity of functionally identical, repeating computational hardware units that are interconnected electrically and optically. Each computational hardware unit includes a reprogrammable local memory and has interconnections to other such units that have reconfigurable weights. Each computational hardware unit is configured to transmit signals into the network for broadcast in a protocol-less manner to other such units in the network, and to respond to protocol-less broadcast messages that it receives from the network. Each computational hardware unit is further configured to reprogram the local memory in response to incoming electrical and/or optical signals.

  4. Human Inspired Self-developmental Model of Neural Network (HIM): Introducing Content/Form Computing

    Science.gov (United States)

    Krajíček, Jiří

    This paper presents cross-disciplinary research between medical/psychological evidence on human abilities and informatics needs to update current models in computer science to support alternative methods for computation and communication. In [10] we have already proposed hypothesis introducing concept of human information model (HIM) as cooperative system. Here we continue on HIM design in detail. In our design, first we introduce Content/Form computing system which is new principle of present methods in evolutionary computing (genetic algorithms, genetic programming). Then we apply this system on HIM (type of artificial neural network) model as basic network self-developmental paradigm. Main inspiration of our natural/human design comes from well known concept of artificial neural networks, medical/psychological evidence and Sheldrake theory of "Nature as Alive" [22].

  5. Computing with Biologically Inspired Neural Oscillators: Application to Colour Image Segmentation

    Directory of Open Access Journals (Sweden)

    Ammar Belatreche

    2010-01-01

    Full Text Available This paper investigates the computing capabilities and potential applications of neural oscillators, a biologically inspired neural model, to grey scale and colour image segmentation, an important task in image understanding and object recognition. A proposed neural system that exploits the synergy between neural oscillators and Kohonen self-organising maps (SOMs is presented. It consists of a two-dimensional grid of neural oscillators which are locally connected through excitatory connections and globally connected to a common inhibitor. Each neuron is mapped to a pixel of the input image and existing objects, represented by homogenous areas, are temporally segmented through synchronisation of the activity of neural oscillators that are mapped to pixels of the same object. Self-organising maps form the basis of a colour reduction system whose output is fed to a 2D grid of neural oscillators for temporal correlation-based object segmentation. Both chromatic and local spatial features are used. The system is simulated in Matlab and its demonstration on real world colour images shows promising results and the emergence of a new bioinspired approach for colour image segmentation. The paper concludes with a discussion of the performance of the proposed system and its comparison with traditional image segmentation approaches.

  6. Biologically Inspired Modular Neural Networks

    OpenAIRE

    Azam, Farooq

    2000-01-01

    This dissertation explores the modular learning in artificial neural networks that mainly driven by the inspiration from the neurobiological basis of the human learning. The presented modularization approaches to the neural network design and learning are inspired by the engineering, complexity, psychological and neurobiological aspects. The main theme of this dissertation is to explore the organization and functioning of the brain to discover new structural and learning ...

  7. Perceptually-Inspired Computing

    Directory of Open Access Journals (Sweden)

    Ming Lin

    2015-08-01

    Full Text Available Human sensory systems allow individuals to see, hear, touch, and interact with the surrounding physical environment. Understanding human perception and its limit enables us to better exploit the psychophysics of human perceptual systems to design more efficient, adaptive algorithms and develop perceptually-inspired computational models. In this talk, I will survey some of recent efforts on perceptually-inspired computing with applications to crowd simulation and multimodal interaction. In particular, I will present data-driven personality modeling based on the results of user studies, example-guided physics-based sound synthesis using auditory perception, as well as perceptually-inspired simplification for multimodal interaction. These perceptually guided principles can be used to accelerating multi-modal interaction and visual computing, thereby creating more natural human-computer interaction and providing more immersive experiences. I will also present their use in interactive applications for entertainment, such as video games, computer animation, and shared social experience. I will conclude by discussing possible future research directions.

  8. A Reconfigurable and Biologically Inspired Paradigm for Computation Using Network-On-Chip and Spiking Neural Networks

    Directory of Open Access Journals (Sweden)

    Jim Harkin

    2009-01-01

    Full Text Available FPGA devices have emerged as a popular platform for the rapid prototyping of biological Spiking Neural Networks (SNNs applications, offering the key requirement of reconfigurability. However, FPGAs do not efficiently realise the biologically plausible neuron and synaptic models of SNNs, and current FPGA routing structures cannot accommodate the high levels of interneuron connectivity inherent in complex SNNs. This paper highlights and discusses the current challenges of implementing scalable SNNs on reconfigurable FPGAs. The paper proposes a novel field programmable neural network architecture (EMBRACE, incorporating low-power analogue spiking neurons, interconnected using a Network-on-Chip architecture. Results on the evaluation of the EMBRACE architecture using the XOR benchmark problem are presented, and the performance of the architecture is discussed. The paper also discusses the adaptability of the EMBRACE architecture in supporting fault tolerant computing.

  9. Novel quantum inspired binary neural network algorithm

    Indian Academy of Sciences (India)

    In this paper, a quantum based binary neural network algorithm is proposed, named as novel quantum binary neural network algorithm (NQ-BNN). It forms a neural network structure by deciding weights and separability parameter in quantum based manner. Quantum computing concept represents solution probabilistically ...

  10. Bio-inspired computation in telecommunications

    CERN Document Server

    Yang, Xin-She; Ting, TO

    2015-01-01

    Bio-inspired computation, especially those based on swarm intelligence, has become increasingly popular in the last decade. Bio-Inspired Computation in Telecommunications reviews the latest developments in bio-inspired computation from both theory and application as they relate to telecommunications and image processing, providing a complete resource that analyzes and discusses the latest and future trends in research directions. Written by recognized experts, this is a must-have guide for researchers, telecommunication engineers, computer scientists and PhD students.

  11. Bits from Brains for Biologically-Inspired Computing

    Directory of Open Access Journals (Sweden)

    Michael eWibral

    2015-03-01

    Full Text Available Inspiration for artificial biologically-inspired computing is often drawn from neural systems. This article shows how to analyze neural systems using information theory with the aim of obtaining constraints that help to identify the algorithms run by neural systems and the information they represent. Algorithms and representations identified this way may then guide the design of biologically inspired computing systems. The material covered includes the necessary introduction to information theory and to the estimation of information theoretic quantities from neural recordings. We then show how to analyze the information encoded in a system about its environment, and also discuss recent methodological developments on the question of how much information each agent carries about the environment either uniquely, or redundantly or synergistically together with others. Last, we introduce the framework of local information dynamics, where information processing is partitioned into component processes of information storage, transfer, and modification -- locally in space and time. We close by discussing example applications of these measures to neural data and other complex systems.

  12. Nature-inspired computation in engineering

    CERN Document Server

    2016-01-01

    This timely review book summarizes the state-of-the-art developments in nature-inspired optimization algorithms and their applications in engineering. Algorithms and topics include the overview and history of nature-inspired algorithms, discrete firefly algorithm, discrete cuckoo search, plant propagation algorithm, parameter-free bat algorithm, gravitational search, biogeography-based algorithm, differential evolution, particle swarm optimization and others. Applications include vehicle routing, swarming robots, discrete and combinatorial optimization, clustering of wireless sensor networks, cell formation, economic load dispatch, metamodeling, surrogated-assisted cooperative co-evolution, data fitting and reverse engineering as well as other case studies in engineering. This book will be an ideal reference for researchers, lecturers, graduates and engineers who are interested in nature-inspired computation, artificial intelligence and computational intelligence. It can also serve as a reference for relevant...

  13. Towards brain-inspired computing

    CERN Document Server

    Gingl, Zoltan; Kish, Laszlo

    2010-01-01

    We present introductory considerations and analysis toward computing applications based on the recently introduced deterministic logic scheme with random spike (pulse) trains [Phys. Lett. A 373 (2009) 2338-2342]. Also, in considering the questions, "Why random?" and "Why pulses?", we show that the random pulse based scheme provides the advantages of realizing multivalued deterministic logic. Pulse trains are realized by an element called orthogonator. We discuss two different types of orthogonators, parallel (intersection-based) and serial (demultiplexer-based) orthogonators. The last one can be slower but it makes sequential logic design straightforward. We propose generating a multidimensional logic hyperspace [Physics Letters A 373 (2009) 1928-1934] by using the zero-crossing events of uncorrelated Gaussian electrical noises available in the chips. The spike trains in the hyperspace are non-overlapping, and are referred to as neuro-bits. To demonstrate this idea, we generate 3-dimensional hyperspace bases ...

  14. Cognitively Inspired Neural Network for Situation Recognition

    Science.gov (United States)

    2010-01-14

    jointly with IEEE International Symposium on Computational Intelligence in Robotics and Automation (ClRA), Intelligent Systems and Semiotics (ISAS...1996), Godel Theorem and Semiotics . In Proc. Conference on Intelligent Systems and Semiotics 󈨤. Gaithersburg, MD, vol.2, pp. 14-18. Perlovsky, l.1...New Math. and Natural Computation, 2(1), pp.43-55. Perlovsky, L.I . (2006d) . Symbols: Integrated Cognition and Language. Chapter in Semiotics and

  15. Neuro-Inspired Computing with Stochastic Electronics

    KAUST Repository

    Naous, Rawan

    2016-01-06

    The extensive scaling and integration within electronic systems have set the standards for what is addressed to as stochastic electronics. The individual components are increasingly diverting away from their reliable behavior and producing un-deterministic outputs. This stochastic operation highly mimics the biological medium within the brain. Hence, building on the inherent variability, particularly within novel non-volatile memory technologies, paves the way for unconventional neuromorphic designs. Neuro-inspired networks with brain-like structures of neurons and synapses allow for computations and levels of learning for diverse recognition tasks and applications.

  16. Optimal neural computations require analog processors

    Energy Technology Data Exchange (ETDEWEB)

    Beiu, V.

    1998-12-31

    This paper discusses some of the limitations of hardware implementations of neural networks. The authors start by presenting neural structures and their biological inspirations, while mentioning the simplifications leading to artificial neural networks. Further, the focus will be on hardware imposed constraints. They will present recent results for three different alternatives of parallel implementations of neural networks: digital circuits, threshold gate circuits, and analog circuits. The area and the delay will be related to the neurons` fan-in and to the precision of their synaptic weights. The main conclusion is that hardware-efficient solutions require analog computations, and suggests the following two alternatives: (i) cope with the limitations imposed by silicon, by speeding up the computation of the elementary silicon neurons; (2) investigate solutions which would allow the use of the third dimension (e.g. using optical interconnections).

  17. Neuro-inspired computing using resistive synaptic devices

    CERN Document Server

    2017-01-01

    This book summarizes the recent breakthroughs in hardware implementation of neuro-inspired computing using resistive synaptic devices. The authors describe how two-terminal solid-state resistive memories can emulate synaptic weights in a neural network. Readers will benefit from state-of-the-art summaries of resistive synaptic devices, from the individual cell characteristics to the large-scale array integration. This book also discusses peripheral neuron circuits design challenges and design strategies. Finally, the authors describe the impact of device non-ideal properties (e.g. noise, variation, yield) and their impact on the learning performance at the system-level, using a device-algorithm co-design methodology. • Provides single-source reference to recent breakthroughs in resistive synaptic devices, not only at individual cell-level, but also at integrated array-level; • Includes detailed discussion of the peripheral circuits and array architecture design of the neuro-crossbar system; • Focuses on...

  18. Optics in neural computation

    Science.gov (United States)

    Levene, Michael John

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

  19. Biologically inspired path to quantum computer

    Science.gov (United States)

    Ogryzko, Vasily; Ozhigov, Yuri

    2014-12-01

    We describe an approach to quantum computer inspired by the information processing at the molecular level in living cells. It is based on the separation of a small ensemble of qubits inside the living system (e.g., a bacterial cell), such that coherent quantum states of this ensemble remain practically unchanged for a long time. We use the notion of a quantum kernel to describe such an ensemble. Quantum kernel is not strictly connected with certain particles; it permanently exchanges atoms and molecules with the environment, which makes quantum kernel a virtual notion. There are many reasons to expect that the state of quantum kernel of a living system can be treated as the stationary state of some Hamiltonian. While the quantum kernel is responsible for the stability of dynamics at the time scale of cellular life, at the longer inter-generation time scale it can change, varying smoothly in the course of biological evolution. To the first level of approximation, quantum kernel can be described in the framework of qubit modification of Jaynes-Cummings-Hubbard model, in which the relaxation corresponds to the exchange of matter between quantum kernel and the rest of the cell and is represented as Lindblad super-operators.

  20. Emerging trends in neuro engineering and neural computation

    CERN Document Server

    Lee, Kendall; Garmestani, Hamid; Lim, Chee

    2017-01-01

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

  1. Neural Computations in Binaural Hearing

    Science.gov (United States)

    Wagner, Hermann

    Binaural hearing helps humans and animals to localize and unmask sounds. Here, binaural computations in the barn owl's auditory system are discussed. Barn owls use the interaural time difference (ITD) for azimuthal sound localization, and they use the interaural level difference (ELD) for elevational sound localization. ITD and ILD and their precursors are processed in separate neural pathways, the time pathway and the intensity pathway, respectively. Representation of ITD involves four main computational steps, while the representation of ILD is accomplished in three steps. In the discussion neural processing in the owl's auditory system is compared with neural computations present in mammals.

  2. Bio-inspired computation in unmanned aerial vehicles

    CERN Document Server

    Duan, Haibin

    2014-01-01

    Bio-inspired Computation in Unmanned Aerial Vehicles focuses on the aspects of path planning, formation control, heterogeneous cooperative control and vision-based surveillance and navigation in Unmanned Aerial Vehicles (UAVs) from the perspective of bio-inspired computation. It helps readers to gain a comprehensive understanding of control-related problems in UAVs, presenting the latest advances in bio-inspired computation. By combining bio-inspired computation and UAV control problems, key questions are explored in depth, and each piece is content-rich while remaining accessible. With abundant illustrations of simulation work, this book links theory, algorithms and implementation procedures, demonstrating the simulation results with graphics that are intuitive without sacrificing academic rigor. Further, it pays due attention to both the conceptual framework and the implementation procedures. The book offers a valuable resource for scientists, researchers and graduate students in the field of Control, Aeros...

  3. Bio-Inspired Neural Model for Learning Dynamic Models

    Science.gov (United States)

    Duong, Tuan; Duong, Vu; Suri, Ronald

    2009-01-01

    A neural-network mathematical model that, relative to prior such models, places greater emphasis on some of the temporal aspects of real neural physical processes, has been proposed as a basis for massively parallel, distributed algorithms that learn dynamic models of possibly complex external processes by means of learning rules that are local in space and time. The algorithms could be made to perform such functions as recognition and prediction of words in speech and of objects depicted in video images. The approach embodied in this model is said to be "hardware-friendly" in the following sense: The algorithms would be amenable to execution by special-purpose computers implemented as very-large-scale integrated (VLSI) circuits that would operate at relatively high speeds and low power demands.

  4. Motor-Skill Learning in an Insect Inspired Neuro-Computational Control System

    OpenAIRE

    Arena, Eleonora; Arena, Paolo; Strauss, Roland; Patané, Luca

    2017-01-01

    In nature, insects show impressive adaptation and learning capabilities. The proposed computational model takes inspiration from specific structures of the insect brain: after proposing key hypotheses on the direct involvement of the mushroom bodies (MBs) and on their neural organization, we developed a new architecture for motor learning to be applied in insect-like walking robots. The proposed model is a nonlinear control system based on spiking neurons. MBs are modeled as a nonlinear recur...

  5. A biologically inspired neural network controller for ballistic arm movements

    Directory of Open Access Journals (Sweden)

    Schmid Maurizio

    2007-09-01

    Full Text Available Abstract Background In humans, the implementation of multijoint tasks of the arm implies a highly complex integration of sensory information, sensorimotor transformations and motor planning. Computational models can be profitably used to better understand the mechanisms sub-serving motor control, thus providing useful perspectives and investigating different control hypotheses. To this purpose, the use of Artificial Neural Networks has been proposed to represent and interpret the movement of upper limb. In this paper, a neural network approach to the modelling of the motor control of a human arm during planar ballistic movements is presented. Methods The developed system is composed of three main computational blocks: 1 a parallel distributed learning scheme that aims at simulating the internal inverse model in the trajectory formation process; 2 a pulse generator, which is responsible for the creation of muscular synergies; and 3 a limb model based on two joints (two degrees of freedom and six muscle-like actuators, that can accommodate for the biomechanical parameters of the arm. The learning paradigm of the neural controller is based on a pure exploration of the working space with no feedback signal. Kinematics provided by the system have been compared with those obtained in literature from experimental data of humans. Results The model reproduces kinematics of arm movements, with bell-shaped wrist velocity profiles and approximately straight trajectories, and gives rise to the generation of synergies for the execution of movements. The model allows achieving amplitude and direction errors of respectively 0.52 cm and 0.2 radians. Curvature values are similar to those encountered in experimental measures with humans. The neural controller also manages environmental modifications such as the insertion of different force fields acting on the end-effector. Conclusion The proposed system has been shown to properly simulate the development of

  6. Nature-inspired computing and optimization theory and applications

    CERN Document Server

    Yang, Xin-She; Nakamatsu, Kazumi

    2017-01-01

    The book provides readers with a snapshot of the state of the art in the field of nature-inspired computing and its application in optimization. The approach is mainly practice-oriented: each bio-inspired technique or algorithm is introduced together with one of its possible applications. Applications cover a wide range of real-world optimization problems: from feature selection and image enhancement to scheduling and dynamic resource management, from wireless sensor networks and wiring network diagnosis to sports training planning and gene expression, from topology control and morphological filters to nutritional meal design and antenna array design. There are a few theoretical chapters comparing different existing techniques, exploring the advantages of nature-inspired computing over other methods, and investigating the mixing time of genetic algorithms. The book also introduces a wide range of algorithms, including the ant colony optimization, the bat algorithm, genetic algorithms, the collision-based opti...

  7. Web Solutions Inspire Cloud Computing Software

    Science.gov (United States)

    2013-01-01

    An effort at Ames Research Center to standardize NASA websites unexpectedly led to a breakthrough in open source cloud computing technology. With the help of Rackspace Inc. of San Antonio, Texas, the resulting product, OpenStack, has spurred the growth of an entire industry that is already employing hundreds of people and generating hundreds of millions in revenue.

  8. A computational model of conditioning inspired by Drosophila olfactory system.

    Science.gov (United States)

    Faghihi, Faramarz; Moustafa, Ahmed A; Heinrich, Ralf; Wörgötter, Florentin

    2017-03-01

    Recent studies have demonstrated that Drosophila melanogaster (briefly Drosophila) can successfully perform higher cognitive processes including second order olfactory conditioning. Understanding the neural mechanism of this behavior can help neuroscientists to unravel the principles of information processing in complex neural systems (e.g. the human brain) and to create efficient and robust robotic systems. In this work, we have developed a biologically-inspired spiking neural network which is able to execute both first and second order conditioning. Experimental studies demonstrated that volume signaling (e.g. by the gaseous transmitter nitric oxide) contributes to memory formation in vertebrates and invertebrates including insects. Based on the existing knowledge of odor encoding in Drosophila, the role of retrograde signaling in memory function, and the integration of synaptic and non-synaptic neural signaling, a neural system is implemented as Simulated fly. Simulated fly navigates in a two-dimensional environment in which it receives odors and electric shocks as sensory stimuli. The model suggests some experimental research on retrograde signaling to investigate neural mechanisms of conditioning in insects and other animals. Moreover, it illustrates a simple strategy to implement higher cognitive capabilities in machines including robots. Copyright © 2016 Elsevier Ltd. All rights reserved.

  9. Fundamentals of computational intelligence neural networks, fuzzy systems, and evolutionary computation

    CERN Document Server

    Keller, James M; Fogel, David B

    2016-01-01

    This book covers the three fundamental topics that form the basis of computational intelligence: neural networks, fuzzy systems, and evolutionary computation. The text focuses on inspiration, design, theory, and practical aspects of implementing procedures to solve real-world problems. While other books in the three fields that comprise computational intelligence are written by specialists in one discipline, this book is co-written by current former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems, a former Editor-in-Chief of IEEE Transactions on Fuzzy Systems, and the founding Editor-in-Chief of IEEE Transactions on Evolutionary Computation. The coverage across the three topics is both uniform and consistent in style and notation. Discusses single-layer and multilayer neural networks, radial-basi function networks, and recurrent neural networks Covers fuzzy set theory, fuzzy relations, fuzzy logic interference, fuzzy clustering and classification, fuzzy measures and fuzz...

  10. 7th World Congress on Nature and Biologically Inspired Computing

    CERN Document Server

    Engelbrecht, Andries; Abraham, Ajith; Plessis, Mathys; Snášel, Václav; Muda, Azah

    2016-01-01

    World Congress on Nature and Biologically Inspired Computing (NaBIC) is organized to discuss the state-of-the-art as well as to address various issues with respect to Nurturing Intelligent Computing Towards Advancement of Machine Intelligence. This Volume contains the papers presented in the Seventh World Congress (NaBIC’15) held in Pietermaritzburg, South Africa during December 01-03, 2015. The 39 papers presented in this Volume were carefully reviewed and selected. The Volume would be a valuable reference to researchers, students and practitioners in the computational intelligence field.

  11. Soft computing integrating evolutionary, neural, and fuzzy systems

    CERN Document Server

    Tettamanzi, Andrea

    2001-01-01

    Soft computing encompasses various computational methodologies, which, unlike conventional algorithms, are tolerant of imprecision, uncertainty, and partial truth. Soft computing technologies offer adaptability as a characteristic feature and thus permit the tracking of a problem through a changing environment. Besides some recent developments in areas like rough sets and probabilistic networks, fuzzy logic, evolutionary algorithms, and artificial neural networks are core ingredients of soft computing, which are all bio-inspired and can easily be combined synergetically. This book presents a well-balanced integration of fuzzy logic, evolutionary computing, and neural information processing. The three constituents are introduced to the reader systematically and brought together in differentiated combinations step by step. The text was developed from courses given by the authors and offers numerous illustrations as

  12. A bio-inspired computational model for motion detection

    OpenAIRE

    Silva, Ana Carolina Quintela Alves Vilares da

    2015-01-01

    Tese de Doutoramento (Programa Doutoral em Engenharia Biomédica) Last years have witnessed a considerable interest in research dedicated to show that solutions to challenges in autonomous robot navigation can be found by taking inspiration from biology. Despite their small size and relatively simple nervous systems, insects have evolved vision systems able to perform the computations required for a safe navigation in dynamic and unstructured environments, by using simple, el...

  13. Design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization

    CERN Document Server

    Castillo, Oscar; Kacprzyk, Janusz

    2015-01-01

    This book presents recent advances on the design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization and their application in areas such as, intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. The book is organized in eight main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of theoretical aspects of fuzzy logic, which basically consists of papers that propose new concepts and algorithms based on fuzzy systems. The second part contains papers with the main theme of neural networks theory, which are basically papers dealing with new concepts and algorithms in neural networks. The third part contains papers describing applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The fourth part contains papers describing new nature-inspired optimization algorithms. The fifth part presents div...

  14. Natural Inspired Intelligent Visual Computing and Its Application to Viticulture.

    Science.gov (United States)

    Ang, Li Minn; Seng, Kah Phooi; Ge, Feng Lu

    2017-05-23

    This paper presents an investigation of natural inspired intelligent computing and its corresponding application towards visual information processing systems for viticulture. The paper has three contributions: (1) a review of visual information processing applications for viticulture; (2) the development of natural inspired computing algorithms based on artificial immune system (AIS) techniques for grape berry detection; and (3) the application of the developed algorithms towards real-world grape berry images captured in natural conditions from vineyards in Australia. The AIS algorithms in (2) were developed based on a nature-inspired clonal selection algorithm (CSA) which is able to detect the arcs in the berry images with precision, based on a fitness model. The arcs detected are then extended to perform the multiple arcs and ring detectors information processing for the berry detection application. The performance of the developed algorithms were compared with traditional image processing algorithms like the circular Hough transform (CHT) and other well-known circle detection methods. The proposed AIS approach gave a Fscore of 0.71 compared with Fscores of 0.28 and 0.30 for the CHT and a parameter-free circle detection technique (RPCD) respectively.

  15. Neural Computation and the Computational Theory of Cognition

    Science.gov (United States)

    Piccinini, Gualtiero; Bahar, Sonya

    2013-01-01

    We begin by distinguishing computationalism from a number of other theses that are sometimes conflated with it. We also distinguish between several important kinds of computation: computation in a generic sense, digital computation, and analog computation. Then, we defend a weak version of computationalism--neural processes are computations in the…

  16. Advances in neural networks computational and theoretical issues

    CERN Document Server

    Esposito, Anna; Morabito, Francesco

    2015-01-01

    This book collects research works that exploit neural networks and machine learning techniques from a multidisciplinary perspective. Subjects covered include theoretical, methodological and computational topics which are grouped together into chapters devoted to the discussion of novelties and innovations related to the field of Artificial Neural Networks as well as the use of neural networks for applications, pattern recognition, signal processing, and special topics such as the detection and recognition of multimodal emotional expressions and daily cognitive functions, and  bio-inspired memristor-based networks.  Providing insights into the latest research interest from a pool of international experts coming from different research fields, the volume becomes valuable to all those with any interest in a holistic approach to implement believable, autonomous, adaptive, and context-aware Information Communication Technologies.

  17. ERNN: a biologically inspired feedforward neural network to discriminate emotion from EEG signal.

    Science.gov (United States)

    Khosrowabadi, Reza; Quek, Chai; Ang, Kai Keng; Wahab, Abdul

    2014-03-01

    Emotions play an important role in human cognition, perception, decision making, and interaction. This paper presents a six-layer biologically inspired feedforward neural network to discriminate human emotions from EEG. The neural network comprises a shift register memory after spectral filtering for the input layer, and the estimation of coherence between each pair of input signals for the hidden layer. EEG data are collected from 57 healthy participants from eight locations while subjected to audio-visual stimuli. Discrimination of emotions from EEG is investigated based on valence and arousal levels. The accuracy of the proposed neural network is compared with various feature extraction methods and feedforward learning algorithms. The results showed that the highest accuracy is achieved when using the proposed neural network with a type of radial basis function.

  18. Inspire

    CERN Document Server

    CERN. Geneva

    2008-01-01

    Inspire is the project name of a new High Energy Physics information system which will integrate present databases and repositories to host the entire corpus of the HEP literature and become the reference HEP scientific information platform worldwide. It is a common project between CERN, DESY, FERMILAB and SLAC. It will empower scientists with new tools to discover and access the results most relevant to their research; enable novel text- and data-mining applications; deploy new metrics to assess the impact of articles and authors. In addition, it will introduce the Web2.0 paradigm of user-enriched content in the domain of sciences with community-based approaches to the peer-review process.

  19. Jet-images: computer vision inspired techniques for jet tagging

    Energy Technology Data Exchange (ETDEWEB)

    Cogan, Josh; Kagan, Michael; Strauss, Emanuel; Schwarztman, Ariel [SLAC National Accelerator Laboratory,Menlo Park, CA 94028 (United States)

    2015-02-18

    We introduce a novel approach to jet tagging and classification through the use of techniques inspired by computer vision. Drawing parallels to the problem of facial recognition in images, we define a jet-image using calorimeter towers as the elements of the image and establish jet-image preprocessing methods. For the jet-image processing step, we develop a discriminant for classifying the jet-images derived using Fisher discriminant analysis. The effectiveness of the technique is shown within the context of identifying boosted hadronic W boson decays with respect to a background of quark- and gluon-initiated jets. Using Monte Carlo simulation, we demonstrate that the performance of this technique introduces additional discriminating power over other substructure approaches, and gives significant insight into the internal structure of jets.

  20. On the Computational Power of Spiking Neural P Systems with Self-Organization

    Science.gov (United States)

    Wang, Xun; Song, Tao; Gong, Faming; Zheng, Pan

    2016-06-01

    Neural-like computing models are versatile computing mechanisms in the field of artificial intelligence. Spiking neural P systems (SN P systems for short) are one of the recently developed spiking neural network models inspired by the way neurons communicate. The communications among neurons are essentially achieved by spikes, i. e. short electrical pulses. In terms of motivation, SN P systems fall into the third generation of neural network models. In this study, a novel variant of SN P systems, namely SN P systems with self-organization, is introduced, and the computational power of the system is investigated and evaluated. It is proved that SN P systems with self-organization are capable of computing and accept the family of sets of Turing computable natural numbers. Moreover, with 87 neurons the system can compute any Turing computable recursive function, thus achieves Turing universality. These results demonstrate promising initiatives to solve an open problem arisen by Gh Păun.

  1. Boolean and brain-inspired computing using spin-transfer torque devices

    Science.gov (United States)

    Fan, Deliang

    Several completely new approaches (such as spintronic, carbon nanotube, graphene, TFETs, etc.) to information processing and data storage technologies are emerging to address the time frame beyond current Complementary Metal-Oxide-Semiconductor (CMOS) roadmap. The high speed magnetization switching of a nano-magnet due to current induced spin-transfer torque (STT) have been demonstrated in recent experiments. Such STT devices can be explored in compact, low power memory and logic design. In order to truly leverage STT devices based computing, researchers require a re-think of circuit, architecture, and computing model, since the STT devices are unlikely to be drop-in replacements for CMOS. The potential of STT devices based computing will be best realized by considering new computing models that are inherently suited to the characteristics of STT devices, and new applications that are enabled by their unique capabilities, thereby attaining performance that CMOS cannot achieve. The goal of this research is to conduct synergistic exploration in architecture, circuit and device levels for Boolean and brain-inspired computing using nanoscale STT devices. Specifically, we first show that the non-volatile STT devices can be used in designing configurable Boolean logic blocks. We propose a spin-memristor threshold logic (SMTL) gate design, where memristive cross-bar array is used to perform current mode summation of binary inputs and the low power current mode spintronic threshold device carries out the energy efficient threshold operation. Next, for brain-inspired computing, we have exploited different spin-transfer torque device structures that can implement the hard-limiting and soft-limiting artificial neuron transfer functions respectively. We apply such STT based neuron (or 'spin-neuron') in various neural network architectures, such as hierarchical temporal memory and feed-forward neural network, for performing "human-like" cognitive computing, which show more than

  2. Motor-Skill Learning in an Insect Inspired Neuro-Computational Control System

    Science.gov (United States)

    Arena, Eleonora; Arena, Paolo; Strauss, Roland; Patané, Luca

    2017-01-01

    In nature, insects show impressive adaptation and learning capabilities. The proposed computational model takes inspiration from specific structures of the insect brain: after proposing key hypotheses on the direct involvement of the mushroom bodies (MBs) and on their neural organization, we developed a new architecture for motor learning to be applied in insect-like walking robots. The proposed model is a nonlinear control system based on spiking neurons. MBs are modeled as a nonlinear recurrent spiking neural network (SNN) with novel characteristics, able to memorize time evolutions of key parameters of the neural motor controller, so that existing motor primitives can be improved. The adopted control scheme enables the structure to efficiently cope with goal-oriented behavioral motor tasks. Here, a six-legged structure, showing a steady-state exponentially stable locomotion pattern, is exposed to the need of learning new motor skills: moving through the environment, the structure is able to modulate motor commands and implements an obstacle climbing procedure. Experimental results on a simulated hexapod robot are reported; they are obtained in a dynamic simulation environment and the robot mimicks the structures of Drosophila melanogaster. PMID:28337138

  3. Motor-Skill Learning in an Insect Inspired Neuro-Computational Control System.

    Science.gov (United States)

    Arena, Eleonora; Arena, Paolo; Strauss, Roland; Patané, Luca

    2017-01-01

    In nature, insects show impressive adaptation and learning capabilities. The proposed computational model takes inspiration from specific structures of the insect brain: after proposing key hypotheses on the direct involvement of the mushroom bodies (MBs) and on their neural organization, we developed a new architecture for motor learning to be applied in insect-like walking robots. The proposed model is a nonlinear control system based on spiking neurons. MBs are modeled as a nonlinear recurrent spiking neural network (SNN) with novel characteristics, able to memorize time evolutions of key parameters of the neural motor controller, so that existing motor primitives can be improved. The adopted control scheme enables the structure to efficiently cope with goal-oriented behavioral motor tasks. Here, a six-legged structure, showing a steady-state exponentially stable locomotion pattern, is exposed to the need of learning new motor skills: moving through the environment, the structure is able to modulate motor commands and implements an obstacle climbing procedure. Experimental results on a simulated hexapod robot are reported; they are obtained in a dynamic simulation environment and the robot mimicks the structures of Drosophila melanogaster.

  4. 7th International Conference on Bio-Inspired Computing : Theories and Applications

    CERN Document Server

    Singh, Pramod; Deep, Kusum; Pant, Millie; Nagar, Atulya

    2013-01-01

    The book is a collection of high quality peer reviewed research papers presented in Seventh International Conference on Bio-Inspired Computing (BIC-TA 2012) held at ABV-IIITM Gwalior, India. These research papers provide the latest developments in the broad area of "Computational Intelligence". The book discusses wide variety of industrial, engineering and scientific applications of nature/bio-inspired computing and presents invited papers from the inventors/originators of novel computational techniques.

  5. Computational modeling of spiking neural network with learning rules from STDP and intrinsic plasticity

    Science.gov (United States)

    Li, Xiumin; Wang, Wei; Xue, Fangzheng; Song, Yongduan

    2018-02-01

    Recently there has been continuously increasing interest in building up computational models of spiking neural networks (SNN), such as the Liquid State Machine (LSM). The biologically inspired self-organized neural networks with neural plasticity can enhance the capability of computational performance, with the characteristic features of dynamical memory and recurrent connection cycles which distinguish them from the more widely used feedforward neural networks. Despite a variety of computational models for brain-like learning and information processing have been proposed, the modeling of self-organized neural networks with multi-neural plasticity is still an important open challenge. The main difficulties lie in the interplay among different forms of neural plasticity rules and understanding how structures and dynamics of neural networks shape the computational performance. In this paper, we propose a novel approach to develop the models of LSM with a biologically inspired self-organizing network based on two neural plasticity learning rules. The connectivity among excitatory neurons is adapted by spike-timing-dependent plasticity (STDP) learning; meanwhile, the degrees of neuronal excitability are regulated to maintain a moderate average activity level by another learning rule: intrinsic plasticity (IP). Our study shows that LSM with STDP+IP performs better than LSM with a random SNN or SNN obtained by STDP alone. The noticeable improvement with the proposed method is due to the better reflected competition among different neurons in the developed SNN model, as well as the more effectively encoded and processed relevant dynamic information with its learning and self-organizing mechanism. This result gives insights to the optimization of computational models of spiking neural networks with neural plasticity.

  6. Simple Algorithms for Distributed Leader Election in Anonymous Synchronous Rings and Complete Networks Inspired by Neural Development in Fruit Flies.

    Science.gov (United States)

    Xu, Lei; Jeavons, Peter

    2015-11-01

    Leader election in anonymous rings and complete networks is a very practical problem in distributed computing. Previous algorithms for this problem are generally designed for a classical message passing model where complex messages are exchanged. However, the need to send and receive complex messages makes such algorithms less practical for some real applications. We present some simple synchronous algorithms for distributed leader election in anonymous rings and complete networks that are inspired by the development of the neural system of the fruit fly. Our leader election algorithms all assume that only one-bit messages are broadcast by nodes in the network and processors are only able to distinguish between silence and the arrival of one or more messages. These restrictions allow implementations to use a simpler message-passing architecture. Even with these harsh restrictions our algorithms are shown to achieve good time and message complexity both analytically and experimentally.

  7. Advances in bio-inspired computing for combinatorial optimization problems

    CERN Document Server

    Pintea, Camelia-Mihaela

    2013-01-01

    Advances in Bio-inspired Combinatorial Optimization Problems' illustrates several recent bio-inspired efficient algorithms for solving NP-hard problems.Theoretical bio-inspired concepts and models, in particular for agents, ants and virtual robots are described. Large-scale optimization problems, for example: the Generalized Traveling Salesman Problem and the Railway Traveling Salesman Problem, are solved and their results are discussed.Some of the main concepts and models described in this book are: inner rule to guide ant search - a recent model in ant optimization, heterogeneous sensitive a

  8. Real-Coded Quantum-Inspired Genetic Algorithm-Based BP Neural Network Algorithm

    Directory of Open Access Journals (Sweden)

    Jianyong Liu

    2015-01-01

    Full Text Available The method that the real-coded quantum-inspired genetic algorithm (RQGA used to optimize the weights and threshold of BP neural network is proposed to overcome the defect that the gradient descent method makes the algorithm easily fall into local optimal value in the learning process. Quantum genetic algorithm (QGA is with good directional global optimization ability, but the conventional QGA is based on binary coding; the speed of calculation is reduced by the coding and decoding processes. So, RQGA is introduced to explore the search space, and the improved varied learning rate is adopted to train the BP neural network. Simulation test shows that the proposed algorithm is effective to rapidly converge to the solution conformed to constraint conditions.

  9. Bio-Inspired Computation: Clock-Free, Grid-Free, Scale-Free and Symbol Free

    Science.gov (United States)

    2015-06-11

    AFRL-AFOSR-JP-TR-2015-0002 Bio -inspired computation: clock-free, grid-free, scale-free, and symbol free Janet Wiles THE UNIVERSITY OF QUEENSLAND...SUBTITLE Bio -inspired computation: clock-free, grid-free, scale-free, and symbol free 5a. CONTRACT NUMBER FA2386-12-1-4050 5b. GRANT NUMBER 5c...SUPPLEMENTARY NOTES 14. ABSTRACT The project developed a new fundamental component for bio -inspired computing, based on a new way of modelling

  10. Fifth International Conference on Innovations in Bio-Inspired Computing and Applications

    CERN Document Server

    Abraham, Ajith; Snášel, Václav

    2014-01-01

    This volume of Advances in Intelligent Systems and Computing contains accepted papers presented at IBICA2014, the 5th International Conference on Innovations in Bio-inspired Computing and Applications. The aim of IBICA 2014 was to provide a platform for world research leaders and practitioners, to discuss the full spectrum of current theoretical developments, emerging technologies, and innovative applications of Bio-inspired Computing. Bio-inspired Computing remains to be one of the most exciting research areas, and it is continuously demonstrating exceptional strength in solving complex real life problems. The main driving force of the conference was to further explore the intriguing potential of Bio-inspired Computing. IBICA 2014 was held in Ostrava, Czech Republic and hosted by the VSB - Technical University of Ostrava.

  11. 4th International Conference on Innovations in Bio-Inspired Computing and Applications

    CERN Document Server

    Krömer, Pavel; Snášel, Václav

    2014-01-01

    This volume of Advances in Intelligent Systems and Computing contains accepted papers presented at IBICA2013, the 4th International Conference on Innovations in Bio-inspired Computing and Applications. The aim of IBICA 2013 was to provide a platform for world research leaders and practitioners, to discuss the full spectrum of current theoretical developments, emerging technologies, and innovative applications of Bio-inspired Computing. Bio-inspired Computing is currently one of the most exciting research areas, and it is continuously demonstrating exceptional strength in solving complex real life problems. The main driving force of the conference is to further explore the intriguing potential of Bio-inspired Computing. IBICA 2013 was held in Ostrava, Czech Republic and hosted by the VSB - Technical University of Ostrava.

  12. Handbook of nature-inspired and innovative computing integrating classical models with emerging technologies

    CERN Document Server

    2006-01-01

    As computing devices proliferate, demand increases for an understanding of emerging computing paradigms and models based on natural phenomena. This handbook explores the connection between nature-inspired and traditional computational paradigms. It presents computing paradigms and models based on natural phenomena.

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

    Science.gov (United States)

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

    2017-08-01

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

  14. Cortical Neural Computation by Discrete Results Hypothesis.

    Science.gov (United States)

    Castejon, Carlos; Nuñez, Angel

    2016-01-01

    One of the most challenging problems we face in neuroscience is to understand how the cortex performs computations. There is increasing evidence that the power of the cortical processing is produced by populations of neurons forming dynamic neuronal ensembles. Theoretical proposals and multineuronal experimental studies have revealed that ensembles of neurons can form emergent functional units. However, how these ensembles are implicated in cortical computations is still a mystery. Although cell ensembles have been associated with brain rhythms, the functional interaction remains largely unclear. It is still unknown how spatially distributed neuronal activity can be temporally integrated to contribute to cortical computations. A theoretical explanation integrating spatial and temporal aspects of cortical processing is still lacking. In this Hypothesis and Theory article, we propose a new functional theoretical framework to explain the computational roles of these ensembles in cortical processing. We suggest that complex neural computations underlying cortical processing could be temporally discrete and that sensory information would need to be quantized to be computed by the cerebral cortex. Accordingly, we propose that cortical processing is produced by the computation of discrete spatio-temporal functional units that we have called "Discrete Results" (Discrete Results Hypothesis). This hypothesis represents a novel functional mechanism by which information processing is computed in the cortex. Furthermore, we propose that precise dynamic sequences of "Discrete Results" is the mechanism used by the cortex to extract, code, memorize and transmit neural information. The novel "Discrete Results" concept has the ability to match the spatial and temporal aspects of cortical processing. We discuss the possible neural underpinnings of these functional computational units and describe the empirical evidence supporting our hypothesis. We propose that fast-spiking (FS

  15. A neuroplasticity-inspired neural circuit for acoustic navigation with obstacle avoidance that learns smooth motion paths

    DEFF Research Database (Denmark)

    Shaikh, Danish; Manoonpong, Poramate

    2018-01-01

    avoiding obstacles. We have reported earlier on a neural circuit for acoustic navigation, inspired by neuroplasticity mechanisms, which learned stable robot motion paths for a simulated mobile robot. The circuit realised a reactive behaviour-based navigation architecture where a phonotaxis behaviour...

  16. Computational capabilities of graph neural networks.

    Science.gov (United States)

    Scarselli, Franco; Gori, Marco; Tsoi, Ah Chung; Hagenbuchner, Markus; Monfardini, Gabriele

    2009-01-01

    In this paper, we will consider the approximation properties of a recently introduced neural network model called graph neural network (GNN), which can be used to process-structured data inputs, e.g., acyclic graphs, cyclic graphs, and directed or undirected graphs. This class of neural networks implements a function tau(G,n) is an element of IR(m) that maps a graph G and one of its nodes n onto an m-dimensional Euclidean space. We characterize the functions that can be approximated by GNNs, in probability, up to any prescribed degree of precision. This set contains the maps that satisfy a property called preservation of the unfolding equivalence, and includes most of the practically useful functions on graphs; the only known exception is when the input graph contains particular patterns of symmetries when unfolding equivalence may not be preserved. The result can be considered an extension of the universal approximation property established for the classic feedforward neural networks (FNNs). Some experimental examples are used to show the computational capabilities of the proposed model.

  17. Fuzzy logic, neural networks, and soft computing

    Science.gov (United States)

    Zadeh, Lofti A.

    1994-01-01

    The past few years have witnessed a rapid growth of interest in a cluster of modes of modeling and computation which may be described collectively as soft computing. The distinguishing characteristic of soft computing is that its primary aims are to achieve tractability, robustness, low cost, and high MIQ (machine intelligence quotient) through an exploitation of the tolerance for imprecision and uncertainty. Thus, in soft computing what is usually sought is an approximate solution to a precisely formulated problem or, more typically, an approximate solution to an imprecisely formulated problem. A simple case in point is the problem of parking a car. Generally, humans can park a car rather easily because the final position of the car is not specified exactly. If it were specified to within, say, a few millimeters and a fraction of a degree, it would take hours or days of maneuvering and precise measurements of distance and angular position to solve the problem. What this simple example points to is the fact that, in general, high precision carries a high cost. The challenge, then, is to exploit the tolerance for imprecision by devising methods of computation which lead to an acceptable solution at low cost. By its nature, soft computing is much closer to human reasoning than the traditional modes of computation. At this juncture, the major components of soft computing are fuzzy logic (FL), neural network theory (NN), and probabilistic reasoning techniques (PR), including genetic algorithms, chaos theory, and part of learning theory. Increasingly, these techniques are used in combination to achieve significant improvement in performance and adaptability. Among the important application areas for soft computing are control systems, expert systems, data compression techniques, image processing, and decision support systems. It may be argued that it is soft computing, rather than the traditional hard computing, that should be viewed as the foundation for artificial

  18. On Biblical Hebrew and Computer Science: Inspiration, Models, Tools, And Cross-fertilization

    DEFF Research Database (Denmark)

    Sandborg-Petersen, Ulrik

    2011-01-01

    Eep Talstra's work has been an inspiration to maby researchers, both within and outside of the field of Old Testament scholarship. Among others, Crist-Jan Doedens and the present author have been heavily influenced by Talstra in their own work within the field of computer science. The present...... of the present author. In addition, the tools surrounding Emdros, including SESB, Libronis, and the Emdros Query Tool, are described. Ecamples Biblical Hebrew scholar. Thus the inspiration of Talstra comes full-circle: from Biblical Hebrew databases to computer science and back into Biblical Hebrew scholarship....

  19. Biologically inspired computation and learning in Sensorimotor Systems

    Science.gov (United States)

    Lee, Daniel D.; Seung, H. S.

    2001-11-01

    Networking systems presently lack the ability to intelligently process the rich multimedia content of the data traffic they carry. Endowing artificial systems with the ability to adapt to changing conditions requires algorithms that can rapidly learn from examples. We demonstrate the application of such learning algorithms on an inexpensive quadruped robot constructed to perform simple sensorimotor tasks. The robot learns to track a particular object by discovering the salient visual and auditory cues unique to that object. The system uses a convolutional neural network that automatically combines color, luminance, motion, and auditory information. The weights of the networks are adjusted using feedback from a teacher to reflect the reliability of the various input channels in the surrounding environment. Additionally, the robot is able to compensate for its own motion by adapting the parameters of a vestibular ocular reflex system.

  20. The super-Turing computational power of plastic recurrent neural networks.

    Science.gov (United States)

    Cabessa, Jérémie; Siegelmann, Hava T

    2014-12-01

    We study the computational capabilities of a biologically inspired neural model where the synaptic weights, the connectivity pattern, and the number of neurons can evolve over time rather than stay static. Our study focuses on the mere concept of plasticity of the model so that the nature of the updates is assumed to be not constrained. In this context, we show that the so-called plastic recurrent neural networks (RNNs) are capable of the precise super-Turing computational power--as the static analog neural networks--irrespective of whether their synaptic weights are modeled by rational or real numbers, and moreover, irrespective of whether their patterns of plasticity are restricted to bi-valued updates or expressed by any other more general form of updating. Consequently, the incorporation of only bi-valued plastic capabilities in a basic model of RNNs suffices to break the Turing barrier and achieve the super-Turing level of computation. The consideration of more general mechanisms of architectural plasticity or of real synaptic weights does not further increase the capabilities of the networks. These results support the claim that the general mechanism of plasticity is crucially involved in the computational and dynamical capabilities of biological neural networks. They further show that the super-Turing level of computation reflects in a suitable way the capabilities of brain-like models of computation.

  1. Semiconductor-inspired design principles for superconducting quantum computing.

    Science.gov (United States)

    Shim, Yun-Pil; Tahan, Charles

    2016-03-17

    Superconducting circuits offer tremendous design flexibility in the quantum regime culminating most recently in the demonstration of few qubit systems supposedly approaching the threshold for fault-tolerant quantum information processing. Competition in the solid-state comes from semiconductor qubits, where nature has bestowed some very useful properties which can be utilized for spin qubit-based quantum computing. Here we begin to explore how selective design principles deduced from spin-based systems could be used to advance superconducting qubit science. We take an initial step along this path proposing an encoded qubit approach realizable with state-of-the-art tunable Josephson junction qubits. Our results show that this design philosophy holds promise, enables microwave-free control, and offers a pathway to future qubit designs with new capabilities such as with higher fidelity or, perhaps, operation at higher temperature. The approach is also especially suited to qubits on the basis of variable super-semi junctions.

  2. Computational intelligence synergies of fuzzy logic, neural networks and evolutionary computing

    CERN Document Server

    Siddique, Nazmul

    2013-01-01

    Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing presents an introduction to some of the cutting edge technological paradigms under the umbrella of computational intelligence. Computational intelligence schemes are investigated with the development of a suitable framework for fuzzy logic, neural networks and evolutionary computing, neuro-fuzzy systems, evolutionary-fuzzy systems and evolutionary neural systems. Applications to linear and non-linear systems are discussed with examples. Key features: Covers all the aspect

  3. 6th International Conference on Innovations in Bio-Inspired Computing and Applications

    CERN Document Server

    Abraham, Ajith; Krömer, Pavel; Pant, Millie; Muda, Azah

    2016-01-01

    This Volume contains the papers presented during the 6th International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2015 which was held in Kochi, India during December 16-18, 2015. The 51 papers presented in this Volume were carefully reviewed and selected. The 6th International Conference IBICA 2015 has been organized to discuss the state-of-the-art as well as to address various issues in the growing research field of Bio-inspired Computing which is currently one of the most exciting research areas, and is continuously demonstrating exceptional strength in solving complex real life problems. The Volume will be a valuable reference to researchers, students and practitioners in the computational intelligence field.

  4. Cerebellum-inspired neural network solution of the inverse kinematics problem.

    Science.gov (United States)

    Asadi-Eydivand, Mitra; Ebadzadeh, Mohammad Mehdi; Solati-Hashjin, Mehran; Darlot, Christian; Abu Osman, Noor Azuan

    2015-12-01

    The demand today for more complex robots that have manipulators with higher degrees of freedom is increasing because of technological advances. Obtaining the precise movement for a desired trajectory or a sequence of arm and positions requires the computation of the inverse kinematic (IK) function, which is a major problem in robotics. The solution of the IK problem leads robots to the precise position and orientation of their end-effector. We developed a bioinspired solution comparable with the cerebellar anatomy and function to solve the said problem. The proposed model is stable under all conditions merely by parameter determination, in contrast to recursive model-based solutions, which remain stable only under certain conditions. We modified the proposed model for the simple two-segmented arm to prove the feasibility of the model under a basic condition. A fuzzy neural network through its learning method was used to compute the parameters of the system. Simulation results show the practical feasibility and efficiency of the proposed model in robotics. The main advantage of the proposed model is its generalizability and potential use in any robot.

  5. Biologically Inspired Modular Neural Control for a Leg-Wheel Hybrid Robot

    DEFF Research Database (Denmark)

    Manoonpong, Poramate; Wörgötter, Florentin; Laksanacharoen, Pudit

    2014-01-01

    In this article we present modular neural control for a leg-wheel hybrid robot consisting of three legs with omnidirectional wheels. This neural control has four main modules having their functional origin in biological neural systems. A minimal recurrent control (MRC) module is for sensory signal...... processing and state memorization. Its outputs drive two front wheels while the rear wheel is controlled through a velocity regulating network (VRN) module. In parallel, a neural oscillator network module serves as a central pattern generator (CPG) controls leg movements for sidestepping. Stepping directions...... or they can serve as useful modules for other module-based neural control applications....

  6. Sound Source Localization Through 8 MEMS Microphones Array Using a Sand-Scorpion-Inspired Spiking Neural Network

    Directory of Open Access Journals (Sweden)

    Christoph Beck

    2016-10-01

    Full Text Available Sand-scorpions and many other arachnids perceive their environment by using their feet to sense ground waves. They are able to determine amplitudes the size of an atom and locate the acoustic stimuli with an accuracy of within 13° based on their neuronal anatomy. We present here a prototype sound source localization system, inspired from this impressive performance. The system presented utilizes custom-built hardware with eight MEMS microphones, one for each foot, to acquire the acoustic scene, and a spiking neural model to localize the sound source. The current implementation shows smaller localization error than those observed in nature.

  7. Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing.

    Science.gov (United States)

    Kuzum, Duygu; Jeyasingh, Rakesh G D; Lee, Byoungil; Wong, H-S Philip

    2012-05-09

    Brain-inspired computing is an emerging field, which aims to extend the capabilities of information technology beyond digital logic. A compact nanoscale device, emulating biological synapses, is needed as the building block for brain-like computational systems. Here, we report a new nanoscale electronic synapse based on technologically mature phase change materials employed in optical data storage and nonvolatile memory applications. We utilize continuous resistance transitions in phase change materials to mimic the analog nature of biological synapses, enabling the implementation of a synaptic learning rule. We demonstrate different forms of spike-timing-dependent plasticity using the same nanoscale synapse with picojoule level energy consumption.

  8. Bio-inspired computational heuristics to study Lane-Emden systems arising in astrophysics model.

    Science.gov (United States)

    Ahmad, Iftikhar; Raja, Muhammad Asif Zahoor; Bilal, Muhammad; Ashraf, Farooq

    2016-01-01

    This study reports novel hybrid computational methods for the solutions of nonlinear singular Lane-Emden type differential equation arising in astrophysics models by exploiting the strength of unsupervised neural network models and stochastic optimization techniques. In the scheme the neural network, sub-part of large field called soft computing, is exploited for modelling of the equation in an unsupervised manner. The proposed approximated solutions of higher order ordinary differential equation are calculated with the weights of neural networks trained with genetic algorithm, and pattern search hybrid with sequential quadratic programming for rapid local convergence. The results of proposed solvers for solving the nonlinear singular systems are in good agreements with the standard solutions. Accuracy and convergence the design schemes are demonstrated by the results of statistical performance measures based on the sufficient large number of independent runs.

  9. Brains--Computers--Machines: Neural Engineering in Science Classrooms

    Science.gov (United States)

    Chudler, Eric H.; Bergsman, Kristen Clapper

    2016-01-01

    Neural engineering is an emerging field of high relevance to students, teachers, and the general public. This feature presents online resources that educators and scientists can use to introduce students to neural engineering and to integrate core ideas from the life sciences, physical sciences, social sciences, computer science, and engineering…

  10. Computational modeling of neural plasticity for self-organization of neural networks.

    Science.gov (United States)

    Chrol-Cannon, Joseph; Jin, Yaochu

    2014-11-01

    Self-organization in biological nervous systems during the lifetime is known to largely occur through a process of plasticity that is dependent upon the spike-timing activity in connected neurons. In the field of computational neuroscience, much effort has been dedicated to building up computational models of neural plasticity to replicate experimental data. Most recently, increasing attention has been paid to understanding the role of neural plasticity in functional and structural neural self-organization, as well as its influence on the learning performance of neural networks for accomplishing machine learning tasks such as classification and regression. Although many ideas and hypothesis have been suggested, the relationship between the structure, dynamics and learning performance of neural networks remains elusive. The purpose of this article is to review the most important computational models for neural plasticity and discuss various ideas about neural plasticity's role. Finally, we suggest a few promising research directions, in particular those along the line that combines findings in computational neuroscience and systems biology, and their synergetic roles in understanding learning, memory and cognition, thereby bridging the gap between computational neuroscience, systems biology and computational intelligence. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  11. Fast and Efficient Asynchronous Neural Computation with Adapting Spiking Neural Networks

    NARCIS (Netherlands)

    D. Zambrano (Davide); S.M. Bohte (Sander)

    2016-01-01

    textabstractBiological neurons communicate with a sparing exchange of pulses - spikes. It is an open question how real spiking neurons produce the kind of powerful neural computation that is possible with deep artificial neural networks, using only so very few spikes to communicate. Building on

  12. 8th International Conference on Bio-Inspired Computing : Theories and Applications

    CERN Document Server

    Pan, Linqiang; Fang, Xianwen

    2013-01-01

    International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) is one of the flagship conferences on Bio-Computing, bringing together the world’s leading scientists from different areas of Natural Computing. Since 2006, the conferences have taken place at Wuhan (2006), Zhengzhou (2007), Adelaide (2008), Beijing (2009), Liverpool & Changsha (2010), Malaysia (2011) and India (2012). Following the successes of previous events, the 8th conference is organized and hosted by Anhui University of Science and Technology in China. This conference aims to provide a high-level international forum that researchers with different backgrounds and who are working in the related areas can use to present their latest results and exchange ideas. Additionally, the growing trend in Emergent Systems has resulted in the inclusion of two other closely related fields in the BIC-TA 2013 event, namely Complex Systems and Computational Neuroscience. These proceedings are intended for researchers in the fiel...

  13. A new parallel DNA algorithm to solve the task scheduling problem based on inspired computational model.

    Science.gov (United States)

    Wang, Zhaocai; Ji, Zuwen; Wang, Xiaoming; Wu, Tunhua; Huang, Wei

    2017-12-01

    As a promising approach to solve the computationally intractable problem, the method based on DNA computing is an emerging research area including mathematics, computer science and molecular biology. The task scheduling problem, as a well-known NP-complete problem, arranges n jobs to m individuals and finds the minimum execution time of last finished individual. In this paper, we use a biologically inspired computational model and describe a new parallel algorithm to solve the task scheduling problem by basic DNA molecular operations. In turn, we skillfully design flexible length DNA strands to represent elements of the allocation matrix, take appropriate biological experiment operations and get solutions of the task scheduling problem in proper length range with less than O(n 2 ) time complexity. Copyright © 2017. Published by Elsevier B.V.

  14. Anomalous Diffusion within the Transcriptome as a Bio-Inspired Computing Framework for Resilience

    Directory of Open Access Journals (Sweden)

    William Seffens

    2017-07-01

    Full Text Available Much of biology-inspired computer science is based on the Central Dogma, as implemented with genetic algorithms or evolutionary computation. That 60-year-old biological principle based on the genome, transcriptome and proteasome is becoming overshadowed by a new paradigm of complex ordered associations and connections between layers of biological entities, such as interactomes, metabolomics, etc. We define a new hierarchical concept as the “Connectosome”, and propose new venues of computational data structures based on a conceptual framework called “Grand Ensemble” which contains the Central Dogma as a subset. Connectedness and communication within and between living or biology-inspired systems comprise ensembles from which a physical computing system can be conceived. In this framework the delivery of messages is filtered by size and a simple and rapid semantic analysis of their content. This work aims to initiate discussion on the Grand Ensemble in network biology as a representation of a Persistent Turing Machine. This framework adding interaction and persistency to the classic Turing-machine model uses metrics based on resilience that has application to dynamic optimization problem solving in Genetic Programming.

  15. Metal oxide resistive random access memory based synaptic devices for brain-inspired computing

    Science.gov (United States)

    Gao, Bin; Kang, Jinfeng; Zhou, Zheng; Chen, Zhe; Huang, Peng; Liu, Lifeng; Liu, Xiaoyan

    2016-04-01

    The traditional Boolean computing paradigm based on the von Neumann architecture is facing great challenges for future information technology applications such as big data, the Internet of Things (IoT), and wearable devices, due to the limited processing capability issues such as binary data storage and computing, non-parallel data processing, and the buses requirement between memory units and logic units. The brain-inspired neuromorphic computing paradigm is believed to be one of the promising solutions for realizing more complex functions with a lower cost. To perform such brain-inspired computing with a low cost and low power consumption, novel devices for use as electronic synapses are needed. Metal oxide resistive random access memory (ReRAM) devices have emerged as the leading candidate for electronic synapses. This paper comprehensively addresses the recent work on the design and optimization of metal oxide ReRAM-based synaptic devices. A performance enhancement methodology and optimized operation scheme to achieve analog resistive switching and low-energy training behavior are provided. A three-dimensional vertical synapse network architecture is proposed for high-density integration and low-cost fabrication. The impacts of the ReRAM synaptic device features on the performances of neuromorphic systems are also discussed on the basis of a constructed neuromorphic visual system with a pattern recognition function. Possible solutions to achieve the high recognition accuracy and efficiency of neuromorphic systems are presented.

  16. Artificial Neural Network Metamodels of Stochastic Computer Simulations

    Science.gov (United States)

    1994-08-10

    23 Haddock, J. and O’Keefe, R., "Using Artificial Intelligence to Facilitate Manufacturing Systems Simulation," Computers & Industrial Engineering , Vol...Feedforward Neural Networks," Computers & Industrial Engineering , Vol. 21, No. 1- 4, (1991), pp. 247-251. 87 Proceedings of the 1992 Summer Computer...Using Simulation Experiments," Computers & Industrial Engineering , Vol. 22, No. 2 (1992), pp. 195-209. 119 Kuei, C. and Madu, C., "Polynomial

  17. Handwritten Digits Recognition Using Neural Computing

    Directory of Open Access Journals (Sweden)

    Călin Enăchescu

    2009-12-01

    Full Text Available In this paper we present a method for the recognition of handwritten digits and a practical implementation of this method for real-time recognition. A theoretical framework for the neural networks used to classify the handwritten digits is also presented.The classification task is performed using a Convolutional Neural Network (CNN. CNN is a special type of multy-layer neural network, being trained with an optimized version of the back-propagation learning algorithm.CNN is designed to recognize visual patterns directly from pixel images with minimal preprocessing, being capable to recognize patterns with extreme variability (such as handwritten characters, and with robustness to distortions and simple geometric transformations.The main contributions of this paper are related to theoriginal methods for increasing the efficiency of the learning algorithm by preprocessing the images before the learning process and a method for increasing the precision and performance for real-time applications, by removing the non useful information from the background.By combining these strategies we have obtained an accuracy of 96.76%, using as training set the NIST (National Institute of Standards and Technology database.

  18. 16th International Conference on Hybrid Intelligent Systems and the 8th World Congress on Nature and Biologically Inspired Computing

    CERN Document Server

    Haqiq, Abdelkrim; Alimi, Adel; Mezzour, Ghita; Rokbani, Nizar; Muda, Azah

    2017-01-01

    This book presents the latest research in hybrid intelligent systems. It includes 57 carefully selected papers from the 16th International Conference on Hybrid Intelligent Systems (HIS 2016) and the 8th World Congress on Nature and Biologically Inspired Computing (NaBIC 2016), held on November 21–23, 2016 in Marrakech, Morocco. HIS - NaBIC 2016 was jointly organized by the Machine Intelligence Research Labs (MIR Labs), USA; Hassan 1st University, Settat, Morocco and University of Sfax, Tunisia. Hybridization of intelligent systems is a promising research field in modern artificial/computational intelligence and is concerned with the development of the next generation of intelligent systems. The conference’s main aim is to inspire further exploration of the intriguing potential of hybrid intelligent systems and bio-inspired computing. As such, the book is a valuable resource for practicing engineers /scientists and researchers working in the field of computational intelligence and artificial intelligence.

  19. Predicting Student Academic Performance: A Comparison of Two Meta-Heuristic Algorithms Inspired by Cuckoo Birds for Training Neural Networks

    Directory of Open Access Journals (Sweden)

    Jeng-Fung Chen

    2014-10-01

    Full Text Available Predicting student academic performance with a high accuracy facilitates admission decisions and enhances educational services at educational institutions. This raises the need to propose a model that predicts student performance, based on the results of standardized exams, including university entrance exams, high school graduation exams, and other influential factors. In this study, an approach to the problem based on the artificial neural network (ANN with the two meta-heuristic algorithms inspired by cuckoo birds and their lifestyle, namely, Cuckoo Search (CS and Cuckoo Optimization Algorithm (COA is proposed. In particular, we used previous exam results and other factors, such as the location of the student’s high school and the student’s gender as input variables, and predicted the student academic performance. The standard CS and standard COA were separately utilized to train the feed-forward network for prediction. The algorithms optimized the weights between layers and biases of the neuron network. The simulation results were then discussed and analyzed to investigate the prediction ability of the neural network trained by these two algorithms. The findings demonstrated that both CS and COA have potential in training ANN and ANN-COA obtained slightly better results for predicting student academic performance in this case. It is expected that this work may be used to support student admission procedures and strengthen the service system in educational institutions.

  20. Computationally Efficient Neural Network Intrusion Security Awareness

    Energy Technology Data Exchange (ETDEWEB)

    Todd Vollmer; Milos Manic

    2009-08-01

    An enhanced version of an algorithm to provide anomaly based intrusion detection alerts for cyber security state awareness is detailed. A unique aspect is the training of an error back-propagation neural network with intrusion detection rule features to provide a recognition basis. Network packet details are subsequently provided to the trained network to produce a classification. This leverages rule knowledge sets to produce classifications for anomaly based systems. Several test cases executed on ICMP protocol revealed a 60% identification rate of true positives. This rate matched the previous work, but 70% less memory was used and the run time was reduced to less than 1 second from 37 seconds.

  1. Engineering incremental resistive switching in TaOx based memristors for brain-inspired computing

    Science.gov (United States)

    Wang, Zongwei; Yin, Minghui; Zhang, Teng; Cai, Yimao; Wang, Yangyuan; Yang, Yuchao; Huang, Ru

    2016-07-01

    Brain-inspired neuromorphic computing is expected to revolutionize the architecture of conventional digital computers and lead to a new generation of powerful computing paradigms, where memristors with analog resistive switching are considered to be potential solutions for synapses. Here we propose and demonstrate a novel approach to engineering the analog switching linearity in TaOx based memristors, that is, by homogenizing the filament growth/dissolution rate via the introduction of an ion diffusion limiting layer (DLL) at the TiN/TaOx interface. This has effectively mitigated the commonly observed two-regime conductance modulation behavior and led to more uniform filament growth (dissolution) dynamics with time, therefore significantly improving the conductance modulation linearity that is desirable in neuromorphic systems. In addition, the introduction of the DLL also served to reduce the power consumption of the memristor, and important synaptic learning rules in biological brains such as spike timing dependent plasticity were successfully implemented using these optimized devices. This study could provide general implications for continued optimizations of memristor performance for neuromorphic applications, by carefully tuning the dynamics involved in filament growth and dissolution.Brain-inspired neuromorphic computing is expected to revolutionize the architecture of conventional digital computers and lead to a new generation of powerful computing paradigms, where memristors with analog resistive switching are considered to be potential solutions for synapses. Here we propose and demonstrate a novel approach to engineering the analog switching linearity in TaOx based memristors, that is, by homogenizing the filament growth/dissolution rate via the introduction of an ion diffusion limiting layer (DLL) at the TiN/TaOx interface. This has effectively mitigated the commonly observed two-regime conductance modulation behavior and led to more uniform filament

  2. Inherently stochastic spiking neurons for probabilistic neural computation

    KAUST Repository

    Al-Shedivat, Maruan

    2015-04-01

    Neuromorphic engineering aims to design hardware that efficiently mimics neural circuitry and provides the means for emulating and studying neural systems. In this paper, we propose a new memristor-based neuron circuit that uniquely complements the scope of neuron implementations and follows the stochastic spike response model (SRM), which plays a cornerstone role in spike-based probabilistic algorithms. We demonstrate that the switching of the memristor is akin to the stochastic firing of the SRM. Our analysis and simulations show that the proposed neuron circuit satisfies a neural computability condition that enables probabilistic neural sampling and spike-based Bayesian learning and inference. Our findings constitute an important step towards memristive, scalable and efficient stochastic neuromorphic platforms. © 2015 IEEE.

  3. A biologically inspired neural model for visual and proprioceptive integration including sensory training.

    Science.gov (United States)

    Saidi, Maryam; Towhidkhah, Farzad; Gharibzadeh, Shahriar; Lari, Abdolaziz Azizi

    2013-12-01

    Humans perceive the surrounding world by integration of information through different sensory modalities. Earlier models of multisensory integration rely mainly on traditional Bayesian and causal Bayesian inferences for single causal (source) and two causal (for two senses such as visual and auditory systems), respectively. In this paper a new recurrent neural model is presented for integration of visual and proprioceptive information. This model is based on population coding which is able to mimic multisensory integration of neural centers in the human brain. The simulation results agree with those achieved by casual Bayesian inference. The model can also simulate the sensory training process of visual and proprioceptive information in human. Training process in multisensory integration is a point with less attention in the literature before. The effect of proprioceptive training on multisensory perception was investigated through a set of experiments in our previous study. The current study, evaluates the effect of both modalities, i.e., visual and proprioceptive training and compares them with each other through a set of new experiments. In these experiments, the subject was asked to move his/her hand in a circle and estimate its position. The experiments were performed on eight subjects with proprioception training and eight subjects with visual training. Results of the experiments show three important points: (1) visual learning rate is significantly more than that of proprioception; (2) means of visual and proprioceptive errors are decreased by training but statistical analysis shows that this decrement is significant for proprioceptive error and non-significant for visual error, and (3) visual errors in training phase even in the beginning of it, is much less than errors of the main test stage because in the main test, the subject has to focus on two senses. The results of the experiments in this paper is in agreement with the results of the neural model

  4. A novel nature inspired firefly algorithm with higher order neural network: Performance analysis

    Directory of Open Access Journals (Sweden)

    Janmenjoy Nayak

    2016-03-01

    Full Text Available The applications of both Feed Forward Neural network and Multilayer perceptron are very diverse and saturated. But the linear threshold unit of feed forward networks causes fast learning with limited capabilities, while due to multilayering, the back propagation of errors exhibits slow training speed in MLP. So, a higher order network can be constructed by correlating between the input variables to perform nonlinear mapping using the single layer of input units for overcoming the above drawbacks. In this paper, a Firefly based higher order neural network has been proposed for data classification for maintaining fast learning and avoids the exponential increase of processing units. A vast literature survey has been conducted to review the state of the art of the previous developed models. The performance of the proposed method has been tested with various benchmark datasets from UCI machine learning repository and compared with the performance of other established models. Experimental results imply that the proposed method is fast, steady, reliable and provides better classification accuracy than others.

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

    Science.gov (United States)

    Mi, Yuanyuan; Lin, Xiaohan; Wu, Si

    2016-01-01

    Neural systems display rich short-term dynamics at various levels, e.g., spike-frequency adaptation (SFA) at the single-neuron level, and short-term facilitation (STF) and depression (STD) at the synapse level. These dynamical features typically cover a broad range of time scales and exhibit large diversity in different brain regions. It remains unclear what is the computational benefit for the brain to have such variability in short-term dynamics. In this study, we propose that the brain can exploit such dynamical features to implement multiple seemingly contradictory computations in a single neural circuit. To demonstrate this idea, we use continuous attractor neural network (CANN) as a working model and include STF, SFA and STD with increasing time constants in its dynamics. Three computational tasks are considered, which are persistent activity, adaptation, and anticipative tracking. These tasks require conflicting neural mechanisms, and hence cannot be implemented by a single dynamical feature or any combination with similar time constants. However, with properly coordinated STF, SFA and STD, we show that the network is able to implement the three computational tasks concurrently. We hope this study will shed light on the understanding of how the brain orchestrates its rich dynamics at various levels to realize diverse cognitive functions. PMID:27679569

  6. Fish species recognition using computer vision and a neural network

    NARCIS (Netherlands)

    Storbeck, F.; Daan, B.

    2001-01-01

    A system is described to recognize fish species by computer vision and a neural network program. The vision system measures a number of features of fish as seen by a camera perpendicular to a conveyor belt. The features used here are the widths and heights at various locations along the fish. First

  7. A neural circuit for angular velocity computation

    Directory of Open Access Journals (Sweden)

    Samuel B Snider

    2010-12-01

    Full Text Available In one of the most remarkable feats of motor control in the animal world, some Diptera, such as the housefly, can accurately execute corrective flight maneuvers in tens of milliseconds. These reflexive movements are achieved by the halteres, gyroscopic force sensors, in conjunction with rapidly-tunable wing-steering muscles. Specifically, the mechanosensory campaniform sensilla located at the base of the halteres transduce and transform rotation-induced gyroscopic forces into information about the angular velocity of the fly's body. But how exactly does the fly's neural architecture generate the angular velocity from the lateral strain forces on the left and right halteres? To explore potential algorithms, we built a neuro-mechanical model of the rotation detection circuit. We propose a neurobiologically plausible method by which the fly could accurately separate and measure the three-dimensional components of an imposed angular velocity. Our model assumes a single sign-inverting synapse and formally resembles some models of directional selectivity by the retina. Using multidimensional error analysis, we demonstrate the robustness of our model under a variety of input conditions. Our analysis reveals the maximum information available to the fly given its physical architecture and the mathematics governing the rotation-induced forces at the haltere's end knob.

  8. A neural circuit for angular velocity computation.

    Science.gov (United States)

    Snider, Samuel B; Yuste, Rafael; Packer, Adam M

    2010-01-01

    In one of the most remarkable feats of motor control in the animal world, some Diptera, such as the housefly, can accurately execute corrective flight maneuvers in tens of milliseconds. These reflexive movements are achieved by the halteres, gyroscopic force sensors, in conjunction with rapidly tunable wing steering muscles. Specifically, the mechanosensory campaniform sensilla located at the base of the halteres transduce and transform rotation-induced gyroscopic forces into information about the angular velocity of the fly's body. But how exactly does the fly's neural architecture generate the angular velocity from the lateral strain forces on the left and right halteres? To explore potential algorithms, we built a neuromechanical model of the rotation detection circuit. We propose a neurobiologically plausible method by which the fly could accurately separate and measure the three-dimensional components of an imposed angular velocity. Our model assumes a single sign-inverting synapse and formally resembles some models of directional selectivity by the retina. Using multidimensional error analysis, we demonstrate the robustness of our model under a variety of input conditions. Our analysis reveals the maximum information available to the fly given its physical architecture and the mathematics governing the rotation-induced forces at the haltere's end knob.

  9. Computation and control with neural nets

    Energy Technology Data Exchange (ETDEWEB)

    Corneliusen, A.; Terdal, P.; Knight, T.; Spencer, J.

    1989-10-04

    As energies have increased exponentially with time so have the size and complexity of accelerators and control systems. NN may offer the kinds of improvements in computation and control that are needed to maintain acceptable functionality. For control their associative characteristics could provide signal conversion or data translation. Because they can do any computation such as least squares, they can close feedback loops autonomously to provide intelligent control at the point of action rather than at a central location that requires transfers, conversions, hand-shaking and other costly repetitions like input protection. Both computation and control can be integrated on a single chip, printed circuit or an optical equivalent that is also inherently faster through full parallel operation. For such reasons one expects lower costs and better results. Such systems could be optimized by integrating sensor and signal processing functions. Distributed nets of such hardware could communicate and provide global monitoring and multiprocessing in various ways e.g. via token, slotted or parallel rings (or Steiner trees) for compatibility with existing systems. Problems and advantages of this approach such as an optimal, real-time Turing machine are discussed. Simple examples are simulated and hardware implemented using discrete elements that demonstrate some basic characteristics of learning and parallelism. Future microprocessors' are predicted and requested on this basis. 19 refs., 18 figs.

  10. Computational neural learning formalisms for manipulator inverse kinematics

    Science.gov (United States)

    Gulati, Sandeep; Barhen, Jacob; Iyengar, S. Sitharama

    1989-01-01

    An efficient, adaptive neural learning paradigm for addressing the inverse kinematics of redundant manipulators is presented. The proposed methodology exploits the infinite local stability of terminal attractors - a new class of mathematical constructs which provide unique information processing capabilities to artificial neural systems. For robotic applications, synaptic elements of such networks can rapidly acquire the kinematic invariances embedded within the presented samples. Subsequently, joint-space configurations, required to follow arbitrary end-effector trajectories, can readily be computed. In a significant departure from prior neuromorphic learning algorithms, this methodology provides mechanisms for incorporating an in-training skew to handle kinematics and environmental constraints.

  11. Internal models and neural computation in the vestibular system.

    Science.gov (United States)

    Green, Andrea M; Angelaki, Dora E

    2010-01-01

    The vestibular system is vital for motor control and spatial self-motion perception. Afferents from the otolith organs and the semicircular canals converge with optokinetic, somatosensory and motor-related signals in the vestibular nuclei, which are reciprocally interconnected with the vestibulocerebellar cortex and deep cerebellar nuclei. Here, we review the properties of the many cell types in the vestibular nuclei, as well as some fundamental computations implemented within this brainstem-cerebellar circuitry. These include the sensorimotor transformations for reflex generation, the neural computations for inertial motion estimation, the distinction between active and passive head movements, as well as the integration of vestibular and proprioceptive information for body motion estimation. A common theme in the solution to such computational problems is the concept of internal models and their neural implementation. Recent studies have shed new insights into important organizational principles that closely resemble those proposed for other sensorimotor systems, where their neural basis has often been more difficult to identify. As such, the vestibular system provides an excellent model to explore common neural processing strategies relevant both for reflexive and for goal-directed, voluntary movement as well as perception.

  12. Quantum inspired PSO for the optimization of simultaneous recurrent neural networks as MIMO learning systems.

    Science.gov (United States)

    Luitel, Bipul; Venayagamoorthy, Ganesh Kumar

    2010-06-01

    Training a single simultaneous recurrent neural network (SRN) to learn all outputs of a multiple-input-multiple-output (MIMO) system is a difficult problem. A new training algorithm developed from combined concepts of swarm intelligence and quantum principles is presented. The training algorithm is called particle swarm optimization with quantum infusion (PSO-QI). To improve the effectiveness of learning, a two-step learning approach is introduced in the training. The objective of the learning in the first step is to find the optimal set of weights in the SRN considering all output errors. In the second step, the objective is to maximize the learning of each output dynamics by fine tuning the respective SRN output weights. To demonstrate the effectiveness of the PSO-QI training algorithm and the two-step learning approach, two examples of an SRN learning MIMO systems are presented. The first example is learning a benchmark MIMO system and the second one is the design of a wide area monitoring system for a multimachine power system. From the results, it is observed that SRNs can effectively learn MIMO systems when trained using the PSO-QI algorithm and the two-step learning approach. Copyright 2009 Elsevier Ltd. All rights reserved.

  13. A Neural Computational Model of Incentive Salience

    Science.gov (United States)

    Zhang, Jun; Berridge, Kent C.; Tindell, Amy J.; Smith, Kyle S.; Aldridge, J. Wayne

    2009-01-01

    Incentive salience is a motivational property with ‘magnet-like’ qualities. When attributed to reward-predicting stimuli (cues), incentive salience triggers a pulse of ‘wanting’ and an individual is pulled toward the cues and reward. A key computational question is how incentive salience is generated during a cue re-encounter, which combines both learning and the state of limbic brain mechanisms. Learning processes, such as temporal-difference models, provide one way for stimuli to acquire cached predictive values of rewards. However, empirical data show that subsequent incentive values are also modulated on the fly by dynamic fluctuation in physiological states, altering cached values in ways requiring additional motivation mechanisms. Dynamic modulation of incentive salience for a Pavlovian conditioned stimulus (CS or cue) occurs during certain states, without necessarily requiring (re)learning about the cue. In some cases, dynamic modulation of cue value occurs during states that are quite novel, never having been experienced before, and even prior to experience of the associated unconditioned reward in the new state. Such cases can include novel drug-induced mesolimbic activation and addictive incentive-sensitization, as well as natural appetite states such as salt appetite. Dynamic enhancement specifically raises the incentive salience of an appropriate CS, without necessarily changing that of other CSs. Here we suggest a new computational model that modulates incentive salience by integrating changing physiological states with prior learning. We support the model with behavioral and neurobiological data from empirical tests that demonstrate dynamic elevations in cue-triggered motivation (involving natural salt appetite, and drug-induced intoxication and sensitization). Our data call for a dynamic model of incentive salience, such as presented here. Computational models can adequately capture fluctuations in cue-triggered ‘wanting’ only by

  14. Neural computing architectures: The design of brain-like machines

    Energy Technology Data Exchange (ETDEWEB)

    Aleksander, I.

    1989-01-01

    Theoretical and applications aspects of neural-network (NN) computers are discussed in chapters contributed by European experts. Topics addressed include speech recognition based on topology-preserving neural maps, neural-map applications, backpropagation in nonfeedforward NNs, a parallel-distributed-processing learning approach to natural language, the learning capabilities of Boolean NNs, the logic of connectionist systems, and a probabilistic-logic NN for associative learning. Consideration is given to N-tuple sampling and genetic algorithms for speech recognition; the dynamic behavior of Boolean NNs; statistical mechanics and NNs; digital NNs, matched filters, and optical implementations; heteroassociative NNs using cabling vs link-disabling local modification rules; and the generation of movement trajectories in primates and robots. Also provided is an overview of parallel distributed processing.

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

    Science.gov (United States)

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

    2014-02-01

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

  16. A neural algorithm for a fundamental computing problem.

    Science.gov (United States)

    Dasgupta, Sanjoy; Stevens, Charles F; Navlakha, Saket

    2017-11-10

    Similarity search-for example, identifying similar images in a database or similar documents on the web-is a fundamental computing problem faced by large-scale information retrieval systems. We discovered that the fruit fly olfactory circuit solves this problem with a variant of a computer science algorithm (called locality-sensitive hashing). The fly circuit assigns similar neural activity patterns to similar odors, so that behaviors learned from one odor can be applied when a similar odor is experienced. The fly algorithm, however, uses three computational strategies that depart from traditional approaches. These strategies can be translated to improve the performance of computational similarity searches. This perspective helps illuminate the logic supporting an important sensory function and provides a conceptually new algorithm for solving a fundamental computational problem. Copyright © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

  17. Advances in neural networks computational intelligence for ICT

    CERN Document Server

    Esposito, Anna; Morabito, Francesco; Pasero, Eros

    2016-01-01

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

  18. Large Scale Evolution of Convolutional Neural Networks Using Volunteer Computing

    OpenAIRE

    Desell, Travis

    2017-01-01

    This work presents a new algorithm called evolutionary exploration of augmenting convolutional topologies (EXACT), which is capable of evolving the structure of convolutional neural networks (CNNs). EXACT is in part modeled after the neuroevolution of augmenting topologies (NEAT) algorithm, with notable exceptions to allow it to scale to large scale distributed computing environments and evolve networks with convolutional filters. In addition to multithreaded and MPI versions, EXACT has been ...

  19. Regional Computation of TEC Using a Neural Network Model

    Science.gov (United States)

    Leandro, R. F.; Santos, M. C.

    2004-05-01

    One of the main sources of errors of GPS measurements is the ionosphere refraction. As a dispersive medium, the ionosphere allow its influence to be computed by using dual frequency receivers. In the case of single frequency receivers it is necessary to use models that tell us how big the ionospheric refraction is. The GPS broadcast message carries parameters of this model, namely Klobuchar model. Dual frequency receivers allow to estimate the influence of ionosphere in the GPS signal by the computation of TEC (Total Electron Content) values, that have a direct relationship with the magnitude of the delay caused by the ionosphere. One alternative is to create a regional model based on a network of dual frequency receivers. In this case, the regional behaviour of ionosphere is modelled in a way that it is possible to estimate the TEC values into or near this region. This regional model can be based on polynomials, for example. In this work we will present a Neural Network-based model to the regional computation of TEC. The advantage of using a Neural Network is that it is not necessary to have a great knowledge on the behaviour of the modelled surface due to the adaptation capability of neural networks training process, that is an iterative adjust of the synaptic weights in function of residuals, using the training parameters. Therefore, the previous knowledge of the modelled phenomena is important to define what kind of and how many parameters are needed to train the neural network so that reasonable results are obtained from the estimations. We have used data from the GPS tracking network in Brazil, and we have tested the accuracy of the new model to all locations where there is a station, accessing the efficiency of the model everywhere. TEC values were computed for each station of the network. After that the training parameters data set for the test station was formed, with the TEC values of all others (all stations, except the test one). The Neural Network was

  20. Neuroscience-inspired computational systems for speech recognition under noisy conditions

    Science.gov (United States)

    Schafer, Phillip B.

    Humans routinely recognize speech in challenging acoustic environments with background music, engine sounds, competing talkers, and other acoustic noise. However, today's automatic speech recognition (ASR) systems perform poorly in such environments. In this dissertation, I present novel methods for ASR designed to approach human-level performance by emulating the brain's processing of sounds. I exploit recent advances in auditory neuroscience to compute neuron-based representations of speech, and design novel methods for decoding these representations to produce word transcriptions. I begin by considering speech representations modeled on the spectrotemporal receptive fields of auditory neurons. These representations can be tuned to optimize a variety of objective functions, which characterize the response properties of a neural population. I propose an objective function that explicitly optimizes the noise invariance of the neural responses, and find that it gives improved performance on an ASR task in noise compared to other objectives. The method as a whole, however, fails to significantly close the performance gap with humans. I next consider speech representations that make use of spiking model neurons. The neurons in this method are feature detectors that selectively respond to spectrotemporal patterns within short time windows in speech. I consider a number of methods for training the response properties of the neurons. In particular, I present a method using linear support vector machines (SVMs) and show that this method produces spikes that are robust to additive noise. I compute the spectrotemporal receptive fields of the neurons for comparison with previous physiological results. To decode the spike-based speech representations, I propose two methods designed to work on isolated word recordings. The first method uses a classical ASR technique based on the hidden Markov model. The second method is a novel template-based recognition scheme that takes

  1. The Path Planning of AUV Based on D-S Information Fusion Map Building and Bio-Inspired Neural Network in Unknown Dynamic Environment

    Directory of Open Access Journals (Sweden)

    Daqi Zhu

    2014-03-01

    Full Text Available In this paper a biologically inspired neural dynamics and map planning based approach are simultaneously proposed for AUV (Autonomous Underwater Vehicle path planning and obstacle avoidance in an unknown dynamic environment. Firstly the readings of an ultrasonic sensor are fused into the map using the D-S (Dempster-Shafer inference rule and a two-dimensional occupancy grid map is built. Secondly the dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation. The AUV path is autonomously generated from the dynamic activity landscape of the neural network and previous AUV location. Finally, simulation results show high quality path optimization and obstacle avoidance behaviour for the AUV.

  2. A modular architecture for transparent computation in recurrent neural networks.

    Science.gov (United States)

    Carmantini, Giovanni S; Beim Graben, Peter; Desroches, Mathieu; Rodrigues, Serafim

    2017-01-01

    Computation is classically studied in terms of automata, formal languages and algorithms; yet, the relation between neural dynamics and symbolic representations and operations is still unclear in traditional eliminative connectionism. Therefore, we suggest a unique perspective on this central issue, to which we would like to refer as transparent connectionism, by proposing accounts of how symbolic computation can be implemented in neural substrates. In this study we first introduce a new model of dynamics on a symbolic space, the versatile shift, showing that it supports the real-time simulation of a range of automata. We then show that the Gödelization of versatile shifts defines nonlinear dynamical automata, dynamical systems evolving on a vectorial space. Finally, we present a mapping between nonlinear dynamical automata and recurrent artificial neural networks. The mapping defines an architecture characterized by its granular modularity, where data, symbolic operations and their control are not only distinguishable in activation space, but also spatially localizable in the network itself, while maintaining a distributed encoding of symbolic representations. The resulting networks simulate automata in real-time and are programmed directly, in the absence of network training. To discuss the unique characteristics of the architecture and their consequences, we present two examples: (i) the design of a Central Pattern Generator from a finite-state locomotive controller, and (ii) the creation of a network simulating a system of interactive automata that supports the parsing of garden-path sentences as investigated in psycholinguistics experiments. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    Biaobiao Zhang

    2011-01-01

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

  4. Review On Applications Of Neural Network To Computer Vision

    Science.gov (United States)

    Li, Wei; Nasrabadi, Nasser M.

    1989-03-01

    Neural network models have many potential applications to computer vision due to their parallel structures, learnability, implicit representation of domain knowledge, fault tolerance, and ability of handling statistical data. This paper demonstrates the basic principles, typical models and their applications in this field. Variety of neural models, such as associative memory, multilayer back-propagation perceptron, self-stabilized adaptive resonance network, hierarchical structured neocognitron, high order correlator, network with gating control and other models, can be applied to visual signal recognition, reinforcement, recall, stereo vision, motion, object tracking and other vision processes. Most of the algorithms have been simulated on com-puters. Some have been implemented with special hardware. Some systems use features, such as edges and profiles, of images as the data form for input. Other systems use raw data as input signals to the networks. We will present some novel ideas contained in these approaches and provide a comparison of these methods. Some unsolved problems are mentioned, such as extracting the intrinsic properties of the input information, integrating those low level functions to a high-level cognitive system, achieving invariances and other problems. Perspectives of applications of some human vision models and neural network models are analyzed.

  5. Computational and microhydrodynamic modeling and experiments with bio-inspired swimming robots in cylindrical channels

    OpenAIRE

    Tabak, Ahmet Fatih

    2012-01-01

    Modeling and control of swimming untethered micro robots are important for future therapeutic medical applications. Bio-inspired propulsion methods emerge as realistic substitutes for hydrodynamic thrust generation in micro realm. Accurate modeling, power supply, and propulsion-means directly affect the mobility and maneuverability of swimming micro robots with helical or planar wave propagation. Flow field around a bio-inspired micro swimmer comprised of a spherical body and a rotating helic...

  6. A bio-inspired computing model for ovarian carcinoma classification and oncogene detection.

    Science.gov (United States)

    Tsai, Meng-Hsiun; Chen, Mu-Yen; Huang, Steve G; Hung, Yao-Ching; Wang, Hsin-Chieh

    2015-04-01

    Ovarian cancer is the fifth leading cause of cancer deaths in women in the western world for 2013. In ovarian cancer, benign tumors turn malignant, but the point of transition is difficult to predict and diagnose. The 5-year survival rate of all types of ovarian cancer is 44%, but this can be improved to 92% if the cancer is found and treated before it spreads beyond the ovary. However, only 15% of all ovarian cancers are found at this early stage. Therefore, the ability to automatically identify and diagnose ovarian cancer precisely and efficiently as the tissue changes from benign to invasive is important for clinical treatment and for increasing the cure rate. This study proposes a new ovarian carcinoma classification model using two algorithms: a novel discretization of food sources for an artificial bee colony (DfABC), and a support vector machine (SVM). For the first time in the literature, oncogene detection using this method is also investigated. A novel bio-inspired computing model and hybrid algorithms combining DfABC and SVM was applied to ovarian carcinoma and oncogene classification. This study used the human ovarian cDNA expression database to collect 41 patient samples and 9600 genes in each pathological stage. Feature selection methods were used to detect and extract 15 notable oncogenes. We then used the DfABC-SVM model to examine these 15 oncogenes, dividing them into eight different classifications according to their gene expressions of various pathological stages. The average accuracyof the eight classification experiments was 94.76%. This research also found some oncogenes that had not been discovered or indicated in previous scientific studies. The main contribution of this research is the proof that these newly discovered oncogenes are highly related to ovarian or other cancers. http://mht.mis.nchu.edu.tw/moodle/course/view.php?id=7. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email

  7. Application of artificial neural networks in computer-aided diagnosis.

    Science.gov (United States)

    Liu, Bei

    2015-01-01

    Computer-aided diagnosis is a diagnostic procedure in which a radiologist uses the outputs of computer analysis of medical images as a second opinion in the interpretation of medical images, either to help with lesion detection or to help determine if the lesion is benign or malignant. Artificial neural networks (ANNs) are usually employed to formulate the statistical models for computer analysis. Receiver operating characteristic curves are used to evaluate the performance of the ANN alone, as well as the diagnostic performance of radiologists who take into account the ANN output as a second opinion. In this chapter, we use mammograms to illustrate how an ANN model is trained, tested, and evaluated, and how a radiologist should use the ANN output as a second opinion in CAD.

  8. Computational modeling of neural activities for statistical inference

    CERN Document Server

    Kolossa, Antonio

    2016-01-01

    This authored monograph supplies empirical evidence for the Bayesian brain hypothesis by modeling event-related potentials (ERP) of the human electroencephalogram (EEG) during successive trials in cognitive tasks. The employed observer models are useful to compute probability distributions over observable events and hidden states, depending on which are present in the respective tasks. Bayesian model selection is then used to choose the model which best explains the ERP amplitude fluctuations. Thus, this book constitutes a decisive step towards a better understanding of the neural coding and computing of probabilities following Bayesian rules. The target audience primarily comprises research experts in the field of computational neurosciences, but the book may also be beneficial for graduate students who want to specialize in this field. .

  9. A computational analysis of the neural bases of Bayesian inference.

    Science.gov (United States)

    Kolossa, Antonio; Kopp, Bruno; Fingscheidt, Tim

    2015-02-01

    Empirical support for the Bayesian brain hypothesis, although of major theoretical importance for cognitive neuroscience, is surprisingly scarce. This hypothesis posits simply that neural activities code and compute Bayesian probabilities. Here, we introduce an urn-ball paradigm to relate event-related potentials (ERPs) such as the P300 wave to Bayesian inference. Bayesian model comparison is conducted to compare various models in terms of their ability to explain trial-by-trial variation in ERP responses at different points in time and over different regions of the scalp. Specifically, we are interested in dissociating specific ERP responses in terms of Bayesian updating and predictive surprise. Bayesian updating refers to changes in probability distributions given new observations, while predictive surprise equals the surprise about observations under current probability distributions. Components of the late positive complex (P3a, P3b, Slow Wave) provide dissociable measures of Bayesian updating and predictive surprise. Specifically, the updating of beliefs about hidden states yields the best fit for the anteriorly distributed P3a, whereas the updating of predictions of observations accounts best for the posteriorly distributed Slow Wave. In addition, parietally distributed P3b responses are best fit by predictive surprise. These results indicate that the three components of the late positive complex reflect distinct neural computations. As such they are consistent with the Bayesian brain hypothesis, but these neural computations seem to be subject to nonlinear probability weighting. We integrate these findings with the free-energy principle that instantiates the Bayesian brain hypothesis. Copyright © 2014 Elsevier Inc. All rights reserved.

  10. The neural and computational bases of semantic cognition.

    Science.gov (United States)

    Ralph, Matthew A Lambon; Jefferies, Elizabeth; Patterson, Karalyn; Rogers, Timothy T

    2017-01-01

    Semantic cognition refers to our ability to use, manipulate and generalize knowledge that is acquired over the lifespan to support innumerable verbal and non-verbal behaviours. This Review summarizes key findings and issues arising from a decade of research into the neurocognitive and neurocomputational underpinnings of this ability, leading to a new framework that we term controlled semantic cognition (CSC). CSC offers solutions to long-standing queries in philosophy and cognitive science, and yields a convergent framework for understanding the neural and computational bases of healthy semantic cognition and its dysfunction in brain disorders.

  11. Eye tracking using artificial neural networks for human computer interaction.

    Science.gov (United States)

    Demjén, E; Aboši, V; Tomori, Z

    2011-01-01

    This paper describes an ongoing project that has the aim to develop a low cost application to replace a computer mouse for people with physical impairment. The application is based on an eye tracking algorithm and assumes that the camera and the head position are fixed. Color tracking and template matching methods are used for pupil detection. Calibration is provided by neural networks as well as by parametric interpolation methods. Neural networks use back-propagation for learning and bipolar sigmoid function is chosen as the activation function. The user's eye is scanned with a simple web camera with backlight compensation which is attached to a head fixation device. Neural networks significantly outperform parametric interpolation techniques: 1) the calibration procedure is faster as they require less calibration marks and 2) cursor control is more precise. The system in its current stage of development is able to distinguish regions at least on the level of desktop icons. The main limitation of the proposed method is the lack of head-pose invariance and its relative sensitivity to illumination (especially to incidental pupil reflections).

  12. Computational models of the neural control of breathing.

    Science.gov (United States)

    Molkov, Yaroslav I; Rubin, Jonathan E; Rybak, Ilya A; Smith, Jeffrey C

    2017-03-01

    The ongoing process of breathing underlies the gas exchange essential for mammalian life. Each respiratory cycle ensues from the activity of rhythmic neural circuits in the brainstem, shaped by various modulatory signals, including mechanoreceptor feedback sensitive to lung inflation and chemoreceptor feedback dependent on gas composition in blood and tissues. This paper reviews a variety of computational models designed to reproduce experimental findings related to the neural control of breathing and generate predictions for future experimental testing. The review starts from the description of the core respiratory network in the brainstem, representing the central pattern generator (CPG) responsible for producing rhythmic respiratory activity, and progresses to encompass additional complexities needed to simulate different metabolic challenges, closed-loop feedback control including the lungs, and interactions between the respiratory and autonomic nervous systems. The integrated models considered in this review share a common framework including a distributed CPG core network responsible for generating the baseline three-phase pattern of rhythmic neural activity underlying normal breathing. WIREs Syst Biol Med 2017, 9:e1371. doi: 10.1002/wsbm.1371 For further resources related to this article, please visit the WIREs website. © 2016 Wiley Periodicals, Inc.

  13. A taxonomy of Deep Convolutional Neural Nets for Computer Vision

    Directory of Open Access Journals (Sweden)

    Suraj eSrinivas

    2016-01-01

    Full Text Available Traditional architectures for solving computer vision problems and the degree of success they enjoyed have been heavily reliant on hand-crafted features. However, of late, deep learning techniques have offered a compelling alternative -- that of automatically learning problem-specific features. With this new paradigm, every problem in computer vision is now being re-examined from a deep learning perspective. Therefore, it has become important to understand what kind of deep networks are suitable for a given problem. Although general surveys of this fast-moving paradigm (i.e. deep-networks exist, a survey specific to computer vision is missing. We specifically consider one form of deep networks widely used in computer vision - convolutional neural networks (CNNs. We start with AlexNet'' as our base CNN and then examine the broad variations proposed over time to suit different applications. We hope that our recipe-style survey will serve as a guide, particularly for novice practitioners intending to use deep-learning techniques for computer vision.

  14. Artificial intelligence in pharmaceutical product formulation: neural computing

    Directory of Open Access Journals (Sweden)

    Svetlana Ibrić

    2009-10-01

    Full Text Available The properties of a formulation are determined not only by the ratios in which the ingredients are combined but also by the processing conditions. Although the relationships between the ingredient levels, processing conditions, and product performance may be known anecdotally, they can rarely be quantified. In the past, formulators tended to use statistical techniques to model their formulations, relying on response surfaces to provide a mechanism for optimazation. However, the optimization by such a method can be misleading, especially if the formulation is complex. More recently, advances in mathematics and computer science have led to the development of alternative modeling and data mining techniques which work with a wider range of data sources: neural networks (an attempt to mimic the processing of the human brain; genetic algorithms (an attempt to mimic the evolutionary process by which biological systems self-organize and adapt, and fuzzy logic (an attempt to mimic the ability of the human brain to draw conclusions and generate responses based on incomplete or imprecise information. In this review the current technology will be examined, as well as its application in pharmaceutical formulation and processing. The challenges, benefits and future possibilities of neural computing will be discussed.

  15. Corticostriatal response selection in sentence production: Insights from neural network simulation with reservoir computing.

    Science.gov (United States)

    Hinaut, Xavier; Lance, Florian; Droin, Colas; Petit, Maxime; Pointeau, Gregoire; Dominey, Peter Ford

    2015-11-01

    Language production requires selection of the appropriate sentence structure to accommodate the communication goal of the speaker - the transmission of a particular meaning. Here we consider event meanings, in terms of predicates and thematic roles, and we address the problem that a given event can be described from multiple perspectives, which poses a problem of response selection. We present a model of response selection in sentence production that is inspired by the primate corticostriatal system. The model is implemented in the context of reservoir computing where the reservoir - a recurrent neural network with fixed connections - corresponds to cortex, and the readout corresponds to the striatum. We demonstrate robust learning, and generalization properties of the model, and demonstrate its cross linguistic capabilities in English and Japanese. The results contribute to the argument that the corticostriatal system plays a role in response selection in language production, and to the stance that reservoir computing is a valid potential model of corticostriatal processing. Copyright © 2015 Elsevier Inc. All rights reserved.

  16. Electricity market price forecasting by grid computing optimizing artificial neural networks

    OpenAIRE

    Niimura, T.; Ozawa, K.; Sakamoto, N.

    2007-01-01

    This paper presents a grid computing approach to parallel-process a neural network time-series model for forecasting electricity market prices. A grid computing environment introduced in a university computing laboratory provides access to otherwise underused computing resources. The grid computing of the neural network model not only processes several times faster than a single iterative process, but also provides chances of improving forecasting accuracy. Results of numerical tests using re...

  17. Instrumentation for Scientific Computing in Neural Networks, Information Science, Artificial Intelligence, and Applied Mathematics.

    Science.gov (United States)

    1987-10-01

    include Security Classification) Instrumentation for scientific computing in neural networks, information science, artificial intelligence, and...instrumentation grant to purchase equipment for support of research in neural networks, information science, artificail intellignece , and applied mathematics...in Neural Networks, Information Science, Artificial Intelligence, and Applied Mathematics Contract AFOSR 86-0282 Principal Investigator: Stephen

  18. Brain-inspired Stochastic Models and Implementations

    KAUST Repository

    Al-Shedivat, Maruan

    2015-05-12

    One of the approaches to building artificial intelligence (AI) is to decipher the princi- ples of the brain function and to employ similar mechanisms for solving cognitive tasks, such as visual perception or natural language understanding, using machines. The recent breakthrough, named deep learning, demonstrated that large multi-layer networks of arti- ficial neural-like computing units attain remarkable performance on some of these tasks. Nevertheless, such artificial networks remain to be very loosely inspired by the brain, which rich structures and mechanisms may further suggest new algorithms or even new paradigms of computation. In this thesis, we explore brain-inspired probabilistic mechanisms, such as neural and synaptic stochasticity, in the context of generative models. The two questions we ask here are: (i) what kind of models can describe a neural learning system built of stochastic components? and (ii) how can we implement such systems e ̆ciently? To give specific answers, we consider two well known models and the corresponding neural architectures: the Naive Bayes model implemented with a winner-take-all spiking neural network and the Boltzmann machine implemented in a spiking or non-spiking fashion. We propose and analyze an e ̆cient neuromorphic implementation of the stochastic neu- ral firing mechanism and study the e ̄ects of synaptic unreliability on learning generative energy-based models implemented with neural networks.

  19. System impairment compensation in coherent optical communications by using a bio-inspired detector based on artificial neural network and genetic algorithm

    Science.gov (United States)

    Wang, Danshi; Zhang, Min; Li, Ze; Song, Chuang; Fu, Meixia; Li, Jin; Chen, Xue

    2017-09-01

    A bio-inspired detector based on the artificial neural network (ANN) and genetic algorithm is proposed in the context of a coherent optical transmission system. The ANN is designed to mitigate 16-quadrature amplitude modulation system impairments, including linear impairment: Gaussian white noise, laser phase noise, in-phase/quadrature component imbalance, and nonlinear impairment: nonlinear phase. Without prior information or heuristic assumptions, the ANN, functioning as a machine learning algorithm, can learn and capture the characteristics of impairments from observed data. Numerical simulations were performed, and dispersion-shifted, dispersion-managed, and dispersion-unmanaged fiber links were investigated. The launch power dynamic range and maximum transmission distance for the bio-inspired method were 2.7 dBm and 240 km greater, respectively, than those of the maximum likelihood estimation algorithm. Moreover, the linewidth tolerance of the bio-inspired technique was 170 kHz greater than that of the k-means method, demonstrating its usability for digital signal processing in coherent systems.

  20. A computational neural model of orientation detection based on multiple guesses: comparison of geometrical and algebraic models.

    Science.gov (United States)

    Wei, Hui; Ren, Yuan; Wang, Zi Yan

    2013-10-01

    The implementation of Hubel-Wiesel hypothesis that orientation selectivity of a simple cell is based on ordered arrangement of its afferent cells has some difficulties. It requires the receptive fields (RFs) of those ganglion cells (GCs) and LGN cells to be similar in size and sub-structure and highly arranged in a perfect order. It also requires an adequate number of regularly distributed simple cells to match ubiquitous edges. However, the anatomical and electrophysiological evidence is not strong enough to support this geometry-based model. These strict regularities also make the model very uneconomical in both evolution and neural computation. We propose a new neural model based on an algebraic method to estimate orientations. This approach synthesizes the guesses made by multiple GCs or LGN cells and calculates local orientation information subject to a group of constraints. This algebraic model need not obey the constraints of Hubel-Wiesel hypothesis, and is easily implemented with a neural network. By using the idea of a satisfiability problem with constraints, we also prove that the precision and efficiency of this model are mathematically practicable. The proposed model makes clear several major questions which Hubel-Wiesel model does not account for. Image-rebuilding experiments are conducted to check whether this model misses any important boundary in the visual field because of the estimation strategy. This study is significant in terms of explaining the neural mechanism of orientation detection, and finding the circuit structure and computational route in neural networks. For engineering applications, our model can be used in orientation detection and as a simulation platform for cell-to-cell communications to develop bio-inspired eye chips.

  1. Computational intelligence in multi-feature visual pattern recognition hand posture and face recognition using biologically inspired approaches

    CERN Document Server

    Pisharady, Pramod Kumar; Poh, Loh Ai

    2014-01-01

    This book presents a collection of computational intelligence algorithms that addresses issues in visual pattern recognition such as high computational complexity, abundance of pattern features, sensitivity to size and shape variations and poor performance against complex backgrounds. The book has 3 parts. Part 1 describes various research issues in the field with a survey of the related literature. Part 2 presents computational intelligence based algorithms for feature selection and classification. The algorithms are discriminative and fast. The main application area considered is hand posture recognition. The book also discusses utility of these algorithms in other visual as well as non-visual pattern recognition tasks including face recognition, general object recognition and cancer / tumor classification. Part 3 presents biologically inspired algorithms for feature extraction. The visual cortex model based features discussed have invariance with respect to appearance and size of the hand, and provide good...

  2. Neuroscience-Inspired Artificial Intelligence.

    Science.gov (United States)

    Hassabis, Demis; Kumaran, Dharshan; Summerfield, Christopher; Botvinick, Matthew

    2017-07-19

    The fields of neuroscience and artificial intelligence (AI) have a long and intertwined history. In more recent times, however, communication and collaboration between the two fields has become less commonplace. In this article, we argue that better understanding biological brains could play a vital role in building intelligent machines. We survey historical interactions between the AI and neuroscience fields and emphasize current advances in AI that have been inspired by the study of neural computation in humans and other animals. We conclude by highlighting shared themes that may be key for advancing future research in both fields. Copyright © 2017. Published by Elsevier Inc.

  3. Neural Cognition and Affective Computing on Cyber Language.

    Science.gov (United States)

    Huang, Shuang; Zhou, Xuan; Xue, Ke; Wan, Xiqiong; Yang, Zhenyi; Xu, Duo; Ivanović, Mirjana; Yu, Xueer

    2015-01-01

    Characterized by its customary symbol system and simple and vivid expression patterns, cyber language acts as not only a tool for convenient communication but also a carrier of abundant emotions and causes high attention in public opinion analysis, internet marketing, service feedback monitoring, and social emergency management. Based on our multidisciplinary research, this paper presents a classification of the emotional symbols in cyber language, analyzes the cognitive characteristics of different symbols, and puts forward a mechanism model to show the dominant neural activities in that process. Through the comparative study of Chinese, English, and Spanish, which are used by the largest population in the world, this paper discusses the expressive patterns of emotions in international cyber languages and proposes an intelligent method for affective computing on cyber language in a unified PAD (Pleasure-Arousal-Dominance) emotional space.

  4. Neural Cognition and Affective Computing on Cyber Language

    Science.gov (United States)

    Huang, Shuang; Zhou, Xuan; Xue, Ke; Wan, Xiqiong; Yang, Zhenyi; Xu, Duo; Ivanović, Mirjana

    2015-01-01

    Characterized by its customary symbol system and simple and vivid expression patterns, cyber language acts as not only a tool for convenient communication but also a carrier of abundant emotions and causes high attention in public opinion analysis, internet marketing, service feedback monitoring, and social emergency management. Based on our multidisciplinary research, this paper presents a classification of the emotional symbols in cyber language, analyzes the cognitive characteristics of different symbols, and puts forward a mechanism model to show the dominant neural activities in that process. Through the comparative study of Chinese, English, and Spanish, which are used by the largest population in the world, this paper discusses the expressive patterns of emotions in international cyber languages and proposes an intelligent method for affective computing on cyber language in a unified PAD (Pleasure-Arousal-Dominance) emotional space. PMID:26491431

  5. Neural Cognition and Affective Computing on Cyber Language

    Directory of Open Access Journals (Sweden)

    Shuang Huang

    2015-01-01

    Full Text Available Characterized by its customary symbol system and simple and vivid expression patterns, cyber language acts as not only a tool for convenient communication but also a carrier of abundant emotions and causes high attention in public opinion analysis, internet marketing, service feedback monitoring, and social emergency management. Based on our multidisciplinary research, this paper presents a classification of the emotional symbols in cyber language, analyzes the cognitive characteristics of different symbols, and puts forward a mechanism model to show the dominant neural activities in that process. Through the comparative study of Chinese, English, and Spanish, which are used by the largest population in the world, this paper discusses the expressive patterns of emotions in international cyber languages and proposes an intelligent method for affective computing on cyber language in a unified PAD (Pleasure-Arousal-Dominance emotional space.

  6. Neural computation and particle accelerators research, technology and applications

    CERN Document Server

    D'Arras, Horace

    2010-01-01

    This book discusses neural computation, a network or circuit of biological neurons and relatedly, particle accelerators, a scientific instrument which accelerates charged particles such as protons, electrons and deuterons. Accelerators have a very broad range of applications in many industrial fields, from high energy physics to medical isotope production. Nuclear technology is one of the fields discussed in this book. The development that has been reached by particle accelerators in energy and particle intensity has opened the possibility to a wide number of new applications in nuclear technology. This book reviews the applications in the nuclear energy field and the design features of high power neutron sources are explained. Surface treatments of niobium flat samples and superconducting radio frequency cavities by a new technique called gas cluster ion beam are also studied in detail, as well as the process of electropolishing. Furthermore, magnetic devises such as solenoids, dipoles and undulators, which ...

  7. Evolution of Neural Computations: Mantis Shrimp and Human Color Decoding

    Directory of Open Access Journals (Sweden)

    Qasim Zaidi

    2014-10-01

    Full Text Available Mantis shrimp and primates both possess good color vision, but the neural implementation in the two species is very different, a reflection of the largely unrelated evolutionary lineages of these creatures. Mantis shrimp have scanning compound eyes with 12 classes of photoreceptors, and have evolved a system to decode color information at the front-end of the sensory stream. Primates have image-focusing eyes with three classes of cones, and decode color further along the visual-processing hierarchy. Despite these differences, we report a fascinating parallel between the computational strategies at the color-decoding stage in the brains of stomatopods and primates. Both species appear to use narrowly tuned cells that support interval decoding color identification.

  8. Real-time immune-inspired optimum state-of-charge trajectory estimation using upcoming route information preview and neural networks for plug-in hybrid electric vehicles fuel economy

    Science.gov (United States)

    Mozaffari, Ahmad; Vajedi, Mahyar; Azad, Nasser L.

    2015-06-01

    The main proposition of the current investigation is to develop a computational intelligence-based framework which can be used for the real-time estimation of optimum battery state-of-charge (SOC) trajectory in plug-in hybrid electric vehicles (PHEVs). The estimated SOC trajectory can be then employed for an intelligent power management to significantly improve the fuel economy of the vehicle. The devised intelligent SOC trajectory builder takes advantage of the upcoming route information preview to achieve the lowest possible total cost of electricity and fossil fuel. To reduce the complexity of real-time optimization, the authors propose an immune system-based clustering approach which allows categorizing the route information into a predefined number of segments. The intelligent real-time optimizer is also inspired on the basis of interactions in biological immune systems, and is called artificial immune algorithm (AIA). The objective function of the optimizer is derived from a computationally efficient artificial neural network (ANN) which is trained by a database obtained from a high-fidelity model of the vehicle built in the Autonomie software. The simulation results demonstrate that the integration of immune inspired clustering tool, AIA and ANN, will result in a powerful framework which can generate a near global optimum SOC trajectory for the baseline vehicle, that is, the Toyota Prius PHEV. The outcomes of the current investigation prove that by taking advantage of intelligent approaches, it is possible to design a computationally efficient and powerful SOC trajectory builder for the intelligent power management of PHEVs.

  9. Computer simulations of neural mechanisms explaining upper and lower limb excitatory neural coupling

    Directory of Open Access Journals (Sweden)

    Ferris Daniel P

    2010-12-01

    Full Text Available Abstract Background When humans perform rhythmic upper and lower limb locomotor-like movements, there is an excitatory effect of upper limb exertion on lower limb muscle recruitment. To investigate potential neural mechanisms for this behavioral observation, we developed computer simulations modeling interlimb neural pathways among central pattern generators. We hypothesized that enhancement of muscle recruitment from interlimb spinal mechanisms was not sufficient to explain muscle enhancement levels observed in experimental data. Methods We used Matsuoka oscillators for the central pattern generators (CPG and determined parameters that enhanced amplitudes of rhythmic steady state bursts. Potential mechanisms for output enhancement were excitatory and inhibitory sensory feedback gains, excitatory and inhibitory interlimb coupling gains, and coupling geometry. We first simulated the simplest case, a single CPG, and then expanded the model to have two CPGs and lastly four CPGs. In the two and four CPG models, the lower limb CPGs did not receive supraspinal input such that the only mechanisms available for enhancing output were interlimb coupling gains and sensory feedback gains. Results In a two-CPG model with inhibitory sensory feedback gains, only excitatory gains of ipsilateral flexor-extensor/extensor-flexor coupling produced reciprocal upper-lower limb bursts and enhanced output up to 26%. In a two-CPG model with excitatory sensory feedback gains, excitatory gains of contralateral flexor-flexor/extensor-extensor coupling produced reciprocal upper-lower limb bursts and enhanced output up to 100%. However, within a given excitatory sensory feedback gain, enhancement due to excitatory interlimb gains could only reach levels up to 20%. Interconnecting four CPGs to have ipsilateral flexor-extensor/extensor-flexor coupling, contralateral flexor-flexor/extensor-extensor coupling, and bilateral flexor-extensor/extensor-flexor coupling could enhance

  10. Ensuring critical event sequences in high consequence computer based systems as inspired by path expressions

    Energy Technology Data Exchange (ETDEWEB)

    Kidd, M.E.C.

    1997-02-01

    The goal of our work is to provide a high level of confidence that critical software driven event sequences are maintained in the face of hardware failures, malevolent attacks and harsh or unstable operating environments. This will be accomplished by providing dynamic fault management measures directly to the software developer and to their varied development environments. The methodology employed here is inspired by previous work in path expressions. This paper discusses the perceived problems, a brief overview of path expressions, the proposed methods, and a discussion of the differences between the proposed methods and traditional path expression usage and implementation.

  11. Hardware Neural Networks Modeling for Computing Different Performance Parameters of Rectangular, Circular, and Triangular Microstrip Antennas

    Directory of Open Access Journals (Sweden)

    Taimoor Khan

    2014-01-01

    Full Text Available In the last one decade, neural networks-based modeling has been used for computing different performance parameters of microstrip antennas because of learning and generalization features. Most of the created neural models are based on software simulation. As the neural networks show massive parallelism inherently, a parallel hardware needs to be created for creating faster computing machine by taking the advantages of the parallelism of the neural networks. This paper demonstrates a generalized neural networks model created on field programmable gate array- (FPGA- based reconfigurable hardware platform for computing different performance parameters of microstrip antennas. Thus, the proposed approach provides a platform for developing low-cost neural network-based FPGA simulators for microwave applications. Also, the results obtained by this approach are in very good agreement with the measured results available in the literature.

  12. Independent Neural Computation of Value from Other People's Confidence.

    Science.gov (United States)

    Campbell-Meiklejohn, Daniel; Simonsen, Arndis; Frith, Chris D; Daw, Nathaniel D

    2017-01-18

    Expectation of reward can be shaped by the observation of actions and expressions of other people in one's environment. A person's apparent confidence in the likely reward of an action, for instance, makes qualities of their evidence, not observed directly, socially accessible. This strategy is computationally distinguished from associative learning methods that rely on direct observation, by its use of inference from indirect evidence. In twenty-three healthy human subjects, we isolated effects of first-hand experience, other people's choices, and the mediating effect of their confidence, on decision-making and neural correlates of value within ventromedial prefrontal cortex (vmPFC). Value derived from first-hand experience and other people's choices (regardless of confidence) were indiscriminately represented across vmPFC. However, value computed from agent choices weighted by their associated confidence was represented with specificity for ventromedial area 10. This pattern corresponds to shifts of connectivity and overlapping cognitive processes along a posterior-anterior vmPFC axis. Task behavior and self-reported self-reliance for decision-making in other social contexts correlated. The tendency to conform in other social contexts corresponded to increased activation in cortical regions previously shown to respond to social conflict in proportion to subsequent conformity (Campbell-Meiklejohn et al., 2010). The tendency to self-monitor predicted a selectively enhanced response to accordance with others in the right temporoparietal junction (rTPJ). The findings anatomically decompose vmPFC value representations according to computational requirements and provide biological insight into the social transmission of preference and reassurance gained from the confidence of others. Decades of research have provided evidence that the ventromedial prefrontal cortex (vmPFC) signals the satisfaction we expect from imminent actions. However, we have a surprisingly modest

  13. Combined bio-inspired/evolutionary computational methods in cross-layer protocol optimization for wireless ad hoc sensor networks

    Science.gov (United States)

    Hortos, William S.

    2011-06-01

    Published studies have focused on the application of one bio-inspired or evolutionary computational method to the functions of a single protocol layer in a wireless ad hoc sensor network (WSN). For example, swarm intelligence in the form of ant colony optimization (ACO), has been repeatedly considered for the routing of data/information among nodes, a network-layer function, while genetic algorithms (GAs) have been used to select transmission frequencies and power levels, physical-layer functions. Similarly, artificial immune systems (AISs) as well as trust models of quantized data reputation have been invoked for detection of network intrusions that cause anomalies in data and information; these act on the application and presentation layers. Most recently, a self-organizing scheduling scheme inspired by frog-calling behavior for reliable data transmission in wireless sensor networks, termed anti-phase synchronization, has been applied to realize collision-free transmissions between neighboring nodes, a function of the MAC layer. In a novel departure from previous work, the cross-layer approach to WSN protocol design suggests applying more than one evolutionary computational method to the functions of the appropriate layers to improve the QoS performance of the cross-layer design beyond that of one method applied to a single layer's functions. A baseline WSN protocol design, embedding GAs, anti-phase synchronization, ACO, and a trust model based on quantized data reputation at the physical, MAC, network, and application layers, respectively, is constructed. Simulation results demonstrate the synergies among the bioinspired/ evolutionary methods of the proposed baseline design improve the overall QoS performance of networks over that of a single computational method.

  14. Writing Inspired

    Science.gov (United States)

    Tischhauser, Karen

    2015-01-01

    Students need inspiration to write. Assigning is not teaching. In order to inspire students to write fiction worth reading, teachers must take them through the process of writing. Physical objects inspire good writing with depth. In this article, the reader will be taken through the process of inspiring young writers through the use of boxes.…

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

    Science.gov (United States)

    Takiyama, Ken

    2017-12-01

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

  16. Exploration of a physiologically-inspired hearing-aid algorithm using a computer model mimicking impaired hearing.

    Science.gov (United States)

    Jürgens, Tim; Clark, Nicholas R; Lecluyse, Wendy; Meddis, Ray

    2016-01-01

    To use a computer model of impaired hearing to explore the effects of a physiologically-inspired hearing-aid algorithm on a range of psychoacoustic measures. A computer model of a hypothetical impaired listener's hearing was constructed by adjusting parameters of a computer model of normal hearing. Absolute thresholds, estimates of compression, and frequency selectivity (summarized to a hearing profile) were assessed using this model with and without pre-processing the stimuli by a hearing-aid algorithm. The influence of different settings of the algorithm on the impaired profile was investigated. To validate the model predictions, the effect of the algorithm on hearing profiles of human impaired listeners was measured. A computer model simulating impaired hearing (total absence of basilar membrane compression) was used, and three hearing-impaired listeners participated. The hearing profiles of the model and the listeners showed substantial changes when the test stimuli were pre-processed by the hearing-aid algorithm. These changes consisted of lower absolute thresholds, steeper temporal masking curves, and sharper psychophysical tuning curves. The hearing-aid algorithm affected the impaired hearing profile of the model to approximate a normal hearing profile. Qualitatively similar results were found with the impaired listeners' hearing profiles.

  17. Honey characterization using computer vision system and artificial neural networks.

    Science.gov (United States)

    Shafiee, Sahameh; Minaei, Saeid; Moghaddam-Charkari, Nasrollah; Barzegar, Mohsen

    2014-09-15

    This paper reports the development of a computer vision system (CVS) for non-destructive characterization of honey based on colour and its correlated chemical attributes including ash content (AC), antioxidant activity (AA), and total phenolic content (TPC). Artificial neural network (ANN) models were applied to transform RGB values of images to CIE L*a*b* colourimetric measurements and to predict AC, TPC and AA from colour features of images. The developed ANN models were able to convert RGB values to CIE L*a*b* colourimetric parameters with low generalization error of 1.01±0.99. In addition, the developed models for prediction of AC, TPC and AA showed high performance based on colour parameters of honey images, as the R(2) values for prediction were 0.99, 0.98, and 0.87, for AC, AA and TPC, respectively. The experimental results show the effectiveness and possibility of applying CVS for non-destructive honey characterization by the industry. Copyright © 2014 Elsevier Ltd. All rights reserved.

  18. Advanced neural network-based computational schemes for robust fault diagnosis

    CERN Document Server

    Mrugalski, Marcin

    2014-01-01

    The present book is devoted to problems of adaptation of artificial neural networks to robust fault diagnosis schemes. It presents neural networks-based modelling and estimation techniques used for designing robust fault diagnosis schemes for non-linear dynamic systems. A part of the book focuses on fundamental issues such as architectures of dynamic neural networks, methods for designing of neural networks and fault diagnosis schemes as well as the importance of robustness. The book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. The book is also devoted to advanced schemes of description of neural model uncertainty. In particular, the methods of computation of neural networks uncertainty with robust parameter estimation are presented. Moreover, a novel approach for system identification with the state-space GMDH neural network is delivered. All the concepts described in this book are illustrated by both simple academic illustrative examples and practica...

  19. Computational Cognition and Robust Decision Making

    Science.gov (United States)

    2013-03-06

    methodology for identifying sensory neural circuits of the fruit fly brain. Technical approach: Dynamic signal processing systems; convex optimization...14 DISTRIBUTION STATEMENT A – Unclassified, Unlimited Distribution Bio-inspired Computation J. Wiles (U. Queensland , ITEE) iRAT: Neurorobotic

  20. 3-D components of a biological neural network visualized in computer generated imagery. II - Macular neural network organization

    Science.gov (United States)

    Ross, Muriel D.; Meyer, Glenn; Lam, Tony; Cutler, Lynn; Vaziri, Parshaw

    1990-01-01

    Computer-assisted reconstructions of small parts of the macular neural network show how the nerve terminals and receptive fields are organized in 3-dimensional space. This biological neural network is anatomically organized for parallel distributed processing of information. Processing appears to be more complex than in computer-based neural network, because spatiotemporal factors figure into synaptic weighting. Serial reconstruction data show anatomical arrangements which suggest that (1) assemblies of cells analyze and distribute information with inbuilt redundancy, to improve reliability; (2) feedforward/feedback loops provide the capacity for presynaptic modulation of output during processing; (3) constrained randomness in connectivities contributes to adaptability; and (4) local variations in network complexity permit differing analyses of incoming signals to take place simultaneously. The last inference suggests that there may be segregation of information flow to central stations subserving particular functions.

  1. Transient diagnosis system using quantum-inspired computing and Minkowski distance

    Energy Technology Data Exchange (ETDEWEB)

    Nicolau, Andressa dos Santos; Schirru, Roberto, E-mail: andressa@lmp.ufrj.b, E-mail: schirru@lmp.ufrj.b [Federal University of Rio de Janeiro (PEN/COPPE/UFRJ), Rio de Janeiro, RJ (Brazil). Nuclear Engineering Program

    2011-07-01

    This paper proposes a diagnosis system model for identification of transient in a PWR nuclear power plant, optimized by the Quantum Inspired Evolutionary Algorithm - QEA in order to help nuclear power plant operator reduce his cognitive load and increase his available time to maintain the plant operating in a safe condition. This method was developed in order to be able to recognize the normal condition and three accidents of the design basis list of the nuclear power plant Angra 2, postulated in the Final Safety Analysis Report (FSAR). This System compares the similarly distance between the set of variables of the anomalous event, in a given time t, and the centroids of the design-basis transient variables. The lower similarly distance indicates the class of the transient to which the anomalous event belongs. The QEA was then used to find the best position of the centroids of each class of the selected transients. Such positions maximize the number of the correct classifications. Unlike the diagnosis system proposed in the literature, Minkowski distance was employed to calculate the similarity distance. The signatures of four transients were submitted to 1% and 2% of noise, and tested with prototype vector found by QEA. The results showed that the present transient diagnostic system was successfully implemented in the nuclear accident identification problem and was compatible with the techniques presented in the literature. (author)

  2. Just-in-Time Compilation-Inspired Methodology for Parallelization of Compute Intensive Java Code

    OpenAIRE

    GHULAM MUSTAFA; WAQAR MAHMOOD; MUHAMMAD USMAN GHANI

    2017-01-01

    Compute intensive programs generally consume significant fraction of execution time in a small amount of repetitive code. Such repetitive code is commonly known as hotspot code. We observed that compute intensive hotspots often possess exploitable loop level parallelism. A JIT (Just-in-Time) compiler profiles a running program to identify its hotspots. Hotspots are then translated into native code, for efficient execution. Using similar approach, we propose a methodology to identify hotspots ...

  3. Neural Computations for Biosonar Imaging in the Big Brown Bat

    Science.gov (United States)

    Saillant, Prestor Augusto

    1995-11-01

    The study of the intimate relationship between space and time has taken many forms, ranging from the Theory of Relativity down to the problem of avoiding traffic jams. However, nowhere has this relationship been more fully developed and exploited than in dolphins and bats, which have the ability to utilize biosonar. This thesis describes research on the behavioral and computational basis of echolocation carried out in order to explore the neural mechanisms which may account for the space-time constructs which are of psychological importance to the big brown bat. The SCAT (Spectrogram Correlation and Transformation) computational model was developed to provide a framework for understanding the computational requirements of FM echolocation as determined from psychophysical experiments (i.e., high resolution imaging) and neurobiological constraints (Saillant et al., 1993). The second part of the thesis consisted in developing a new behavioral paradigm for simultaneously studying acoustic behavior and flight behavior of big brown bats in pursuit of stationary or moving targets. In the third part of the thesis a complete acoustic "artificial bat" was constructed, making use of the SCAT process. The development of the artificial bat allowed us to begin experimentation with real world echoes from various targets, in order to gain a better appreciation for the additional complexities and sources of information encountered by bats in flight. Finally, the continued development of the SCAT model has allowed a deeper understanding of the phenomenon of "time expansion" and of the phenomenon of phase sensitivity in the ultrasonic range. Time expansion, first predicted through the use of the SCAT model, and later found in auditory local evoked potential recordings, opens up a new realm of information processing and representation in the brain which as of yet has not been considered. It seems possible, from the work in the auditory system, that time expansion may provide a novel

  4. A Neural Information Field Approach to Computational Cognition

    Science.gov (United States)

    2016-11-18

    of irrelevant information) during the task. The spiking neural model accounts for the probability of first recall, recency effects , primacy effects ...neuron models, allowing the simulated testing of drug effects on cognitive performance; demonstrated a scalable neural model of motor planning... effects of distraction in working memory; shown a hippocampal model able to perform context sensitive sequence encoding and retrieval; proposed what is

  5. A neuron-inspired computational architecture for spatiotemporal visual processing: real-time visual sensory integration for humanoid robots.

    Science.gov (United States)

    Holzbach, Andreas; Cheng, Gordon

    2014-06-01

    In this article, we present a neurologically motivated computational architecture for visual information processing. The computational architecture's focus lies in multiple strategies: hierarchical processing, parallel and concurrent processing, and modularity. The architecture is modular and expandable in both hardware and software, so that it can also cope with multisensory integrations - making it an ideal tool for validating and applying computational neuroscience models in real time under real-world conditions. We apply our architecture in real time to validate a long-standing biologically inspired visual object recognition model, HMAX. In this context, the overall aim is to supply a humanoid robot with the ability to perceive and understand its environment with a focus on the active aspect of real-time spatiotemporal visual processing. We show that our approach is capable of simulating information processing in the visual cortex in real time and that our entropy-adaptive modification of HMAX has a higher efficiency and classification performance than the standard model (up to ∼+6%).

  6. Aware Computing in Spatial Language Understanding Guided by Cognitively Inspired Knowledge Representation

    Directory of Open Access Journals (Sweden)

    Masao Yokota

    2012-01-01

    Full Text Available Mental image directed semantic theory (MIDST has proposed an omnisensory mental image model and its description language Lmd. This language is designed to represent and compute human intuitive knowledge of space and can provide multimedia expressions with intermediate semantic descriptions in predicate logic. It is hypothesized that such knowledge and semantic descriptions are controlled by human attention toward the world and therefore subjective to each human individual. This paper describes Lmd expression of human subjective knowledge of space and its application to aware computing in cross-media operation between linguistic and pictorial expressions as spatial language understanding.

  7. Neural network computation for the evaluation of process rendering: application to thermally sprayed coatings

    Directory of Open Access Journals (Sweden)

    Guessasma Sofiane

    2017-01-01

    Full Text Available In this work, neural network computation is attempted to relate alumina and titania phase changes of a coating microstructure with respect to energetic parameters of atmospheric plasma straying (APS process. Experimental results were analysed using standard fitting routines and neural computation to quantify the effect of arc current, hydrogen ratio and total plasma flow rate. For a large parameter domain, phase changes were 10% for alumina and 8% for titania with a significant control of titania phase.

  8. A novel Morse code-inspired method for multiclass motor imagery brain-computer interface (BCI) design.

    Science.gov (United States)

    Jiang, Jun; Zhou, Zongtan; Yin, Erwei; Yu, Yang; Liu, Yadong; Hu, Dewen

    2015-11-01

    Motor imagery (MI)-based brain-computer interfaces (BCIs) allow disabled individuals to control external devices voluntarily, helping us to restore lost motor functions. However, the number of control commands available in MI-based BCIs remains limited, limiting the usability of BCI systems in control applications involving multiple degrees of freedom (DOF), such as control of a robot arm. To address this problem, we developed a novel Morse code-inspired method for MI-based BCI design to increase the number of output commands. Using this method, brain activities are modulated by sequences of MI (sMI) tasks, which are constructed by alternately imagining movements of the left or right hand or no motion. The codes of the sMI task was detected from EEG signals and mapped to special commands. According to permutation theory, an sMI task with N-length allows 2 × (2(N)-1) possible commands with the left and right MI tasks under self-paced conditions. To verify its feasibility, the new method was used to construct a six-class BCI system to control the arm of a humanoid robot. Four subjects participated in our experiment and the averaged accuracy of the six-class sMI tasks was 89.4%. The Cohen's kappa coefficient and the throughput of our BCI paradigm are 0.88 ± 0.060 and 23.5bits per minute (bpm), respectively. Furthermore, all of the subjects could operate an actual three-joint robot arm to grasp an object in around 49.1s using our approach. These promising results suggest that the Morse code-inspired method could be used in the design of BCIs for multi-DOF control. Copyright © 2015 Elsevier Ltd. All rights reserved.

  9. Biological modelling of a computational spiking neural network with neuronal avalanches

    Science.gov (United States)

    Li, Xiumin; Chen, Qing; Xue, Fangzheng

    2017-05-01

    In recent years, an increasing number of studies have demonstrated that networks in the brain can self-organize into a critical state where dynamics exhibit a mixture of ordered and disordered patterns. This critical branching phenomenon is termed neuronal avalanches. It has been hypothesized that the homeostatic level balanced between stability and plasticity of this critical state may be the optimal state for performing diverse neural computational tasks. However, the critical region for high performance is narrow and sensitive for spiking neural networks (SNNs). In this paper, we investigated the role of the critical state in neural computations based on liquid-state machines, a biologically plausible computational neural network model for real-time computing. The computational performance of an SNN when operating at the critical state and, in particular, with spike-timing-dependent plasticity for updating synaptic weights is investigated. The network is found to show the best computational performance when it is subjected to critical dynamic states. Moreover, the active-neuron-dominant structure refined from synaptic learning can remarkably enhance the robustness of the critical state and further improve computational accuracy. These results may have important implications in the modelling of spiking neural networks with optimal computational performance. This article is part of the themed issue `Mathematical methods in medicine: neuroscience, cardiology and pathology'.

  10. Hardware Acceleration of Adaptive Neural Algorithms.

    Energy Technology Data Exchange (ETDEWEB)

    James, Conrad D. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-11-01

    As tradit ional numerical computing has faced challenges, researchers have turned towards alternative computing approaches to reduce power - per - computation metrics and improve algorithm performance. Here, we describe an approach towards non - conventional computing that strengthens the connection between machine learning and neuroscience concepts. The Hardware Acceleration of Adaptive Neural Algorithms (HAANA) project ha s develop ed neural machine learning algorithms and hardware for applications in image processing and cybersecurity. While machine learning methods are effective at extracting relevant features from many types of data, the effectiveness of these algorithms degrades when subjected to real - world conditions. Our team has generated novel neural - inspired approa ches to improve the resiliency and adaptability of machine learning algorithms. In addition, we have also designed and fabricated hardware architectures and microelectronic devices specifically tuned towards the training and inference operations of neural - inspired algorithms. Finally, our multi - scale simulation framework allows us to assess the impact of microelectronic device properties on algorithm performance.

  11. Bio-Inspired Computing, Information Swarms, and the Problem of Data Fusion

    Science.gov (United States)

    Nordmann, Brian

    The problem of information overload becomes a huge challenge, particularly when attempting to understanding how to introduce more and more disparate data streams into a data system. Little has been done on how to make those data streams understandable and usable by an analyst. A new paradigm is constructed here, unconstrained by the limits of the current desktop computer, to develop new ways of processing and analyzing data based on the behavior of cellular scale organisms. The additional issue of analytic "groupthink" or "information swarms" is also addressed, with potential solutions to the problem of "paralysis by analysis."

  12. Just-in-Time Compilation-Inspired Methodology for Parallelization of Compute Intensive Java Code

    Directory of Open Access Journals (Sweden)

    GHULAM MUSTAFA

    2017-01-01

    Full Text Available Compute intensive programs generally consume significant fraction of execution time in a small amount of repetitive code. Such repetitive code is commonly known as hotspot code. We observed that compute intensive hotspots often possess exploitable loop level parallelism. A JIT (Just-in-Time compiler profiles a running program to identify its hotspots. Hotspots are then translated into native code, for efficient execution. Using similar approach, we propose a methodology to identify hotspots and exploit their parallelization potential on multicore systems. Proposed methodology selects and parallelizes each DOALL loop that is either contained in a hotspot method or calls a hotspot method. The methodology could be integrated in front-end of a JIT compiler to parallelize sequential code, just before native translation. However, compilation to native code is out of scope of this work. As a case study, we analyze eighteen JGF (Java Grande Forum benchmarks to determine parallelization potential of hotspots. Eight benchmarks demonstrate a speedup of up to 7.6x on an 8-core system

  13. Deep ART Neural Model for Biologically Inspired Episodic Memory and Its Application to Task Performance of Robots.

    Science.gov (United States)

    Park, Gyeong-Moon; Yoo, Yong-Ho; Kim, Deok-Hwa; Kim, Jong-Hwan

    2017-06-26

    Robots are expected to perform smart services and to undertake various troublesome or difficult tasks in the place of humans. Since these human-scale tasks consist of a temporal sequence of events, robots need episodic memory to store and retrieve the sequences to perform the tasks autonomously in similar situations. As episodic memory, in this paper we propose a novel Deep adaptive resonance theory (ART) neural model and apply it to the task performance of the humanoid robot, Mybot, developed in the Robot Intelligence Technology Laboratory at KAIST. Deep ART has a deep structure to learn events, episodes, and even more like daily episodes. Moreover, it can retrieve the correct episode from partial input cues robustly. To demonstrate the effectiveness and applicability of the proposed Deep ART, experiments are conducted with the humanoid robot, Mybot, for performing the three tasks of arranging toys, making cereal, and disposing of garbage.

  14. Pwning Level Bosses in MATLAB: Student Reactions to a Game-Inspired Computational Physics Course

    CERN Document Server

    Beatty, Ian D

    2014-01-01

    We investigated student reactions to two computational physics courses incorporating several videogame-like aspects. These included use of gaming terminology such as "levels," "weapons," and "bosses"; a game-style point system linked to course grades; a self-paced schedule with no deadlines; a mastery design in which only entirely correct attempts earn credit, but students can retry until they succeed; immediate feedback via self-test code; an assignment progression from "minions" (small, focused tasks) to "level bosses" (integrative tasks); and believable, authentic assignment scenarios. Through semi-structured interviews and course evaluations, we found that a majority of students considered the courses effective and the game-like aspects beneficial. In particular, many claimed that the point system increased their motivation; the self-paced nature caused them to reflect on their self-discipline; the possibility and necessity of repeating assignments until perfect aided learning; and the authentic tasks hel...

  15. Computational design of bio-inspired carnosine-based HOBr antioxidants

    Science.gov (United States)

    Sarrami, Farzaneh; Yu, Li-Juan; Karton, Amir

    2017-09-01

    During a respiratory burst the enzyme myeloperoxidase generates significant amounts of hypohalous acids (HOX, X = Cl and Br) in order to inflict oxidative damage upon invading pathogens. However, excessive production of these potent oxidants is associated with numerous inflammatory diseases. It has been suggested that the endogenous antioxidant carnosine is an effective HOCl scavenger. Recent computational and experimental studies suggested that an intramolecular Cl+ transfer from the imidazole ring to the terminal amine might play an important role in the antioxidant activity of carnosine. Based on high-level ab initio calculations, we propose a similar reaction mechanism for the intramolecular Br+ transfer in carnosine. These results suggest that carnosine may be an effective HOBr scavenger. On the basis of the proposed reaction mechanism, we proceed to design systems that share similar structural features to carnosine but with enhanced HOX scavenging capabilities for X = Cl and Br. We find that (i) elongating the β-alanyl-glycyl side chain by one carbon reduces the reaction barriers by up to 44%, and (ii) substituting the imidazole ring with strong electron-donating groups reduces the reaction barriers by similar amounts. We also show that the above structural and electronic effects are largely additive. In an antioxidant candidate that involves both of these effects the reaction barriers are reduced by 71%.

  16. 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).

  17. Quantum perceptron over a field and neural network architecture selection in a quantum computer.

    Science.gov (United States)

    da Silva, Adenilton José; Ludermir, Teresa Bernarda; de Oliveira, Wilson Rosa

    2016-04-01

    In this work, we propose a quantum neural network named quantum perceptron over a field (QPF). Quantum computers are not yet a reality and the models and algorithms proposed in this work cannot be simulated in actual (or classical) computers. QPF is a direct generalization of a classical perceptron and solves some drawbacks found in previous models of quantum perceptrons. We also present a learning algorithm named Superposition based Architecture Learning algorithm (SAL) that optimizes the neural network weights and architectures. SAL searches for the best architecture in a finite set of neural network architectures with linear time over the number of patterns in the training set. SAL is the first learning algorithm to determine neural network architectures in polynomial time. This speedup is obtained by the use of quantum parallelism and a non-linear quantum operator. Copyright © 2016 Elsevier Ltd. All rights reserved.

  18. Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling.

    Science.gov (United States)

    Struzyna, Laura A; Adewole, Dayo O; Gordián-Vélez, Wisberty J; Grovola, Michael R; Burrell, Justin C; Katiyar, Kritika S; Petrov, Dmitriy; Harris, James P; Cullen, D Kacy

    2017-05-31

    Functional recovery rarely occurs following injury or disease-induced degeneration within the central nervous system (CNS) due to the inhibitory environment and the limited capacity for neurogenesis. We are developing a strategy to simultaneously address neuronal and axonal pathway loss within the damaged CNS. This manuscript presents the fabrication protocol for micro-tissue engineered neural networks (micro-TENNs), implantable constructs consisting of neurons and aligned axonal tracts spanning the extracellular matrix (ECM) lumen of a preformed hydrogel cylinder hundreds of microns in diameter that may extend centimeters in length. Neuronal aggregates are delimited to the extremes of the three-dimensional encasement and are spanned by axonal projections. Micro-TENNs are uniquely poised as a strategy for CNS reconstruction, emulating aspects of brain connectome cytoarchitecture and potentially providing means for network replacement. The neuronal aggregates may synapse with host tissue to form new functional relays to restore and/or modulate missing or damaged circuitry. These constructs may also act as pro-regenerative "living scaffolds" capable of exploiting developmental mechanisms for cell migration and axonal pathfinding, providing synergistic structural and soluble cues based on the state of regeneration. Micro-TENNs are fabricated by pouring liquid hydrogel into a cylindrical mold containing a longitudinally centered needle. Once the hydrogel has gelled, the needle is removed, leaving a hollow micro-column. An ECM solution is added to the lumen to provide an environment suitable for neuronal adhesion and axonal outgrowth. Dissociated neurons are mechanically aggregated for precise seeding within one or both ends of the micro-column. This methodology reliably produces self-contained miniature constructs with long-projecting axonal tracts that may recapitulate features of brain neuroanatomy. Synaptic immunolabeling and genetically encoded calcium

  19. Bio-inspired networking

    CERN Document Server

    Câmara, Daniel

    2015-01-01

    Bio-inspired techniques are based on principles, or models, of biological systems. In general, natural systems present remarkable capabilities of resilience and adaptability. In this book, we explore how bio-inspired methods can solve different problems linked to computer networks. Future networks are expected to be autonomous, scalable and adaptive. During millions of years of evolution, nature has developed a number of different systems that present these and other characteristics required for the next generation networks. Indeed, a series of bio-inspired methods have been successfully used to solve the most diverse problems linked to computer networks. This book presents some of these techniques from a theoretical and practical point of view. Discusses the key concepts of bio-inspired networking to aid you in finding efficient networking solutions Delivers examples of techniques both in theoretical concepts and practical applications Helps you apply nature's dynamic resource and task management to your co...

  20. Space-time system architecture for the neural optical computing

    Science.gov (United States)

    Lo, Yee-Man V.

    1991-02-01

    The brain can perform the tasks of associative recall detection recognition and optimization. In this paper space-time system field models of the brain are introduced. They are called the space-time maximum likelihood associative memory system (ST-ML-AMS) and the space-time adaptive learning system (ST-ALS). Performance of the system is analyzed using the probability of error in memory recall (PEMR) and the space-time neural capacity (ST-NC). 1.

  1. A state space approach for piecewise-linear recurrent neural networks for identifying computational dynamics from neural measurements.

    Directory of Open Access Journals (Sweden)

    Daniel Durstewitz

    2017-06-01

    Full Text Available The computational and cognitive properties of neural systems are often thought to be implemented in terms of their (stochastic network dynamics. Hence, recovering the system dynamics from experimentally observed neuronal time series, like multiple single-unit recordings or neuroimaging data, is an important step toward understanding its computations. Ideally, one would not only seek a (lower-dimensional state space representation of the dynamics, but would wish to have access to its statistical properties and their generative equations for in-depth analysis. Recurrent neural networks (RNNs are a computationally powerful and dynamically universal formal framework which has been extensively studied from both the computational and the dynamical systems perspective. Here we develop a semi-analytical maximum-likelihood estimation scheme for piecewise-linear RNNs (PLRNNs within the statistical framework of state space models, which accounts for noise in both the underlying latent dynamics and the observation process. The Expectation-Maximization algorithm is used to infer the latent state distribution, through a global Laplace approximation, and the PLRNN parameters iteratively. After validating the procedure on toy examples, and using inference through particle filters for comparison, the approach is applied to multiple single-unit recordings from the rodent anterior cingulate cortex (ACC obtained during performance of a classical working memory task, delayed alternation. Models estimated from kernel-smoothed spike time data were able to capture the essential computational dynamics underlying task performance, including stimulus-selective delay activity. The estimated models were rarely multi-stable, however, but rather were tuned to exhibit slow dynamics in the vicinity of a bifurcation point. In summary, the present work advances a semi-analytical (thus reasonably fast maximum-likelihood estimation framework for PLRNNs that may enable to recover

  2. A state space approach for piecewise-linear recurrent neural networks for identifying computational dynamics from neural measurements.

    Science.gov (United States)

    Durstewitz, Daniel

    2017-06-01

    The computational and cognitive properties of neural systems are often thought to be implemented in terms of their (stochastic) network dynamics. Hence, recovering the system dynamics from experimentally observed neuronal time series, like multiple single-unit recordings or neuroimaging data, is an important step toward understanding its computations. Ideally, one would not only seek a (lower-dimensional) state space representation of the dynamics, but would wish to have access to its statistical properties and their generative equations for in-depth analysis. Recurrent neural networks (RNNs) are a computationally powerful and dynamically universal formal framework which has been extensively studied from both the computational and the dynamical systems perspective. Here we develop a semi-analytical maximum-likelihood estimation scheme for piecewise-linear RNNs (PLRNNs) within the statistical framework of state space models, which accounts for noise in both the underlying latent dynamics and the observation process. The Expectation-Maximization algorithm is used to infer the latent state distribution, through a global Laplace approximation, and the PLRNN parameters iteratively. After validating the procedure on toy examples, and using inference through particle filters for comparison, the approach is applied to multiple single-unit recordings from the rodent anterior cingulate cortex (ACC) obtained during performance of a classical working memory task, delayed alternation. Models estimated from kernel-smoothed spike time data were able to capture the essential computational dynamics underlying task performance, including stimulus-selective delay activity. The estimated models were rarely multi-stable, however, but rather were tuned to exhibit slow dynamics in the vicinity of a bifurcation point. In summary, the present work advances a semi-analytical (thus reasonably fast) maximum-likelihood estimation framework for PLRNNs that may enable to recover relevant aspects

  3. From biological neural networks to thinking machines: Transitioning biological organizational principles to computer technology

    Science.gov (United States)

    Ross, Muriel D.

    1991-01-01

    The three-dimensional organization of the vestibular macula is under study by computer assisted reconstruction and simulation methods as a model for more complex neural systems. One goal of this research is to transition knowledge of biological neural network architecture and functioning to computer technology, to contribute to the development of thinking computers. Maculas are organized as weighted neural networks for parallel distributed processing of information. The network is characterized by non-linearity of its terminal/receptive fields. Wiring appears to develop through constrained randomness. A further property is the presence of two main circuits, highly channeled and distributed modifying, that are connected through feedforward-feedback collaterals and biasing subcircuit. Computer simulations demonstrate that differences in geometry of the feedback (afferent) collaterals affects the timing and the magnitude of voltage changes delivered to the spike initiation zone. Feedforward (efferent) collaterals act as voltage followers and likely inhibit neurons of the distributed modifying circuit. These results illustrate the importance of feedforward-feedback loops, of timing, and of inhibition in refining neural network output. They also suggest that it is the distributed modifying network that is most involved in adaptation, memory, and learning. Tests of macular adaptation, through hyper- and microgravitational studies, support this hypothesis since synapses in the distributed modifying circuit, but not the channeled circuit, are altered. Transitioning knowledge of biological systems to computer technology, however, remains problematical.

  4. Multiscale methodology for bone remodelling simulation using coupled finite element and neural network computation.

    Science.gov (United States)

    Hambli, Ridha; Katerchi, Houda; Benhamou, Claude-Laurent

    2011-02-01

    The aim of this paper is to develop a multiscale hierarchical hybrid model based on finite element analysis and neural network computation to link mesoscopic scale (trabecular network level) and macroscopic (whole bone level) to simulate the process of bone remodelling. As whole bone simulation, including the 3D reconstruction of trabecular level bone, is time consuming, finite element calculation is only performed at the macroscopic level, whilst trained neural networks are employed as numerical substitutes for the finite element code needed for the mesoscale prediction. The bone mechanical properties are updated at the macroscopic scale depending on the morphological and mechanical adaptation at the mesoscopic scale computed by the trained neural network. The digital image-based modelling technique using μ-CT and voxel finite element analysis is used to capture volume elements representative of 2 mm³ at the mesoscale level of the femoral head. The input data for the artificial neural network are a set of bone material parameters, boundary conditions and the applied stress. The output data are the updated bone properties and some trabecular bone factors. The current approach is the first model, to our knowledge, that incorporates both finite element analysis and neural network computation to rapidly simulate multilevel bone adaptation.

  5. Social influence modulates the neural computation of value.

    Science.gov (United States)

    Zaki, Jamil; Schirmer, Jessica; Mitchell, Jason P

    2011-07-01

    Social influence--individuals' tendency to conform to the beliefs and attitudes of others--has interested psychologists for decades. However, it has traditionally been difficult to distinguish true modification of attitudes from mere public compliance with social norms; this study addressed this challenge using functional neuroimaging. Participants rated the attractiveness of faces and subsequently learned how their peers ostensibly rated each face. Participants were then scanned using functional MRI while they rated each face a second time. The second ratings were influenced by social norms: Participants changed their ratings to conform to those of their peers. This social influence was accompanied by modulated engagement of two brain regions associated with coding subjective value--the nucleus accumbens and orbitofrontal cortex--a finding suggesting that exposure to social norms affected participants' neural representations of value assigned to stimuli. These findings document the utility of neuroimaging to demonstrate the private acceptance of social norms.

  6. Minimalist social-affective value for use in joint action: A neural-computational hypothesis

    DEFF Research Database (Denmark)

    Lowe, Robert; Almér, Alexander; Lindblad, Gustaf

    2016-01-01

    Joint Action is typically described as social interaction that requires coordination among two or more co-actors in order to achieve a common goal. In this article, we put forward a hypothesis for the existence of a neural-computational mechanism of affective valuation that may be critically expl...

  7. Artificial neural networks and support vector machine in banking computer systems

    Directory of Open Access Journals (Sweden)

    Jerzy Balicki

    2013-12-01

    Full Text Available In this paper, some artificial neural networks as well as a support vector machines have been studied due to bank computer system development. These approaches with the contact-less microprocessor technologies can upsurge the bank competitiveness by adding new functionalities. Moreover, some financial crisis influences can be declines.

  8. Distributed dynamical computation in neural circuits with propagating coherent activity patterns.

    Directory of Open Access Journals (Sweden)

    Pulin Gong

    2009-12-01

    Full Text Available Activity in neural circuits is spatiotemporally organized. Its spatial organization consists of multiple, localized coherent patterns, or patchy clusters. These patterns propagate across the circuits over time. This type of collective behavior has ubiquitously been observed, both in spontaneous activity and evoked responses; its function, however, has remained unclear. We construct a spatially extended, spiking neural circuit that generates emergent spatiotemporal activity patterns, thereby capturing some of the complexities of the patterns observed empirically. We elucidate what kind of fundamental function these patterns can serve by showing how they process information. As self-sustained objects, localized coherent patterns can signal information by propagating across the neural circuit. Computational operations occur when these emergent patterns interact, or collide with each other. The ongoing behaviors of these patterns naturally embody both distributed, parallel computation and cascaded logical operations. Such distributed computations enable the system to work in an inherently flexible and efficient way. Our work leads us to propose that propagating coherent activity patterns are the underlying primitives with which neural circuits carry out distributed dynamical computation.

  9. Accelerating Inspire

    CERN Document Server

    AUTHOR|(CDS)2266999

    2017-01-01

    CERN has been involved in the dissemination of scientific results since its early days and has continuously updated the distribution channels. Currently, Inspire hosts catalogues of articles, authors, institutions, conferences, jobs, experiments, journals and more. Successful orientation among this amount of data requires comprehensive linking between the content. Inspire has lacked a system for linking experiments and articles together based on which accelerator they were conducted at. The purpose of this project has been to create such a system. Records for 156 accelerators were created and all 2913 experiments on Inspire were given corresponding MARC tags. Records of 18404 accelerator physics related bibliographic entries were also tagged with corresponding accelerator tags. Finally, as a part of the endeavour to broaden CERN's presence on Wikipedia, existing Wikipedia articles of accelerators were updated with short descriptions and links to Inspire. In total, 86 Wikipedia articles were updated. This repo...

  10. Neural computation of visual imaging based on Kronecker product in the primary visual cortex

    Directory of Open Access Journals (Sweden)

    Guozheng Yao

    2010-03-01

    Full Text Available Abstract Background What kind of neural computation is actually performed by the primary visual cortex and how is this represented mathematically at the system level? It is an important problem in the visual information processing, but has not been well answered. In this paper, according to our understanding of retinal organization and parallel multi-channel topographical mapping between retina and primary visual cortex V1, we divide an image into orthogonal and orderly array of image primitives (or patches, in which each patch will evoke activities of simple cells in V1. From viewpoint of information processing, this activated process, essentially, involves optimal detection and optimal matching of receptive fields of simple cells with features contained in image patches. For the reconstruction of the visual image in the visual cortex V1 based on the principle of minimum mean squares error, it is natural to use the inner product expression in neural computation, which then is transformed into matrix form. Results The inner product is carried out by using Kronecker product between patches and function architecture (or functional column in localized and oriented neural computing. Compared with Fourier Transform, the mathematical description of Kronecker product is simple and intuitive, so is the algorithm more suitable for neural computation of visual cortex V1. Results of computer simulation based on two-dimensional Gabor pyramid wavelets show that the theoretical analysis and the proposed model are reasonable. Conclusions Our results are: 1. The neural computation of the retinal image in cortex V1 can be expressed to Kronecker product operation and its matrix form, this algorithm is implemented by the inner operation between retinal image primitives and primary visual cortex's column. It has simple, efficient and robust features, which is, therefore, such a neural algorithm, which can be completed by biological vision. 2. It is more suitable

  11. Biological neural networks as model systems for designing future parallel processing computers

    Science.gov (United States)

    Ross, Muriel D.

    1991-01-01

    One of the more interesting debates of the present day centers on whether human intelligence can be simulated by computer. The author works under the premise that neurons individually are not smart at all. Rather, they are physical units which are impinged upon continuously by other matter that influences the direction of voltage shifts across the units membranes. It is only the action of a great many neurons, billions in the case of the human nervous system, that intelligent behavior emerges. What is required to understand even the simplest neural system is painstaking analysis, bit by bit, of the architecture and the physiological functioning of its various parts. The biological neural network studied, the vestibular utricular and saccular maculas of the inner ear, are among the most simple of the mammalian neural networks to understand and model. While there is still a long way to go to understand even this most simple neural network in sufficient detail for extrapolation to computers and robots, a start was made. Moreover, the insights obtained and the technologies developed help advance the understanding of the more complex neural networks that underlie human intelligence.

  12. Neuromechanic: a computational platform for simulation and analysis of the neural control of movement

    Science.gov (United States)

    Bunderson, Nathan E.; Bingham, Jeffrey T.; Sohn, M. Hongchul; Ting, Lena H.; Burkholder, Thomas J.

    2015-01-01

    Neuromusculoskeletal models solve the basic problem of determining how the body moves under the influence of external and internal forces. Existing biomechanical modeling programs often emphasize dynamics with the goal of finding a feed-forward neural program to replicate experimental data or of estimating force contributions or individual muscles. The computation of rigid-body dynamics, muscle forces, and activation of the muscles are often performed separately. We have developed an intrinsically forward computational platform (Neuromechanic, www.neuromechanic.com) that explicitly represents the interdependencies among rigid body dynamics, frictional contact, muscle mechanics, and neural control modules. This formulation has significant advantages for optimization and forward simulation, particularly with application to neural controllers with feedback or regulatory features. Explicit inclusion of all state dependencies allows calculation of system derivatives with respect to kinematic states as well as muscle and neural control states, thus affording a wealth of analytical tools, including linearization, stability analyses and calculation of initial conditions for forward simulations. In this review, we describe our algorithm for generating state equations and explain how they may be used in integration, linearization and stability analysis tools to provide structural insights into the neural control of movement. PMID:23027632

  13. Internal models and neural computation in the vestibular system

    OpenAIRE

    Green, Andrea M.; Dora E. Angelaki

    2010-01-01

    The vestibular system is vital for motor control and spatial self-motion perception. Afferents from the otolith organs and the semicircular canals converge with optokinetic, somatosensory and motor-related signals in the vestibular nuclei, which are reciprocally interconnected with the vestibulocerebellar cortex and deep cerebellar nuclei. Here, we review the properties of the many cell types in the vestibular nuclei, as well as some fundamental computations implemented within this brainstem–...

  14. Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons.

    Directory of Open Access Journals (Sweden)

    Lars Buesing

    2011-11-01

    Full Text Available The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of neural systems in the brain. In principle there exists a powerful computational framework for stochastic computations, probabilistic inference by sampling, which can explain a large number of macroscopic experimental data in neuroscience and cognitive science. But it has turned out to be surprisingly difficult to create a link between these abstract models for stochastic computations and more detailed models of the dynamics of networks of spiking neurons. Here we create such a link and show that under some conditions the stochastic firing activity of networks of spiking neurons can be interpreted as probabilistic inference via Markov chain Monte Carlo (MCMC sampling. Since common methods for MCMC sampling in distributed systems, such as Gibbs sampling, are inconsistent with the dynamics of spiking neurons, we introduce a different approach based on non-reversible Markov chains that is able to reflect inherent temporal processes of spiking neuronal activity through a suitable choice of random variables. We propose a neural network model and show by a rigorous theoretical analysis that its neural activity implements MCMC sampling of a given distribution, both for the case of discrete and continuous time. This provides a step towards closing the gap between abstract functional models of cortical computation and more detailed models of networks of spiking neurons.

  15. Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons.

    Science.gov (United States)

    Buesing, Lars; Bill, Johannes; Nessler, Bernhard; Maass, Wolfgang

    2011-11-01

    The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of neural systems in the brain. In principle there exists a powerful computational framework for stochastic computations, probabilistic inference by sampling, which can explain a large number of macroscopic experimental data in neuroscience and cognitive science. But it has turned out to be surprisingly difficult to create a link between these abstract models for stochastic computations and more detailed models of the dynamics of networks of spiking neurons. Here we create such a link and show that under some conditions the stochastic firing activity of networks of spiking neurons can be interpreted as probabilistic inference via Markov chain Monte Carlo (MCMC) sampling. Since common methods for MCMC sampling in distributed systems, such as Gibbs sampling, are inconsistent with the dynamics of spiking neurons, we introduce a different approach based on non-reversible Markov chains that is able to reflect inherent temporal processes of spiking neuronal activity through a suitable choice of random variables. We propose a neural network model and show by a rigorous theoretical analysis that its neural activity implements MCMC sampling of a given distribution, both for the case of discrete and continuous time. This provides a step towards closing the gap between abstract functional models of cortical computation and more detailed models of networks of spiking neurons.

  16. Depth perception in frogs and toads a study in neural computing

    CERN Document Server

    House, Donald

    1989-01-01

    Depth Perception in Frogs and Toads provides a comprehensive exploration of the phenomenon of depth perception in frogs and toads, as seen from a neuro-computational point of view. Perhaps the most important feature of the book is the development and presentation of two neurally realizable depth perception algorithms that utilize both monocular and binocular depth cues in a cooperative fashion. One of these algorithms is specialized for computation of depth maps for navigation, and the other for the selection and localization of a single prey for prey catching. The book is also unique in that it thoroughly reviews the known neuroanatomical, neurophysiological and behavioral data, and then synthesizes, organizes and interprets that information to explain a complex sensory-motor task. The book will be of special interest to that segment of the neural computing community interested in understanding natural neurocomputational structures, particularly to those working in perception and sensory-motor coordination. ...

  17. Predictive Behavior of a Computational Foot/Ankle Model through Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Ruchi D. Chande

    2017-01-01

    Full Text Available Computational models are useful tools to study the biomechanics of human joints. Their predictive performance is heavily dependent on bony anatomy and soft tissue properties. Imaging data provides anatomical requirements while approximate tissue properties are implemented from literature data, when available. We sought to improve the predictive capability of a computational foot/ankle model by optimizing its ligament stiffness inputs using feedforward and radial basis function neural networks. While the former demonstrated better performance than the latter per mean square error, both networks provided reasonable stiffness predictions for implementation into the computational model.

  18. Behavioral, Neural, and Computational Principles of Bodily Self-Consciousness.

    Science.gov (United States)

    Blanke, Olaf; Slater, Mel; Serino, Andrea

    2015-10-07

    Recent work in human cognitive neuroscience has linked self-consciousness to the processing of multisensory bodily signals (bodily self-consciousness [BSC]) in fronto-parietal cortex and more posterior temporo-parietal regions. We highlight the behavioral, neurophysiological, neuroimaging, and computational laws that subtend BSC in humans and non-human primates. We propose that BSC includes body-centered perception (hand, face, and trunk), based on the integration of proprioceptive, vestibular, and visual bodily inputs, and involves spatio-temporal mechanisms integrating multisensory bodily stimuli within peripersonal space (PPS). We develop four major constraints of BSC (proprioception, body-related visual information, PPS, and embodiment) and argue that the fronto-parietal and temporo-parietal processing of trunk-centered multisensory signals in PPS is of particular relevance for theoretical models and simulations of BSC and eventually of self-consciousness. Copyright © 2015 Elsevier Inc. All rights reserved.

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

    Science.gov (United States)

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

    2014-12-01

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

  20. Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network

    Directory of Open Access Journals (Sweden)

    Lukas Falat

    2016-01-01

    Full Text Available This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.

  1. Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network.

    Science.gov (United States)

    Falat, Lukas; Marcek, Dusan; Durisova, Maria

    2016-01-01

    This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.

  2. Computing single step operators of logic programming in radial basis function neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong [School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang (Malaysia)

    2014-07-10

    Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (T{sub p}:I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks.

  3. ARACHNE: A neural-neuroglial network builder with remotely controlled parallel computing

    Science.gov (United States)

    Rusakov, Dmitri A.; Savtchenko, Leonid P.

    2017-01-01

    Creating and running realistic models of neural networks has hitherto been a task for computing professionals rather than experimental neuroscientists. This is mainly because such networks usually engage substantial computational resources, the handling of which requires specific programing skills. Here we put forward a newly developed simulation environment ARACHNE: it enables an investigator to build and explore cellular networks of arbitrary biophysical and architectural complexity using the logic of NEURON and a simple interface on a local computer or a mobile device. The interface can control, through the internet, an optimized computational kernel installed on a remote computer cluster. ARACHNE can combine neuronal (wired) and astroglial (extracellular volume-transmission driven) network types and adopt realistic cell models from the NEURON library. The program and documentation (current version) are available at GitHub repository https://github.com/LeonidSavtchenko/Arachne under the MIT License (MIT). PMID:28362877

  4. An experimental study on nonlinear function computation for neural/fuzzy hardware design.

    Science.gov (United States)

    Basterretxea, Koldo; Tarela, José Manuel; del Campo, Inés; Bosque, Guillermo

    2007-01-01

    An experimental study on the influence of the computation of basic nodal nonlinear functions on the performance of (NFSs) is described in this paper. Systems' architecture size, their approximation capability, and the smoothness of provided mappings are used as performance indexes for this comparative paper. Two widely used kernel functions, the sigmoid-logistic function and the Gaussian function, are analyzed by their computation through an accuracy-controllable approximation algorithm designed for hardware implementation. Two artificial neural network (ANN) paradigms are selected for the analysis: backpropagation neural networks (BPNNs) with one hidden layer and radial basis function (RBF) networks. Extensive simulation of simple benchmark approximation problems is used in order to achieve generalizable conclusions. For the performance analysis of fuzzy systems, a functional equivalence theorem is used to extend obtained results to fuzzy inference systems (FISs). Finally, the adaptive neurofuzzy inference system (ANFIS) paradigm is used to observe the behavior of neurofuzzy systems with learning capabilities.

  5. Modeling and computing of stock index forecasting based on neural network and Markov chain.

    Science.gov (United States)

    Dai, Yonghui; Han, Dongmei; Dai, Weihui

    2014-01-01

    The stock index reflects the fluctuation of the stock market. For a long time, there have been a lot of researches on the forecast of stock index. However, the traditional method is limited to achieving an ideal precision in the dynamic market due to the influences of many factors such as the economic situation, policy changes, and emergency events. Therefore, the approach based on adaptive modeling and conditional probability transfer causes the new attention of researchers. This paper presents a new forecast method by the combination of improved back-propagation (BP) neural network and Markov chain, as well as its modeling and computing technology. This method includes initial forecasting by improved BP neural network, division of Markov state region, computing of the state transition probability matrix, and the prediction adjustment. Results of the empirical study show that this method can achieve high accuracy in the stock index prediction, and it could provide a good reference for the investment in stock market.

  6. Inspired Landscapes

    Science.gov (United States)

    Brandon, Robert; Spruch, Arthur

    2008-01-01

    It has been nearly 400 years since Harvard College was created, and since then, thousands of colleges and universities have been built across the United States. From the classically inspired lines of Thomas Jefferson's University of Virginia to the Spanish architecture at Stanford University, every campus has its own personality. It's not unusual,…

  7. Inspire Day

    Science.gov (United States)

    Bohach, Barbara M.; Meade, Birgitta

    2014-01-01

    The authors collaborated on hosting a "Spring Inspire Day." planned and delivered by preservice elementary teachers as a social studies/science methods project. Projects that have authentic application opportunities can make learning meaningful for prospective teachers as well as elementary students. With the impetus for an integrated…

  8. A Computational Estimation of Cyclic Material Properties Using Artificial Neural Networks

    OpenAIRE

    Tomasella, A.; Dsoki, C. el; H. Hanselka; Kaufmann, H.

    2011-01-01

    The structural durability design of components requires the knowledge of cyclic material properties. These parameters are strongly dependent on environmental conditions and manufacturing processes, and require many experimental tests to be correctly determined. Considering time and costs, it is not possible to include in the tests all the variables that influence the material behaviour. For this reason, the computational method of the Artificial Neural Network (ANN) can be implemented to supp...

  9. Utilizing neural networks in magnetic media modeling and field computation: A review

    OpenAIRE

    Amr A. Adly; Abd-El-Hafiz, Salwa K.

    2013-01-01

    Magnetic materials are considered as crucial components for a wide range of products and devices. Usually, complexity of such materials is defined by their permeability classification and coupling extent to non-magnetic properties. Hence, development of models that could accurately simulate the complex nature of these materials becomes crucial to the multi-dimensional field-media interactions and computations. In the past few decades, artificial neural networks (ANNs) have been utilized in ma...

  10. Characterizing Deep Brain Stimulation effects in computationally efficient neural network models.

    Science.gov (United States)

    Latteri, Alberta; Arena, Paolo; Mazzone, Paolo

    2011-04-15

    Recent studies on the medical treatment of Parkinson's disease (PD) led to the introduction of the so called Deep Brain Stimulation (DBS) technique. This particular therapy allows to contrast actively the pathological activity of various Deep Brain structures, responsible for the well known PD symptoms. This technique, frequently joined to dopaminergic drugs administration, replaces the surgical interventions implemented to contrast the activity of specific brain nuclei, called Basal Ganglia (BG). This clinical protocol gave the possibility to analyse and inspect signals measured from the electrodes implanted into the deep brain regions. The analysis of these signals led to the possibility to study the PD as a specific case of dynamical synchronization in biological neural networks, with the advantage to apply the theoretical analysis developed in such scientific field to find efficient treatments to face with this important disease. Experimental results in fact show that the PD neurological diseases are characterized by a pathological signal synchronization in BG. Parkinsonian tremor, for example, is ascribed to be caused by neuron populations of the Thalamic and Striatal structures that undergo an abnormal synchronization. On the contrary, in normal conditions, the activity of the same neuron populations do not appear to be correlated and synchronized. To study in details the effect of the stimulation signal on a pathological neural medium, efficient models of these neural structures were built, which are able to show, without any external input, the intrinsic properties of a pathological neural tissue, mimicking the BG synchronized dynamics.We start considering a model already introduced in the literature to investigate the effects of electrical stimulation on pathologically synchronized clusters of neurons. This model used Morris Lecar type neurons. This neuron model, although having a high level of biological plausibility, requires a large computational effort

  11. Fish and chips: implementation of a neural network model into computer chips to maximize swimming efficiency in autonomous underwater vehicles.

    Science.gov (United States)

    Blake, R W; Ng, H; Chan, K H S; Li, J

    2008-09-01

    Recent developments in the design and propulsion of biomimetic autonomous underwater vehicles (AUVs) have focused on boxfish as models (e.g. Deng and Avadhanula 2005 Biomimetic micro underwater vehicle with oscillating fin propulsion: system design and force measurement Proc. 2005 IEEE Int. Conf. Robot. Auto. (Barcelona, Spain) pp 3312-7). Whilst such vehicles have many potential advantages in operating in complex environments (e.g. high manoeuvrability and stability), limited battery life and payload capacity are likely functional disadvantages. Boxfish employ undulatory median and paired fins during routine swimming which are characterized by high hydromechanical Froude efficiencies (approximately 0.9) at low forward speeds. Current boxfish-inspired vehicles are propelled by a low aspect ratio, 'plate-like' caudal fin (ostraciiform tail) which can be shown to operate at a relatively low maximum Froude efficiency (approximately 0.5) and is mainly employed as a rudder for steering and in rapid swimming bouts (e.g. escape responses). Given this and the fact that bioinspired engineering designs are not obligated to wholly duplicate a biological model, computer chips were developed using a multilayer perception neural network model of undulatory fin propulsion in the knifefish Xenomystus nigri that would potentially allow an AUV to achieve high optimum values of propulsive efficiency at any given forward velocity, giving a minimum energy drain on the battery. We envisage that externally monitored information on flow velocity (sensory system) would be conveyed to the chips residing in the vehicle's control unit, which in turn would signal the locomotor unit to adopt kinematics (e.g. fin frequency, amplitude) associated with optimal propulsion efficiency. Power savings could protract vehicle operational life and/or provide more power to other functions (e.g. communications).

  12. A cortically-inspired model for inverse kinematics computation of a humanoid finger with mechanically coupled joints.

    Science.gov (United States)

    Gentili, Rodolphe J; Oh, Hyuk; Kregling, Alissa V; Reggia, James A

    2016-05-19

    The human hand's versatility allows for robust and flexible grasping. To obtain such efficiency, many robotic hands include human biomechanical features such as fingers having their two last joints mechanically coupled. Although such coupling enables human-like grasping, controlling the inverse kinematics of such mechanical systems is challenging. Here we propose a cortical model for fine motor control of a humanoid finger, having its two last joints coupled, that learns the inverse kinematics of the effector. This neural model functionally mimics the population vector coding as well as sensorimotor prediction processes of the brain's motor/premotor and parietal regions, respectively. After learning, this neural architecture could both overtly (actual execution) and covertly (mental execution or motor imagery) perform accurate, robust and flexible finger movements while reproducing the main human finger kinematic states. This work contributes to developing neuro-mimetic controllers for dexterous humanoid robotic/prosthetic upper-extremities, and has the potential to promote human-robot interactions.

  13. International Conference on Artificial Neural Networks (ICANN)

    CERN Document Server

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

    2015-01-01

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

  14. Biologically Inspired Self-Stabilizing Control for Bipedal Robots

    Directory of Open Access Journals (Sweden)

    Woosung Yang

    2013-02-01

    Full Text Available Despite recent major advances in computational power and control algorithms, the stable and robust control of a bipedal robot is still a challenging issue due to the complexity and high nonlinearity of robot dynamics. To address the issue an efficient and powerful alternative based on a biologically inspired control framework employing neural oscillators is proposed and tested. In a numerical test the virtual force controller combined with the neural oscillator of a humanoid robot generated rhythmic control signals and stable bipedal locomotion when coupled with proper impedance components. The entrainment nature inherent to neural oscillators also achieved stable and robust walking even in the presence of unexpected disturbances, in that the centre of mass (COM was successfully kept in phase with the zero moment point (ZMP input trajectory. The efficiency of the proposed control scheme is discussed alongside simulation results.

  15. Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images

    Directory of Open Access Journals (Sweden)

    Wei Li

    2016-01-01

    Full Text Available Computer aided detection (CAD systems can assist radiologists by offering a second opinion on early diagnosis of lung cancer. Classification and feature representation play critical roles in false-positive reduction (FPR in lung nodule CAD. We design a deep convolutional neural networks method for nodule classification, which has an advantage of autolearning representation and strong generalization ability. A specified network structure for nodule images is proposed to solve the recognition of three types of nodules, that is, solid, semisolid, and ground glass opacity (GGO. Deep convolutional neural networks are trained by 62,492 regions-of-interest (ROIs samples including 40,772 nodules and 21,720 nonnodules from the Lung Image Database Consortium (LIDC database. Experimental results demonstrate the effectiveness of the proposed method in terms of sensitivity and overall accuracy and that it consistently outperforms the competing methods.

  16. Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images

    Science.gov (United States)

    Li, Wei; Zhao, Dazhe; Wang, Junbo

    2016-01-01

    Computer aided detection (CAD) systems can assist radiologists by offering a second opinion on early diagnosis of lung cancer. Classification and feature representation play critical roles in false-positive reduction (FPR) in lung nodule CAD. We design a deep convolutional neural networks method for nodule classification, which has an advantage of autolearning representation and strong generalization ability. A specified network structure for nodule images is proposed to solve the recognition of three types of nodules, that is, solid, semisolid, and ground glass opacity (GGO). Deep convolutional neural networks are trained by 62,492 regions-of-interest (ROIs) samples including 40,772 nodules and 21,720 nonnodules from the Lung Image Database Consortium (LIDC) database. Experimental results demonstrate the effectiveness of the proposed method in terms of sensitivity and overall accuracy and that it consistently outperforms the competing methods. PMID:28070212

  17. The prediction in computer color matching of dentistry based on GA+BP neural network.

    Science.gov (United States)

    Li, Haisheng; Lai, Long; Chen, Li; Lu, Cheng; Cai, Qiang

    2015-01-01

    Although the use of computer color matching can reduce the influence of subjective factors by technicians, matching the color of a natural tooth with a ceramic restoration is still one of the most challenging topics in esthetic prosthodontics. Back propagation neural network (BPNN) has already been introduced into the computer color matching in dentistry, but it has disadvantages such as unstable and low accuracy. In our study, we adopt genetic algorithm (GA) to optimize the initial weights and threshold values in BPNN for improving the matching precision. To our knowledge, we firstly combine the BPNN with GA in computer color matching in dentistry. Extensive experiments demonstrate that the proposed method improves the precision and prediction robustness of the color matching in restorative dentistry.

  18. Artificial Neural Networks for Reducing Computational Effort in Active Truncated Model Testing of Mooring Lines

    DEFF Research Database (Denmark)

    Christiansen, Niels Hørbye; Voie, Per Erlend Torbergsen; Høgsberg, Jan Becker

    2015-01-01

    simultaneously, this method is very demanding in terms of numerical efficiency and computational power. Therefore, this method has not yet proved to be feasible. It has recently been shown how a hybrid method combining classical numerical models and artificial neural networks (ANN) can provide a dramatic...... model. Hence, in principal it is possible to achieve reliable experimental data for much larger water depths than what the actual depth of the test basin would suggest. However, since the computations must be faster than real time, as the numerical simulations and the physical experiment run...... reduction in computational effort when performing time domain simulation of mooring lines. The hybrid method uses a classical numerical model to generate simulation data, which are then subsequently used to train the ANN. After successful training the ANN is able to take over the simulation at a speed two...

  19. A Spiking Neural Simulator Integrating Event-Driven and Time-Driven Computation Schemes Using Parallel CPU-GPU Co-Processing: A Case Study.

    Science.gov (United States)

    Naveros, Francisco; Luque, Niceto R; Garrido, Jesús A; Carrillo, Richard R; Anguita, Mancia; Ros, Eduardo

    2015-07-01

    Time-driven simulation methods in traditional CPU architectures perform well and precisely when simulating small-scale spiking neural networks. Nevertheless, they still have drawbacks when simulating large-scale systems. Conversely, event-driven simulation methods in CPUs and time-driven simulation methods in graphic processing units (GPUs) can outperform CPU time-driven methods under certain conditions. With this performance improvement in mind, we have developed an event-and-time-driven spiking neural network simulator suitable for a hybrid CPU-GPU platform. Our neural simulator is able to efficiently simulate bio-inspired spiking neural networks consisting of different neural models, which can be distributed heterogeneously in both small layers and large layers or subsystems. For the sake of efficiency, the low-activity parts of the neural network can be simulated in CPU using event-driven methods while the high-activity subsystems can be simulated in either CPU (a few neurons) or GPU (thousands or millions of neurons) using time-driven methods. In this brief, we have undertaken a comparative study of these different simulation methods. For benchmarking the different simulation methods and platforms, we have used a cerebellar-inspired neural-network model consisting of a very dense granular layer and a Purkinje layer with a smaller number of cells (according to biological ratios). Thus, this cerebellar-like network includes a dense diverging neural layer (increasing the dimensionality of its internal representation and sparse coding) and a converging neural layer (integration) similar to many other biologically inspired and also artificial neural networks.

  20. Computing with Spiking Neuron Networks

    NARCIS (Netherlands)

    H. Paugam-Moisy; S.M. Bohte (Sander); G. Rozenberg; T.H.W. Baeck (Thomas); J.N. Kok (Joost)

    2012-01-01

    htmlabstractAbstract Spiking Neuron Networks (SNNs) are often referred to as the 3rd gener- ation of neural networks. Highly inspired from natural computing in the brain and recent advances in neurosciences, they derive their strength and interest from an ac- curate modeling of synaptic interactions

  1. Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia

    Science.gov (United States)

    Kim, Sung-Phil; Simeral, John D.; Hochberg, Leigh R.; Donoghue, John P.; Black, Michael J.

    2008-12-01

    Computer-mediated connections between human motor cortical neurons and assistive devices promise to improve or restore lost function in people with paralysis. Recently, a pilot clinical study of an intracortical neural interface system demonstrated that a tetraplegic human was able to obtain continuous two-dimensional control of a computer cursor using neural activity recorded from his motor cortex. This control, however, was not sufficiently accurate for reliable use in many common computer control tasks. Here, we studied several central design choices for such a system including the kinematic representation for cursor movement, the decoding method that translates neuronal ensemble spiking activity into a control signal and the cursor control task used during training for optimizing the parameters of the decoding method. In two tetraplegic participants, we found that controlling a cursor's velocity resulted in more accurate closed-loop control than controlling its position directly and that cursor velocity control was achieved more rapidly than position control. Control quality was further improved over conventional linear filters by using a probabilistic method, the Kalman filter, to decode human motor cortical activity. Performance assessment based on standard metrics used for the evaluation of a wide range of pointing devices demonstrated significantly improved cursor control with velocity rather than position decoding. Disclosure. JPD is the Chief Scientific Officer and a director of Cyberkinetics Neurotechnology Systems (CYKN); he holds stock and receives compensation. JDS has been a consultant for CYKN. LRH receives clinical trial support from CYKN.

  2. On the efficient swimming of a ray-inspired underwater vehicle. Part II: Computational analysis of fin hydrodynamics

    Science.gov (United States)

    Liu, Geng; Ren, Yan; Zhu, Jianzhou; Bart-Smith, Hilary; Dong, Haibo

    2014-11-01

    High-fidelity numerical simulations are being used to examine the key hydrodynamic features and thrust performance of the fin of a manta ray-inspired underwater vehicle (MantaBot) which is moving at a constant forward velocity. The numerical modeling approach employs a parallelized DNS immersed boundary solver for low-Reynolds number flows past highly deformable bodies such as fish pectoral fins and insect wings. The three-dimensional, time-dependent fin kinematics is obtained via a stereo-videographic technique. The primary objectives of the CFD effort are to quantify the thrust performance of the MantaBot fin with different bending stiffness as well as to establish the mechanisms responsible for thrust production. Simulations show that the bending angle and bending rate of the fin play important roles in thrust producing. A distinct system of connected vortices produced by the deformable fins is also examined in detail for understanding the thrust producing mechanisms. This research was supported by the Office of Naval Research (ONR) under the Multidisciplinary University Research Initiative (MURI) Grant N00014-14-1-0533.

  3. Complexity, chaos and human physiology: the justification for non-linear neural computational analysis.

    Science.gov (United States)

    Baxt, W G

    1994-03-15

    Background is presented to suggest that a great many biologic processes are chaotic. It is well known that chaotic processes can be accurately characterized by non-linear technologies. Evidence is presented that an artificial neural network, which is a known method for the application of non-linear statistics, is able to perform more accurately in identifying patients with and without myocardial infarction than either physicians or other computer paradigms. It is suggested that the improved performance may be due to the network's better ability to characterize what is a chaotic process imbedded in the problem of the clinical diagnosis of this entity.

  4. Simulation of Neurocomputing Based on Photophobic Reactions of Euglena: Toward Microbe-Based Neural Network Computing

    Science.gov (United States)

    Ozasa, Kazunari; Aono, Masashi; Maeda, Mizuo; Hara, Masahiko

    In order to develop an adaptive computing system, we investigate microscopic optical feedback to a group of microbes (Euglena gracilis in this study) with a neural network algorithm, expecting that the unique characteristics of microbes, especially their strategies to survive/adapt against unfavorable environmental stimuli, will explicitly determine the temporal evolution of the microbe-based feedback system. The photophobic reactions of Euglena are extracted from experiments, and built in the Monte-Carlo simulation of a microbe-based neurocomputing. The simulation revealed a good performance of Euglena-based neurocomputing. Dynamic transition among the solutions is discussed from the viewpoint of feedback instability.

  5. Springer handbook of computational intelligence

    CERN Document Server

    Pedrycz, Witold

    2015-01-01

    This is the first book covering the basics and the state of the art and important applications of the complete growing discipline of computational intelligence. This comprehensive handbook presents a unique synergy of various approaches and new qualities to be gained by using hybrid approaches, incl. inspirations from biology and living organisms and animate systems. The text is organized in 7 main parts foundations, fuzzy sets, rough sets, evolutionary computation, neural networks, swarm intelligence and hybrid computational intelligence systems.

  6. Biologically inspired emotion recognition from speech

    Directory of Open Access Journals (Sweden)

    Buscicchio Cosimo

    2011-01-01

    Full Text Available Abstract Emotion recognition has become a fundamental task in human-computer interaction systems. In this article, we propose an emotion recognition approach based on biologically inspired methods. Specifically, emotion classification is performed using a long short-term memory (LSTM recurrent neural network which is able to recognize long-range dependencies between successive temporal patterns. We propose to represent data using features derived from two different models: mel-frequency cepstral coefficients (MFCC and the Lyon cochlear model. In the experimental phase, results obtained from the LSTM network and the two different feature sets are compared, showing that features derived from the Lyon cochlear model give better recognition results in comparison with those obtained with the traditional MFCC representation.

  7. An efficient neural network approach to dynamic robot motion planning.

    Science.gov (United States)

    Yang, S X; Meng, M

    2000-03-01

    In this paper, a biologically inspired neural network approach to real-time collision-free motion planning of mobile robots or robot manipulators in a nonstationary environment is proposed. Each neuron in the topologically organized neural network has only local connections, whose neural dynamics is characterized by a shunting equation. Thus the computational complexity linearly depends on the neural network size. The real-time robot motion is planned through the dynamic activity landscape of the neural network without any prior knowledge of the dynamic environment, without explicitly searching over the free workspace or the collision paths, and without any learning procedures. Therefore it is computationally efficient. The global stability of the neural network is guaranteed by qualitative analysis and the Lyapunov stability theory. The effectiveness and efficiency of the proposed approach are demonstrated through simulation studies.

  8. Neural Computation via Neural Geometry: A Place Code for Inter-whisker Timing in the Barrel Cortex?

    Science.gov (United States)

    Wilson, Stuart P.; Bednar, James A.; Prescott, Tony J.; Mitchinson, Ben

    2011-01-01

    The place theory proposed by Jeffress (1948) is still the dominant model of how the brain represents the movement of sensory stimuli between sensory receptors. According to the place theory, delays in signalling between neurons, dependent on the distances between them, compensate for time differences in the stimulation of sensory receptors. Hence the location of neurons, activated by the coincident arrival of multiple signals, reports the stimulus movement velocity. Despite its generality, most evidence for the place theory has been provided by studies of the auditory system of auditory specialists like the barn owl, but in the study of mammalian auditory systems the evidence is inconclusive. We ask to what extent the somatosensory systems of tactile specialists like rats and mice use distance dependent delays between neurons to compute the motion of tactile stimuli between the facial whiskers (or ‘vibrissae’). We present a model in which synaptic inputs evoked by whisker deflections arrive at neurons in layer 2/3 (L2/3) somatosensory ‘barrel’ cortex at different times. The timing of synaptic inputs to each neuron depends on its location relative to sources of input in layer 4 (L4) that represent stimulation of each whisker. Constrained by the geometry and timing of projections from L4 to L2/3, the model can account for a range of experimentally measured responses to two-whisker stimuli. Consistent with that data, responses of model neurons located between the barrels to paired stimulation of two whiskers are greater than the sum of the responses to either whisker input alone. The model predicts that for neurons located closer to either barrel these supralinear responses are tuned for longer inter-whisker stimulation intervals, yielding a topographic map for the inter-whisker deflection interval across the surface of L2/3. This map constitutes a neural place code for the relative timing of sensory stimuli. PMID:22022245

  9. Neural computation via neural geometry: a place code for inter-whisker timing in the barrel cortex?

    Directory of Open Access Journals (Sweden)

    Stuart P Wilson

    2011-10-01

    Full Text Available The place theory proposed by Jeffress (1948 is still the dominant model of how the brain represents the movement of sensory stimuli between sensory receptors. According to the place theory, delays in signalling between neurons, dependent on the distances between them, compensate for time differences in the stimulation of sensory receptors. Hence the location of neurons, activated by the coincident arrival of multiple signals, reports the stimulus movement velocity. Despite its generality, most evidence for the place theory has been provided by studies of the auditory system of auditory specialists like the barn owl, but in the study of mammalian auditory systems the evidence is inconclusive. We ask to what extent the somatosensory systems of tactile specialists like rats and mice use distance dependent delays between neurons to compute the motion of tactile stimuli between the facial whiskers (or 'vibrissae'. We present a model in which synaptic inputs evoked by whisker deflections arrive at neurons in layer 2/3 (L2/3 somatosensory 'barrel' cortex at different times. The timing of synaptic inputs to each neuron depends on its location relative to sources of input in layer 4 (L4 that represent stimulation of each whisker. Constrained by the geometry and timing of projections from L4 to L2/3, the model can account for a range of experimentally measured responses to two-whisker stimuli. Consistent with that data, responses of model neurons located between the barrels to paired stimulation of two whiskers are greater than the sum of the responses to either whisker input alone. The model predicts that for neurons located closer to either barrel these supralinear responses are tuned for longer inter-whisker stimulation intervals, yielding a topographic map for the inter-whisker deflection interval across the surface of L2/3. This map constitutes a neural place code for the relative timing of sensory stimuli.

  10. Computer vision system for egg volume prediction using backpropagation neural network

    Science.gov (United States)

    Siswantoro, J.; Hilman, M. Y.; Widiasri, M.

    2017-11-01

    Volume is one of considered aspects in egg sorting process. A rapid and accurate volume measurement method is needed to develop an egg sorting system. Computer vision system (CVS) provides a promising solution for volume measurement problem. Artificial neural network (ANN) has been used to predict the volume of egg in several CVSs. However, volume prediction from ANN could have less accuracy due to inappropriate input features or inappropriate ANN structure. This paper proposes a CVS for predicting the volume of egg using ANN. The CVS acquired an image of egg from top view and then processed the image to extract its 1D and 2 D size features. The features were used as input for ANN in predicting the volume of egg. The experiment results show that the proposed CSV can predict the volume of egg with a good accuracy and less computation time.

  11. Characterization of physiological networks in sleep apnea patients using artificial neural networks for Granger causality computation

    Science.gov (United States)

    Cárdenas, Jhon; Orjuela-Cañón, Alvaro D.; Cerquera, Alexander; Ravelo, Antonio

    2017-11-01

    Different studies have used Transfer Entropy (TE) and Granger Causality (GC) computation to quantify interconnection between physiological systems. These methods have disadvantages in parametrization and availability in analytic formulas to evaluate the significance of the results. Other inconvenience is related with the assumptions in the distribution of the models generated from the data. In this document, the authors present a way to measure the causality that connect the Central Nervous System (CNS) and the Cardiac System (CS) in people diagnosed with obstructive sleep apnea syndrome (OSA) before and during treatment with continuous positive air pressure (CPAP). For this purpose, artificial neural networks were used to obtain models for GC computation, based on time series of normalized powers calculated from electrocardiography (EKG) and electroencephalography (EEG) signals recorded in polysomnography (PSG) studies.

  12. Utilizing neural networks in magnetic media modeling and field computation: A review.

    Science.gov (United States)

    Adly, Amr A; Abd-El-Hafiz, Salwa K

    2014-11-01

    Magnetic materials are considered as crucial components for a wide range of products and devices. Usually, complexity of such materials is defined by their permeability classification and coupling extent to non-magnetic properties. Hence, development of models that could accurately simulate the complex nature of these materials becomes crucial to the multi-dimensional field-media interactions and computations. In the past few decades, artificial neural networks (ANNs) have been utilized in many applications to perform miscellaneous tasks such as identification, approximation, optimization, classification and forecasting. The purpose of this review article is to give an account of the utilization of ANNs in modeling as well as field computation involving complex magnetic materials. Mostly used ANN types in magnetics, advantages of this usage, detailed implementation methodologies as well as numerical examples are given in the paper.

  13. Utilizing neural networks in magnetic media modeling and field computation: A review

    Directory of Open Access Journals (Sweden)

    Amr A. Adly

    2014-11-01

    Full Text Available Magnetic materials are considered as crucial components for a wide range of products and devices. Usually, complexity of such materials is defined by their permeability classification and coupling extent to non-magnetic properties. Hence, development of models that could accurately simulate the complex nature of these materials becomes crucial to the multi-dimensional field-media interactions and computations. In the past few decades, artificial neural networks (ANNs have been utilized in many applications to perform miscellaneous tasks such as identification, approximation, optimization, classification and forecasting. The purpose of this review article is to give an account of the utilization of ANNs in modeling as well as field computation involving complex magnetic materials. Mostly used ANN types in magnetics, advantages of this usage, detailed implementation methodologies as well as numerical examples are given in the paper.

  14. Adaptiveness in monotone pseudo-Boolean optimization and stochastic neural computation.

    Science.gov (United States)

    Grossi, Giuliano

    2009-08-01

    Hopfield neural network (HNN) is a nonlinear computational model successfully applied in finding near-optimal solutions of several difficult combinatorial problems. In many cases, the network energy function is obtained through a learning procedure so that its minima are states falling into a proper subspace (feasible region) of the search space. However, because of the network nonlinearity, a number of undesirable local energy minima emerge from the learning procedure, significantly effecting the network performance. In the neural model analyzed here, we combine both a penalty and a stochastic process in order to enhance the performance of a binary HNN. The penalty strategy allows us to gradually lead the search towards states representing feasible solutions, so avoiding oscillatory behaviors or asymptotically instable convergence. Presence of stochastic dynamics potentially prevents the network to fall into shallow local minima of the energy function, i.e., quite far from global optimum. Hence, for a given fixed network topology, the desired final distribution on the states can be reached by carefully modulating such process. The model uses pseudo-Boolean functions both to express problem constraints and cost function; a combination of these two functions is then interpreted as energy of the neural network. A wide variety of NP-hard problems fall in the class of problems that can be solved by the model at hand, particularly those having a monotonic quadratic pseudo-Boolean function as constraint function. That is, functions easily derived by closed algebraic expressions representing the constraint structure and easy (polynomial time) to maximize. We show the asymptotic convergence properties of this model characterizing its state space distribution at thermal equilibrium in terms of Markov chain and give evidence of its ability to find high quality solutions on benchmarks and randomly generated instances of two specific problems taken from the computational graph

  15. Quantum-Inspired Maximizer

    Science.gov (United States)

    Zak, Michail

    2008-01-01

    A report discusses an algorithm for a new kind of dynamics based on a quantum- classical hybrid-quantum-inspired maximizer. The model is represented by a modified Madelung equation in which the quantum potential is replaced by different, specially chosen 'computational' potential. As a result, the dynamics attains both quantum and classical properties: it preserves superposition and entanglement of random solutions, while allowing one to measure its state variables, using classical methods. Such optimal combination of characteristics is a perfect match for quantum-inspired computing. As an application, an algorithm for global maximum of an arbitrary integrable function is proposed. The idea of the proposed algorithm is very simple: based upon the Quantum-inspired Maximizer (QIM), introduce a positive function to be maximized as the probability density to which the solution is attracted. Then the larger value of this function will have the higher probability to appear. Special attention is paid to simulation of integer programming and NP-complete problems. It is demonstrated that the problem of global maximum of an integrable function can be found in polynomial time by using the proposed quantum- classical hybrid. The result is extended to a constrained maximum with applications to integer programming and TSP (Traveling Salesman Problem).

  16. Lexical organization and competition in first and second languages: computational and neural mechanisms.

    Science.gov (United States)

    Li, Ping

    2009-06-01

    How does a child rapidly acquire and develop a structured mental organization for the vast number of words in the first years of life? How does a bilingual individual deal with the even more complicated task of learning and organizing two lexicons? It is only until recently have we started to examine the lexicon as a dynamical system with regard to its acquisition, representation, and organization. In this article, I outline a proposal based on our research that takes the dynamical approach to the lexicon, and I discuss how this proposal can be applied to account for lexical organization, structural representation, and competition within and between languages. In particular, I provide computational evidence based on the DevLex model, a self-organizing neural network model, and neuroimaging evidence based on functional magnetic resonance imaging (fMRI) studies, to illustrate how children and adults learn and represent the lexicon in their first and second languages. In the computational research, our goal has been to identify, through linguistically and developmentally realistic models, detailed cognitive mechanisms underlying the dynamic self-organizing processes in monolingual and bilingual lexical development; in the neuroimaging research, our goal has been to identify the neural substrates that subserve lexical organization and competition in the monolingual and the bilingual brain. In both cases, our findings lead to a better understanding of the interactive dynamics involved in the acquisition and representation of one or multiple languages. Copyright © 2009 Cognitive Science Society, Inc.

  17. Modeling and Computing of Stock Index Forecasting Based on Neural Network and Markov Chain

    Directory of Open Access Journals (Sweden)

    Yonghui Dai

    2014-01-01

    Full Text Available The stock index reflects the fluctuation of the stock market. For a long time, there have been a lot of researches on the forecast of stock index. However, the traditional method is limited to achieving an ideal precision in the dynamic market due to the influences of many factors such as the economic situation, policy changes, and emergency events. Therefore, the approach based on adaptive modeling and conditional probability transfer causes the new attention of researchers. This paper presents a new forecast method by the combination of improved back-propagation (BP neural network and Markov chain, as well as its modeling and computing technology. This method includes initial forecasting by improved BP neural network, division of Markov state region, computing of the state transition probability matrix, and the prediction adjustment. Results of the empirical study show that this method can achieve high accuracy in the stock index prediction, and it could provide a good reference for the investment in stock market.

  18. Bio-Inspired Optimization of Sustainable Energy Systems: A Review

    Directory of Open Access Journals (Sweden)

    Yu-Jun Zheng

    2013-01-01

    Full Text Available Sustainable energy development always involves complex optimization problems of design, planning, and control, which are often computationally difficult for conventional optimization methods. Fortunately, the continuous advances in artificial intelligence have resulted in an increasing number of heuristic optimization methods for effectively handling those complicated problems. Particularly, algorithms that are inspired by the principles of natural biological evolution and/or collective behavior of social colonies have shown a promising performance and are becoming more and more popular nowadays. In this paper we summarize the recent advances in bio-inspired optimization methods, including artificial neural networks, evolutionary algorithms, swarm intelligence, and their hybridizations, which are applied to the field of sustainable energy development. Literature reviewed in this paper shows the current state of the art and discusses the potential future research trends.

  19. Forecast and restoration of geomagnetic activity indices by using the software-computational neural network complex

    Science.gov (United States)

    Barkhatov, Nikolay; Revunov, Sergey

    2010-05-01

    It is known that currently used indices of geomagnetic activity to some extent reflect the physical processes occurring in the interaction of the perturbed solar wind with Earth's magnetosphere. Therefore, they are connected to each other and with the parameters of near-Earth space. The establishment of such nonlinear connections is interest. For such purposes when the physical problem is complex or has many parameters the technology of artificial neural networks is applied. Such approach for development of the automated forecast and restoration method of geomagnetic activity indices with the establishment of creative software-computational neural network complex is used. Each neural network experiments were carried out at this complex aims to search for a specific nonlinear relation between the analyzed indices and parameters. At the core of the algorithm work program a complex scheme of the functioning of artificial neural networks (ANN) of different types is contained: back propagation Elman network, feed forward network, fuzzy logic network and Kohonen layer classification network. Tools of the main window of the complex (the application) the settings used by neural networks allow you to change: the number of hidden layers, the number of neurons in the layer, the input and target data, the number of cycles of training. Process and the quality of training the ANN is a dynamic plot of changing training error. Plot of comparison of network response with the test sequence is result of the network training. The last-trained neural network with established nonlinear connection for repeated numerical experiments can be run. At the same time additional training is not executed and the previously trained network as a filter input parameters get through and output parameters with the test event are compared. At statement of the large number of different experiments provided the ability to run the program in a "batch" mode is stipulated. For this purpose the user a

  20. A computer vision system for rapid search inspired by surface-based attention mechanisms from human perception.

    Science.gov (United States)

    Mohr, Johannes; Park, Jong-Han; Obermayer, Klaus

    2014-12-01

    Humans are highly efficient at visual search tasks by focusing selective attention on a small but relevant region of a visual scene. Recent results from biological vision suggest that surfaces of distinct physical objects form the basic units of this attentional process. The aim of this paper is to demonstrate how such surface-based attention mechanisms can speed up a computer vision system for visual search. The system uses fast perceptual grouping of depth cues to represent the visual world at the level of surfaces. This representation is stored in short-term memory and updated over time. A top-down guided attention mechanism sequentially selects one of the surfaces for detailed inspection by a recognition module. We show that the proposed attention framework requires little computational overhead (about 11 ms), but enables the system to operate in real-time and leads to a substantial increase in search efficiency. Copyright © 2014 Elsevier Ltd. All rights reserved.

  1. Multiscale approach including microfibril scale to assess elastic constants of cortical bone based on neural network computation and homogenization method

    CERN Document Server

    Barkaoui, Abdelwahed; Tarek, Merzouki; Hambli, Ridha; Ali, Mkaddem

    2014-01-01

    The complexity and heterogeneity of bone tissue require a multiscale modelling to understand its mechanical behaviour and its remodelling mechanisms. In this paper, a novel multiscale hierarchical approach including microfibril scale based on hybrid neural network computation and homogenisation equations was developed to link nanoscopic and macroscopic scales to estimate the elastic properties of human cortical bone. The multiscale model is divided into three main phases: (i) in step 0, the elastic constants of collagen-water and mineral-water composites are calculated by averaging the upper and lower Hill bounds; (ii) in step 1, the elastic properties of the collagen microfibril are computed using a trained neural network simulation. Finite element (FE) calculation is performed at nanoscopic levels to provide a database to train an in-house neural network program; (iii) in steps 2 to 10 from fibril to continuum cortical bone tissue, homogenisation equations are used to perform the computation at the higher s...

  2. Encoding neural and synaptic functionalities in electron spin: A pathway to efficient neuromorphic computing

    Science.gov (United States)

    Sengupta, Abhronil; Roy, Kaushik

    2017-12-01

    Present day computers expend orders of magnitude more computational resources to perform various cognitive and perception related tasks that humans routinely perform every day. This has recently resulted in a seismic shift in the field of computation where research efforts are being directed to develop a neurocomputer that attempts to mimic the human brain by nanoelectronic components and thereby harness its efficiency in recognition problems. Bridging the gap between neuroscience and nanoelectronics, this paper attempts to provide a review of the recent developments in the field of spintronic device based neuromorphic computing. Description of various spin-transfer torque mechanisms that can be potentially utilized for realizing device structures mimicking neural and synaptic functionalities is provided. A cross-layer perspective extending from the device to the circuit and system level is presented to envision the design of an All-Spin neuromorphic processor enabled with on-chip learning functionalities. Device-circuit-algorithm co-simulation framework calibrated to experimental results suggest that such All-Spin neuromorphic systems can potentially achieve almost two orders of magnitude energy improvement in comparison to state-of-the-art CMOS implementations.

  3. Brain without mind: Computer simulation of neural networks with modifiable neuronal interactions

    Science.gov (United States)

    Clark, John W.; Rafelski, Johann; Winston, Jeffrey V.

    1985-07-01

    Aspects of brain function are examined in terms of a nonlinear dynamical system of highly interconnected neuron-like binary decision elements. The model neurons operate synchronously in discrete time, according to deterministic or probabilistic equations of motion. Plasticity of the nervous system, which underlies such cognitive collective phenomena as adaptive development, learning, and memory, is represented by temporal modification of interneuronal connection strengths depending on momentary or recent neural activity. A formal basis is presented for the construction of local plasticity algorithms, or connection-modification routines, spanning a large class. To build an intuitive understanding of the behavior of discrete-time network models, extensive computer simulations have been carried out (a) for nets with fixed, quasirandom connectivity and (b) for nets with connections that evolve under one or another choice of plasticity algorithm. From the former experiments, insights are gained concerning the spontaneous emergence of order in the form of cyclic modes of neuronal activity. In the course of the latter experiments, a simple plasticity routine (“brainwashing,” or “anti-learning”) was identified which, applied to nets with initially quasirandom connectivity, creates model networks which provide more felicitous starting points for computer experiments on the engramming of content-addressable memories and on learning more generally. The potential relevance of this algorithm to developmental neurobiology and to sleep states is discussed. The model considered is at the same time a synthesis of earlier synchronous neural-network models and an elaboration upon them; accordingly, the present article offers both a focused review of the dynamical properties of such systems and a selection of new findings derived from computer simulation.

  4. Bio-inspired varying subspace based computational framework for a class of nonlinear constrained optimal trajectory planning problems.

    Science.gov (United States)

    Xu, Y; Li, N

    2014-09-01

    Biological species have produced many simple but efficient rules in their complex and critical survival activities such as hunting and mating. A common feature observed in several biological motion strategies is that the predator only moves along paths in a carefully selected or iteratively refined subspace (or manifold), which might be able to explain why these motion strategies are effective. In this paper, a unified linear algebraic formulation representing such a predator-prey relationship is developed to simplify the construction and refinement process of the subspace (or manifold). Specifically, the following three motion strategies are studied and modified: motion camouflage, constant absolute target direction and local pursuit. The framework constructed based on this varying subspace concept could significantly reduce the computational cost in solving a class of nonlinear constrained optimal trajectory planning problems, particularly for the case with severe constraints. Two non-trivial examples, a ground robot and a hypersonic aircraft trajectory optimization problem, are used to show the capabilities of the algorithms in this new computational framework.

  5. Neural network technologies

    Science.gov (United States)

    Villarreal, James A.

    1991-01-01

    A whole new arena of computer technologies is now beginning to form. Still in its infancy, neural network technology is a biologically inspired methodology which draws on nature's own cognitive processes. The Software Technology Branch has provided a software tool, Neural Execution and Training System (NETS), to industry, government, and academia to facilitate and expedite the use of this technology. NETS is written in the C programming language and can be executed on a variety of machines. Once a network has been debugged, NETS can produce a C source code which implements the network. This code can then be incorporated into other software systems. Described here are various software projects currently under development with NETS and the anticipated future enhancements to NETS and the technology.

  6. Biologically inspired intelligent robots

    Science.gov (United States)

    Bar-Cohen, Yoseph; Breazeal, Cynthia

    2003-07-01

    Humans throughout history have always sought to mimic the appearance, mobility, functionality, intelligent operation, and thinking process of biological creatures. This field of biologically inspired technology, having the moniker biomimetics, has evolved from making static copies of human and animals in the form of statues to the emergence of robots that operate with realistic behavior. Imagine a person walking towards you where suddenly you notice something weird about him--he is not real but rather he is a robot. Your reaction would probably be "I can't believe it but this robot looks very real" just as you would react to an artificial flower that is a good imitation. You may even proceed and touch the robot to check if your assessment is correct but, as oppose to the flower case, the robot may be programmed to respond physical and verbally. This science fiction scenario could become a reality as the current trend continues in developing biologically inspired technologies. Technology evolution led to such fields as artificial muscles, artificial intelligence, and artificial vision as well as biomimetic capabilities in materials science, mechanics, electronics, computing science, information technology and many others. This paper will review the state of the art and challenges to biologically-inspired technologies and the role that EAP is expected to play as the technology evolves.

  7. Dual Coding Theory Explains Biphasic Collective Computation in Neural Decision-Making.

    Science.gov (United States)

    Daniels, Bryan C; Flack, Jessica C; Krakauer, David C

    2017-01-01

    A central question in cognitive neuroscience is how unitary, coherent decisions at the whole organism level can arise from the distributed behavior of a large population of neurons with only partially overlapping information. We address this issue by studying neural spiking behavior recorded from a multielectrode array with 169 channels during a visual motion direction discrimination task. It is well known that in this task there are two distinct phases in neural spiking behavior. Here we show Phase I is a distributed or incompressible phase in which uncertainty about the decision is substantially reduced by pooling information from many cells. Phase II is a redundant or compressible phase in which numerous single cells contain all the information present at the population level in Phase I, such that the firing behavior of a single cell is enough to predict the subject's decision. Using an empirically grounded dynamical modeling framework, we show that in Phase I large cell populations with low redundancy produce a slow timescale of information aggregation through critical slowing down near a symmetry-breaking transition. Our model indicates that increasing collective amplification in Phase II leads naturally to a faster timescale of information pooling and consensus formation. Based on our results and others in the literature, we propose that a general feature of collective computation is a "coding duality" in which there are accumulation and consensus formation processes distinguished by different timescales.

  8. Computer-Aided Diagnosis of Parkinson's Disease Using Enhanced Probabilistic Neural Network.

    Science.gov (United States)

    Hirschauer, Thomas J; Adeli, Hojjat; Buford, John A

    2015-11-01

    Early and accurate diagnosis of Parkinson's disease (PD) remains challenging. Neuropathological studies using brain bank specimens have estimated that a large percentages of clinical diagnoses of PD may be incorrect especially in the early stages. In this paper, a comprehensive computer model is presented for the diagnosis of PD based on motor, non-motor, and neuroimaging features using the recently-developed enhanced probabilistic neural network (EPNN). The model is tested for differentiating PD patients from those with scans without evidence of dopaminergic deficit (SWEDDs) using the Parkinson's Progression Markers Initiative (PPMI) database, an observational, multi-center study designed to identify PD biomarkers for diagnosis and disease progression. The results are compared to four other commonly-used machine learning algorithms: the probabilistic neural network (PNN), support vector machine (SVM), k-nearest neighbors (k-NN) algorithm, and classification tree (CT). The EPNN had the highest classification accuracy at 92.5% followed by the PNN (91.6%), k-NN (90.8%) and CT (90.2%). The EPNN exhibited an accuracy of 98.6% when classifying healthy control (HC) versus PD, higher than any previous studies.

  9. Learning to minimize efforts versus maximizing rewards: computational principles and neural correlates.

    Science.gov (United States)

    Skvortsova, Vasilisa; Palminteri, Stefano; Pessiglione, Mathias

    2014-11-19

    The mechanisms of reward maximization have been extensively studied at both the computational and neural levels. By contrast, little is known about how the brain learns to choose the options that minimize action cost. In principle, the brain could have evolved a general mechanism that applies the same learning rule to the different dimensions of choice options. To test this hypothesis, we scanned healthy human volunteers while they performed a probabilistic instrumental learning task that varied in both the physical effort and the monetary outcome associated with choice options. Behavioral data showed that the same computational rule, using prediction errors to update expectations, could account for both reward maximization and effort minimization. However, these learning-related variables were encoded in partially dissociable brain areas. In line with previous findings, the ventromedial prefrontal cortex was found to positively represent expected and actual rewards, regardless of effort. A separate network, encompassing the anterior insula, the dorsal anterior cingulate, and the posterior parietal cortex, correlated positively with expected and actual efforts. These findings suggest that the same computational rule is applied by distinct brain systems, depending on the choice dimension-cost or benefit-that has to be learned. Copyright © 2014 the authors 0270-6474/14/3415621-10$15.00/0.

  10. Convolutional neural networks for P300 detection with application to brain-computer interfaces.

    Science.gov (United States)

    Cecotti, Hubert; Gräser, Axel

    2011-03-01

    A Brain-Computer Interface (BCI) is a specific type of human-computer interface that enables the direct communication between human and computers by analyzing brain measurements. Oddball paradigms are used in BCI to generate event-related potentials (ERPs), like the P300 wave, on targets selected by the user. A P300 speller is based on this principle, where the detection of P300 waves allows the user to write characters. The P300 speller is composed of two classification problems. The first classification is to detect the presence of a P300 in the electroencephalogram (EEG). The second one corresponds to the combination of different P300 responses for determining the right character to spell. A new method for the detection of P300 waves is presented. This model is based on a convolutional neural network (CNN). The topology of the network is adapted to the detection of P300 waves in the time domain. Seven classifiers based on the CNN are proposed: four single classifiers with different features set and three multiclassifiers. These models are tested and compared on the Data set II of the third BCI competition. The best result is obtained with a multiclassifier solution with a recognition rate of 95.5 percent, without channel selection before the classification. The proposed approach provides also a new way for analyzing brain activities due to the receptive field of the CNN models.

  11. Can computational efficiency alone drive the evolution of modularity in neural networks?

    Science.gov (United States)

    Tosh, Colin R

    2016-08-30

    Some biologists have abandoned the idea that computational efficiency in processing multipart tasks or input sets alone drives the evolution of modularity in biological networks. A recent study confirmed that small modular (neural) networks are relatively computationally-inefficient but large modular networks are slightly more efficient than non-modular ones. The present study determines whether these efficiency advantages with network size can drive the evolution of modularity in networks whose connective architecture can evolve. The answer is no, but the reason why is interesting. All simulations (run in a wide variety of parameter states) involving gradualistic connective evolution end in non-modular local attractors. Thus while a high performance modular attractor exists, such regions cannot be reached by gradualistic evolution. Non-gradualistic evolutionary simulations in which multi-modularity is obtained through duplication of existing architecture appear viable. Fundamentally, this study indicates that computational efficiency alone does not drive the evolution of modularity, even in large biological networks, but it may still be a viable mechanism when networks evolve by non-gradualistic means.

  12. Computer-Aided Cobb Measurement Based on Automatic Detection of Vertebral Slopes Using Deep Neural Network.

    Science.gov (United States)

    Zhang, Junhua; Li, Hongjian; Lv, Liang; Zhang, Yufeng

    2017-01-01

    To develop a computer-aided method that reduces the variability of Cobb angle measurement for scoliosis assessment. A deep neural network (DNN) was trained with vertebral patches extracted from spinal model radiographs. The Cobb angle of the spinal curve was calculated automatically from the vertebral slopes predicted by the DNN. Sixty-five in vivo radiographs and 40 model radiographs were analyzed. An experienced surgeon performed manual measurements on the aforementioned radiographs. Two examiners used both the proposed and the manual measurement methods to analyze the aforementioned radiographs. For model radiographs, the intraclass correlation coefficients were greater than 0.98, and the mean absolute differences were less than 3°. This indicates that the proposed system showed high repeatability for measurements of model radiographs. For the in vivo radiographs, the reliabilities were lower than those from the model radiographs, and the differences between the computer-aided measurement and the manual measurement by the surgeon were higher than 5°. The variability of Cobb angle measurements can be reduced if the DNN system is trained with enough vertebral patches. Training data of in vivo radiographs must be included to improve the performance of DNN. Vertebral slopes can be predicted by DNN. The computer-aided system can be used to perform automatic measurements of Cobb angle, which is used to make reliable and objective assessments of scoliosis.

  13. Neural and computational processes underlying dynamic changes in self-esteem

    Science.gov (United States)

    Rutledge, Robb B; Moutoussis, Michael; Dolan, Raymond J

    2017-01-01

    Self-esteem is shaped by the appraisals we receive from others. Here, we characterize neural and computational mechanisms underlying this form of social influence. We introduce a computational model that captures fluctuations in self-esteem engendered by prediction errors that quantify the difference between expected and received social feedback. Using functional MRI, we show these social prediction errors correlate with activity in ventral striatum/subgenual anterior cingulate cortex, while updates in self-esteem resulting from these errors co-varied with activity in ventromedial prefrontal cortex (vmPFC). We linked computational parameters to psychiatric symptoms using canonical correlation analysis to identify an ‘interpersonal vulnerability’ dimension. Vulnerability modulated the expression of prediction error responses in anterior insula and insula-vmPFC connectivity during self-esteem updates. Our findings indicate that updating of self-evaluative beliefs relies on learning mechanisms akin to those used in learning about others. Enhanced insula-vmPFC connectivity during updating of those beliefs may represent a marker for psychiatric vulnerability. PMID:29061228

  14. Exact computation of the Maximum Entropy Potential of spiking neural networks models

    CERN Document Server

    Cofre, Rodrigo

    2014-01-01

    Understanding how stimuli and synaptic connectivity in uence the statistics of spike patterns in neural networks is a central question in computational neuroscience. Maximum Entropy approach has been successfully used to characterize the statistical response of simultaneously recorded spiking neurons responding to stimuli. But, in spite of good performance in terms of prediction, the ?tting parameters do not explain the underlying mechanistic causes of the observed correlations. On the other hand, mathematical models of spiking neurons (neuro-mimetic models) provide a probabilistic mapping between stimulus, network architecture and spike patterns in terms of conditional proba- bilities. In this paper we build an exact analytical mapping between neuro-mimetic and Maximum Entropy models.

  15. Application of artificial neural networks to identify equilibration in computer simulations

    Science.gov (United States)

    Leibowitz, Mitchell H.; Miller, Evan D.; Henry, Michael M.; Jankowski, Eric

    2017-11-01

    Determining which microstates generated by a thermodynamic simulation are representative of the ensemble for which sampling is desired is a ubiquitous, underspecified problem. Artificial neural networks are one type of machine learning algorithm that can provide a reproducible way to apply pattern recognition heuristics to underspecified problems. Here we use the open-source TensorFlow machine learning library and apply it to the problem of identifying which hypothetical observation sequences from a computer simulation are “equilibrated” and which are not. We generate training populations and test populations of observation sequences with embedded linear and exponential correlations. We train a two-neuron artificial network to distinguish the correlated and uncorrelated sequences. We find that this simple network is good enough for > 98% accuracy in identifying exponentially-decaying energy trajectories from molecular simulations.

  16. Recurrent neural networks in computer-based clinical decision support for laryngopathies: an experimental study.

    Science.gov (United States)

    Szkoła, Jarosław; Pancerz, Krzysztof; Warchoł, Jan

    2011-01-01

    The main goal of this paper is to give the basis for creating a computer-based clinical decision support (CDS) system for laryngopathies. One of approaches which can be used in the proposed CDS is based on the speech signal analysis using recurrent neural networks (RNNs). RNNs can be used for pattern recognition in time series data due to their ability of memorizing some information from the past. The Elman networks (ENs) are a classical representative of RNNs. To improve learning ability of ENs, we may modify and combine them with another kind of RNNs, namely, with the Jordan networks. The modified Elman-Jordan networks (EJNs) manifest a faster and more exact achievement of the target pattern. Validation experiments were carried out on speech signals of patients from the control group and with two kinds of laryngopathies.

  17. Assessment of locomotion in chlorine exposed mice by computer vision and neural networks.

    Science.gov (United States)

    Filippidis, Aristotelis S; Zarogiannis, Sotirios G; Randich, Alan; Ness, Timothy J; Matalon, Sadis

    2012-03-01

    Assessment of locomotion following exposure of animals to noxious or painful stimuli can offer significant insights into underlying mechanisms of injury and the effectiveness of various treatments. We developed a novel method to track the movement of mice in two dimensions using computer vision and neural network algorithms. By using this system we demonstrated that mice exposed to chlorine (Cl(2)) gas developed impaired locomotion and increased immobility for up to 9 h postexposure. Postexposure administration of buprenorphine, a common analgesic agent, increased locomotion and decreased immobility times in Cl(2)- but not air-exposed mice, most likely by decreasing Cl(2)-induced pain. This method can be adapted to assess the effectiveness of various therapies following exposure to a variety of chemical and behavioral noxious stimuli.

  18. A convolutional neural network approach to calibrating the rotation axis for X-ray computed tomography.

    Science.gov (United States)

    Yang, Xiaogang; De Carlo, Francesco; Phatak, Charudatta; Gürsoy, Dogˇa

    2017-03-01

    This paper presents an algorithm to calibrate the center-of-rotation for X-ray tomography by using a machine learning approach, the Convolutional Neural Network (CNN). The algorithm shows excellent accuracy from the evaluation of synthetic data with various noise ratios. It is further validated with experimental data of four different shale samples measured at the Advanced Photon Source and at the Swiss Light Source. The results are as good as those determined by visual inspection and show better robustness than conventional methods. CNN has also great potential for reducing or removing other artifacts caused by instrument instability, detector non-linearity, etc. An open-source toolbox, which integrates the CNN methods described in this paper, is freely available through GitHub at tomography/xlearn and can be easily integrated into existing computational pipelines available at various synchrotron facilities. Source code, documentation and information on how to contribute are also provided.

  19. Goal-Directed Behavior and Instrumental Devaluation: A Neural System-Level Computational Model.

    Science.gov (United States)

    Mannella, Francesco; Mirolli, Marco; Baldassarre, Gianluca

    2016-01-01

    Devaluation is the key experimental paradigm used to demonstrate the presence of instrumental behaviors guided by goals in mammals. We propose a neural system-level computational model to address the question of which brain mechanisms allow the current value of rewards to control instrumental actions. The model pivots on and shows the computational soundness of the hypothesis for which the internal representation of instrumental manipulanda (e.g., levers) activate the representation of rewards (or "action-outcomes", e.g., foods) while attributing to them a value which depends on the current internal state of the animal (e.g., satiation for some but not all foods). The model also proposes an initial hypothesis of the integrated system of key brain components supporting this process and allowing the recalled outcomes to bias action selection: (a) the sub-system formed by the basolateral amygdala and insular cortex acquiring the manipulanda-outcomes associations and attributing the current value to the outcomes; (b) three basal ganglia-cortical loops selecting respectively goals, associative sensory representations, and actions; (c) the cortico-cortical and striato-nigro-striatal neural pathways supporting the selection, and selection learning, of actions based on habits and goals. The model reproduces and explains the results of several devaluation experiments carried out with control rats and rats with pre- and post-training lesions of the basolateral amygdala, the nucleus accumbens core, the prelimbic cortex, and the dorso-medial striatum. The results support the soundness of the hypotheses of the model and show its capacity to integrate, at the system-level, the operations of the key brain structures underlying devaluation. Based on its hypotheses and predictions, the model also represents an operational framework to support the design and analysis of new experiments on the motivational aspects of goal-directed behavior.

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

    Directory of Open Access Journals (Sweden)

    Markus A Wenzel

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

  1. The Study of Learners' Preference for Visual Complexity on Small Screens of Mobile Computers Using Neural Networks

    Science.gov (United States)

    Wang, Lan-Ting; Lee, Kun-Chou

    2014-01-01

    The vision plays an important role in educational technologies because it can produce and communicate quite important functions in teaching and learning. In this paper, learners' preference for the visual complexity on small screens of mobile computers is studied by neural networks. The visual complexity in this study is divided into five…

  2. Soft Computing in Construction Information Technology

    NARCIS (Netherlands)

    Ciftcioglu, O.; Durmisevic, S.; Sariyildiz, S.

    2001-01-01

    The last decade, civil engineering has exercised a rapidly growing interest in the application of neurally inspired computing techniques. The motive for this interest was the promises of certain information processing characteristics, which are similar to some extend, to those of human brain. The

  3. Memory Computation in the Real Brain

    Science.gov (United States)

    Treves, Alessandro

    2007-08-01

    I will illustrate one example of neural computation which is beginning to be understood in detail, and which may inspire applications in synthetic systems: the storage and retrieval of memories in the mammalian hippocampus. The hippocampus is one part of the cortex that has, in all mammalian species, strictly the same organization in a few neural networks with distinct architecture and properties. It shows how neural computation is implemented not with a generic “connectionist” paradigm, but combining ingredients made available by evolution in a manner specific to the operation that has to be carried out.

  4. Neural networks advances and applications 2

    CERN Document Server

    Gelenbe, E

    1992-01-01

    The present volume is a natural follow-up to Neural Networks: Advances and Applications which appeared one year previously. As the title indicates, it combines the presentation of recent methodological results concerning computational models and results inspired by neural networks, and of well-documented applications which illustrate the use of such models in the solution of difficult problems. The volume is balanced with respect to these two orientations: it contains six papers concerning methodological developments and five papers concerning applications and examples illustrating the theoret

  5. Data systems and computer science: Neural networks base R/T program overview

    Science.gov (United States)

    Gulati, Sandeep

    1991-01-01

    The research base, in the U.S. and abroad, for the development of neural network technology is discussed. The technical objectives are to develop and demonstrate adaptive, neural information processing concepts. The leveraging of external funding is also discussed.

  6. Medical image analysis with artificial neural networks.

    Science.gov (United States)

    Jiang, J; Trundle, P; Ren, J

    2010-12-01

    Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging. Copyright © 2010 Elsevier Ltd. All rights reserved.

  7. A hybrid fuzzy-neural system for computer-aided diagnosis of ultrasound kidney images using prominent features.

    Science.gov (United States)

    Bommanna Raja, K; Madheswaran, M; Thyagarajah, K

    2008-02-01

    The objective of this work is to develop and implement a computer-aided decision support system for an automated diagnosis and classification of ultrasound kidney images. The proposed method distinguishes three kidney categories namely normal, medical renal diseases and cortical cyst. For the each pre-processed ultrasound kidney image, 36 features are extracted. Two types of decision support systems, optimized multi-layer back propagation network and hybrid fuzzy-neural system have been developed with these features for classifying the kidney categories. The performance of the hybrid fuzzy-neural system is compared with the optimized multi-layer back propagation network in terms of classification efficiency, training and testing time. The results obtained show that fuzzy-neural system provides higher classification efficiency with minimum training and testing time. It has also been found that instead of using all 36 features, ranking the features enhance classification efficiency. The outputs of the decision support systems are validated with medical expert to measure the actual efficiency. The overall discriminating capability of the systems is accessed with performance evaluation measure, f-score. It has been observed that the performance of fuzzy-neural system is superior compared to optimized multi-layer back propagation network. Such hybrid fuzzy-neural system with feature extraction algorithms and pre-processing scheme helps in developing computer-aided diagnosis system for ultrasound kidney images and can be used as a secondary observer in clinical decision making.

  8. Computational connectionism within neurons: A model of cytoskeletal automata subserving neural networks

    Science.gov (United States)

    Rasmussen, Steen; Karampurwala, Hasnain; Vaidyanath, Rajesh; Jensen, Klaus S.; Hameroff, Stuart

    1990-06-01

    Neural network” models of brain function assume neurons and their synaptic connections to be the fundamental units of information processing, somewhat like switches within computers. However, neurons and synapses are extremely complex and resemble entire computers rather than switches. The interiors of the neurons (and other eucaryotic cells) are now known to contain highly ordered parallel networks of filamentous protein polymers collectively termed the cytoskeleton. Originally assumed to provide merely structural “bone-like” support, cytoskeletal structures such as microtubules are now recognized to organize cell interiors dynamically. The cytoskeleton is the internal communication network for the eucaryotic cell, both by means of simple transport and by means of coordinating extremely complicated events like cell division, growth and differentiation. The cytoskeleton may therefore be viewed as the cell's “nervous system”. Consequently the neuronal cytoskeleton may be involved in molecular level information processing which subserves higher, collective neuronal functions ultimately relating to cognition. Numerous models of information processing within the cytoskeleton (in particular, microtubules) have been proposed. We have utilized cellular automata as a means to model and demonstrate the potential for information processing in cytoskeletal microtubules. In this paper, we extend previous work and simulate associative learning in a cytoskeletal network as well as assembly and disassembly of microtubules. We also discuss possible relevance and implications of cytoskeletal information processing to cognition.

  9. Computer vision-based method for classification of wheat grains using artificial neural network.

    Science.gov (United States)

    Sabanci, Kadir; Kayabasi, Ahmet; Toktas, Abdurrahim

    2017-06-01

    A simplified computer vision-based application using artificial neural network (ANN) depending on multilayer perceptron (MLP) for accurately classifying wheat grains into bread or durum is presented. The images of 100 bread and 100 durum wheat grains are taken via a high-resolution camera and subjected to pre-processing. The main visual features of four dimensions, three colors and five textures are acquired using image-processing techniques (IPTs). A total of 21 visual features are reproduced from the 12 main features to diversify the input population for training and testing the ANN model. The data sets of visual features are considered as input parameters of the ANN model. The ANN with four different input data subsets is modelled to classify the wheat grains into bread or durum. The ANN model is trained with 180 grains and its accuracy tested with 20 grains from a total of 200 wheat grains. Seven input parameters that are most effective on the classifying results are determined using the correlation-based CfsSubsetEval algorithm to simplify the ANN model. The results of the ANN model are compared in terms of accuracy rate. The best result is achieved with a mean absolute error (MAE) of 9.8 × 10 -6 by the simplified ANN model. This shows that the proposed classifier based on computer vision can be successfully exploited to automatically classify a variety of grains. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

  10. Classification of dried vegetables using computer image analysis and artificial neural networks

    Science.gov (United States)

    Koszela, K.; Łukomski, M.; Mueller, W.; Górna, K.; Okoń, P.; Boniecki, P.; Zaborowicz, M.; Wojcieszak, D.

    2017-07-01

    In the recent years, there has been a continuously increasing demand for vegetables and dried vegetables. This trend affects the growth of the dehydration industry in Poland helping to exploit excess production. More and more often dried vegetables are used in various sectors of the food industry, both due to their high nutritional qualities and changes in consumers' food preferences. As we observe an increase in consumer awareness regarding a healthy lifestyle and a boom in health food, there is also an increase in the consumption of such food, which means that the production and crop area can increase further. Among the dried vegetables, dried carrots play a strategic role due to their wide application range and high nutritional value. They contain high concentrations of carotene and sugar which is present in the form of crystals. Carrots are also the vegetables which are most often subjected to a wide range of dehydration processes; this makes it difficult to perform a reliable qualitative assessment and classification of this dried product. The many qualitative properties of dried carrots determining their positive or negative quality assessment include colour and shape. The aim of the research was to develop and implement the model of a computer system for the recognition and classification of freeze-dried, convection-dried and microwave vacuum dried products using the methods of computer image analysis and artificial neural networks.

  11. The neural correlates of problem states: testing FMRI predictions of a computational model of multitasking.

    Directory of Open Access Journals (Sweden)

    Jelmer P Borst

    Full Text Available BACKGROUND: It has been shown that people can only maintain one problem state, or intermediate mental representation, at a time. When more than one problem state is required, for example in multitasking, performance decreases considerably. This effect has been explained in terms of a problem state bottleneck. METHODOLOGY: In the current study we use the complimentary methodologies of computational cognitive modeling and neuroimaging to investigate the neural correlates of this problem state bottleneck. In particular, an existing computational cognitive model was used to generate a priori fMRI predictions for a multitasking experiment in which the problem state bottleneck plays a major role. Hemodynamic responses were predicted for five brain regions, corresponding to five cognitive resources in the model. Most importantly, we predicted the intraparietal sulcus to show a strong effect of the problem state manipulations. CONCLUSIONS: Some of the predictions were confirmed by a subsequent fMRI experiment, while others were not matched by the data. The experiment supported the hypothesis that the problem state bottleneck is a plausible cause of the interference in the experiment and that it could be located in the intraparietal sulcus.

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

    Directory of Open Access Journals (Sweden)

    Hossein Bashashati

    2017-07-01

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

  13. Autocatalytic loop, amplification and diffusion: a mathematical and computational model of cell polarization in neural chemotaxis.

    Directory of Open Access Journals (Sweden)

    Paola Causin

    2009-08-01

    Full Text Available The chemotactic response of cells to graded fields of chemical cues is a complex process that requires the coordination of several intracellular activities. Fundamental steps to obtain a front vs. back differentiation in the cell are the localized distribution of internal molecules and the amplification of the external signal. The goal of this work is to develop a mathematical and computational model for the quantitative study of such phenomena in the context of axon chemotactic pathfinding in neural development. In order to perform turning decisions, axons develop front-back polarization in their distal structure, the growth cone. Starting from the recent experimental findings of the biased redistribution of receptors on the growth cone membrane, driven by the interaction with the cytoskeleton, we propose a model to investigate the significance of this process. Our main contribution is to quantitatively demonstrate that the autocatalytic loop involving receptors, cytoplasmic species and cytoskeleton is adequate to give rise to the chemotactic behavior of neural cells. We assess the fact that spatial bias in receptors is a precursory key event for chemotactic response, establishing the necessity of a tight link between upstream gradient sensing and downstream cytoskeleton dynamics. We analyze further crosslinked effects and, among others, the contribution to polarization of internal enzymatic reactions, which entail the production of molecules with a one-to-more factor. The model shows that the enzymatic efficiency of such reactions must overcome a threshold in order to give rise to a sufficient amplification, another fundamental precursory step for obtaining polarization. Eventually, we address the characteristic behavior of the attraction/repulsion of axons subjected to the same cue, providing a quantitative indicator of the parameters which more critically determine this nontrivial chemotactic response.

  14. Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

    Science.gov (United States)

    Hoo-Chang, Shin; Roth, Holger R.; Gao, Mingchen; Lu, Le; Xu, Ziyue; Nogues, Isabella; Yao, Jianhua; Mollura, Daniel

    2016-01-01

    Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets (i.e. ImageNet) and the revival of deep convolutional neural networks (CNN). CNNs enable learning data-driven, highly representative, layered hierarchical image features from sufficient training data. However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge. There are currently three major techniques that successfully employ CNNs to medical image classification: training the CNN from scratch, using off-the-shelf pre-trained CNN features, and conducting unsupervised CNN pre-training with supervised fine-tuning. Another effective method is transfer learning, i.e., fine-tuning CNN models (supervised) pre-trained from natural image dataset to medical image tasks (although domain transfer between two medical image datasets is also possible). In this paper, we exploit three important, but previously understudied factors of employing deep convolutional neural networks to computer-aided detection problems. We first explore and evaluate different CNN architectures. The studied models contain 5 thousand to 160 million parameters, and vary in numbers of layers. We then evaluate the influence of dataset scale and spatial image context on performance. Finally, we examine when and why transfer learning from pre-trained ImageNet (via fine-tuning) can be useful. We study two specific computeraided detection (CADe) problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification. We achieve the state-of-the-art performance on the mediastinal LN detection, with 85% sensitivity at 3 false positive per patient, and report the first five-fold cross-validation classification results on predicting axial CT slices with ILD categories. Our extensive empirical evaluation, CNN model analysis and valuable insights can be extended to the design of high performance

  15. A Neural Network Architecture For Rapid Model Indexing In Computer Vision Systems

    Science.gov (United States)

    Pawlicki, Ted

    1988-03-01

    Models of objects stored in memory have been shown to be useful for guiding the processing of computer vision systems. A major consideration in such systems, however, is how stored models are initially accessed and indexed by the system. As the number of stored models increases, the time required to search memory for the correct model becomes high. Parallel distributed, connectionist, neural networks' have been shown to have appealing content addressable memory properties. This paper discusses an architecture for efficient storage and reference of model memories stored as stable patterns of activity in a parallel, distributed, connectionist, neural network. The emergent properties of content addressability and resistance to noise are exploited to perform indexing of the appropriate object centered model from image centered primitives. The system consists of three network modules each of which represent information relative to a different frame of reference. The model memory network is a large state space vector where fields in the vector correspond to ordered component objects and relative, object based spatial relationships between the component objects. The component assertion network represents evidence about the existence of object primitives in the input image. It establishes local frames of reference for object primitives relative to the image based frame of reference. The spatial relationship constraint network is an intermediate representation which enables the association between the object based and the image based frames of reference. This intermediate level represents information about possible object orderings and establishes relative spatial relationships from the image based information in the component assertion network below. It is also constrained by the lawful object orderings in the model memory network above. The system design is consistent with current psychological theories of recognition by component. It also seems to support Marr's notions

  16. Bio-inspired nano tools for neuroscience.

    Science.gov (United States)

    Das, Suradip; Carnicer-Lombarte, Alejandro; Fawcett, James W; Bora, Utpal

    2016-07-01

    Research and treatment in the nervous system is challenged by many physiological barriers posing a major hurdle for neurologists. The CNS is protected by a formidable blood brain barrier (BBB) which limits surgical, therapeutic and diagnostic interventions. The hostile environment created by reactive astrocytes in the CNS along with the limited regeneration capacity of the PNS makes functional recovery after tissue damage difficult and inefficient. Nanomaterials have the unique ability to interface with neural tissue in the nano-scale and are capable of influencing the function of a single neuron. The ability of nanoparticles to transcend the BBB through surface modifications has been exploited in various neuro-imaging techniques and for targeted drug delivery. The tunable topography of nanofibers provides accurate spatio-temporal guidance to regenerating axons. This review is an attempt to comprehend the progress in understanding the obstacles posed by the complex physiology of the nervous system and the innovations in design and fabrication of advanced nanomaterials drawing inspiration from natural phenomenon. We also discuss the development of nanomaterials for use in Neuro-diagnostics, Neuro-therapy and the fabrication of advanced nano-devices for use in opto-electronic and ultrasensitive electrophysiological applications. The energy efficient and parallel computing ability of the human brain has inspired the design of advanced nanotechnology based computational systems. However, extensive use of nanomaterials in neuroscience also raises serious toxicity issues as well as ethical concerns regarding nano implants in the brain. In conclusion we summarize these challenges and provide an insight into the huge potential of nanotechnology platforms in neuroscience. Copyright © 2016 Elsevier Ltd. All rights reserved.

  17. Computational Assessment of Neural Probe and Brain Tissue Interface under Transient Motion

    Directory of Open Access Journals (Sweden)

    Michael Polanco

    2016-06-01

    Full Text Available The functional longevity of a neural probe is dependent upon its ability to minimize injury risk during the insertion and recording period in vivo, which could be related to motion-related strain between the probe and surrounding tissue. A series of finite element analyses was conducted to study the extent of the strain induced within the brain in an area around a neural probe. This study focuses on the transient behavior of neural probe and brain tissue interface with a viscoelastic model. Different stages of the interface from initial insertion of neural probe to full bonding of the probe by astro-glial sheath formation are simulated utilizing analytical tools to investigate the effects of relative motion between the neural probe and the brain while friction coefficients and kinematic frequencies are varied. The analyses can provide an in-depth look at the quantitative benefits behind using soft materials for neural probes.

  18. Development of a computational model on the neural activity patterns of a visual working memory in a hierarchical feedforward Network

    Science.gov (United States)

    An, Soyoung; Choi, Woochul; Paik, Se-Bum

    2015-11-01

    Understanding the mechanism of information processing in the human brain remains a unique challenge because the nonlinear interactions between the neurons in the network are extremely complex and because controlling every relevant parameter during an experiment is difficult. Therefore, a simulation using simplified computational models may be an effective approach. In the present study, we developed a general model of neural networks that can simulate nonlinear activity patterns in the hierarchical structure of a neural network system. To test our model, we first examined whether our simulation could match the previously-observed nonlinear features of neural activity patterns. Next, we performed a psychophysics experiment for a simple visual working memory task to evaluate whether the model could predict the performance of human subjects. Our studies show that the model is capable of reproducing the relationship between memory load and performance and may contribute, in part, to our understanding of how the structure of neural circuits can determine the nonlinear neural activity patterns in the human brain.

  19. Learning Pitch with STDP: A Computational Model of Place and Temporal Pitch Perception Using Spiking Neural Networks.

    Directory of Open Access Journals (Sweden)

    Nafise Erfanian Saeedi

    2016-04-01

    Full Text Available Pitch perception is important for understanding speech prosody, music perception, recognizing tones in tonal languages, and perceiving speech in noisy environments. The two principal pitch perception theories consider the place of maximum neural excitation along the auditory nerve and the temporal pattern of the auditory neurons' action potentials (spikes as pitch cues. This paper describes a biophysical mechanism by which fine-structure temporal information can be extracted from the spikes generated at the auditory periphery. Deriving meaningful pitch-related information from spike times requires neural structures specialized in capturing synchronous or correlated activity from amongst neural events. The emergence of such pitch-processing neural mechanisms is described through a computational model of auditory processing. Simulation results show that a correlation-based, unsupervised, spike-based form of Hebbian learning can explain the development of neural structures required for recognizing the pitch of simple and complex tones, with or without the fundamental frequency. The temporal code is robust to variations in the spectral shape of the signal and thus can explain the phenomenon of pitch constancy.

  20. Computer vision-based limestone rock-type classification using probabilistic neural network

    Directory of Open Access Journals (Sweden)

    Ashok Kumar Patel

    2016-01-01

    Full Text Available Proper quality planning of limestone raw materials is an essential job of maintaining desired feed in cement plant. Rock-type identification is an integrated part of quality planning for limestone mine. In this paper, a computer vision-based rock-type classification algorithm is proposed for fast and reliable identification without human intervention. A laboratory scale vision-based model was developed using probabilistic neural network (PNN where color histogram features are used as input. The color image histogram-based features that include weighted mean, skewness and kurtosis features are extracted for all three color space red, green, and blue. A total nine features are used as input for the PNN classification model. The smoothing parameter for PNN model is selected judicially to develop an optimal or close to the optimum classification model. The developed PPN is validated using the test data set and results reveal that the proposed vision-based model can perform satisfactorily for classifying limestone rock-types. Overall the error of mis-classification is below 6%. When compared with other three classification algorithms, it is observed that the proposed method performs substantially better than all three classification algorithms.

  1. Minimalist Social-Affective Value for Use in Joint Action: A Neural-Computational Hypothesis

    Science.gov (United States)

    Lowe, Robert; Almér, Alexander; Lindblad, Gustaf; Gander, Pierre; Michael, John; Vesper, Cordula

    2016-01-01

    Joint Action is typically described as social interaction that requires coordination among two or more co-actors in order to achieve a common goal. In this article, we put forward a hypothesis for the existence of a neural-computational mechanism of affective valuation that may be critically exploited in Joint Action. Such a mechanism would serve to facilitate coordination between co-actors permitting a reduction of required information. Our hypothesized affective mechanism provides a value function based implementation of Associative Two-Process (ATP) theory that entails the classification of external stimuli according to outcome expectancies. This approach has been used to describe animal and human action that concerns differential outcome expectancies. Until now it has not been applied to social interaction. We describe our Affective ATP model as applied to social learning consistent with an “extended common currency” perspective in the social neuroscience literature. We contrast this to an alternative mechanism that provides an example implementation of the so-called social-specific value perspective. In brief, our Social-Affective ATP mechanism builds upon established formalisms for reinforcement learning (temporal difference learning models) nuanced to accommodate expectations (consistent with ATP theory) and extended to integrate non-social and social cues for use in Joint Action. PMID:27601989

  2. A convolutional neural network approach to calibrating the rotation axis for X-ray computed tomography

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Xiaogang; De Carlo, Francesco; Phatak, Charudatta; Gürsoy, Dogˇa

    2017-01-24

    This paper presents an algorithm to calibrate the center-of-rotation for X-ray tomography by using a machine learning approach, the Convolutional Neural Network (CNN). The algorithm shows excellent accuracy from the evaluation of synthetic data with various noise ratios. It is further validated with experimental data of four different shale samples measured at the Advanced Photon Source and at the Swiss Light Source. The results are as good as those determined by visual inspection and show better robustness than conventional methods. CNN has also great potential forreducing or removingother artifacts caused by instrument instability, detector non-linearity,etc. An open-source toolbox, which integrates the CNN methods described in this paper, is freely available through GitHub at tomography/xlearn and can be easily integrated into existing computational pipelines available at various synchrotron facilities. Source code, documentation and information on how to contribute are also provided.

  3. Brain-Computer Interface for Control of Wheelchair Using Fuzzy Neural Networks

    Directory of Open Access Journals (Sweden)

    Rahib H. Abiyev

    2016-01-01

    Full Text Available The design of brain-computer interface for the wheelchair for physically disabled people is presented. The design of the proposed system is based on receiving, processing, and classification of the electroencephalographic (EEG signals and then performing the control of the wheelchair. The number of experimental measurements of brain activity has been done using human control commands of the wheelchair. Based on the mental activity of the user and the control commands of the wheelchair, the design of classification system based on fuzzy neural networks (FNN is considered. The design of FNN based algorithm is used for brain-actuated control. The training data is used to design the system and then test data is applied to measure the performance of the control system. The control of the wheelchair is performed under real conditions using direction and speed control commands of the wheelchair. The approach used in the paper allows reducing the probability of misclassification and improving the control accuracy of the wheelchair.

  4. Minimalist Social-Affective Value for Use in Joint Action: A Neural-Computational Hypothesis

    Directory of Open Access Journals (Sweden)

    Robert J Lowe

    2016-08-01

    Full Text Available Joint Action is typically described as social interaction that requires coordination among two or more co-actors in order to achieve a common goal. In this article, we put forward a hypothesis for the existence of a neural-computational mechanism of affective valuation that may be critically exploited in Joint Action. Such a mechanism would serve to facilitate coordination between co-actors permitting a reduction of required information. Our hypothesized affective mechanism provides a value function based implementation of Associative Two-Process theory that entails the classification of external stimuli according to outcome expectancies. This approach has been used to describe animal and human action that concerns differential outcome expectancies. Until now it has not been applied to social interaction. We describe our Affective Associative Two-Process (ATP model as applied to social learning consistent with an ‘extended common currency’ perspective in the social neuroscience literature. We contrast this to an alternative mechanism that provides an example implementation of the so-called social-specific value perspective. In brief, our Social-Affective ATP mechanism builds upon established formalisms for reinforcement learning (temporal difference learning models nuanced to accommodate expectations (consistent with ATP theory and extended to integrate non-social and social cues for use in Joint Action.

  5. Neural and cortisol responses during play with human and computer partners in children with autism.

    Science.gov (United States)

    Edmiston, Elliot Kale; Merkle, Kristen; Corbett, Blythe A

    2015-08-01

    Children with autism spectrum disorder (ASD) exhibit impairment in reciprocal social interactions, including play, which can manifest as failure to show social preference or discrimination between social and nonsocial stimuli. To explore mechanisms underlying these deficits, we collected salivary cortisol from 42 children 8-12 years with ASD or typical development during a playground interaction with a confederate child. Participants underwent functional MRI during a prisoner's dilemma game requiring cooperation or defection with a human (confederate) or computer partner. Search region of interest analyses were based on previous research (e.g. insula, amygdala, temporal parietal junction-TPJ). There were significant group differences in neural activation based on partner and response pattern. When playing with a human partner, children with ASD showed limited engagement of a social salience brain circuit during defection. Reduced insula activation during defection in the ASD children relative to TD children, regardless of partner type, was also a prominent finding. Insula and TPJ BOLD during defection was also associated with stress responsivity and behavior in the ASD group under playground conditions. Children with ASD engage social salience networks less than TD children during conditions of social salience, supporting a fundamental disturbance of social engagement. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  6. Abstract Computation in Schizophrenia Detection through Artificial Neural Network Based Systems

    Directory of Open Access Journals (Sweden)

    L. Cardoso

    2015-01-01

    Full Text Available Schizophrenia stands for a long-lasting state of mental uncertainty that may bring to an end the relation among behavior, thought, and emotion; that is, it may lead to unreliable perception, not suitable actions and feelings, and a sense of mental fragmentation. Indeed, its diagnosis is done over a large period of time; continuos signs of the disturbance persist for at least 6 (six months. Once detected, the psychiatrist diagnosis is made through the clinical interview and a series of psychic tests, addressed mainly to avoid the diagnosis of other mental states or diseases. Undeniably, the main problem with identifying schizophrenia is the difficulty to distinguish its symptoms from those associated to different untidiness or roles. Therefore, this work will focus on the development of a diagnostic support system, in terms of its knowledge representation and reasoning procedures, based on a blended of Logic Programming and Artificial Neural Networks approaches to computing, taking advantage of a novel approach to knowledge representation and reasoning, which aims to solve the problems associated in the handling (i.e., to stand for and reason of defective information.

  7. Enabling functional neural circuit simulations with distributed computing of neuromodulated plasticity

    Directory of Open Access Journals (Sweden)

    Wiebke ePotjans

    2010-11-01

    Full Text Available A major puzzle in the field of computational neuroscience is how to relate system-level learning in higher organisms to synaptic plasticity. Recently, plasticity rules depending not only on pre- and post-synaptic activity but also on a third, non-local neuromodulatory signal have emerged as key candidates to bridge the gap between the macroscopic and the microscopic level of learning. Crucial insights into this topic are expected to be gained from simulations of neural systems, as these allow the simultaneous study of the multiple spatial and temporal scales that are involved in the problem. In particular, synaptic plasticity can be studied during the whole learning process, i.e. on a time scale of minutes to hours and across multiple brain areas. Implementing neuromodulated plasticity in large-scale network simulations where the neuromodulatory signal is dynamically generated by the network itself is challenging, because the network structure is commonly defined purely by the connectivity graph without explicit reference to the embedding of the nodes in physical space. Furthermore, the simulation of networks with realistic connectivity entails the use of distributed computing. A neuromodulated synapse must therefore be informed in an efficient way about the neuromodulatory signal, which is typically generated by a population of neurons located on different machines than either the pre- or post-synaptic neuron. Here, we develop a general framework to solve the problem of implementing neuromodulated plasticity in a time-driven distributed simulation, without reference to a particular implementation language, neuromodulator or neuromodulated plasticity mechanism. We implement our framework in the simulator NEST and demonstrate excellent scaling up to 1024 processors for simulations of a recurrent network incorporating neuromodulated spike-timing dependent plasticity.

  8. Building bridges between perceptual and economic decision-making: neural and computational mechanisms

    Directory of Open Access Journals (Sweden)

    Christopher eSummerfield

    2012-05-01

    Full Text Available Investigation into the neural and computational bases of decision-making has proceeded in two parallel but distinct streams. Perceptual decision making (PDM is concerned with how observers detect, discriminate and categorise noisy sensory information. Economic decision making (EDM explores how options are selected on the basis of their reinforcement history. Traditionally, the subfields of PDM and EDM have employed different paradigms, proposed different mechanistic models, explored different brain regions, disagreed about whether decisions approach optimality. Nevertheless, we argue that there is a common framework for understanding decisions made in both domains, under which an agent has to combine sensory information (what is the stimulus with value information (what is it worth. We review computational models of the decision process typically used in PDM, based around the idea that decisions involve a serial integration of evidence, and assess their applicability to decisions between good and gambles. Subsequently, we consider the contribution of three key brain regions – the parietal cortex, the basal ganglia, and the orbitofrontal cortex – to perceptual and economic decision-making, with a focus on the mechanisms by which sensory and reward information are integrated during choice. We find that although the parietal cortex is often implicated in the integration of sensory evidence, there is evidence for its role in encoding the expected value of a decision. Similarly, although much research has emphasised the role of the striatum and orbitofrontal cortex in value-guided choices, they may play an important role in categorisation of perceptual information. In conclusion, we consider how findings from the two fields might be brought together, in order to move towards a general framework for understanding decision-making in humans and other primates.

  9. Unsupervised statistical learning underpins computational, behavioural, and neural manifestations of musical expectation.

    Science.gov (United States)

    Pearce, Marcus T; Ruiz, María Herrojo; Kapasi, Selina; Wiggins, Geraint A; Bhattacharya, Joydeep

    2010-03-01

    The ability to anticipate forthcoming events has clear evolutionary advantages, and predictive successes or failures often entail significant psychological and physiological consequences. In music perception, the confirmation and violation of expectations are critical to the communication of emotion and aesthetic effects of a composition. Neuroscientific research on musical expectations has focused on harmony. Although harmony is important in Western tonal styles, other musical traditions, emphasizing pitch and melody, have been rather neglected. In this study, we investigated melodic pitch expectations elicited by ecologically valid musical stimuli by drawing together computational, behavioural, and electrophysiological evidence. Unlike rule-based models, our computational model acquires knowledge through unsupervised statistical learning of sequential structure in music and uses this knowledge to estimate the conditional probability (and information content) of musical notes. Unlike previous behavioural paradigms that interrupt a stimulus, we devised a new paradigm for studying auditory expectation without compromising ecological validity. A strong negative correlation was found between the probability of notes predicted by our model and the subjectively perceived degree of expectedness. Our electrophysiological results showed that low-probability notes, as compared to high-probability notes, elicited a larger (i) negative ERP component at a late time period (400-450 ms), (ii) beta band (14-30 Hz) oscillation over the parietal lobe, and (iii) long-range phase synchronization between multiple brain regions. Altogether, the study demonstrated that statistical learning produces information-theoretic descriptions of musical notes that are proportional to their perceived expectedness and are associated with characteristic patterns of neural activity. Copyright (c) 2009 Elsevier Inc. All rights reserved.

  10. Engineering Applications of Neural Computing: A State-of-the-Art Survey

    Science.gov (United States)

    1991-05-01

    Papachristou. C. A., "Training of a Neural Network for Pattern Classifi- cation Based on Entropy Measure," Proceedings of 1988 IEEE International Conference...diction and System Modeling," Technical Report LA-UR-87-2662, Los Alamos National Lab- oratoiy, 1987. 10. Levy, W. B., "Maximum Entropy Prediction in Neural...Lawrence Erlbaum, Hillsdale, NJ, 1990. 53. MacGregor, R. J., Neural and Brain Modeling, The Academic Press, New York, 1987. 54. Marr , D., Vision, San

  11. The dynamics of discrete-time computation, with application to recurrent neural networks and finite state machine extraction.

    Science.gov (United States)

    Casey, M

    1996-08-15

    Recurrent neural networks (RNNs) can learn to perform finite state computations. It is shown that an RNN performing a finite state computation must organize its state space to mimic the states in the minimal deterministic finite state machine that can perform that computation, and a precise description of the attractor structure of such systems is given. This knowledge effectively predicts activation space dynamics, which allows one to understand RNN computation dynamics in spite of complexity in activation dynamics. This theory provides a theoretical framework for understanding finite state machine (FSM) extraction techniques and can be used to improve training methods for RNNs performing FSM computations. This provides an example of a successful approach to understanding a general class of complex systems that has not been explicitly designed, e.g., systems that have evolved or learned their internal structure.

  12. The Inspirational Leader

    Science.gov (United States)

    Benigni, Mark D.; Hughes, Mark A

    2012-01-01

    Amid the focus on improved standardized test scores, differentiated instruction, value-added initiatives and improved teacher evaluation, one must not ignore an education leader's need to inspire and be inspired. But how do education leaders inspire their students and teachers during some of the most difficult economic times the nation has ever…

  13. COMPUTER-SIMULATED NEURAL NETWORKS - AN APPROPRIATE MODEL FOR MOTOR DEVELOPMENT

    NARCIS (Netherlands)

    VOS, JE; SCHEEPSTRA, KA

    The idea of an artificial neural network is introduced in a historical context, and the essential aspect of it, viz., the modifiable synapse, is compared to the aspect of plasticity in the natural nervous system. Based on such an artificial neural network, a model is presented for the way in which

  14. Computational multi-scale constitutive model for wood cell wall and its application to the design of bio-inspired composites

    Science.gov (United States)

    Saavedra Flores, E. I.; Murugan, M. S.; Friswell, M. I.; de Souza Neto, E. A.

    2011-04-01

    This paper presents a fully coupled multi-scale finite element model for the description of the dissipative mechanical response of wood cell walls under large strains. Results show the ability of the present model to capture the main phenomenological responses found typically in wood at the microscopic scale. In addition, the structural and mechanical concepts involved in wood cells are exploited further in order to design new wood inspired composites. Numerical tests are conducted in prototypes of bio-inspired composites and demonstrate substantial gains in terms of resistance to failure and in the control of the overall flexibility/stiffness balance in the material.

  15. Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing.

    Science.gov (United States)

    Kriegeskorte, Nikolaus

    2015-11-24

    Recent advances in neural network modeling have enabled major strides in computer vision and other artificial intelligence applications. Human-level visual recognition abilities are coming within reach of artificial systems. Artificial neural networks are inspired by the brain, and their computations could be implemented in biological neurons. Convolutional feedforward networks, which now dominate computer vision, take further inspiration from the architecture of the primate visual hierarchy. However, the current models are designed with engineering goals, not to model brain computations. Nevertheless, initial studies comparing internal representations between these models and primate brains find surprisingly similar representational spaces. With human-level performance no longer out of reach, we are entering an exciting new era, in which we will be able to build biologically faithful feedforward and recurrent computational models of how biological brains perform high-level feats of intelligence, including vision.

  16. Computer-Aided Diagnosis Based on Convolutional Neural Network System for Colorectal Polyp Classification: Preliminary Experience.

    Science.gov (United States)

    Komeda, Yoriaki; Handa, Hisashi; Watanabe, Tomohiro; Nomura, Takanobu; Kitahashi, Misaki; Sakurai, Toshiharu; Okamoto, Ayana; Minami, Tomohiro; Kono, Masashi; Arizumi, Tadaaki; Takenaka, Mamoru; Hagiwara, Satoru; Matsui, Shigenaga; Nishida, Naoshi; Kashida, Hiroshi; Kudo, Masatoshi

    2017-01-01

    Computer-aided diagnosis (CAD) is becoming a next-generation tool for the diagnosis of human disease. CAD for colon polyps has been suggested as a particularly useful tool for trainee colonoscopists, as the use of a CAD system avoids the complications associated with endoscopic resections. In addition to conventional CAD, a convolutional neural network (CNN) system utilizing artificial intelligence (AI) has been developing rapidly over the past 5 years. We attempted to generate a unique CNN-CAD system with an AI function that studied endoscopic images extracted from movies obtained with colonoscopes used in routine examinations. Here, we report our preliminary results of this novel CNN-CAD system for the diagnosis of colon polyps. A total of 1,200 images from cases of colonoscopy performed between January 2010 and December 2016 at Kindai University Hospital were used. These images were extracted from the video of actual endoscopic examinations. Additional video images from 10 cases of unlearned processes were retrospectively assessed in a pilot study. They were simply diagnosed as either an adenomatous or nonadenomatous polyp. The number of images used by AI to learn to distinguish adenomatous from nonadenomatous was 1,200:600. These images were extracted from the videos of actual endoscopic examinations. The size of each image was adjusted to 256 × 256 pixels. A 10-hold cross-validation was carried out. The accuracy of the 10-hold cross-validation is 0.751, where the accuracy is the ratio of the number of correct answers over the number of all the answers produced by the CNN. The decisions by the CNN were correct in 7 of 10 cases. A CNN-CAD system using routine colonoscopy might be useful for the rapid diagnosis of colorectal polyp classification. Further prospective studies in an in vivo setting are required to confirm the effectiveness of a CNN-CAD system in routine colonoscopy. © 2017 S. Karger AG, Basel.

  17. Deep neural network-based computer-assisted detection of cerebral aneurysms in MR angiography.

    Science.gov (United States)

    Nakao, Takahiro; Hanaoka, Shouhei; Nomura, Yukihiro; Sato, Issei; Nemoto, Mitsutaka; Miki, Soichiro; Maeda, Eriko; Yoshikawa, Takeharu; Hayashi, Naoto; Abe, Osamu

    2017-08-24

    The usefulness of computer-assisted detection (CAD) for detecting cerebral aneurysms has been reported; therefore, the improved performance of CAD will help to detect cerebral aneurysms. To develop a CAD system for intracranial aneurysms on unenhanced magnetic resonance angiography (MRA) images based on a deep convolutional neural network (CNN) and a maximum intensity projection (MIP) algorithm, and to demonstrate the usefulness of the system by training and evaluating it using a large dataset. Retrospective study. There were 450 cases with intracranial aneurysms. The diagnoses of brain aneurysms were made on the basis of MRA, which was performed as part of a brain screening program. Noncontrast-enhanced 3D time-of-flight (TOF) MRA on 3T MR scanners. In our CAD, we used a CNN classifier that predicts whether each voxel is inside or outside aneurysms by inputting MIP images generated from a volume of interest (VOI) around the voxel. The CNN was trained in advance using manually inputted labels. We evaluated our method using 450 cases with intracranial aneurysms, 300 of which were used for training, 50 for parameter tuning, and 100 for the final evaluation. Free-response receiver operating characteristic (FROC) analysis. Our CAD system detected 94.2% (98/104) of aneurysms with 2.9 false positives per case (FPs/case). At a sensitivity of 70%, the number of FPs/case was 0.26. We showed that the combination of a CNN and an MIP algorithm is useful for the detection of intracranial aneurysms. 4 Technical Efficacy Stage 1 J. Magn. Reson. Imaging 2017. © 2017 International Society for Magnetic Resonance in Medicine.

  18. Deciding not to decide: computational and neural evidence for hidden behavior in sequential choice.

    Directory of Open Access Journals (Sweden)

    Sebastian Gluth

    2013-10-01

    Full Text Available Understanding the cognitive and neural processes that underlie human decision making requires the successful prediction of how, but also of when, people choose. Sequential sampling models (SSMs have greatly advanced the decision sciences by assuming decisions to emerge from a bounded evidence accumulation process so that response times (RTs become predictable. Here, we demonstrate a difficulty of SSMs that occurs when people are not forced to respond at once but are allowed to sample information sequentially: The decision maker might decide to delay the choice and terminate the accumulation process temporarily, a scenario not accounted for by the standard SSM approach. We developed several SSMs for predicting RTs from two independent samples of an electroencephalography (EEG and a functional magnetic resonance imaging (fMRI study. In these studies, participants bought or rejected fictitious stocks based on sequentially presented cues and were free to respond at any time. Standard SSM implementations did not describe RT distributions adequately. However, by adding a mechanism for postponing decisions to the model we obtained an accurate fit to the data. Time-frequency analysis of EEG data revealed alternating states of de- and increasing oscillatory power in beta-band frequencies (14-30 Hz, indicating that responses were repeatedly prepared and inhibited and thus lending further support for the existence of a decision not to decide. Finally, the extended model accounted for the results of an adapted version of our paradigm in which participants had to press a button for sampling more information. Our results show how computational modeling of decisions and RTs support a deeper understanding of the hidden dynamics in cognition.

  19. Evidence for Neural Computations of Temporal Coherence in an Auditory Scene and Their Enhancement during Active Listening.

    Science.gov (United States)

    O'Sullivan, James A; Shamma, Shihab A; Lalor, Edmund C

    2015-05-06

    The human brain has evolved to operate effectively in highly complex acoustic environments, segregating multiple sound sources into perceptually distinct auditory objects. A recent theory seeks to explain this ability by arguing that stream segregation occurs primarily due to the temporal coherence of the neural populations that encode the various features of an individual acoustic source. This theory has received support from both psychoacoustic and functional magnetic resonance imaging (fMRI) studies that use stimuli which model complex acoustic environments. Termed stochastic figure-ground (SFG) stimuli, they are composed of a "figure" and background that overlap in spectrotemporal space, such that the only way to segregate the figure is by computing the coherence of its frequency components over time. Here, we extend these psychoacoustic and fMRI findings by using the greater temporal resolution of electroencephalography to investigate the neural computation of temporal coherence. We present subjects with modified SFG stimuli wherein the temporal coherence of the figure is modulated stochastically over time, which allows us to use linear regression methods to extract a signature of the neural processing of this temporal coherence. We do this under both active and passive listening conditions. Our findings show an early effect of coherence during passive listening, lasting from ∼115 to 185 ms post-stimulus. When subjects are actively listening to the stimuli, these responses are larger and last longer, up to ∼265 ms. These findings provide evidence for early and preattentive neural computations of temporal coherence that are enhanced by active analysis of an auditory scene. Copyright © 2015 the authors 0270-6474/15/357256-08$15.00/0.

  20. A computational model incorporating neural stem cell dynamics reproduces glioma incidence across the lifespan in the human population.

    Directory of Open Access Journals (Sweden)

    Roman Bauer

    Full Text Available Glioma is the most common form of primary brain tumor. Demographically, the risk of occurrence increases until old age. Here we present a novel computational model to reproduce the probability of glioma incidence across the lifespan. Previous mathematical models explaining glioma incidence are framed in a rather abstract way, and do not directly relate to empirical findings. To decrease this gap between theory and experimental observations, we incorporate recent data on cellular and molecular factors underlying gliomagenesis. Since evidence implicates the adult neural stem cell as the likely cell-of-origin of glioma, we have incorporated empirically-determined estimates of neural stem cell number, cell division rate, mutation rate and oncogenic potential into our model. We demonstrate that our model yields results which match actual demographic data in the human population. In particular, this model accounts for the observed peak incidence of glioma at approximately 80 years of age, without the need to assert differential susceptibility throughout the population. Overall, our model supports the hypothesis that glioma is caused by randomly-occurring oncogenic mutations within the neural stem cell population. Based on this model, we assess the influence of the (experimentally indicated decrease in the number of neural stem cells and increase of cell division rate during aging. Our model provides multiple testable predictions, and suggests that different temporal sequences of oncogenic mutations can lead to tumorigenesis. Finally, we conclude that four or five oncogenic mutations are sufficient for the formation of glioma.

  1. Neural recording and modulation technologies

    Science.gov (United States)

    Chen, Ritchie; Canales, Andres; Anikeeva, Polina

    2017-01-01

    In the mammalian nervous system, billions of neurons connected by quadrillions of synapses exchange electrical, chemical and mechanical signals. Disruptions to this network manifest as neurological or psychiatric conditions. Despite decades of neuroscience research, our ability to treat or even to understand these conditions is limited by the capability of tools to probe the signalling complexity of the nervous system. Although orders of magnitude smaller and computationally faster than neurons, conventional substrate-bound electronics do not recapitulate the chemical and mechanical properties of neural tissue. This mismatch results in a foreign-body response and the encapsulation of devices by glial scars, suggesting that the design of an interface between the nervous system and a synthetic sensor requires additional materials innovation. Advances in genetic tools for manipulating neural activity have fuelled the demand for devices that are capable of simultaneously recording and controlling individual neurons at unprecedented scales. Recently, flexible organic electronics and bio- and nanomaterials have been developed for multifunctional and minimally invasive probes for long-term interaction with the nervous system. In this Review, we discuss the design lessons from the quarter-century-old field of neural engineering, highlight recent materials-driven progress in neural probes and look at emergent directions inspired by the principles of neural transduction.

  2. Cat Swarm Optimization Based Functional Link Artificial Neural Network Filter for Gaussian Noise Removal from Computed Tomography Images

    Directory of Open Access Journals (Sweden)

    M. Kumar

    2016-01-01

    Full Text Available Gaussian noise is one of the dominant noises, which degrades the quality of acquired Computed Tomography (CT image data. It creates difficulties in pathological identification or diagnosis of any disease. Gaussian noise elimination is desirable to improve the clarity of a CT image for clinical, diagnostic, and postprocessing applications. This paper proposes an evolutionary nonlinear adaptive filter approach, using Cat Swarm Functional Link Artificial Neural Network (CS-FLANN to remove the unwanted noise. The structure of the proposed filter is based on the Functional Link Artificial Neural Network (FLANN and the Cat Swarm Optimization (CSO is utilized for the selection of optimum weight of the neural network filter. The applied filter has been compared with the existing linear filters, like the mean filter and the adaptive Wiener filter. The performance indices, such as peak signal to noise ratio (PSNR, have been computed for the quantitative analysis of the proposed filter. The experimental evaluation established the superiority of the proposed filtering technique over existing methods.

  3. Implementation of a Computational Model for Information Processing and Signaling from a Biological Neural Network of Neostriatum Nucleus

    Directory of Open Access Journals (Sweden)

    C. Sanchez-Vazquez

    2014-06-01

    Full Text Available Recently, several mathematical models have been developed to study and explain the way information is processed in the brain. The models published account for a myriad of perspectives from single neuron segments to neural networks, and lately, with the use of supercomputing facilities, to the study of whole environments of nuclei interacting for massive stimuli and processing. Some of the most complex neural structures -and also most studied- are basal ganglia nuclei in the brain; amongst which we can find the Neostriatum. Currently, just a few papers about high scale biological-based computational modeling of this region have been published. It has been demonstrated that the Basal Ganglia region contains functions related to learning and decision making based on rules of the action-selection type, which are of particular interest for the machine autonomous-learning field. This knowledge could be clearly transferred between areas of research. The present work proposes a model of information processing, by integrating knowledge generated from widely accepted experiments in both morphology and biophysics, through integrating theories such as the compartmental electrical model, the Rall’s cable equation, and the Hodking-Huxley particle potential regulations, among others. Additionally, the leaky integrator framework is incorporated in an adapted function. This was accomplished through a computational environment prepared for high scale neural simulation which delivers data output equivalent to that from the original model, and that can not only be analyzed as a Bayesian problem, but also successfully compared to the biological specimen.

  4. Social insects inspire human design

    Science.gov (United States)

    Holbrook, C. Tate; Clark, Rebecca M.; Moore, Dani; Overson, Rick P.; Penick, Clint A.; Smith, Adrian A.

    2010-01-01

    The international conference ‘Social Biomimicry: Insect Societies and Human Design’, hosted by Arizona State University, USA, 18–20 February 2010, explored how the collective behaviour and nest architecture of social insects can inspire innovative and effective solutions to human design challenges. It brought together biologists, designers, engineers, computer scientists, architects and businesspeople, with the dual aims of enriching biology and advancing biomimetic design. PMID:20392721

  5. Neural networks and applications tutorial

    Science.gov (United States)

    Guyon, I.

    1991-09-01

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

  6. Computational Models of Financial Price Prediction: A Survey of Neural Networks, Kernel Machines and Evolutionary Computation Approaches

    Directory of Open Access Journals (Sweden)

    Javier Sandoval

    2011-12-01

    Full Text Available A review of the representative models of machine learning research applied to the foreign exchange rate and stock price prediction problem is conducted.  The article is organized as follows: The first section provides a context on the definitions and importance of foreign exchange rate and stock markets.  The second section reviews machine learning models for financial prediction focusing on neural networks, SVM and evolutionary methods. Lastly, the third section draws some conclusions.

  7. Deep Deconvolutional Neural Network for Target Segmentation of Nasopharyngeal Cancer in Planning Computed Tomography Images

    Directory of Open Access Journals (Sweden)

    Kuo Men

    2017-12-01

    Full Text Available BackgroundRadiotherapy is one of the main treatment methods for nasopharyngeal carcinoma (NPC. It requires exact delineation of the nasopharynx gross tumor volume (GTVnx, the metastatic lymph node gross tumor volume (GTVnd, the clinical target volume (CTV, and organs at risk in the planning computed tomography images. However, this task is time-consuming and operator dependent. In the present study, we developed an end-to-end deep deconvolutional neural network (DDNN for segmentation of these targets.MethodsThe proposed DDNN is an end-to-end architecture enabling fast training and testing. It consists of two important components: an encoder network and a decoder network. The encoder network was used to extract the visual features of a medical image and the decoder network was used to recover the original resolution by deploying deconvolution. A total of 230 patients diagnosed with NPC stage I or stage II were included in this study. Data from 184 patients were chosen randomly as a training set to adjust the parameters of DDNN, and the remaining 46 patients were the test set to assess the performance of the model. The Dice similarity coefficient (DSC was used to quantify the segmentation results of the GTVnx, GTVnd, and CTV. In addition, the performance of DDNN was compared with the VGG-16 model.ResultsThe proposed DDNN method outperformed the VGG-16 in all the segmentation. The mean DSC values of DDNN were 80.9% for GTVnx, 62.3% for the GTVnd, and 82.6% for CTV, whereas VGG-16 obtained 72.3, 33.7, and 73.7% for the DSC values, respectively.ConclusionDDNN can be used to segment the GTVnx and CTV accurately. The accuracy for the GTVnd segmentation was relatively low due to the considerable differences in its shape, volume, and location among patients. The accuracy is expected to increase with more training data and combination of MR images. In conclusion, DDNN has the potential to improve the consistency of contouring and streamline radiotherapy

  8. Genome-wide identification of specific oligonucleotides using artificial neural network and computational genomic analysis

    Directory of Open Access Journals (Sweden)

    Chen Jiun-Ching

    2007-05-01

    Full Text Available Abstract Background Genome-wide identification of specific oligonucleotides (oligos is a computationally-intensive task and is a requirement for designing microarray probes, primers, and siRNAs. An artificial neural network (ANN is a machine learning technique that can effectively process complex and high noise data. Here, ANNs are applied to process the unique subsequence distribution for prediction of specific oligos. Results We present a novel and efficient algorithm, named the integration of ANN and BLAST (IAB algorithm, to identify specific oligos. We establish the unique marker database for human and rat gene index databases using the hash table algorithm. We then create the input vectors, via the unique marker database, to train and test the ANN. The trained ANN predicted the specific oligos with high efficiency, and these oligos were subsequently verified by BLAST. To improve the prediction performance, the ANN over-fitting issue was avoided by early stopping with the best observed error and a k-fold validation was also applied. The performance of the IAB algorithm was about 5.2, 7.1, and 6.7 times faster than the BLAST search without ANN for experimental results of 70-mer, 50-mer, and 25-mer specific oligos, respectively. In addition, the results of polymerase chain reactions showed that the primers predicted by the IAB algorithm could specifically amplify the corresponding genes. The IAB algorithm has been integrated into a previously published comprehensive web server to support microarray analysis and genome-wide iterative enrichment analysis, through which users can identify a group of desired genes and then discover the specific oligos of these genes. Conclusion The IAB algorithm has been developed to construct SpecificDB, a web server that provides a specific and valid oligo database of the probe, siRNA, and primer design for the human genome. We also demonstrate the ability of the IAB algorithm to predict specific oligos through

  9. Genome-wide identification of specific oligonucleotides using artificial neural network and computational genomic analysis.

    Science.gov (United States)

    Liu, Chun-Chi; Lin, Chin-Chung; Li, Ker-Chau; Chen, Wen-Shyen E; Chen, Jiun-Ching; Yang, Ming-Te; Yang, Pan-Chyr; Chang, Pei-Chun; Chen, Jeremy J W

    2007-05-22

    Genome-wide identification of specific oligonucleotides (oligos) is a computationally-intensive task and is a requirement for designing microarray probes, primers, and siRNAs. An artificial neural network (ANN) is a machine learning technique that can effectively process complex and high noise data. Here, ANNs are applied to process the unique subsequence distribution for prediction of specific oligos. We present a novel and efficient algorithm, named the integration of ANN and BLAST (IAB) algorithm, to identify specific oligos. We establish the unique marker database for human and rat gene index databases using the hash table algorithm. We then create the input vectors, via the unique marker database, to train and test the ANN. The trained ANN predicted the specific oligos with high efficiency, and these oligos were subsequently verified by BLAST. To improve the prediction performance, the ANN over-fitting issue was avoided by early stopping with the best observed error and a k-fold validation was also applied. The performance of the IAB algorithm was about 5.2, 7.1, and 6.7 times faster than the BLAST search without ANN for experimental results of 70-mer, 50-mer, and 25-mer specific oligos, respectively. In addition, the results of polymerase chain reactions showed that the primers predicted by the IAB algorithm could specifically amplify the corresponding genes. The IAB algorithm has been integrated into a previously published comprehensive web server to support microarray analysis and genome-wide iterative enrichment analysis, through which users can identify a group of desired genes and then discover the specific oligos of these genes. The IAB algorithm has been developed to construct SpecificDB, a web server that provides a specific and valid oligo database of the probe, siRNA, and primer design for the human genome. We also demonstrate the ability of the IAB algorithm to predict specific oligos through polymerase chain reaction experiments. Specific

  10. The human hand as an inspiration for robot hand development

    CERN Document Server

    Santos, Veronica

    2014-01-01

    “The Human Hand as an Inspiration for Robot Hand Development” presents an edited collection of authoritative contributions in the area of robot hands. The results described in the volume are expected to lead to more robust, dependable, and inexpensive distributed systems such as those endowed with complex and advanced sensing, actuation, computation, and communication capabilities. The twenty-four chapters discuss the field of robotic grasping and manipulation viewed in light of the human hand’s capabilities and push the state-of-the-art in robot hand design and control. Topics discussed include human hand biomechanics, neural control, sensory feedback and perception, and robotic grasp and manipulation. This book will be useful for researchers from diverse areas such as robotics, biomechanics, neuroscience, and anthropologists.

  11. 3-D components of a biological neural network visualized in computer generated imagery. I - Macular receptive field organization

    Science.gov (United States)

    Ross, Muriel D.; Cutler, Lynn; Meyer, Glenn; Lam, Tony; Vaziri, Parshaw

    1990-01-01

    Computer-assisted, 3-dimensional reconstructions of macular receptive fields and of their linkages into a neural network have revealed new information about macular functional organization. Both type I and type II hair cells are included in the receptive fields. The fields are rounded, oblong, or elongated, but gradations between categories are common. Cell polarizations are divergent. Morphologically, each calyx of oblong and elongated fields appears to be an information processing site. Intrinsic modulation of information processing is extensive and varies with the kind of field. Each reconstructed field differs in detail from every other, suggesting that an element of randomness is introduced developmentally and contributes to endorgan adaptability.

  12. Bio-Inspired Controller on an FPGA Applied to Closed-Loop Diaphragmatic Stimulation.

    Science.gov (United States)

    Zbrzeski, Adeline; Bornat, Yannick; Hillen, Brian; Siu, Ricardo; Abbas, James; Jung, Ranu; Renaud, Sylvie

    2016-01-01

    Cervical spinal cord injury can disrupt connections between the brain respiratory network and the respiratory muscles which can lead to partial or complete loss of ventilatory control and require ventilatory assistance. Unlike current open-loop technology, a closed-loop diaphragmatic pacing system could overcome the drawbacks of manual titration as well as respond to changing ventilation requirements. We present an original bio-inspired assistive technology for real-time ventilation assistance, implemented in a digital configurable Field Programmable Gate Array (FPGA). The bio-inspired controller, which is a spiking neural network (SNN) inspired by the medullary respiratory network, is as robust as a classic controller while having a flexible, low-power and low-cost hardware design. The system was simulated in MATLAB with FPGA-specific constraints and tested with a computational model of rat breathing; the model reproduced experimentally collected respiratory data in eupneic animals. The open-loop version of the bio-inspired controller was implemented on the FPGA. Electrical test bench characterizations confirmed the system functionality. Open and closed-loop paradigm simulations were simulated to test the FPGA system real-time behavior using the rat computational model. The closed-loop system monitors breathing and changes in respiratory demands to drive diaphragmatic stimulation. The simulated results inform future acute animal experiments and constitute the first step toward the development of a neuromorphic, adaptive, compact, low-power, implantable device. The bio-inspired hardware design optimizes the FPGA resource and time costs while harnessing the computational power of spike-based neuromorphic hardware. Its real-time feature makes it suitable for in vivo applications.

  13. Comparison of different computer models of the neural control system of the lower urinary tract

    NARCIS (Netherlands)

    van Duin, F.; Rosier, P. F.; Bemelmans, B. L.; Wijkstra, H.; Debruyne, F. M.; van Oosterom, A.

    2000-01-01

    This paper presents a series of five models that were formulated for describing the neural control of the lower urinary tract in humans. A parsimonious formulation of the effect of the sympathetic system, the pre-optic area, and urethral afferents on the simulated behavior are included. In spite of

  14. Clay Bells: Edo Inspiration

    Science.gov (United States)

    Wagner, Tom

    2010-01-01

    The ceremonial copper and iron bells at the Smithsonian's National Museum of African Art were the author's inspiration for an interdisciplinary unit with a focus on the contributions various cultures make toward the richness of a community. The author of this article describes an Edo bell-inspired ceramic project incorporating slab-building…

  15. 2D neural hardware versus 3D biological ones

    Energy Technology Data Exchange (ETDEWEB)

    Beiu, V.

    1998-12-31

    This paper will present important limitations of hardware neural nets as opposed to biological neural nets (i.e. the real ones). The author starts by discussing neural structures and their biological inspirations, while mentioning the simplifications leading to artificial neural nets. Going further, the focus will be on hardware constraints. The author will present recent results for three different alternatives of implementing neural networks: digital, threshold gate, and analog, while the area and the delay will be related to neurons' fan-in and weights' precision. Based on all of these, it will be shown why hardware implementations cannot cope with their biological inspiration with respect to their power of computation: the mapping onto silicon lacking the third dimension of biological nets. This translates into reduced fan-in, and leads to reduced precision. The main conclusion is that one is faced with the following alternatives: (1) try to cope with the limitations imposed by silicon, by speeding up the computation of the elementary silicon neurons; (2) investigate solutions which would allow one to use the third dimension, e.g. using optical interconnections.

  16. DEVELOPMENT OF A COMPUTER SYSTEM FOR IDENTITY AUTHENTICATION USING ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Timur Kartbayev

    2017-03-01

    Full Text Available The aim of the study is to increase the effectiveness of automated face recognition to authenticate identity, considering features of change of the face parameters over time. The improvement of the recognition accuracy, as well as consideration of the features of temporal changes in a human face can be based on the methodology of artificial neural networks. Hybrid neural networks, combining the advantages of classical neural networks and fuzzy logic systems, allow using the network learnability along with the explanation of the findings. The structural scheme of intelligent system for identification based on artificial neural networks is proposed in this work. It realizes the principles of digital information processing and identity recognition taking into account the forecast of key characteristics’ changes over time (e.g., due to aging. The structural scheme has a three-tier architecture and implements preliminary processing, recognition and identification of images obtained as a result of monitoring. On the basis of expert knowledge, the fuzzy base of products is designed. It allows assessing possible changes in key characteristics, used to authenticate identity based on the image. To take this possibility into consideration, a neuro-fuzzy network of ANFIS type was used, which implements the algorithm of Tagaki-Sugeno. The conducted experiments showed high efficiency of the developed neural network and a low value of learning errors, which allows recommending this approach for practical implementation. Application of the developed system of fuzzy production rules that allow predicting changes in individuals over time, will improve the recognition accuracy, reduce the number of authentication failures and improve the efficiency of information processing and decision-making in applications, such as authentication of bank customers, users of mobile applications, or in video monitoring systems of sensitive sites.

  17. A neutron spectrum unfolding computer code based on artificial neural networks

    Science.gov (United States)

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

    2014-02-01

    The Bonner Spheres Spectrometer consists of a thermal neutron sensor placed at the center of a number of moderating polyethylene spheres of different diameters. From the measured readings, information can be derived about the spectrum of the neutron field where measurements were made. Disadvantages of the Bonner system are the weight associated with each sphere and the need to sequentially irradiate the spheres, requiring long exposure periods. Provided a well-established response matrix and adequate irradiation conditions, the most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. The drawbacks associated with traditional unfolding procedures have motivated the need of complementary approaches. Novel methods based on Artificial Intelligence, mainly Artificial Neural Networks, have been widely investigated. In this work, a neutron spectrum unfolding code based on neural nets technology is presented. This code is called Neutron Spectrometry and Dosimetry with Artificial Neural networks unfolding code that was designed in a graphical interface. The core of the code is an embedded neural network architecture previously optimized using the robust design of artificial neural networks methodology. The main features of the code are: easy to use, friendly and intuitive to the user. This code was designed for a Bonner Sphere System based on a 6LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. The main feature of the code is that as entrance data, for unfolding the neutron spectrum, only seven rate counts measured with seven Bonner spheres are required; simultaneously the code calculates 15 dosimetric quantities as well as the total flux for radiation protection purposes. This code generates a full report with all information of the unfolding in

  18. Spiking neural P systems with multiple channels.

    Science.gov (United States)

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

    2017-11-01

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

  19. A Computationally Inexpensive Optimal Guidance via Radial-Basis-Function Neural Network for Autonomous Soft Landing on Asteroids.

    Directory of Open Access Journals (Sweden)

    Peng Zhang

    Full Text Available Optimal guidance is essential for the soft landing task. However, due to its high computational complexities, it is hardly applied to the autonomous guidance. In this paper, a computationally inexpensive optimal guidance algorithm based on the radial basis function neural network (RBFNN is proposed. The optimization problem of the trajectory for soft landing on asteroids is formulated and transformed into a two-point boundary value problem (TPBVP. Combining the database of initial states with the relative initial co-states, an RBFNN is trained offline. The optimal trajectory of the soft landing is determined rapidly by applying the trained network in the online guidance. The Monte Carlo simulations of soft landing on the Eros433 are performed to demonstrate the effectiveness of the proposed guidance algorithm.

  20. A Computationally Inexpensive Optimal Guidance via Radial-Basis-Function Neural Network for Autonomous Soft Landing on Asteroids.

    Science.gov (United States)

    Zhang, Peng; Liu, Keping; Zhao, Bo; Li, Yuanchun

    2015-01-01

    Optimal guidance is essential for the soft landing task. However, due to its high computational complexities, it is hardly applied to the autonomous guidance. In this paper, a computationally inexpensive optimal guidance algorithm based on the radial basis function neural network (RBFNN) is proposed. The optimization problem of the trajectory for soft landing on asteroids is formulated and transformed into a two-point boundary value problem (TPBVP). Combining the database of initial states with the relative initial co-states, an RBFNN is trained offline. The optimal trajectory of the soft landing is determined rapidly by applying the trained network in the online guidance. The Monte Carlo simulations of soft landing on the Eros433 are performed to demonstrate the effectiveness of the proposed guidance algorithm.

  1. On the relationships between generative encodings, regularity, and learning abilities when evolving plastic artificial neural networks.

    Directory of Open Access Journals (Sweden)

    Paul Tonelli

    Full Text Available A major goal of bio-inspired artificial intelligence is to design artificial neural networks with abilities that resemble those of animal nervous systems. It is commonly believed that two keys for evolving nature-like artificial neural networks are (1 the developmental process that links genes to nervous systems, which enables the evolution of large, regular neural networks, and (2 synaptic plasticity, which allows neural networks to change during their lifetime. So far, these two topics have been mainly studied separately. The present paper shows that they are actually deeply connected. Using a simple operant conditioning task and a classic evolutionary algorithm, we compare three ways to encode plastic neural networks: a direct encoding, a developmental encoding inspired by computational neuroscience models, and a developmental encoding inspired by morphogen gradients (similar to HyperNEAT. Our results suggest that using a developmental encoding could improve the learning abilities of evolved, plastic neural networks. Complementary experiments reveal that this result is likely the consequence of the bias of developmental encodings towards regular structures: (1 in our experimental setup, encodings that tend to produce more regular networks yield networks with better general learning abilities; (2 whatever the encoding is, networks that are the more regular are statistically those that have the best learning abilities.

  2. On the relationships between generative encodings, regularity, and learning abilities when evolving plastic artificial neural networks.

    Science.gov (United States)

    Tonelli, Paul; Mouret, Jean-Baptiste

    2013-01-01

    A major goal of bio-inspired artificial intelligence is to design artificial neural networks with abilities that resemble those of animal nervous systems. It is commonly believed that two keys for evolving nature-like artificial neural networks are (1) the developmental process that links genes to nervous systems, which enables the evolution of large, regular neural networks, and (2) synaptic plasticity, which allows neural networks to change during their lifetime. So far, these two topics have been mainly studied separately. The present paper shows that they are actually deeply connected. Using a simple operant conditioning task and a classic evolutionary algorithm, we compare three ways to encode plastic neural networks: a direct encoding, a developmental encoding inspired by computational neuroscience models, and a developmental encoding inspired by morphogen gradients (similar to HyperNEAT). Our results suggest that using a developmental encoding could improve the learning abilities of evolved, plastic neural networks. Complementary experiments reveal that this result is likely the consequence of the bias of developmental encodings towards regular structures: (1) in our experimental setup, encodings that tend to produce more regular networks yield networks with better general learning abilities; (2) whatever the encoding is, networks that are the more regular are statistically those that have the best learning abilities.

  3. Nature-Inspired Cognitive Evolution to Play MS. Pac-Man

    Science.gov (United States)

    Tan, Tse Guan; Teo, Jason; Anthony, Patricia

    Recent developments in nature-inspired computation have heightened the need for research into the three main areas of scientific, engineering and industrial applications. Some approaches have reported that it is able to solve dynamic problems and very useful for improving the performance of various complex systems. So far however, there has been little discussion about the effectiveness of the application of these models to computer and video games in particular. The focus of this research is to explore the hybridization of nature-inspired computation methods for optimization of neural network-based cognition in video games, in this case the combination of a neural network with an evolutionary algorithm. In essence, a neural network is an attempt to mimic the extremely complex human brain system, which is building an artificial brain that is able to self-learn intelligently. On the other hand, an evolutionary algorithm is to simulate the biological evolutionary processes that evolve potential solutions in order to solve the problems or tasks by applying the genetic operators such as crossover, mutation and selection into the solutions. This paper investigates the abilities of Evolution Strategies (ES) to evolve feed-forward artificial neural network's internal parameters (i.e. weight and bias values) for automatically generating Ms. Pac-man controllers. The main objective of this game is to clear a maze of dots while avoiding the ghosts and to achieve the highest possible score. The experimental results show that an ES-based system can be successfully applied to automatically generate artificial intelligence for a complex, dynamic and highly stochastic video game environment.

  4. Grid cells generate an analog error-correcting code for singularly precise neural computation.

    Science.gov (United States)

    Sreenivasan, Sameet; Fiete, Ila

    2011-09-11

    Entorhinal grid cells in mammals fire as a function of animal location, with spatially periodic response patterns. This nonlocal periodic representation of location, a local variable, is unlike other neural codes. There is no theoretical explanation for why such a code should exist. We examined how accurately the grid code with noisy neurons allows an ideal observer to estimate location and found this code to be a previously unknown type of population code with unprecedented robustness to noise. In particular, the representational accuracy attained by grid cells over the coding range was in a qualitatively different class from what is possible with observed sensory and motor population codes. We found that a simple neural network can effectively correct the grid code. To the best of our knowledge, these results are the first demonstration that the brain contains, and may exploit, powerful error-correcting codes for analog variables. © 2011 Nature America, Inc. All rights reserved.

  5. Better Computer Go Player with Neural Network and Long-term Prediction

    OpenAIRE

    Tian, Yuandong; Zhu, Yan

    2015-01-01

    Competing with top human players in the ancient game of Go has been a long-term goal of artificial intelligence. Go's high branching factor makes traditional search techniques ineffective, even on leading-edge hardware, and Go's evaluation function could change drastically with one stone change. Recent works [Maddison et al. (2015); Clark & Storkey (2015)] show that search is not strictly necessary for machine Go players. A pure pattern-matching approach, based on a Deep Convolutional Neural ...

  6. Spatial consistency of neural firing regulates long-range local field potential synchronization: a computational study.

    Science.gov (United States)

    Sato, Naoyuki

    2015-02-01

    Local field potentials (LFPs) are thought to integrate neuronal processes within the range of a few millimeters of radius, which corresponds to the scale of multiple columns. In this study, the model of LFP in the visual cortex proposed by Mazzoni et al. (2008) was adapted to organize a network of two cortical areas, in which pyramidal neurons were divided into two sub-population modeling columns with spatially organized connections to neurons in other areas. Using the model enabled the relationship between neural firing and LFP to be evaluated, in addition to the LFP coherence between the two areas. Results showed that: (1) neurons in a particular sub-population generated the LFP in the area; (2) the spatial consistency of neural firing in the two areas was strongly correlated with LFP coherence; and (3) this consistency was capable of regulating LFP coherence in a lower frequency band, which was originally introduced to neurons in a particular sub-population. These results were derived from a winner-take-all operation in the columnar structure; thus, they are expected to be common in the cortex. It is suggested that the spatial consistency of neural firing is essential for regulating long-range LFP synchronization, which would facilitate neuronal integration processes over multiple cortical areas. Copyright © 2014 Elsevier Ltd. All rights reserved.

  7. Evolving Spiking Neural Networks for Control of Artificial Creatures

    Directory of Open Access Journals (Sweden)

    Arash Ahmadi

    2013-10-01

    Full Text Available To understand and analysis behavior of complicated and intelligent organisms, scientists apply bio-inspired concepts including evolution and learning to mathematical models and analyses. Researchers utilize these perceptions in different applications, searching for improved methods andapproaches for modern computational systems. This paper presents a genetic algorithm based evolution framework in which Spiking Neural Network (SNN of artificial creatures are evolved for higher chance of survival in a virtual environment. The artificial creatures are composed ofrandomly connected Izhikevich spiking reservoir neural networks using population activity rate coding. Inspired by biological neurons, the neuronal connections are considered with different axonal conduction delays. Simulations results prove that the evolutionary algorithm has thecapability to find or synthesis artificial creatures which can survive in the environment successfully.

  8. Evolution of Neural Dynamics in an Ecological Model

    Directory of Open Access Journals (Sweden)

    Steven Williams

    2017-07-01

    Full Text Available What is the optimal level of chaos in a computational system? If a system is too chaotic, it cannot reliably store information. If it is too ordered, it cannot transmit information. A variety of computational systems exhibit dynamics at the “edge of chaos”, the transition between the ordered and chaotic regimes. In this work, we examine the evolved neural networks of Polyworld, an artificial life model consisting of a simulated ecology populated with biologically inspired agents. As these agents adapt to their environment, their initially simple neural networks become increasingly capable of exhibiting rich dynamics. Dynamical systems analysis reveals that natural selection drives these networks toward the edge of chaos until the agent population is able to sustain itself. After this point, the evolutionary trend stabilizes, with neural dynamics remaining on average significantly far from the transition to chaos.

  9. Evolutionary and Neural Computing Based Decision Support System for Disease Diagnosis from Clinical Data Sets in Medical Practice.

    Science.gov (United States)

    Sudha, M

    2017-09-27

    As a recent trend, various computational intelligence and machine learning approaches have been used for mining inferences hidden in the large clinical databases to assist the clinician in strategic decision making. In any target data the irrelevant information may be detrimental, causing confusion for the mining algorithm and degrades the prediction outcome. To address this issue, this study attempts to identify an intelligent approach to assist disease diagnostic procedure using an optimal set of attributes instead of all attributes present in the clinical data set. In this proposed Application Specific Intelligent Computing (ASIC) decision support system, a rough set based genetic algorithm is employed in pre-processing phase and a back propagation neural network is applied in training and testing phase. ASIC has two phases, the first phase handles outliers, noisy data, and missing values to obtain a qualitative target data to generate appropriate attribute reduct sets from the input data using rough computing based genetic algorithm centred on a relative fitness function measure. The succeeding phase of this system involves both training and testing of back propagation neural network classifier on the selected reducts. The model performance is evaluated with widely adopted existing classifiers. The proposed ASIC system for clinical decision support has been tested with breast cancer, fertility diagnosis and heart disease data set from the University of California at Irvine (UCI) machine learning repository. The proposed system outperformed the existing approaches attaining the accuracy rate of 95.33%, 97.61%, and 93.04% for breast cancer, fertility issue and heart disease diagnosis.

  10. Physicists get INSPIREd

    CERN Document Server

    CERN Bulletin

    2010-01-01

    Particle physicists thrive on information. They first create information by performing experiments or elaborating theoretical conjectures and then they share it through publications and various web tools. The INSPIRE service, just released, will bring state of the art information retrieval to the fingertips of researchers.   Keeping track of the information shared within the particle physics community has long been the task of libraries at the larger labs, such as CERN, DESY, Fermilab and SLAC, as well as the focus of indispensible services like arXiv and those of the Particle Data Group. In 2007, many providers of information in the field came together for a summit at SLAC to see how physics information resources could be enhanced, and the INSPIRE project emerged from that meeting. The vision behind INSPIRE was built by a survey launched by the four labs to evaluate the real needs of the community. INSPIRE responds to these directives from the community by combining the most successful aspe...

  11. Supersymmetry Inspired QCD Beta Function

    CERN Document Server

    Ryttov, Thomas A

    2008-01-01

    We propose an all orders beta function for ordinary Yang-Mills theories with or without fermions inspired by the Novikov-Shifman-Vainshtein-Zakharov beta function of N=1 supersymmetric gauge theories. The beta function allows us to bound the conformal window. When restricting to one adjoint Weyl fermion we show how the proposed beta function matches the one of supersymmetric Yang-Mills theory. The running of the pure Yang-Mills coupling is computed and the deviation from the two loop result is presented. We then compare the deviation with the one obtained from lattice data also with respect to the two loop running.

  12. Supersymmetry Inspired QCD Beta Function

    DEFF Research Database (Denmark)

    Ryttov, Thomas; Sannino, Francesco

    2008-01-01

    We propose an all orders beta function for ordinary Yang-Mills theories with or without fermions inspired by the Novikov-Shifman-Vainshtein-Zakharov beta function of N=1 supersymmetric gauge theories. The beta function allows us to bound the conformal window. When restricting to one adjoint Weyl...... fermion we show how the proposed beta function matches the one of supersymmetric Yang-Mills theory. The running of the pure Yang-Mills coupling is computed and the deviation from the two loop result is presented. We then compare the deviation with the one obtained from lattice data also with respect...

  13. Matching Behavior as a Tradeoff Between Reward Maximization and Demands on Neural Computation [version 2; referees: 2 approved

    Directory of Open Access Journals (Sweden)

    Jan Kubanek

    2015-10-01

    Full Text Available When faced with a choice, humans and animals commonly distribute their behavior in proportion to the frequency of payoff of each option. Such behavior is referred to as matching and has been captured by the matching law. However, matching is not a general law of economic choice. Matching in its strict sense seems to be specifically observed in tasks whose properties make matching an optimal or a near-optimal strategy. We engaged monkeys in a foraging task in which matching was not the optimal strategy. Over-matching the proportions of the mean offered reward magnitudes would yield more reward than matching, yet, surprisingly, the animals almost exactly matched them. To gain insight into this phenomenon, we modeled the animals' decision-making using a mechanistic model. The model accounted for the animals' macroscopic and microscopic choice behavior. When the models' three parameters were not constrained to mimic the monkeys' behavior, the model over-matched the reward proportions and in doing so, harvested substantially more reward than the monkeys. This optimized model revealed a marked bottleneck in the monkeys' choice function that compares the value of the two options. The model featured a very steep value comparison function relative to that of the monkeys. The steepness of the value comparison function had a profound effect on the earned reward and on the level of matching. We implemented this value comparison function through responses of simulated biological neurons. We found that due to the presence of neural noise, steepening the value comparison requires an exponential increase in the number of value-coding neurons. Matching may be a compromise between harvesting satisfactory reward and the high demands placed by neural noise on optimal neural computation.

  14. Application of a neural network for reflectance spectrum classification

    Science.gov (United States)

    Yang, Gefei; Gartley, Michael

    2017-05-01

    Traditional reflectance spectrum classification algorithms are based on comparing spectrum across the electromagnetic spectrum anywhere from the ultra-violet to the thermal infrared regions. These methods analyze reflectance on a pixel by pixel basis. Inspired by high performance that Convolution Neural Networks (CNN) have demonstrated in image classification, we applied a neural network to analyze directional reflectance pattern images. By using the bidirectional reflectance distribution function (BRDF) data, we can reformulate the 4-dimensional into 2 dimensions, namely incident direction × reflected direction × channels. Meanwhile, RIT's micro-DIRSIG model is utilized to simulate additional training samples for improving the robustness of the neural networks training. Unlike traditional classification by using hand-designed feature extraction with a trainable classifier, neural networks create several layers to learn a feature hierarchy from pixels to classifier and all layers are trained jointly. Hence, the our approach of utilizing the angular features are different to traditional methods utilizing spatial features. Although training processing typically has a large computational cost, simple classifiers work well when subsequently using neural network generated features. Currently, most popular neural networks such as VGG, GoogLeNet and AlexNet are trained based on RGB spatial image data. Our approach aims to build a directional reflectance spectrum based neural network to help us to understand from another perspective. At the end of this paper, we compare the difference among several classifiers and analyze the trade-off among neural networks parameters.

  15. Neural Correlates of Racial Ingroup Bias in Observing Computer-Animated Social Encounters

    Directory of Open Access Journals (Sweden)

    Yuta Katsumi

    2018-01-01

    Full Text Available Despite evidence for the role of group membership in the neural correlates of social cognition, the mechanisms associated with processing non-verbal behaviors displayed by racially ingroup vs. outgroup members remain unclear. Here, 20 Caucasian participants underwent fMRI recording while observing social encounters with ingroup and outgroup characters displaying dynamic and static non-verbal behaviors. Dynamic behaviors included approach and avoidance behaviors, preceded or not by a handshake; both dynamic and static behaviors were followed by participants’ ratings. Behaviorally, participants showed bias toward their ingroup members, demonstrated by faster/slower reaction times for evaluating ingroup static/approach behaviors, respectively. At the neural level, despite overall similar responses in the action observation network to ingroup and outgroup encounters, the medial prefrontal cortex showed dissociable activation, possibly reflecting spontaneous processing of ingroup static behaviors and positive evaluations of ingroup approach behaviors. The anterior cingulate and superior frontal cortices also showed sensitivity to race, reflected in coordinated and reduced activation for observing ingroup static behaviors. Finally, the posterior superior temporal sulcus showed uniquely increased activity to observing ingroup handshakes. These findings shed light on the mechanisms of racial ingroup bias in observing social encounters, and have implications for understanding factors related to successful interactions with individuals from diverse backgrounds.

  16. COMPUTATIONAL ANALYSIS BASED ON ARTIFICIAL NEURAL NETWORKS FOR AIDING IN DIAGNOSING OSTEOARTHRITIS OF THE LUMBAR SPINE.

    Science.gov (United States)

    Veronezi, Carlos Cassiano Denipotti; de Azevedo Simões, Priscyla Waleska Targino; Dos Santos, Robson Luiz; da Rocha, Edroaldo Lummertz; Meláo, Suelen; de Mattos, Merisandra Côrtes; Cechinel, Cristian

    2011-01-01

    To ascertain the advantages of applying artificial neural networks to recognize patterns on lumbar spine radiographies in order to aid in the process of diagnosing primary osteoarthritis. This was a cross-sectional descriptive analytical study with a quantitative approach and an emphasis on diagnosis. The training set was composed of images collected between January and July 2009 from patients who had undergone lateral-view digital radiographies of the lumbar spine, which were provided by a radiology clinic located in the municipality of Criciúma (SC). Out of the total of 260 images gathered, those with distortions, those presenting pathological conditions that altered the architecture of the lumbar spine and those with patterns that were difficult to characterize were discarded, resulting in 206 images. The image data base (n = 206) was then subdivided, resulting in 68 radiographies for the training stage, 68 images for tests and 70 for validation. A hybrid neural network based on Kohonen self-organizing maps and on Multilayer Perceptron networks was used. After 90 cycles, the validation was carried out on the best results, achieving accuracy of 62.85%, sensitivity of 65.71% and specificity of 60%. Even though the effectiveness shown was moderate, this study is still innovative. The values show that the technique used has a promising future, pointing towards further studies on image and cycle processing methodology with a larger quantity of radiographies.

  17. The Prediction of Bandwidth On Need Computer Network Through Artificial Neural Network Method of Backpropagation

    Directory of Open Access Journals (Sweden)

    Ikhthison Mekongga

    2014-02-01

    Full Text Available The need for bandwidth has been increasing recently. This is because the development of internet infrastructure is also increasing so that we need an economic and efficient provider system. This can be achieved through good planning and a proper system. The prediction of the bandwidth consumption is one of the factors that support the planning for an efficient internet service provider system. Bandwidth consumption is predicted using ANN. ANN is an information processing system which has similar characteristics as the biologic al neural network.  ANN  is  chosen  to  predict  the  consumption  of  the  bandwidth  because  ANN  has  good  approachability  to  non-linearity.  The variable used in ANN is the historical load data. A bandwidth consumption information system was built using neural networks  with a backpropagation algorithm to make the use of bandwidth more efficient in the future both in the rental rate of the bandwidth and in the usage of the bandwidth.Keywords: Forecasting, Bandwidth, Backpropagation

  18. Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm.

    Science.gov (United States)

    Arabasadi, Zeinab; Alizadehsani, Roohallah; Roshanzamir, Mohamad; Moosaei, Hossein; Yarifard, Ali Asghar

    2017-04-01

    Cardiovascular disease is one of the most rampant causes of death around the world and was deemed as a major illness in Middle and Old ages. Coronary artery disease, in particular, is a widespread cardiovascular malady entailing high mortality rates. Angiography is, more often than not, regarded as the best method for the diagnosis of coronary artery disease; on the other hand, it is associated with high costs and major side effects. Much research has, therefore, been conducted using machine learning and data mining so as to seek alternative modalities. Accordingly, we herein propose a highly accurate hybrid method for the diagnosis of coronary artery disease. As a matter of fact, the proposed method is able to increase the performance of neural network by approximately 10% through enhancing its initial weights using genetic algorithm which suggests better weights for neural network. Making use of such methodology, we achieved accuracy, sensitivity and specificity rates of 93.85%, 97% and 92% respectively, on Z-Alizadeh Sani dataset. Copyright © 2017 Elsevier B.V. All rights reserved.

  19. Inspiration, anyone? (Editorial

    Directory of Open Access Journals (Sweden)

    Lindsay Glynn

    2006-09-01

    Full Text Available I have to admit that writing an editorial for this issue was a struggle. Trying to sit down and write when the sun was shining outside and most of my colleagues were on vacation was, to say the least, difficult. Add to that research projects and conferences…let’s just say that I found myself less than inspired. A pitiful plea for ideas to a colleague resulted in the reintroduction to a few recent evidence based papers and resources which inspired further searching and reading. Though I generally find myself surrounded (more like buried in research papers and EBLIP literature, somehow I had missed the great strides that have been made of late in the world of evidence based library and information practice. I realize now that I am inspired by the researchers, authors and innovators who are putting EBLIP on the proverbial map. My biggest beef with library literature in general has been the plethora of articles highlighting what we should be doing. Take a close look at the evidence based practitioners in the information professions: these are some of the people who are actively practicing what has been preached for the past few years. Take, for example, the about‐to‐be released Libraries using Evidence Toolkit by Northern Sydney Central Coast Health and The University of Newcastle, Australia (see their announcement in this issue. An impressive advisory group is responsible for maintaining the currency and relevancy of the site as well as promoting the site and acting as a steering committee for related projects. This group is certainly doing more than “talking the talk”: they took their experience at the 3rd International Evidence Based Librarianship Conference and did something with the information they obtained by implementing solutions that worked in their environment. The result? The creation of a collection of tools for all of us to use. This toolkit is just what EBLIP needs: a portal to resources aimed at supporting the information

  20. Introduction to neural networks

    CERN Document Server

    James, Frederick E

    1994-02-02

    1. Introduction and overview of Artificial Neural Networks. 2,3. The Feed-forward Network as an inverse Problem, and results on the computational complexity of network training. 4.Physics applications of neural networks.

  1. An Application of Artificial Neural Network to Compute the Resonant Frequency of E-Shaped Compact Microstrip Antennas

    Science.gov (United States)

    Akdagli, Ali; Toktas, Abdurrahim; Kayabasi, Ahmet; Develi, Ibrahim

    2013-09-01

    An application of artificial neural network (ANN) based on multilayer perceptrons (MLP) to compute the resonant frequency of E-shaped compact microstrip antennas (ECMAs) is presented in this paper. The resonant frequencies of 144 ECMAs with different dimensions and electrical parameters were firstly determined by using IE3D(tm) software based on the method of moments (MoM), then the ANN model for computing the resonant frequency was built by considering the simulation data. The parameters and respective resonant frequency values of 130 simulated ECMAs were employed for training and the remaining 14 ECMAs were used for testing the model. The computed resonant frequencies for training and testing by ANN were obtained with the average percentage errors (APE) of 0.257% and 0.523%, respectively. The validity and accuracy of the present approach was verified on the measurement results of an ECMA fabricated in this study. Furthermore, the effects of the slots loading method over the resonant frequency were investigated to explain the relationship between the slots and resonant frequency.

  2. A neural network based computational model to predict the output power of different types of photovoltaic cells.

    Directory of Open Access Journals (Sweden)

    WenBo Xiao

    Full Text Available In this article, we introduced an artificial neural network (ANN based computational model to predict the output power of three types of photovoltaic cells, mono-crystalline (mono-, multi-crystalline (multi-, and amorphous (amor- crystalline. The prediction results are very close to the experimental data, and were also influenced by numbers of hidden neurons. The order of the solar generation power output influenced by the external conditions from smallest to biggest is: multi-, mono-, and amor- crystalline silicon cells. In addition, the dependences of power prediction on the number of hidden neurons were studied. For multi- and amorphous crystalline cell, three or four hidden layer units resulted in the high correlation coefficient and low MSEs. For mono-crystalline cell, the best results were achieved at the hidden layer unit of 8.

  3. Neural Correlates of Phrase Quadrature Perception in Harmonic Rhythm: An EEG Study (Using a Brain-Computer Interface).

    Science.gov (United States)

    Fernández-Sotos, Alicia; Martínez-Rodrigo, Arturo; Moncho-Bogani, José; Latorre, José Miguel; Fernández-Caballero, Antonio

    2017-11-13

    For the sake of establishing the neural correlates of phrase quadrature perception in harmonic rhythm, a musical experiment has been designed to induce music-evoked stimuli related to one important aspect of harmonic rhythm, namely the phrase quadrature. Brain activity is translated to action through electroencephalography (EEG) by using a brain-computer interface. The power spectral value of each EEG channel is estimated to obtain how power variance distributes as a function of frequency. The results of processing the acquired signals are in line with previous studies that use different musical parameters to induce emotions. Indeed, our experiment shows statistical differences in theta and alpha bands between the fulfillment and break of phrase quadrature, an important cue of harmonic rhythm, in two classical sonatas.

  4. A neural network based computational model to predict the output power of different types of photovoltaic cells.

    Science.gov (United States)

    Xiao, WenBo; Nazario, Gina; Wu, HuaMing; Zhang, HuaMing; Cheng, Feng

    2017-01-01

    In this article, we introduced an artificial neural network (ANN) based computational model to predict the output power of three types of photovoltaic cells, mono-crystalline (mono-), multi-crystalline (multi-), and amorphous (amor-) crystalline. The prediction results are very close to the experimental data, and were also influenced by numbers of hidden neurons. The order of the solar generation power output influenced by the external conditions from smallest to biggest is: multi-, mono-, and amor- crystalline silicon cells. In addition, the dependences of power prediction on the number of hidden neurons were studied. For multi- and amorphous crystalline cell, three or four hidden layer units resulted in the high correlation coefficient and low MSEs. For mono-crystalline cell, the best results were achieved at the hidden layer unit of 8.

  5. Temporal Modeling of Neural Net Input/Output Behaviors: The Case of XOR

    Directory of Open Access Journals (Sweden)

    Bernard P. Zeigler

    2017-01-01

    Full Text Available In the context of the modeling and simulation of neural nets, we formulate definitions for the behavioral realization of memoryless functions. The definitions of realization are substantively different for deterministic and stochastic systems constructed of neuron-inspired components. In contrast to earlier generations of neural net models, third generation spiking neural nets exhibit important temporal and dynamic properties, and random neural nets provide alternative probabilistic approaches. Our definitions of realization are based on the Discrete Event System Specification (DEVS formalism that fundamentally include temporal and probabilistic characteristics of neuron system inputs, state, and outputs. The realizations that we construct—in particular for the Exclusive Or (XOR logic gate—provide insight into the temporal and probabilistic characteristics that real neural systems might display. Our results provide a solid system-theoretical foundation and simulation modeling framework for the high-performance computational support of such applications.

  6. Detection and diagnosis of colitis on computed tomography using deep convolutional neural networks.

    Science.gov (United States)

    Liu, Jiamin; Wang, David; Lu, Le; Wei, Zhuoshi; Kim, Lauren; Turkbey, Evrim B; Sahiner, Berkman; Petrick, Nicholas A; Summers, Ronald M

    2017-09-01

    Colitis refers to inflammation of the inner lining of the colon that is frequently associated with infection and allergic reactions. In this paper, we propose deep convolutional neural networks methods for lesion-level colitis detection and a support vector machine (SVM) classifier for patient-level colitis diagnosis on routine abdominal CT scans. The recently developed Faster Region-based Convolutional Neural Network (Faster RCNN) is utilized for lesion-level colitis detection. For each 2D slice, rectangular region proposals are generated by region proposal networks (RPN). Then, each region proposal is jointly classified and refined by a softmax classifier and bounding-box regressor. Two convolutional neural networks, eight layers of ZF net and 16 layers of VGG net are compared for colitis detection. Finally, for each patient, the detections on all 2D slices are collected and a SVM classifier is applied to develop a patient-level diagnosis. We trained and evaluated our method with 80 colitis patients and 80 normal cases using 4 × 4-fold cross validation. For lesion-level colitis detection, with ZF net, the mean of average precisions (mAP) were 48.7% and 50.9% for RCNN and Faster RCNN, respectively. The detection system achieved sensitivities of 51.4% and 54.0% at two false positives per patient for RCNN and Faster RCNN, respectively. With VGG net, Faster RCNN increased the mAP to 56.9% and increased the sensitivity to 58.4% at two false positive per patient. For patient-level colitis diagnosis, with ZF net, the average areas under the ROC curve (AUC) were 0.978 ± 0.009 and 0.984 ± 0.008 for RCNN and Faster RCNN method, respectively. The difference was not statistically significant with P = 0.18. At the optimal operating point, the RCNN method correctly identified 90.4% (72.3/80) of the colitis patients and 94.0% (75.2/80) of normal cases. The sensitivity improved to 91.6% (73.3/80) and the specificity improved to 95.0% (76.0/80) for the Faster RCNN

  7. Inspiration and the Oulipo

    Directory of Open Access Journals (Sweden)

    Chris Andrews

    2005-01-01

    Full Text Available In the Ion and the Phaedrus Plato establishes an opposition between technique and inspiration in literary composition. He has Socrates argue that true poets are inspired and thereby completely deprived of reason. It is often said that the writers of the French collective known as the Oulipo have inverted the Platonic opposition, substituting a scientific conception of technique—formalization—for inspiration. Some of the group's members aim to do this, but not the best-known writers. Jacques Roubaud and Georges Perec practice traditional imitation alongside formalization. Imitation is a bodily activity with an important non-technical aspect. Raymond Queneau consistently points to an indispensable factor in literary composition that exceeds both formalization and imitation but is inimical to neither. Sometimes he calls this factor "inspiration"; sometimes he speaks of "the unknown" and the "the unpredictable," which must confirm the writer's efforts and intentions. The lack of consensus within the Oulipo on the question of inspiration is not a fault or a weakness, since the group has never claimed to adhere to a unified doctrine. However, to present Queneau as a radical formalist is to distort his poetics.

  8. Implementing Signature Neural Networks with Spiking Neurons.

    Science.gov (United States)

    Carrillo-Medina, José Luis; Latorre, Roberto

    2016-01-01

    Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm-i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data-to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the absence

  9. Implementing Signature Neural Networks with Spiking Neurons

    Science.gov (United States)

    Carrillo-Medina, José Luis; Latorre, Roberto

    2016-01-01

    Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm—i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data—to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the

  10. A Neural Model of How the Brain Computes Heading from Optic Flow in Realistic Scenes

    Science.gov (United States)

    Browning, N. Andrew; Grossberg, Stephen; Mingolla, Ennio

    2009-01-01

    Visually-based navigation is a key competence during spatial cognition. Animals avoid obstacles and approach goals in novel cluttered environments using optic flow to compute heading with respect to the environment. Most navigation models try either explain data, or to demonstrate navigational competence in real-world environments without regard…

  11. Goal-Directed Decision Making as Probabilistic Inference: A Computational Framework and Potential Neural Correlates

    Science.gov (United States)

    Solway, Alec; Botvinick, Matthew M.

    2012-01-01

    Recent work has given rise to the view that reward-based decision making is governed by two key controllers: a habit system, which stores stimulus-response associations shaped by past reward, and a goal-oriented system that selects actions based on their anticipated outcomes. The current literature provides a rich body of computational theory…

  12. BNCI Horizon 2020 - Towards a Roadmap for Brain/Neural Computer Interaction

    NARCIS (Netherlands)

    Brunner, Clemens; Blankertz, Benjamin; Cincotti, Febo; Kübler, Andrea; Mattia, Donatella; Miralles, Felip; Nijholt, Antinus; Otal, Begonya; Salomon, Patric; Müller-Putz, Gernot R.; Stephanidis, C; Antona, M.

    2014-01-01

    In this paper, we present BNCI Horizon 2020, an EU Coordination and Support Action (CSA) that will provide a roadmap for brain-computer interaction research for the next years, starting in 2013, and aiming at research efforts until 2020 and beyond. The project is a successor of the earlier EU-funded

  13. Morphological neural networks

    Energy Technology Data Exchange (ETDEWEB)

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

    1996-12-31

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

  14. How attention and contrast gain control interact to regulate lightness contrast and assimilation: a computational neural model.

    Science.gov (United States)

    Rudd, Michael E

    2010-12-31

    Recent theories of lightness perception assume that lightness (perceived reflectance) is computed by a process that contrasts the target's luminance with that of one or more regions in its spatial surround. A challenge for any such theory is the phenomenon of lightness assimilation, which occurs when increasing the luminance of a surround region increases the target lightness: the opposite of contrast. Here contrast and assimilation are studied quantitatively in lightness matching experiments utilizing concentric disk-and-ring displays. Whether contrast or assimilation is seen depends on a number of factors including: the luminance relations of the target, surround, and background; surround size; and matching instructions. When assimilation occurs, it is always part of a larger pattern in which assimilation and contrast both occur over different ranges of surround luminance. These findings are quantitatively modeled by a theory that assumes lightness is computed from a weighted sum of responses of edge detector neurons in visual cortex. The magnitude of the neural response to an edge is regulated by a combination of contrast gain control acting between neighboring edge detectors and a top-down attentional gain control that selectively weights the response to stimulus edges according to their task relevance.

  15. Neural computations underlying arbitration between model-based and model-free learning

    Science.gov (United States)

    Lee, Sang Wan; Shimojo, Shinsuke; O’Doherty, John P.

    2014-01-01

    SUMMARY There is accumulating neural evidence to support the existence of two distinct systems for guiding action-selection in the brain, a deliberative “model-based” and a reflexive “model-free” system. However, little is known about how the brain determines which of these systems controls behavior at one moment in time. We provide evidence for an arbitration mechanism that allocates the degree of control over behavior by model-based and model-free systems as a function of the reliability of their respective predictions. We show that inferior lateral prefrontal and frontopolar cortex encode both reliability signals and the output of a comparison between those signals, implicating these regions in the arbitration process. Moreover, connectivity between these regions and model-free valuation areas is negatively modulated by the degree of model-based control in the arbitrator, suggesting that arbitration may work through modulation of the model-free valuation system when the arbitrator deems that the model-based system should drive behavior. PMID:24507199

  16. A neural computation for visual acuity in the presence of eye movements.

    Directory of Open Access Journals (Sweden)

    Xaq Pitkow

    2007-12-01

    Full Text Available Humans can distinguish visual stimuli that differ by features the size of only a few photoreceptors. This is possible despite the incessant image motion due to fixational eye movements, which can be many times larger than the features to be distinguished. To perform well, the brain must identify the retinal firing patterns induced by the stimulus while discounting similar patterns caused by spontaneous retinal activity. This is a challenge since the trajectory of the eye movements, and consequently, the stimulus position, are unknown. We derive a decision rule for using retinal spike trains to discriminate between two stimuli, given that their retinal image moves with an unknown random walk trajectory. This algorithm dynamically estimates the probability of the stimulus at different retinal locations, and uses this to modulate the influence of retinal spikes acquired later. Applied to a simple orientation-discrimination task, the algorithm performance is consistent with human acuity, whereas naive strategies that neglect eye movements perform much worse. We then show how a simple, biologically plausible neural network could implement this algorithm using a local, activity-dependent gain and lateral interactions approximately matched to the statistics of eye movements. Finally, we discuss evidence that such a network could be operating in the primary visual cortex.

  17. 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.

  18. Real-time simulation of a spiking neural network model of the basal ganglia circuitry using general purpose computing on graphics processing units.

    Science.gov (United States)

    Igarashi, Jun; Shouno, Osamu; Fukai, Tomoki; Tsujino, Hiroshi

    2011-11-01

    Real-time simulation of a biologically realistic spiking neural network is necessary for evaluation of its capacity to interact with real environments. However, the real-time simulation of such a neural network is difficult due to its high computational costs that arise from two factors: (1) vast network size and (2) the complicated dynamics of biologically realistic neurons. In order to address these problems, mainly the latter, we chose to use general purpose computing on graphics processing units (GPGPUs) for simulation of such a neural network, taking advantage of the powerful computational capability of a graphics processing unit (GPU). As a target for real-time simulation, we used a model of the basal ganglia that has been developed according to electrophysiological and anatomical knowledge. The model consists of heterogeneous populations of 370 spiking model neurons, including computationally heavy conductance-based models, connected by 11,002 synapses. Simulation of the model has not yet been performed in real-time using a general computing server. By parallelization of the model on the NVIDIA Geforce GTX 280 GPU in data-parallel and task-parallel fashion, faster-than-real-time simulation was robustly realized with only one-third of the GPU's total computational resources. Furthermore, we used the GPU's full computational resources to perform faster-than-real-time simulation of three instances of the basal ganglia model; these instances consisted of 1100 neurons and 33,006 synapses and were synchronized at each calculation step. Finally, we developed software for simultaneous visualization of faster-than-real-time simulation output. These results suggest the potential power of GPGPU techniques in real-time simulation of realistic neural networks. Copyright © 2011 Elsevier Ltd. All rights reserved.

  19. EDITORIAL: Focus on the neural interface Focus on the neural interface

    Science.gov (United States)

    Durand, Dominique M.

    2009-10-01

    The possibility of an effective connection between neural tissue and computers has inspired scientists and engineers to develop new ways of controlling and obtaining information from the nervous system. These applications range from `brain hacking' to neural control of artificial limbs with brain signals. Notwithstanding the significant advances in neural prosthetics in the last few decades and the success of some stimulation devices such as cochlear prosthesis, neurotechnology remains below its potential for restoring neural function in patients with nervous system disorders. One of the reasons for this limited impact can be found at the neural interface and close attention to the integration between electrodes and tissue should improve the possibility of successful outcomes. The neural interfaces research community consists of investigators working in areas such as deep brain stimulation, functional neuromuscular/electrical stimulation, auditory prostheses, cortical prostheses, neuromodulation, microelectrode array technology, brain-computer/machine interfaces. Following the success of previous neuroprostheses and neural interfaces workshops, funding (from NIH) was obtained to establish a biennial conference in the area of neural interfaces. The first Neural Interfaces Conference took place in Cleveland, OH in 2008 and several topics from this conference have been selected for publication in this special section of the Journal of Neural Engineering. Three `perspectives' review the areas of neural regeneration (Corredor and Goldberg), cochlear implants (O'Leary et al) and neural prostheses (Anderson). Seven articles focus on various aspects of neural interfacing. One of the most popular of these areas is the field of brain-computer interfaces. Fraser et al, report on a method to generate robust control with simple signal processing algorithms of signals obtained with electrodes implanted in the brain. One problem with implanted electrode arrays, however, is that

  20. Modeling daily discharge responses of a large karstic aquifer using soft computing methods: Artificial neural network and neuro-fuzzy

    Science.gov (United States)

    Kurtulus, Bedri; Razack, Moumtaz

    2010-02-01

    SummaryThis paper compares two methods for modeling karst aquifers, which are heterogeneous, highly non-linear, and hierarchical systems. There is a clear need to model these systems given the crucial role they play in water supply in many countries. In recent years, the main components of soft computing (fuzzy logic (FL), and Artificial Neural Networks, (ANNs)) have come to prevail in the modeling of complex non-linear systems in different scientific and technologic disciplines. In this study, Artificial Neural Networks and Adaptive Neuro-Fuzzy Interface System (ANFIS) methods were used for the prediction of daily discharge of karstic aquifers and their capability was compared. The approach was applied to 7 years of daily data of La Rochefoucauld karst system in south-western France. In order to predict the karst daily discharges, single-input (rainfall, piezometric level) vs. multiple-input (rainfall and piezometric level) series were used. In addition to these inputs, all models used measured or simulated discharges from the previous days with a specified delay. The models were designed in a Matlab™ environment. An automatic procedure was used to select the best calibrated models. Daily discharge predictions were then performed using the calibrated models. Comparing predicted and observed hydrographs indicates that both models (ANN and ANFIS) provide close predictions of the karst daily discharges. The summary statistics of both series (observed and predicted daily discharges) are comparable. The performance of both models is improved when the number of inputs is increased from one to two. The root mean square error between the observed and predicted series reaches a minimum for two-input models. However, the ANFIS model demonstrates a better performance than the ANN model to predict peak flow. The ANFIS approach demonstrates a better generalization capability and slightly higher performance than the ANN, especially for peak discharges.

  1. Xenopus laevis: an ideal experimental model for studying the developmental dynamics of neural network assembly and sensory-motor computations.

    Science.gov (United States)

    Straka, Hans; Simmers, John

    2012-04-01

    The amphibian Xenopus laevis represents a highly amenable model system for exploring the ontogeny of central neural networks, the functional establishment of sensory-motor transformations, and the generation of effective motor commands for complex behaviors. Specifically, the ability to employ a range of semi-intact and isolated preparations for in vitro morphophysiological experimentation has provided new insights into the developmental and integrative processes associated with the generation of locomotory behavior during changing life styles. In vitro electrophysiological studies have begun to explore the functional assembly, disassembly and dynamic plasticity of spinal pattern generating circuits as Xenopus undergoes the developmental switch from larval tail-based swimming to adult limb-based locomotion. Major advances have also been made in understanding the developmental onset of multisensory signal processing for reactive gaze and posture stabilizing reflexes during self-motion. Additionally, recent evidence from semi-intact animal and isolated CNS experiments has provided compelling evidence that in Xenopus tadpoles, predictive feed-forward signaling from the spinal locomotor pattern generator are engaged in minimizing visual disturbances during tail-based swimming. This new concept questions the traditional view of retinal image stabilization that in vertebrates has been exclusively attributed to sensory-motor transformations of body/head motion-detecting signals. Moreover, changes in visuomotor demands associated with the developmental transition in propulsive strategy from tail- to limb-based locomotion during metamorphosis presumably necessitates corresponding adaptive alterations in the intrinsic spinoextraocular coupling mechanism. Consequently, Xenopus provides a unique opportunity to address basic questions on the developmental dynamics of neural network assembly and sensory-motor computations for vertebrate motor behavior in general. Copyright

  2. Inspiring Exercises for Undergraduates.

    Science.gov (United States)

    Kuo, Tzee-Char

    1999-01-01

    Inspiring exercises are used to guide students at all levels to rediscover the essential meaning of various individual pieces of mathematics. Presents five sets of examples including Abel's identity, Hensel's lemma, finitely generated Abelian groups, Baire's category theorem, and the Weierstrass preparation theorem. (Author/ASK)

  3. Nature as Inspiration

    Science.gov (United States)

    Tank, Kristina; Moore, Tamara; Strnat, Meg

    2015-01-01

    This article describes the final lesson within a seven-day STEM and literacy unit that is part of the Picture STEM curriculum (pictureSTEM. org) and uses engineering to integrate science and mathematics learning in a meaningful way (Tank and Moore 2013). For this engineering challenge, students used nature as a source of inspiration for designs to…

  4. Haring-Inspired Designs

    Science.gov (United States)

    Corfield, Marie

    2007-01-01

    While exploring Keith Haring's Web site one day, the author discovered two untitled pieces from 1982 that became inspiration pieces for a lesson on symmetry and complementary colors for her second graders. Haring's simple shapes and colors and playful images are very similar to those found in the margins or on the cover of any typical child's…

  5. Inspirations in medical genetics.

    Science.gov (United States)

    Asadollahi, Reza

    2016-02-01

    There are abundant instances in the history of genetics and medical genetics to illustrate how curiosity, charisma of mentors, nature, art, the saving of lives and many other matters have inspired great discoveries. These achievements from deciphering genetic concepts to characterizing genetic disorders have been crucial for management of the patients. There remains, however, a long pathway ahead. © The Author(s) 2014.

  6. Ndebele Inspired Houses

    Science.gov (United States)

    Rice, Nicole

    2012-01-01

    The house paintings of the South African Ndebele people are more than just an attempt to improve the aesthetics of a community; they are a source of identity and significance for Ndebele women. In this article, the author describes an art project wherein students use the tradition of Ndebele house painting as inspiration for creating their own…

  7. Airway Measurement for Airway Remodeling Defined by Post-Bronchodilator FEV1/FVC in Asthma: Investigation Using Inspiration-Expiration Computed Tomography

    OpenAIRE

    Chae, Eun Jin; Kim, Tae-Bum; Cho, You Sook; Park, Chan-Sun; Seo, Joon Beom; Kim, Namkug; Moon, Hee-Bom

    2010-01-01

    Purpose Airway remodeling may be responsible for irreversible airway obstruction in asthma, and a low post-bronchodilator FEV1/FVC ratio can be used as a noninvasive marker of airway remodeling. We investigated correlations between airway wall indices on computed tomography (CT) and various clinical indices, including post-bronchodilator FEV1/FVC ratio, in patients with asthma. Methods Volumetric CT was performed on 22 stable asthma patients who were taking inhaled corticosteroids. Airway dim...

  8. Neural networks for data compression and invariant image recognition

    Science.gov (United States)

    Gardner, Sheldon

    1989-01-01

    An approach to invariant image recognition (I2R), based upon a model of biological vision in the mammalian visual system (MVS), is described. The complete I2R model incorporates several biologically inspired features: exponential mapping of retinal images, Gabor spatial filtering, and a neural network associative memory. In the I2R model, exponentially mapped retinal images are filtered by a hierarchical set of Gabor spatial filters (GSF) which provide compression of the information contained within a pixel-based image. A neural network associative memory (AM) is used to process the GSF coded images. We describe a 1-D shape function method for coding of scale and rotationally invariant shape information. This method reduces image shape information to a periodic waveform suitable for coding as an input vector to a neural network AM. The shape function method is suitable for near term applications on conventional computing architectures equipped with VLSI FFT chips to provide a rapid image search capability.

  9. Neural Systems Laboratory

    Data.gov (United States)

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

  10. Nature-inspired design of hybrid intelligent systems

    CERN Document Server

    Castillo, Oscar; Kacprzyk, Janusz

    2017-01-01

    This book highlights recent advances in the design of hybrid intelligent systems based on nature-inspired optimization and their application in areas such as intelligent control and robotics, pattern recognition, time series prediction, and optimization of complex problems. The book is divided into seven main parts, the first of which addresses theoretical aspects of and new concepts and algorithms based on type-2 and intuitionistic fuzzy logic systems. The second part focuses on neural network theory, and explores the applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The book’s third part presents enhancements to meta-heuristics based on fuzzy logic techniques and describes new nature-inspired optimization algorithms that employ fuzzy dynamic adaptation of parameters, while the fourth part presents diverse applications of nature-inspired optimization algorithms. In turn, the fifth part investigates applications of fuzzy logic in diverse areas, such as...

  11. The integration of social influence and reward: Computational approaches and neural evidence.

    Science.gov (United States)

    Tomlin, Damon; Nedic, Andrea; Prentice, Deborah A; Holmes, Philip; Cohen, Jonathan D

    2017-05-24

    Decades of research have established that decision-making is dramatically impacted by both the rewards an individual receives and the behavior of others. How do these distinct influences exert their influence on an individual's actions, and can the resulting behavior be effectively captured in a computational model? To address this question, we employed a novel spatial foraging game in which groups of three participants sought to find the most rewarding location in an unfamiliar two-dimensional space. As the game transitioned from one block to the next, the availability of information regarding other group members was varied systematically, revealing the relative impacts of feedback from the environment and information from other group members on individual decision-making. Both reward-based and socially-based sources of information exerted a significant influence on behavior, and a computational model incorporating these effects was able to recapitulate several key trends in the behavioral data. In addition, our findings suggest how these sources were processed and combined during decision-making. Analysis of reaction time, location of gaze, and functional magnetic resonance imaging (fMRI) data indicated that these distinct sources of information were integrated simultaneously for each decision, rather than exerting their influence in a separate, all-or-none fashion across separate subsets of trials. These findings add to our understanding of how the separate influences of reward from the environment and information derived from other social agents are combined to produce decisions.

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

    Science.gov (United States)

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

    2014-01-01

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

  13. Computational Modelling of the Neural Representation of Object Shape in the Primate Ventral Visual System

    Directory of Open Access Journals (Sweden)

    Akihiro eEguchi

    2015-08-01

    Full Text Available Neurons in successive stages of the primate ventral visual pathway encode the spatial structure of visual objects. In this paper, we investigate through computer simulation how these cell firing properties may develop through unsupervised visually-guided learning. Individual neurons in the model are shown to exploit statistical regularity and temporal continuity of the visual inputs during training to learn firing properties that are similar to neurons in V4 and TEO. Neurons in V4 encode the conformation of boundary contour elements at a particular position within an object regardless of the location of the object on the retina, while neurons in TEO integrate information from multiple boundary contour elements. This representation goes beyond mere object recognition, in which neurons simply respond to the presence of a whole object, but provides an essential foundation from which the brain is subsequently able to recognise the whole object.

  14. Neural correlates of user-initiated motor success and failure - A brain-computer interface perspective.

    Science.gov (United States)

    Yazmir, Boris; Reiner, Miriam

    2016-11-02

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

  15. Advances in photonic reservoir computing

    Science.gov (United States)

    Van der Sande, Guy; Brunner, Daniel; Soriano, Miguel C.

    2017-05-01

    We review a novel paradigm that has emerged in analogue neuromorphic optical computing. The goal is to implement a reservoir computer in optics, where information is encoded in the intensity and phase of the optical field. Reservoir computing is a bio-inspired approach especially suited for processing time-dependent information. The reservoir's complex and high-dimensional transient response to the input signal is capable of universal computation. The reservoir does not need to be trained, which makes it very well suited for optics. As such, much of the promise of photonic reservoirs lies in their minimal hardware requirements, a tremendous advantage over other hardware-intensive neural network models. We review the two main approaches to optical reservoir computing: networks implemented with multiple discrete optical nodes and the continuous system of a single nonlinear device coupled to delayed feedback.

  16. Advances in photonic reservoir computing

    Directory of Open Access Journals (Sweden)

    Van der Sande Guy

    2017-05-01

    Full Text Available We review a novel paradigm that has emerged in analogue neuromorphic optical computing. The goal is to implement a reservoir computer in optics, where information is encoded in the intensity and phase of the optical field. Reservoir computing is a bio-inspired approach especially suited for processing time-dependent information. The reservoir’s complex and high-dimensional transient response to the input signal is capable of universal computation. The reservoir does not need to be trained, which makes it very well suited for optics. As such, much of the promise of photonic reservoirs lies in their minimal hardware requirements, a tremendous advantage over other hardware-intensive neural network models. We review the two main approaches to optical reservoir computing: networks implemented with multiple discrete optical nodes and the continuous system of a single nonlinear device coupled to delayed feedback.

  17. Cognitive and Neural Plasticity in Older Adults’ Prospective Memory Following Training with the Virtual Week Computer Game

    Directory of Open Access Journals (Sweden)

    Nathan S Rose

    2015-10-01

    Full Text Available Prospective memory (PM – the ability to remember and successfully execute our intentions and planned activities – is critical for functional independence and declines with age, yet few studies have attempted to train PM in older adults. We developed a PM training program using the Virtual Week computer game. Trained participants played the game in twelve, 1-hour sessions over one month. Measures of neuropsychological functions, lab-based PM, event-related potentials (ERPs during performance on a lab-based PM task, instrumental activities of daily living, and real-world PM were assessed before and after training. Performance was compared to both no-contact and active (music training control groups. PM on the Virtual Week game dramatically improved following training relative to controls, suggesting PM plasticity is preserved in older adults. Relative to control participants, training did not produce reliable transfer to laboratory-based tasks, but was associated with a reduction of an ERP component (sustained negativity over occipito-parietal cortex associated with processing PM cues, indicative of more automatic PM retrieval. Most importantly, training produced far transfer to real-world outcomes including improvements in performance on real-world PM and activities of daily living. Real-world gains were not observed in either control group. Our findings demonstrate that short-term training with the Virtual Week game produces cognitive and neural plasticity that may result in real-world benefits to supporting functional independence in older adulthood.

  18. Cognitive and neural plasticity in older adults' prospective memory following training with the Virtual Week computer game.

    Science.gov (United States)

    Rose, Nathan S; Rendell, Peter G; Hering, Alexandra; Kliegel, Matthias; Bidelman, Gavin M; Craik, Fergus I M

    2015-01-01

    Prospective memory (PM) - the ability to remember and successfully execute our intentions and planned activities - is critical for functional independence and declines with age, yet few studies have attempted to train PM in older adults. We developed a PM training program using the Virtual Week computer game. Trained participants played the game in 12, 1-h sessions over 1 month. Measures of neuropsychological functions, lab-based PM, event-related potentials (ERPs) during performance on a lab-based PM task, instrumental activities of daily living, and real-world PM were assessed before and after training. Performance was compared to both no-contact and active (music training) control groups. PM on the Virtual Week game dramatically improved following training relative to controls, suggesting PM plasticity is preserved in older adults. Relative to control participants, training did not produce reliable transfer to laboratory-based tasks, but was associated with a reduction of an ERP component (sustained negativity over occipito-parietal cortex) associated with processing PM cues, indicative of more automatic PM retrieval. Most importantly, training produced far transfer to real-world outcomes including improvements in performance on real-world PM and activities of daily living. Real-world gains were not observed in either control group. Our findings demonstrate that short-term training with the Virtual Week game produces cognitive and neural plasticity that may result in real-world benefits to supporting functional independence in older adulthood.

  19. Cognitive and neural plasticity in older adults’ prospective memory following training with the Virtual Week computer game

    Science.gov (United States)

    Rose, Nathan S.; Rendell, Peter G.; Hering, Alexandra; Kliegel, Matthias; Bidelman, Gavin M.; Craik, Fergus I. M.

    2015-01-01

    Prospective memory (PM) – the ability to remember and successfully execute our intentions and planned activities – is critical for functional independence and declines with age, yet few studies have attempted to train PM in older adults. We developed a PM training program using the Virtual Week computer game. Trained participants played the game in 12, 1-h sessions over 1 month. Measures of neuropsychological functions, lab-based PM, event-related potentials (ERPs) during performance on a lab-based PM task, instrumental activities of daily living, and real-world PM were assessed before and after training. Performance was compared to both no-contact and active (music training) control groups. PM on the Virtual Week game dramatically improved following training relative to controls, suggesting PM plasticity is preserved in older adults. Relative to control participants, training did not produce reliable transfer to laboratory-based tasks, but was associated with a reduction of an ERP component (sustained negativity over occipito-parietal cortex) associated with processing PM cues, indicative of more automatic PM retrieval. Most importantly, training produced far transfer to real-world outcomes including improvements in performance on real-world PM and activities of daily living. Real-world gains were not observed in either control group. Our findings demonstrate that short-term training with the Virtual Week game produces cognitive and neural plasticity that may result in real-world benefits to supporting functional independence in older adulthood. PMID:26578936

  20. Computer-aided diagnosis of mammography using an artificial neural network: predicting the invasiveness of breast cancers from image features

    Science.gov (United States)

    Lo, Joseph Y.; Kim, Jeffrey; Baker, Jay A.; Floyd, Carey E., Jr.

    1996-04-01

    The study aim is to develop an artificial neural network (ANN) for computer-aided diagnosis of mammography. Using 9 mammographic image features and patient age, the ANN predicted whether breast lesions were benign, invasive malignant, or noninvasive malignant. Given only 97 malignant patients, the 3-layer backpropagation ANN successfully predicted the invasiveness of those breast cancers, performing with Az of 0.88 plus or minus 0.03. To determine more generalized clinical performance, a different ANN was developed using 266 consecutive patients (97 malignant, 169 benign). This ANN predicted whether those patients were benign or noninvasive malignant vs. invasive malignant with Az of 0.86 plus or minus 0.03. This study is unique because it is the first to predict the invasiveness of breast cancers using mammographic features and age. This knowledge, which was previously available only through surgical biopsy, may assist in the planning of surgical procedures for patients with breast lesions, and may help reduce the cost and morbidity associated with unnecessary surgical biopsies.

  1. Characterizing cartilage microarchitecture on phase-contrast x-ray computed tomography using deep learning with convolutional neural networks

    Science.gov (United States)

    Deng, Botao; Abidin, Anas Z.; D'Souza, Adora M.; Nagarajan, Mahesh B.; Coan, Paola; Wismüller, Axel

    2017-03-01

    The effectiveness of phase contrast X-ray computed tomography (PCI-CT) in visualizing human patellar cartilage matrix has been demonstrated due to its ability to capture soft tissue contrast on a micrometer resolution scale. Recent studies have shown that off-the-shelf Convolutional Neural Network (CNN) features learned from a nonmedical data set can be used for medical image classification. In this paper, we investigate the ability of features extracted from two different CNNs for characterizing chondrocyte patterns in the cartilage matrix. We obtained features from 842 regions of interest annotated on PCI-CT images of human patellar cartilage using CaffeNet and Inception-v3 Network, which were then used in a machine learning task involving support vector machines with radial basis function kernel to classify the ROIs as healthy or osteoarthritic. Classification performance was evaluated using the area (AUC) under the Receiver Operating Characteristic (ROC) curve. The best classification performance was observed with features from Inception-v3 network (AUC = 0.95), which outperforms features extracted from CaffeNet (AUC = 0.91). These results suggest that such characterization of chondrocyte patterns using features from internal layers of CNNs can be used to distinguish between healthy and osteoarthritic tissue with high accuracy.

  2. Evaluation of a Novel Computer Color Matching System Based on the Improved Back-Propagation Neural Network Model.

    Science.gov (United States)

    Wei, Jiaqiang; Peng, Mengdong; Li, Qing; Wang, Yining

    2016-11-09

    To explore the feasibility of a novel computer color-matching (CCM) system based on the improved back-propagation neural network (BPNN) model by comparing it with the traditional visual method. Forty-three metal-ceramic specimens were fabricated by proportionally mixing porcelain powders. Thirty-nine specimens were randomly selected to train the BPNN model, while the remaining four specimens were used to test and calibrate the model. A CCM system based on the improved BPNN model was constructed using MATLAB software. A comparison of the novel CCM system and the traditional visual method was conducted by evaluating the color reproduction results of 10 maxillary central incisors. Metal-ceramic specimens were fabricated using two color reproduction approaches. Color distributions (L*, a*, and b*) of the target teeth and of the corresponding metal-ceramic specimens were measured using a spectroradiometer. Color differences (ΔE) and color distributions (ΔL*, Δa*, and Δb*) between the teeth and their corresponding specimens were calculated. The average ΔE value of the CCM system was 1.89 ± 0.75, which was lower than that of the visual approach (3.54 ± 1.11, p systems, except for ΔL* (p > 0.05). The novel CCM system produced greater accuracy in color reproduction within the given color space than the traditional visual approach. © 2016 by the American College of Prosthodontists.

  3. Application of Reinforcement Learning Algorithms for the Adaptive Computation of the Smoothing Parameter for Probabilistic Neural Network.

    Science.gov (United States)

    Kusy, Maciej; Zajdel, Roman

    2015-09-01

    In this paper, we propose new methods for the choice and adaptation of the smoothing parameter of the probabilistic neural network (PNN). These methods are based on three reinforcement learning algorithms: Q(0)-learning, Q(λ)-learning, and stateless Q-learning. We regard three types of PNN classifiers: the model that uses single smoothing parameter for the whole network, the model that utilizes single smoothing parameter for each data attribute, and the model that possesses the matrix of smoothing parameters different for each data variable and data class. Reinforcement learning is applied as the method of finding such a value of the smoothing parameter, which ensures the maximization of the prediction ability. PNN models with smoothing parameters computed according to the proposed algorithms are tested on eight databases by calculating the test error with the use of the cross validation procedure. The results are compared with state-of-the-art methods for PNN training published in the literature up to date and, additionally, with PNN whose sigma is determined by means of the conjugate gradient approach. The results demonstrate that the proposed approaches can be used as alternative PNN training procedures.

  4. Neural computational modeling reveals a major role of corticospinal gating of central oscillations in the generation of essential tremor

    Directory of Open Access Journals (Sweden)

    Hong-en Qu

    2017-01-01

    Full Text Available Essential tremor, also referred to as familial tremor, is an autosomal dominant genetic disease and the most common movement disorder. It typically involves a postural and motor tremor of the hands, head or other part of the body. Essential tremor is driven by a central oscillation signal in the brain. However, the corticospinal mechanisms involved in the generation of essential tremor are unclear. Therefore, in this study, we used a neural computational model that includes both monosynaptic and multisynaptic corticospinal pathways interacting with a propriospinal neuronal network. A virtual arm model is driven by the central oscillation signal to simulate tremor activity behavior. Cortical descending commands are classified as alpha or gamma through monosynaptic or multisynaptic corticospinal pathways, which converge respectively on alpha or gamma motoneurons in the spinal cord. Several scenarios are evaluated based on the central oscillation signal passing down to the spinal motoneurons via each descending pathway. The simulated behaviors are compared with clinical essential tremor characteristics to identify the corticospinal pathways responsible for transmitting the central oscillation signal. A propriospinal neuron with strong cortical inhibition performs a gating function in the generation of essential tremor. Our results indicate that the propriospinal neuronal network is essential for relaying the central oscillation signal and the production of essential tremor.

  5. Inspiral into Gargantua

    CERN Document Server

    Gralla, Samuel E; Warburton, Niels

    2016-01-01

    We model the inspiral of a compact object into a more massive black hole rotating very near the theoretical maximum. We find that once the body enters the near-horizon regime the gravitational radiation is characterized by a constant frequency, equal to (twice) the horizon frequency, with an exponentially damped profile. This contrasts with the usual "chirping" behavior and, if detected, would constitute a "smoking gun" for a near-extremal black hole in nature.

  6. Inspiral into Gargantua

    Science.gov (United States)

    Gralla, Samuel E.; Hughes, Scott A.; Warburton, Niels

    2016-08-01

    We model the inspiral of a compact object into a more massive black hole rotating very near the theoretical maximum. We find that once the body enters the near-horizon regime the gravitational radiation is characterized by a constant frequency, equal to (twice) the horizon frequency, with an exponentially damped profile. This contrasts with the usual ‘chirping’ behavior and, if detected, would constitute a ‘smoking gun’ for a near-extremal black hole in nature.

  7. Operant conditioning: a minimal components requirement in artificial spiking neurons designed for bio-inspired robot's controller.

    Science.gov (United States)

    Cyr, André; Boukadoum, Mounir; Thériault, Frédéric

    2014-01-01

    In this paper, we investigate the operant conditioning (OC) learning process within a bio-inspired paradigm, using artificial spiking neural networks (ASNN) to act as robot brain controllers. In biological agents, OC results in behavioral changes learned from the consequences of previous actions, based on progressive prediction adjustment from rewarding or punishing signals. In a neurorobotics context, virtual and physical autonomous robots may benefit from a similar learning skill when facing unknown and unsupervised environments. In this work, we demonstrate that a simple invariant micro-circuit can sustain OC in multiple learning scenarios. The motivation for this new OC implementation model stems from the relatively complex alternatives that have been described in the computational literature and recent advances in neurobiology. Our elementary kernel includes only a few crucial neurons, synaptic links and originally from the integration of habituation and spike-timing dependent plasticity as learning rules. Using several tasks of incremental complexity, our results show that a minimal neural component set is sufficient to realize many OC procedures. Hence, with the proposed OC module, designing learning tasks with an ASNN and a bio-inspired robot context leads to simpler neural architectures for achieving complex behaviors.

  8. Inspiring a generation

    CERN Multimedia

    2012-01-01

    The motto of the 2012 Olympic and Paralympic Games is ‘Inspire a generation’ so it was particularly pleasing to see science, the LHC and Higgs bosons featuring so strongly in the opening ceremony of the Paralympics last week.   It’s a sign of just how far our field has come that such a high-profile event featured particle physics so strongly, and we can certainly add our support to that motto. If the legacy of London 2012 is a generation inspired by science as well as sport, then the games will have more than fulfilled their mission. Particle physics has truly inspiring stories to tell, going well beyond Higgs and the LHC, and the entire community has played its part in bringing the excitement of frontier research in particle physics to a wide audience. Nevertheless, we cannot rest on our laurels: maintaining the kind of enthusiasm for science we witnessed at the Paralympic opening ceremony will require constant vigilance, and creative thinking about ways to rea...

  9. Astronomy. Inspiration. Art

    Science.gov (United States)

    Stanic, N.

    2008-10-01

    This paper speculates how poetry and other kind of arts are tightly related to astronomy. Hence the connection between art and natural sciences in general will be discussed in the frame of ongoing multidisciplinary project `Astronomy. Inspiration. Art' at Public Observatory in Belgrade (started in 2004). This project tends to inspire (better to say `infect') artist with a cosmic themes and fantastic sceneries of the Universe. At the very beginning of the project, Serbian poet and philosopher Laza Lazić (who published 49 books of poetry, stories and novels), as well as writer Gordana Maletić (with 25 published novels for children) were interested to work on The Inspiration by Astronomical Phenomena in Serbian Literature. Five young artists and scientists include their new ideas and new approach to multidisciplinary studies too (Srdjan Djukić, Nenad Jeremić, Olivera Obradović, Romana Vujasinović, Elena Dimoski). Two books that will be presented in details in the frame of this Project, "STARRY CITIES" (http://zavod.co.yu) and "ASTROLIES", don't offer only interesting illustrations, images from the latest astronomical observations and currently accepted cosmological theories -- those books induces, provoking curiosity in a specific and witty way, an adventure and challenge to explore and create.

  10. A Computational Model of Torque Generation: Neural, Contractile, Metabolic and Musculoskeletal Components

    Science.gov (United States)

    Callahan, Damien M.; Umberger, Brian R.; Kent-Braun, Jane A.

    2013-01-01

    The pathway of voluntary joint torque production includes motor neuron recruitment and rate-coding, sarcolemmal depolarization and calcium release by the sarcoplasmic reticulum, force generation by motor proteins within skeletal muscle, and force transmission by tendon across the joint. The direct source of energetic support for this process is ATP hydrolysis. It is possible to examine portions of this physiologic pathway using various in vivo and in vitro techniques, but an integrated view of the multiple processes that ultimately impact joint torque remains elusive. To address this gap, we present a comprehensive computational model of the combined neuromuscular and musculoskeletal systems that includes novel components related to intracellular bioenergetics function. Components representing excitatory drive, muscle activation, force generation, metabolic perturbations, and torque production during voluntary human ankle dorsiflexion were constructed, using a combination of experimentally-derived data and literature values. Simulation results were validated by comparison with torque and metabolic data obtained in vivo. The model successfully predicted peak and submaximal voluntary and electrically-elicited torque output, and accurately simulated the metabolic perturbations associated with voluntary contractions. This novel, comprehensive model could be used to better understand impact of global effectors such as age and disease on various components of the neuromuscular system, and ultimately, voluntary torque output. PMID:23405245

  11. Neural computations mediating one-shot learning in the human brain.

    Science.gov (United States)

    Lee, Sang Wan; O'Doherty, John P; Shimojo, Shinsuke

    2015-04-01

    Incremental learning, in which new knowledge is acquired gradually through trial and error, can be distinguished from one-shot learning, in which the brain learns rapidly from only a single pairing of a stimulus and a consequence. Very little is known about how the brain transitions between these two fundamentally different forms of learning. Here we test a computational hypothesis that uncertainty about the causal relationship between a stimulus and an outcome induces rapid changes in the rate of learning, which in turn mediates the transition between incremental and one-shot learning. By using a novel behavioral task in combination with functional magnetic resonance imaging (fMRI) data from human volunteers, we found evidence implicating the ventrolateral prefrontal cortex and hippocampus in this process. The hippocampus was selectively "switched" on when one-shot learning was predicted to occur, while the ventrolateral prefrontal cortex was found to encode uncertainty about the causal association, exhibiting increased coupling with the hippocampus for high-learning rates, suggesting this region may act as a "switch," turning on and off one-shot learning as required.

  12. Eight open questions in the computational modeling of higher sensory cortex.

    Science.gov (United States)

    Yamins, Daniel Lk; DiCarlo, James J

    2016-04-01

    Propelled by advances in biologically inspired computer vision and artificial intelligence, the past five years have seen significant progress in using deep neural networks to model response patterns of neurons in visual cortex. In this paper, we briefly review this progress and then discuss eight key 'open questions' that we believe will drive research in computational models of sensory systems over the next five years, both in visual cortex and beyond. Copyright © 2016 Elsevier Ltd. All rights reserved.

  13. The scientific study of inspiration in the creative process: challenges and opportunities

    Science.gov (United States)

    Oleynick, Victoria C.; Thrash, Todd M.; LeFew, Michael C.; Moldovan, Emil G.; Kieffaber, Paul D.

    2014-01-01

    Inspiration is a motivational state that compels individuals to bring ideas into fruition. Creators have long argued that inspiration is important to the creative process, but until recently, scientists have not investigated this claim. In this article, we review challenges to the study of creative inspiration, as well as solutions to these challenges afforded by theoretical and empirical work on inspiration over the past decade. First, we discuss the problem of definitional ambiguity, which has been addressed through an integrative process of construct conceptualization. Second, we discuss the challenge of how to operationalize inspiration. This challenge has been overcome by the development and validation of the Inspiration Scale (IS), which may be used to assess trait or state inspiration. Third, we address ambiguity regarding how inspiration differs from related concepts (creativity, insight, positive affect) by discussing discriminant validity. Next, we discuss the preconception that inspiration is less important than “perspiration” (effort), and we review empirical evidence that inspiration and effort both play important—but different—roles in the creative process. Finally, with many challenges overcome, we argue that the foundation is now set for a new generation of research focused on neural underpinnings. We discuss potential challenges to and opportunities for the neuroscientific study of inspiration. A better understanding of the biological basis of inspiration will illuminate the process through which creative ideas “fire the soul,” such that individuals are compelled to transform ideas into products and solutions that may benefit society. PMID:25009483

  14. Spike-timing computation properties of a feed-forward neural network model

    Directory of Open Access Journals (Sweden)

    Drew Benjamin Sinha

    2014-01-01

    Full Text Available Brain function is characterized by dynamical interactions among networks of neurons. These interactions are mediated by network topology at many scales ranging from microcircuits to brain areas. Understanding how networks operate can be aided by understanding how the transformation of inputs depends upon network connectivity patterns, e.g. serial and parallel pathways. To tractably determine how single synapses or groups of synapses in such pathways shape transformations, we modeled feed-forward networks of 7-22 neurons in which synaptic strength changed according to a spike-timing dependent plasticity rule. We investigated how activity varied when dynamics were perturbed by an activity-dependent electrical stimulation protocol (spike-triggered stimulation; STS in networks of different topologies and background input correlations. STS can successfully reorganize functional brain networks in vivo, but with a variability in effectiveness that may derive partially from the underlying network topology. In a simulated network with a single disynaptic pathway driven by uncorrelated background activity, structured spike-timing relationships between polysynaptically connected neurons were not observed. When background activity was correlated or parallel disynaptic pathways were added, however, robust polysynaptic spike timing relationships were observed, and application of STS yielded predictable changes in synaptic strengths and spike-timing relationships. These observations suggest that precise input-related or topologically induced temporal relationships in network activity are necessary for polysynaptic signal propagation. Such constraints for polysynaptic computation suggest potential roles for higher-order topological structure in network organization, such as maintaining polysynaptic correlation in the face of relatively weak synapses.

  15. Fourth International Conference on Computer Science and Its Applications (CIIA 2013)

    CERN Document Server

    Mohamed, Otmane; Bellatreche, Ladjel; Recent Advances in Robotics and Automation

    2013-01-01

        "During the last decades Computational Intelligence has emerged and showed its contributions in various broad research communities (computer science, engineering, finance, economic, decision making, etc.). This was done by proposing approaches and algorithms based either on turnkey techniques belonging to the large panoply of solutions offered by computational intelligence such as data mining, genetic algorithms, bio-inspired methods, Bayesian networks, machine learning, fuzzy logic, artificial neural networks, etc. or inspired by computational intelligence techniques to develop new ad-hoc algorithms for the problem under consideration.    This volume is a comprehensive collection of extended contributions from the 4th International Conference on Computer Science and Its Applications (CIIA’2013) organized into four main tracks: Track 1: Computational Intelligence, Track  2: Security & Network Technologies, Track  3: Information Technology and Track 4: Computer Systems and Applications. This ...

  16. Minimal approach to neuro-inspired information processing

    Directory of Open Access Journals (Sweden)

    Miguel C. Soriano

    2015-06-01

    Full Text Available To learn and mimic how the brain processes information has been a major research challenge for decades. Despite the efforts, little is known on how we encode, maintain and retrieve our memories. One of the hypothesis assumes that transient states are generated in our intricate network of neurons when the brain is stimulated by a sensory input. Based on this idea, powerful computational schemes have been developed. These schemes, known as machine-learning techniques, include artificial neural networks, support vector machine and reservoir computing, among others.In this paper, we concentrate on the reservoir computing (RC technique using delay-coupled systems. Unlike traditional RC, where the information is processed in large recurrent networks of interconnected artificial neurons, we choose a minimal design, implemented via a simple nonlinear dynamical system subject to a self-feedback loop with delay. This design is not intended to represent an actual brain circuit, but aims at finding the minimum ingredients that allow developing an efficient information processor. This simple scheme not only allows us to address fundamental questions but also permits simple hardware implementations. By reducing the neuro-inspired reservoir computing approach to its bare essentials, we find that nonlinear transient responses of the simple dynamical system enable the processing of information with excellent performance and at unprecedented speed. We specifically explore different hardware implementations and, by that, we learn about the role of nonlinearity, noise, system responses, connectivity structure, and the quality of projection onto the required high-dimensional state space. Besides the relevance for the understanding of basic mechanisms, this scheme opens direct technological opportunities that could not be addressed with previous approaches.

  17. [Artificial neural networks in Neurosciences].

    Science.gov (United States)

    Porras Chavarino, Carmen; Salinas Martínez de Lecea, José María

    2011-11-01

    This article shows that artificial neural networks are used for confirming the relationships between physiological and cognitive changes. Specifically, we explore the influence of a decrease of neurotransmitters on the behaviour of old people in recognition tasks. This artificial neural network recognizes learned patterns. When we change the threshold of activation in some units, the artificial neural network simulates the experimental results of old people in recognition tasks. However, the main contributions of this paper are the design of an artificial neural network and its operation inspired by the nervous system and the way the inputs are coded and the process of orthogonalization of patterns.

  18. Inspirals into Gargantua

    Science.gov (United States)

    Warburton, Niels; Gralla, Samuel; Hughes, Scott

    2016-03-01

    We model the inspiral of a compact, stellar mass object into a massive black hole rotating at or just below the theoretical maximum. We find that once the compact object enters the near-horizon regime the gravitational radiation is characterized by a constant frequency, equal to the horizon frequency, with an exponentially damped profile. This contrasts with the usual `chirping' behaviour of a black hole binary system and were such a waveform observed it would constitute a `smoking gun' for a (near) extremal black hole in nature.

  19. #IWD2016 Academic Inspiration

    DEFF Research Database (Denmark)

    Meier, Ninna

    2016-01-01

    What academics or books have inspired you in your writing and research, or helped to make sense of the world around you? In this feature essay, Ninna Meier returns to her experience of reading Hannah Arendt as she sought to understand work and how it relates to value production in capitalist...... economies. Meier recounts how Arendt’s book On Revolution (1963) forged connective threads between the ‘smallest parts’ and the ‘largest wholes’ and showed how academic work is never fully relegated to the past, but can return in new iterations across time....

  20. Automated segmentation of synchrotron radiation micro-computed tomography biomedical images using Graph Cuts and neural networks

    Science.gov (United States)

    Alvarenga de Moura Meneses, Anderson; Giusti, Alessandro; de Almeida, André Pereira; Parreira Nogueira, Liebert; Braz, Delson; Cely Barroso, Regina; deAlmeida, Carlos Eduardo

    2011-12-01

    Synchrotron Radiation (SR) X-ray micro-Computed Tomography (μCT) enables magnified images to be used as a non-invasive and non-destructive technique with a high space resolution for the qualitative and quantitative analyses of biomedical samples. The research on applications of segmentation algorithms to SR-μCT is an open problem, due to the interesting and well-known characteristics of SR images for visualization, such as the high resolution and the phase contrast effect. In this article, we describe and assess the application of the Energy Minimization via Graph Cuts (EMvGC) algorithm for the segmentation of SR-μCT biomedical images acquired at the Synchrotron Radiation for MEdical Physics (SYRMEP) beam line at the Elettra Laboratory (Trieste, Italy). We also propose a method using EMvGC with Artificial Neural Networks (EMANNs) for correcting misclassifications due to intensity variation of phase contrast, which are important effects and sometimes indispensable in certain biomedical applications, although they impair the segmentation provided by conventional techniques. Results demonstrate considerable success in the segmentation of SR-μCT biomedical images, with average Dice Similarity Coefficient 99.88% for bony tissue in Wistar Rats rib samples (EMvGC), as well as 98.95% and 98.02% for scans of Rhodnius prolixus insect samples (Chagas's disease vector) with EMANNs, in relation to manual segmentation. The techniques EMvGC and EMANNs cope with the task of performing segmentation in images with the intensity variation due to phase contrast effects, presenting a superior performance in comparison to conventional segmentation techniques based on thresholding and linear/nonlinear image filtering, which is also discussed in the present article.

  1. Quality assessment of microwave-vacuum dried material with the use of computer image analysis and neural model

    Science.gov (United States)

    Koszela, K.; OtrzÄ sek, J.; Zaborowicz, M.; Boniecki, P.; Mueller, W.; Raba, B.; Lewicki, A.; Przybył, K.

    2014-04-01

    The farming area for vegetables in Poland is constantly changed and modified. Each year the cultivation structure of particular vegetables is different. However, it is the cultivation of carrots that plays a significant role among vegetables. According to the Main Statistical Office (GUS), in 2012 carrot held second position among the cultivated root vegetables, and it was estimated at 835 thousand tons. In the world we are perceived as the leading producer of carrot, due to the fourth place in the ranking of global producers. Poland is the largest producer of this vegetable in the EU [1]. It is also noteworthy, that the demand for dried vegetables is still increasing. This tendency affects the development of drying industry in our country, contributing to utilization of the product surplus. Dried vegetables are used increasingly often in various sectors of food products industry, due to high nutrition value, as well as to changing alimentary preferences of consumers [2-3]. Dried carrot plays a crucial role among dried vegetables, because of its wide scope of use and high nutrition value. It contains a lot of carotene and sugar present in the form of crystals. Carrot also undergoes many different drying processes, which makes it difficult to perform a reliable quality assessment and classification of this dried material. One of many qualitative properties of dried carrot, having important influence on a positive or negative result of the quality assessment, is color and shape. The aim of the research project was to develop a method for the analysis of microwave-vacuum dried carrot images, and its application for the classification of individual fractions in the sample studied for quality assessment. During the research digital photographs of dried carrot were taken, which constituted the basis for assessment performed by a dedicated computer programme developed as a part of the research. Consequently, using a neural model, the dried material was classified [4-6].

  2. Automated segmentation of synchrotron radiation micro-computed tomography biomedical images using Graph Cuts and neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Alvarenga de Moura Meneses, Anderson, E-mail: ameneses@ieee.org [Radiological Sciences Laboratory, Rio de Janeiro State University, Rua Sao Francisco Xavier 524, CEP 20550-900, RJ (Brazil); Giusti, Alessandro [IDSIA (Dalle Molle Institute for Artificial Intelligence), University of Lugano (Switzerland); Pereira de Almeida, Andre; Parreira Nogueira, Liebert; Braz, Delson [Nuclear Engineering Program, Federal University of Rio de Janeiro, RJ (Brazil); Cely Barroso, Regina [Laboratory of Applied Physics on Biomedical Sciences, Physics Department, Rio de Janeiro State University, RJ (Brazil); Almeida, Carlos Eduardo de [Radiological Sciences Laboratory, Rio de Janeiro State University, Rua Sao Francisco Xavier 524, CEP 20550-900, RJ (Brazil)

    2011-12-21

    Synchrotron Radiation (SR) X-ray micro-Computed Tomography ({mu}CT) enables magnified images to be used as a non-invasive and non-destructive technique with a high space resolution for the qualitative and quantitative analyses of biomedical samples. The research on applications of segmentation algorithms to SR-{mu}CT is an open problem, due to the interesting and well-known characteristics of SR images for visualization, such as the high resolution and the phase contrast effect. In this article, we describe and assess the application of the Energy Minimization via Graph Cuts (EMvGC) algorithm for the segmentation of SR-{mu}CT biomedical images acquired at the Synchrotron Radiation for MEdical Physics (SYRMEP) beam line at the Elettra Laboratory (Trieste, Italy). We also propose a method using EMvGC with Artificial Neural Networks (EMANNs) for correcting misclassifications due to intensity variation of phase contrast, which are important effects and sometimes indispensable in certain biomedical applications, although they impair the segmentation provided by conventional techniques. Results demonstrate considerable success in the segmentation of SR-{mu}CT biomedical images, with average Dice Similarity Coefficient 99.88% for bony tissue in Wistar Rats rib samples (EMvGC), as well as 98.95% and 98.02% for scans of Rhodnius prolixus insect samples (Chagas's disease vector) with EMANNs, in relation to manual segmentation. The techniques EMvGC and EMANNs cope with the task of performing segmentation in images with the intensity variation due to phase contrast effects, presenting a superior performance in comparison to conventional segmentation techniques based on thresholding and linear/nonlinear image filtering, which is also discussed in the present article.

  3. An Expedient Study on Back-Propagation (BPN) Neural Networks for Modeling Automated Evaluation of the Answers and Progress of Deaf Students' That Possess Basic Knowledge of the English Language and Computer Skills

    Science.gov (United States)

    Vrettaros, John; Vouros, George; Drigas, Athanasios S.

    This article studies the expediency of using neural networks technology and the development of back-propagation networks (BPN) models for modeling automated evaluation of the answers and progress of deaf students' that possess basic knowledge of the English language and computer skills, within a virtual e-learning environment. The performance of the developed neural models is evaluated with the correlation factor between the neural networks' response values and the real value data as well as the percentage measurement of the error between the neural networks' estimate values and the real value data during its training process and afterwards with unknown data that weren't used in the training process.

  4. Application of a computational neural network to optimize the fluorescence signal from a receptor-ligand interaction on a microfluidic chip.

    Science.gov (United States)

    Ortega, Maria; Hanrahan, Grady; Arceo, Marilyn; Gomez, Frank A

    2015-02-01

    We describe the use of a computational neural network platform to optimize the fluorescence upon binding 5-carboxyfluorescein-d-Ala-d-Ala-d-Ala (5-FAM(DA)3 ) (1) to the antibiotic teicoplanin covalently attached to a glass slide. A three-level response surface experimental design was used as the first stage of investigation. Subsequently, three defined experimental parameters were examined by the neural network approach: (i) the concentration of teicoplanin used to derivatize a glass platform on the microfluidic device, (ii) the time required for the immobilization of teicoplanin on the platform, and (iii) the length of time 1 is allowed to equilibrate with teicoplanin in the microfluidic channel. Optimal neural structure provided a best fit model, both for the training set (r(2) = 0.961) and test set (r(2) = 0.934) data. Model simulated results were experimentally validated with excellent agreement (% difference) between experimental and predicted fluorescence shown, thus demonstrating efficiency of the neural network approach. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  5. When science inspires art

    CERN Multimedia

    Anaïs Vernède

    2011-01-01

    On Tuesday 18 January 2011, artist Pipilotti Rist came to CERN to find out how science could provide her with a source of inspiration for her art and perhaps to get ideas for future work. Pipilotti, who is an eclectic artist always on the lookout for an original source of inspiration, is almost as passionate about physics as she is about art.   Ever Is Over All, 1997, audio video installation by Pipilotti Rist.  View of the installation at the National Museum for Foreign Art, Sofia, Bulgaria. © Pipilotti Rist. Courtesy the artist and Hauser & Wirth. Photo by Angel Tzvetanov. Swiss video-maker Pipilotti Rist (her real name is Elisabeth Charlotte Rist), who is well-known in the international art world for her highly colourful videos and creations, visited CERN for the first time on Tuesday 18 January 2011.  Her visit represented a trip down memory lane, since she originally studied physics before becoming interested in pursuing a career as an artist and going on to de...

  6. Intelligent computing systems emerging application areas

    CERN Document Server

    Virvou, Maria; Jain, Lakhmi

    2016-01-01

    This book at hand explores emerging scientific and technological areas in which Intelligent Computing Systems provide efficient solutions and, thus, may play a role in the years to come. It demonstrates how Intelligent Computing Systems make use of computational methodologies that mimic nature-inspired processes to address real world problems of high complexity for which exact mathematical solutions, based on physical and statistical modelling, are intractable. Common intelligent computational methodologies are presented including artificial neural networks, evolutionary computation, genetic algorithms, artificial immune systems, fuzzy logic, swarm intelligence, artificial life, virtual worlds and hybrid methodologies based on combinations of the previous. The book will be useful to researchers, practitioners and graduate students dealing with mathematically-intractable problems. It is intended for both the expert/researcher in the field of Intelligent Computing Systems, as well as for the general reader in t...

  7. VI International Workshop on Nature Inspired Cooperative Strategies for Optimization

    CERN Document Server

    Otero, Fernando; Masegosa, Antonio

    2014-01-01

    Biological and other natural processes have always been a source of inspiration for computer science and information technology. Many emerging problem solving techniques integrate advanced evolution and cooperation strategies, encompassing a range of spatio-temporal scales for visionary conceptualization of evolutionary computation. This book is a collection of research works presented in the VI International Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO) held in Canterbury, UK. Previous editions of NICSO were held in Granada, Spain (2006 & 2010), Acireale, Italy (2007), Tenerife, Spain (2008), and Cluj-Napoca, Romania (2011). NICSO 2013 and this book provides a place where state-of-the-art research, latest ideas and emerging areas of nature inspired cooperative strategies for problem solving are vigorously discussed and exchanged among the scientific community. The breadth and variety of articles in this book report on nature inspired methods and applications such as Swarm In...

  8. Ant- and Ant-Colony-Inspired ALife Visual Art.

    Science.gov (United States)

    Greenfield, Gary; Machado, Penousal

    2015-01-01

    Ant- and ant-colony-inspired ALife art is characterized by the artistic exploration of the emerging collective behavior of computational agents, developed using ants as a metaphor. We present a chronology that documents the emergence and history of such visual art, contextualize ant- and ant-colony-inspired art within generative art practices, and consider how it relates to other ALife art. We survey many of the algorithms that artists have used in this genre, address some of their aims, and explore the relationships between ant- and ant-colony-inspired art and research on ant and ant colony behavior.

  9. Search and optimization by metaheuristics techniques and algorithms inspired by nature

    CERN Document Server

    Du, Ke-Lin

    2016-01-01

    This textbook provides a comprehensive introduction to nature-inspired metaheuristic methods for search and optimization, including the latest trends in evolutionary algorithms and other forms of natural computing. Over 100 different types of these methods are discussed in detail. The authors emphasize non-standard optimization problems and utilize a natural approach to the topic, moving from basic notions to more complex ones. An introductory chapter covers the necessary biological and mathematical backgrounds for understanding the main material. Subsequent chapters then explore almost all of the major metaheuristics for search and optimization created based on natural phenomena, including simulated annealing, recurrent neural networks, genetic algorithms and genetic programming, differential evolution, memetic algorithms, particle swarm optimization, artificial immune systems, ant colony optimization, tabu search and scatter search, bee and bacteria foraging algorithms, harmony search, biomolecular computin...

  10. Combining Bio-inspired Sensing with Bio-inspired Locomotion

    DEFF Research Database (Denmark)

    Shaikh, Danish; Hallam, John; Christensen-Dalsgaard, Jakob

    In this paper we present a preliminary Braitenberg vehicle–like approach to combine bio-inspired audition with bio-inspired quadruped locomotion in simulation. Locomotion gaits of the salamander–like robot Salamandra robotica are modified by a lizard’s peripheral auditory system model...

  11. Cognition-inspired Descriptors for Scalable Cover Song Retrieval

    NARCIS (Netherlands)

    van Balen, J.M.H.; Bountouridis, D.; Wiering, F.; Veltkamp, R.C.

    2014-01-01

    Inspired by representations used in music cognition studies and computational musicology, we propose three simple and interpretable descriptors for use in mid- to high-level computational analysis of musical audio and applications in content-based retrieval. We also argue that the task of scalable

  12. Genetic learning in rule-based and neural systems

    Science.gov (United States)

    Smith, Robert E.

    1993-01-01

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

  13. The neural processing of voluntary completed, real and virtual violent and nonviolent computer game scenarios displaying predefined actions in gamers and nongamers.

    Science.gov (United States)

    Regenbogen, Christina; Herrmann, Manfred; Fehr, Thorsten

    2010-01-01

    Studies investigating the effects of violent computer and video game playing have resulted in heterogeneous outcomes. It has been assumed that there is a decreased ability to differentiate between virtuality and reality in people that play these games intensively. FMRI data of a group of young males with (gamers) and without (controls) a history of long-term violent computer game playing experience were obtained during the presentation of computer game and realistic video sequences. In gamers the processing of real violence in contrast to nonviolence produced activation clusters in right inferior frontal, left lingual and superior temporal brain regions. Virtual violence activated a network comprising bilateral inferior frontal, occipital, postcentral, right middle temporal, and left fusiform regions. Control participants showed extended left frontal, insula and superior frontal activations during the processing of real, and posterior activations during the processing of virtual violent scenarios. The data suggest that the ability to differentiate automatically between real and virtual violence has not been diminished by a long-term history of violent video game play, nor have gamers' neural responses to real violence in particular been subject to desensitization processes. However, analyses of individual data indicated that group-related analyses reflect only a small part of actual individual different neural network involvement, suggesting that the consideration of individual learning history is sufficient for the present discussion.

  14. Neural network approaches to dynamic collision-free trajectory generation.

    Science.gov (United States)

    Yang, S X; Meng, M

    2001-01-01

    In this paper, dynamic collision-free trajectory generation in a nonstationary environment is studied using biologically inspired neural network approaches. The proposed neural network is topologically organized, where the dynamics of each neuron is characterized by a shunting equation or an additive equation. The state space of the neural network can be either the Cartesian workspace or the joint space of multi-joint robot manipulators. There are only local lateral connections among neurons. The real-time optimal trajectory is generated through the dynamic activity landscape of the neural network without explicitly searching over the free space nor the collision paths, without explicitly optimizing any global cost functions, without any prior knowledge of the dynamic environment, and without any learning procedures. Therefore the model algorithm is computationally efficient. The stability of the neural network system is guaranteed by the existence of a Lyapunov function candidate. In addition, this model is not very sensitive to the model parameters. Several model variations are presented and the differences are discussed. As examples, the proposed models are applied to generate collision-free trajectories for a mobile robot to solve a maze-type of problem, to avoid concave U-shaped obstacles, to track a moving target and at the same to avoid varying obstacles, and to generate a trajectory for a two-link planar robot with two targets. The effectiveness and efficiency of the proposed approaches are demonstrated through simulation and comparison studies.

  15. Computational Intelligence in Intelligent Data Analysis

    CERN Document Server

    Nürnberger, Andreas

    2013-01-01

    Complex systems and their phenomena are ubiquitous as they can be found in biology, finance, the humanities, management sciences, medicine, physics and similar fields. For many problems in these fields, there are no conventional ways to mathematically or analytically solve them completely at low cost. On the other hand, nature already solved many optimization problems efficiently. Computational intelligence attempts to mimic nature-inspired problem-solving strategies and methods. These strategies can be used to study, model and analyze complex systems such that it becomes feasible to handle them. Key areas of computational intelligence are artificial neural networks, evolutionary computation and fuzzy systems. As only a few researchers in that field, Rudolf Kruse has contributed in many important ways to the understanding, modeling and application of computational intelligence methods. On occasion of his 60th birthday, a collection of original papers of leading researchers in the field of computational intell...

  16. Operant Conditioning: A Minimal Components Requirement in Artificial Spiking Neurons Designed for Bio-Inspired Robot’s Controller

    Directory of Open Access Journals (Sweden)

    André eCyr

    2014-07-01

    Full Text Available We demonstrate the operant conditioning (OC learning process within a basic bio-inspired robot controller paradigm, using an artificial spiking neural network (ASNN with minimal component count as artificial brain. In biological agents, OC results in behavioral changes that are learned from the consequences of previous actions, using progressive prediction adjustment triggered by reinforcers. In a robotics context, virtual and physical robots may benefit from a similar learning skill when facing unknown environments with no supervision. In this work, we demonstrate that a simple ASNN can efficiently realise many OC scenarios. The elementary learning kernel that we describe relies on a few critical neurons, synaptic links and the integration of habituation and spike-timing dependent plasticity (STDP as learning rules. Using four tasks of incremental complexity, our experimental results show that such minimal neural component set may be sufficient to implement many OC procedures. Hence, with the described bio-inspired module, OC can be implemented in a wide range of robot controllers, including those with limited computational resources.

  17. Inspired by CERN

    CERN Multimedia

    2004-01-01

    Art students inspired by CERN will be returning to show their work 9 to 16 October in Building 500, outside the Auditorium. Seventeen art students from around Europe visited CERN last January for a week of introductions to particle physics and astrophysics, and discussions with CERN scientists about their projects. A CERN scientist "adopted"each artist so they could ask questions during and after the visit. Now the seeds planted during their visit have come to fruition in a show using many media and exploring varied concepts, such as how people experience the online world, the sheer scale of CERN's equipment, and the abstractness of the entities scientists are looking for. "The work is so varied, people are going to love some pieces and detest others," says Andrew Charalambous, the project coordinator from University College London who is also curating the exhibition. "It's contemporary modern art, and that's sometimes difficult to take in." For more information on this thought-provoking show, see: htt...

  18. Microflyers: inspiration from nature

    Science.gov (United States)

    Sirohi, Jayant

    2013-04-01

    Over the past decade, there has been considerable interest in miniaturizing aircraft to create a class of extremely small, robotic vehicles with a gross mass on the order of tens of grams and a dimension on the order of tens of centimeters. These are collectively refered to as micro aerial vehicles (MAVs) or microflyers. Because the size of microflyers is on the same order as that of small birds and large insects, engineers are turning to nature for inspiration. Bioinspired concepts make use of structural or aerodynamic mechanisms that are observed in insects and birds, such as elastic energy storage and unsteady aerodynamics. Biomimetic concepts attempt to replicate the form and function of natural flyers, such as flapping-wing propulsion and external appearance. This paper reviews recent developments in the area of man-made microflyers. The design space for microflyers will be described, along with fundamental physical limits to miniaturization. Key aerodynamic phenomena at the scale of microflyers will be highlighted. Because the focus is on bioinspiration and biomimetics, scaled-down versions of conventional aircraft, such as fixed wing micro air vehicles and microhelicopters will not be addressed. A few representative bioinspired and biomimetic microflyer concepts developed by researchers will be described in detail. Finally, some of the sensing mechanisms used by natural flyers that are being implemented in man-made microflyers will be discussed.

  19. Bio-inspired Artificial Intelligence: А Generalized Net Model of the Regularization Process in MLP

    Directory of Open Access Journals (Sweden)

    Stanimir Surchev

    2013-10-01

    Full Text Available Many objects and processes inspired by the nature have been recreated by the scientists. The inspiration to create a Multilayer Neural Network came from human brain as member of the group. It possesses complicated structure and it is difficult to recreate, because of the existence of too many processes that require different solving methods. The aim of the following paper is to describe one of the methods that improve learning process of Artificial Neural Network. The proposed generalized net method presents Regularization process in Multilayer Neural Network. The purpose of verification is to protect the neural network from overfitting. The regularization is commonly used in neural network training process. Many methods of verification are present, the subject of interest is the one known as Regularization. It contains function in order to set weights and biases with smaller values to protect from overfitting.

  20. Modular Neural Networks and Type-2 Fuzzy Systems for Pattern Recognition

    CERN Document Server

    Melin, Patricia

    2012-01-01

    This book describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern recognition systems. Type-2 fuzzy logic is an extension of traditional type-1 fuzzy logic that enables managing higher levels of uncertainty in complex real world problems, which are of particular importance in the area of pattern recognition. The book is organized in three main parts, each containing a group of chapters built around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which are the basis for achieving intelligent pattern recognition. The second part contains chapters with the main theme of using type-2 fuzzy models and modular neural ne...

  1. Biologically inspired coupled antenna beampattern design

    Energy Technology Data Exchange (ETDEWEB)

    Akcakaya, Murat; Nehorai, Arye, E-mail: makcak2@ese.wustl.ed, E-mail: nehorai@ese.wustl.ed [Department of Electrical and Systems Engineering, Washington University in St Louis, St Louis, MO 63130 (United States)

    2010-12-15

    We propose to design a small-size transmission-coupled antenna array, and corresponding radiation pattern, having high performance inspired by the female Ormia ochracea's coupled ears. For reproduction purposes, the female Ormia is able to locate male crickets' call accurately despite the small distance between its ears compared with the incoming wavelength. This phenomenon has been explained by the mechanical coupling between the Ormia's ears, which has been modeled by a pair of differential equations. In this paper, we first solve these differential equations governing the Ormia ochracea's ear response, and convert the response to the pre-specified radio frequencies. We then apply the converted response of the biological coupling in the array factor of a uniform linear array composed of finite-length dipole antennas, and also include the undesired electromagnetic coupling due to the proximity of the elements. Moreover, we propose an algorithm to optimally choose the biologically inspired coupling for maximum array performance. In our numerical examples, we compute the radiation intensity of the designed system for binomial and uniform ordinary end-fire arrays, and demonstrate the improvement in the half-power beamwidth, sidelobe suppression and directivity of the radiation pattern due to the biologically inspired coupling.

  2. Document analysis with neural net circuits

    Science.gov (United States)

    Graf, Hans Peter

    1994-01-01

    Document analysis is one of the main applications of machine vision today and offers great opportunities for neural net circuits. Despite more and more data processing with computers, the number of paper documents is still increasing rapidly. A fast translation of data from paper into electronic format is needed almost everywhere, and when done manually, this is a time consuming process. Markets range from small scanners for personal use to high-volume document analysis systems, such as address readers for the postal service or check processing systems for banks. A major concern with present systems is the accuracy of the automatic interpretation. Today's algorithms fail miserably when noise is present, when print quality is poor, or when the layout is complex. A common approach to circumvent these problems is to restrict the variations of the documents handled by a system. In our laboratory, we had the best luck with circuits implementing basic functions, such as convolutions, that can be used in many different algorithms. To illustrate the flexibility of this approach, three applications of the NET32K circuit are described in this short viewgraph presentation: locating address blocks, cleaning document images by removing noise, and locating areas of interest in personal checks to improve image compression. Several of the ideas realized in this circuit that were inspired by neural nets, such as analog computation with a low resolution, resulted in a chip that is well suited for real-world document analysis applications and that compares favorably with alternative, 'conventional' circuits.

  3. A Quantum Inspired GVNS: Some Preliminary Results.

    Science.gov (United States)

    Papalitsas, Christos; Karakostas, Panayiotis; Kastampolidou, Kalliopi

    2017-01-01

    GVNS is a well known and widely used metaheuristic for solving efficiently many NP-Hard Combinatorial Optimization problems. In this paper, the qGVNS, which is a new quantum inspired variant of GVNS, is being introduced. This variant differs in terms of the perturbation phase because it achieves the shaking moves by adopting quantum computing principles. The functionality and efficiency of qGVNS have been tested using a comparative study (compared with the equivalent GVNS results) in selected TSPLib instances, both in first and best improvement.

  4. Biology-Inspired Autonomous Control

    Science.gov (United States)

    2011-08-31

    AFRL-RW-EG-TR-2011-021 BIOLOGY -INSPIRED AUTONOMOUS CONTROL Multiple Authors – See Table of Contents Appendices Multiple...From - To) (Oct,1,2007)-(May 31,2011) 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER N/A Biology -Inspired Autonomous Control 5b. GRANT NUMBER N...limitations of conventional approaches by applying principles derived from studying the biology of flying organisms. The research was focused on

  5. Nature-inspired optimization algorithms

    CERN Document Server

    Yang, Xin-She

    2014-01-01

    Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning

  6. Real-time ocular artifact suppression using recurrent neural network for electro-encephalogram based brain-computer interface.

    Science.gov (United States)

    Erfanian, A; Mahmoudi, B

    2005-03-01

    The paper presents an adaptive noise canceller (ANC) filter using an artificial neural network for real-time removal of electro-oculogram (EOG) interference from electro-encephalogram (EEG) signals. Conventional ANC filters are based on linear models of interference. Such linear models provide poorer prediction for biomedical signals. In this work, a recurrent neural network was employed for modelling the interference signals. The eye movement and eye blink artifacts were recorded by the placing of an electrode on the forehead above the left eye and an electrode on the left temple. The reference signal was then generated by the data collected from the forehead electrode being added to data recorded from the temple electrode. The reference signal was also contaminated by the EEG. To reduce the EEG interference, the reference signal was first low-pass filtered by a moving averaged filter and then applied to the ANC. Matlab Simulink was used for real-time data acquisition, filtering and ocular artifact suppression. Simulation results show the validity and effectiveness of the technique with different signal-to-noise ratios (SNRs) of the primary signal. On average, a significant improvement in SNR up to 27 dB was achieved with the recurrent neural network. The results from real data demonstrate that the proposed scheme removes ocular artifacts from contaminated EEG signals and is suitable for real-time and short-time EEG recordings.

  7. Biologically-Inspired Adaptive Obstacle Negotiation Behavior of Hexapod Robots

    DEFF Research Database (Denmark)

    Goldschmidt, Dennis; Wörgötter, Florentin; Manoonpong, Poramate

    2014-01-01

    Neurobiological studies have shown that insects are able to adapt leg movements and posture for obstacle negotiation in changing environments. Moreover, the distance to an obstacle where an insect begins to climb is found to be a major parameter for successful obstacle negotiation. Inspired...... by these findings, we present an adaptive neural control mechanism for obstacle negotiation behavior in hexapod robots. It combines locomotion control, backbone joint control, local leg reflexes, and neural learning. While the first three components generate locomotion including walking and climbing, the neural...... learning mechanism allows the robot to adapt its behavior for obstacle negotiation with respect to changing conditions, e.g., variable obstacle heights and different walking gaits. By successfully learning the association of an early, predictive signal (conditioned stimulus, CS) and a late, reflex signal...

  8. Inspiring to inspire: Developing teaching in higher education

    Directory of Open Access Journals (Sweden)

    Louise Williams

    2016-12-01

    Full Text Available Following a three-year staff development initiative within one faculty in a UK university, the authors reflected on inspiring teaching and the role that staff development can play in enhancing individual practice. Teaching is a core component of Higher Education and is complex and multi-faceted both theoretically and in practice. Through individual reflections to a set of pre-determined questions, a group of Higher Education teachers (n = 5 with a responsibility for the development of learning, teaching and assessment, share their thoughts, feelings and beliefs on inspiring teaching. The interpretive analysis of the data shows from a staff perspective that the notion of inspiring teaching has three main components which are all interrelated, those being; the actual teaching and learning experience; the design of the curriculum and the teacher/student relationship. Staff development initiatives were found to help people explore and develop their own teaching philosophy, to develop new practices and to share and learn from others. However, individual’s mindset, beliefs and attitudes were found to be a challenge. Teachers can frame their development around the different aspects of inspiring teaching and with support from senior leadership as well as a positive culture, teaching communities can work together towards inspiring teaching.

  9. Improved Quantum-Inspired Evolutionary Algorithm for Engineering Design Optimization

    Directory of Open Access Journals (Sweden)

    Jinn-Tsong Tsai

    2012-01-01

    Full Text Available An improved quantum-inspired evolutionary algorithm is proposed for solving mixed discrete-continuous nonlinear problems in engineering design. The proposed Latin square quantum-inspired evolutionary algorithm (LSQEA combines Latin squares and quantum-inspired genetic algorithm (QGA. The novel contribution of the proposed LSQEA is the use of a QGA to explore the optimal feasible region in macrospace and the use of a systematic reasoning mechanism of the Latin square to exploit the better solution in microspace. By combining the advantages of exploration and exploitation, the LSQEA provides higher computational efficiency and robustness compared to QGA and real-coded GA when solving global numerical optimization problems with continuous variables. Additionally, the proposed LSQEA approach effectively solves mixed discrete-continuous nonlinear design optimization problems in which the design variables are integers, discrete values, and continuous values. The computational experiments show that the proposed LSQEA approach obtains better results compared to existing methods reported in the literature.

  10. Silicon synaptic transistor for hardware-based spiking neural network and neuromorphic system

    Science.gov (United States)

    Kim, Hyungjin; Hwang, Sungmin; Park, Jungjin; Park, Byung-Gook

    2017-10-01

    Brain-inspired neuromorphic systems have attracted much attention as new computing paradigms for power-efficient computation. Here, we report a silicon synaptic transistor with two electrically independent gates to realize a hardware-based neural network system without any switching components. The spike-timing dependent plasticity characteristics of the synaptic devices are measured and analyzed. With the help of the device model based on the measured data, the pattern recognition capability of the hardware-based spiking neural network systems is demonstrated using the modified national institute of standards and technology handwritten dataset. By comparing systems with and without inhibitory synapse part, it is confirmed that the inhibitory synapse part is an essential element in obtaining effective and high pattern classification capability.

  11. An Improved Brain-Inspired Emotional Learning Algorithm for Fast Classification

    Directory of Open Access Journals (Sweden)

    Ying Mei

    2017-06-01

    Full Text Available Classification is an important task of machine intelligence in the field of information. The artificial neural network (ANN is widely used for classification. However, the traditional ANN shows slow training speed, and it is hard to meet the real-time requirement for large-scale applications. In this paper, an improved brain-inspired emotional learning (BEL algorithm is proposed for fast classification. The BEL algorithm was put forward to mimic the high speed of the emotional learning mechanism in mammalian brain, which has the superior features of fast learning and low computational complexity. To improve the accuracy of BEL in classification, the genetic algorithm (GA is adopted for optimally tuning the weights and biases of amygdala and orbitofrontal cortex in the BEL neural network. The combinational algorithm named as GA-BEL has been tested on eight University of California at Irvine (UCI datasets and two well-known databases (Japanese Female Facial Expression, Cohn–Kanade. The comparisons of experiments indicate that the proposed GA-BEL is more accurate than the original BEL algorithm, and it is much faster than the traditional algorithm.

  12. Computational simulation: astrocyte-induced depolarization of neighboring neurons mediates synchronous UP states in a neural network.

    Science.gov (United States)

    Kuriu, Takayuki; Kakimoto, Yuta; Araki, Osamu

    2015-09-01

    Although recent reports have suggested that synchronous neuronal UP states are mediated by astrocytic activity, the mechanism responsible for this remains unknown. Astrocytic glutamate release synchronously depolarizes adjacent neurons, while synaptic transmissions are blocked. The purpose of this study was to confirm that astrocytic depolarization, propagated through synaptic connections, can lead to synchronous neuronal UP states. We applied astrocytic currents to local neurons in a neural network consisting of model cortical neurons. Our results show that astrocytic depolarization may generate synchronous UP states for hundreds of milliseconds in neurons even if they do not directly receive glutamate release from the activated astrocyte.

  13. Recent advances in swarm intelligence and evolutionary computation

    CERN Document Server

    2015-01-01

    This timely review volume summarizes the state-of-the-art developments in nature-inspired algorithms and applications with the emphasis on swarm intelligence and bio-inspired computation. Topics include the analysis and overview of swarm intelligence and evolutionary computation, hybrid metaheuristic algorithms, bat algorithm, discrete cuckoo search, firefly algorithm, particle swarm optimization, and harmony search as well as convergent hybridization. Application case studies have focused on the dehydration of fruits and vegetables by the firefly algorithm and goal programming, feature selection by the binary flower pollination algorithm, job shop scheduling, single row facility layout optimization, training of feed-forward neural networks, damage and stiffness identification, synthesis of cross-ambiguity functions by the bat algorithm, web document clustering, truss analysis, water distribution networks, sustainable building designs and others. As a timely review, this book can serve as an ideal reference f...

  14. Word Vectorization Using Relations among Words for Neural Network

    Science.gov (United States)

    Hotta, Hajime; Kittaka, Masanobu; Hagiwara, Masafumi

    In this paper, we propose a new vectorization method for a new generation of computational intelligence including neural networks and natural language processing. In recent years, various techniques of word vectorization have been proposed, many of which rely on the preparation of dictionaries. However, these techniques don't consider the symbol grounding problem for unknown types of data, which is one of the most fundamental issues on artificial intelligence. In order to avoid the symbol-grounding problem, pattern processing based methods, such as neural networks, are often used in various studies on self-directive systems and algorithms, and the merit of neural network is not exception in the natural language processing. The proposed method is a converter from one word input to one real-valued vector, whose algorithm is inspired by neural network architecture. The merits of the method are as follows: (1) the method requires no specific knowledge of linguistics e.g. word classes or grammatical one; (2) the method is a sequence learning technique and it can learn additional knowledge. The experiment showed the efficiency of word vectorization in terms of similarity measurement.

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

    Science.gov (United States)

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

    2016-08-31

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

  16. Symbolic processing in neural networks

    OpenAIRE

    Neto, João Pedro; Hava T Siegelmann; Costa,J.Félix

    2003-01-01

    In this paper we show that programming languages can be translated into recurrent (analog, rational weighted) neural nets. Implementation of programming languages in neural nets turns to be not only theoretical exciting, but has also some practical implications in the recent efforts to merge symbolic and sub symbolic computation. To be of some use, it should be carried in a context of bounded resources. Herein, we show how to use resource bounds to speed up computations over neural nets, thro...

  17. A Project-Based Biologically-Inspired Robotics Module

    Science.gov (United States)

    Crowder, R. M.; Zauner, K.-P.

    2013-01-01

    The design of any robotic system requires input from engineers from a variety of technical fields. This paper describes a project-based module, "Biologically-Inspired Robotics," that is offered to Electronics and Computer Science students at the University of Southampton, U.K. The overall objective of the module is for student groups to…

  18. Materials inspired by mathematics.

    Science.gov (United States)

    Kotani, Motoko; Ikeda, Susumu

    2016-01-01

    Our world is transforming into an interacting system of the physical world and the digital world. What will be the materials science in the new era? With the rising expectations of the rapid development of computers, information science and mathematical science including statistics and probability theory, 'data-driven materials design' has become a common term. There is knowledge and experience gained in the physical world in the form of know-how and recipes for the creation of material. An important key is how we establish vocabulary and grammar to translate them into the language of the digital world. In this article, we outline how materials science develops when it encounters mathematics, showing some emerging directions.

  19. Convolutional neural network architecture and input volume matrix design for ERP classifications in a tactile P300-based Brain-Computer Interface.

    Science.gov (United States)

    Kodama, Takumi; Makino, Shoji

    2017-07-01

    In the presented study we conduct the off-line ERP classification using the convolutional neural network (CNN) classifier for somatosensory ERP intervals acquired in the full- body tactile P300-based Brain-Computer Interface paradigm (fbBCI). The main objective of the study is to enhance fbBCI stimulus pattern classification accuracies by applying the CNN classifier. A 60 × 60 squared input volume transformed by one-dimensional somatosensory ERP intervals in each electrode channel is input to the convolutional architecture for a filter training. The flattened activation maps are evaluated by a multilayer perceptron with one-hidden-layer in order to calculate classification accuracy results. The proposed method reveals that the CNN classifier model can achieve a non-personal- training ERP classification with the fbBCI paradigm, scoring 100 % classification accuracy results for all the participated ten users.

  20. The potential of computer vision, optical backscattering parameters and artificial neural network modelling in monitoring the shrinkage of sweet potato (Ipomoea batatas L.) during drying.

    Science.gov (United States)

    Onwude, Daniel I; Hashim, Norhashila; Abdan, Khalina; Janius, Rimfiel; Chen, Guangnan

    2018-03-01

    Drying is a method used to preserve agricultural crops. During the drying of products with high moisture content, structural changes in shape, volume, area, density and porosity occur. These changes could affect the final quality of dried product and also the effective design of drying equipment. Therefore, this study investigated a novel approach in monitoring and predicting the shrinkage of sweet potato during drying. Drying experiments were conducted at temperatures of 50-70 °C and samples thicknesses of 2-6 mm. The volume and surface area obtained from camera vision, and the perimeter and illuminated area from backscattered optical images were analysed and used to evaluate the shrinkage of sweet potato during drying. The relationship between dimensionless moisture content and shrinkage of sweet potato in terms of volume, surface area, perimeter and illuminated area was found to be linearly correlated. The results also demonstrated that the shrinkage of sweet potato based on computer vision and backscattered optical parameters is affected by the product thickness, drying temperature and drying time. A multilayer perceptron (MLP) artificial neural network with input layer containing three cells, two hidden layers (18 neurons), and five cells for output layer, was used to develop a model that can monitor, control and predict the shrinkage parameters and moisture content of sweet potato slices under different drying conditions. The developed ANN model satisfactorily predicted the shrinkage and dimensionless moisture content of sweet potato with correlation coefficient greater than 0.95. Combined computer vision, laser light backscattering imaging and artificial neural network can be used as a non-destructive, rapid and easily adaptable technique for in-line monitoring, predicting and controlling the shrinkage and moisture changes of food and agricultural crops during drying. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

  1. Inspiration fra NY-times

    DEFF Research Database (Denmark)

    Ejersbo, Lisser Rye

    2015-01-01

    NY-times har en ugentlig klumme med gode råd. For nogle uger siden var ugens inspiration henvendt til lærere/undervisere og drejede sig om, hvordan man skaber taletid til alle uden at have favoritter og overse de mere stille elever.......NY-times har en ugentlig klumme med gode råd. For nogle uger siden var ugens inspiration henvendt til lærere/undervisere og drejede sig om, hvordan man skaber taletid til alle uden at have favoritter og overse de mere stille elever....

  2. Bio-Inspired Antifouling Strategies

    Science.gov (United States)

    Kirschner, Chelsea M.; Brennan, Anthony B.

    2012-08-01

    Biofouling is a complex, dynamic problem that globally impacts both the economy and environment. Interdisciplinary research in marine biology, polymer science, and engineering has led to the implementation of bio-inspired strategies for the development of the next generation of antifouling marine coatings. Natural fouling defense mechanisms have been mimicked through chemical, physical, and/or stimuli-responsive strategies. This review outlines the detrimental effects associated with biofouling, describes the theoretical basis for antifouling coating design, and highlights prominent advances in bio-inspired antifouling technologies.

  3. The Wellcome Prize Lecture. A map of auditory space in the mammalian brain: neural computation and development.

    Science.gov (United States)

    King, A J

    1993-09-01

    The experiments described in this review have demonstrated that the SC contains a two-dimensional map of auditory space, which is synthesized within the brain using a combination of monaural and binaural localization cues. There is also an adaptive fusion of auditory and visual space in this midbrain nucleus, providing for a common access to the motor pathways that control orientation behaviour. This necessitates a highly plastic relationship between the visual and auditory systems, both during postnatal development and in adult life. Because of the independent mobility of difference sense organs, gating mechanisms are incorporated into the auditory representation to provide up-to-date information about the spatial orientation of the eyes and ears. The SC therefore provides a valuable model system for studying a number of important issues in brain function, including the neural coding of sound location, the co-ordination of spatial information between different sensory systems, and the integration of sensory signals with motor outputs.

  4. Application of computational neural networks in predicting atmospheric pollutant concentrations due to fossil-fired electric power generation

    Energy Technology Data Exchange (ETDEWEB)

    El-Hawary, F. [BH Engineering Systems & Technical Univ. of Nova Scotia (Canada)

    1995-12-31

    The ability to accurately predict the behavior of a dynamic system is of essential importance in monitoring and control of complex processes. In this regard recent advances in neural-net based system identification represent a significant step toward development and design of a new generation of control tools for increased system performance and reliability. The enabling functionality is the one of accurate representation of a model of a nonlinear and nonstationary dynamic system. This functionality provides valuable new opportunities including: (1) The ability to predict future system behavior on the basis of actual system observations, (2) On-line evaluation and display of system performance and design of early warning systems, and (3) Controller optimization for improved system performance. In this presentation, we discuss the issues involved in definition and design of learning control systems and their impact on power system control. Several numerical examples are provided for illustrative purpose.

  5. Computer-Aided Diagnosis of Parkinson's Disease Using Complex-Valued Neural Networks and mRMR Feature Selection Algorithm.

    Science.gov (United States)

    Peker, Musa; Sen, Baha; Delen, Dursun

    2015-01-01

    Parkinson's disease (PD) is a neurological disorder which has a significant social and economic impact. PD is diagnosed by clinical observation and evaluations, coupled with a PD rating scale. However, these methods may be insufficient, especially in the initial phase of the disease. The processes are tedious and time-consuming, and hence systems that can automatically offer a diagnosis are needed. In this study, a novel method for the diagnosis of PD is proposed. Biomedical sound measurements obtained from continuous phonation samples were used as attributes. First, a minimum redundancy maximum relevance (mRMR) attribute selection algorithm was applied for the identification of the effective attributes. After conversion to a complex number, the resulting attributes are presented as input data to the complex-valued artificial neural network (CVANN). The proposed novel system might be a powerful tool for effective diagnosis of PD.

  6. Predicting Motivation: Computational Models of PFC Can Explain Neural Coding of Motivation and Effort-based Decision-making in Health and Disease.

    Science.gov (United States)

    Vassena, Eliana; Deraeve, James; Alexander, William H

    2017-10-01

    Human behavior is strongly driven by the pursuit of rewards. In daily life, however, benefits mostly come at a cost, often requiring that effort be exerted to obtain potential benefits. Medial PFC (MPFC) and dorsolateral PFC (DLPFC) are frequently implicated in the expectation of effortful control, showing increased activity as a function of predicted task difficulty. Such activity partially overlaps with expectation of reward and has been observed both during decision-making and during task preparation. Recently, novel computational frameworks have been developed to explain activity in these regions during cognitive control, based on the principle of prediction and prediction error (predicted response-outcome [PRO] model [Alexander, W. H., & Brown, J. W. Medial prefrontal cortex as an action-outcome predictor. Nature Neuroscience, 14, 1338-1344, 2011], hierarchical error representation [HER] model [Alexander, W. H., & Brown, J. W. Hierarchical error representation: A computational model of anterior cingulate and dorsolateral prefrontal cortex. Neural Computation, 27, 2354-2410, 2015]). Despite the broad explanatory power of these models, it is not clear whether they can also accommodate effects related to the expectation of effort observed in MPFC and DLPFC. Here, we propose a translation of these computational frameworks to the domain of effort-based behavior. First, we discuss how the PRO model, based on prediction error, can explain effort-related activity in MPFC, by reframing effort-based behavior in a predictive context. We propose that MPFC activity reflects monitoring of motivationally relevant variables (such as effort and reward), by coding expectations and discrepancies from such expectations. Moreover, we derive behavioral and neural model-based predictions for healthy controls and clinical populations with impairments of motivation. Second, we illustrate the possible translation to effort-based behavior of the HER model, an extended version of PRO

  7. Bio-inspired grasp control in a robotic hand with massive sensorial input.

    Science.gov (United States)

    Ascari, Luca; Bertocchi, Ulisse; Corradi, Paolo; Laschi, Cecilia; Dario, Paolo

    2009-02-01

    The capability of grasping and lifting an object in a suitable, stable and controlled way is an outstanding feature for a robot, and thus far, one of the major problems to be solved in robotics. No robotic tools able to perform an advanced control of the grasp as, for instance, the human hand does, have been demonstrated to date. Due to its capital importance in science and in many applications, namely from biomedics to manufacturing, the issue has been matter of deep scientific investigations in both the field of neurophysiology and robotics. While the former is contributing with a profound understanding of the dynamics of real-time control of the slippage and grasp force in the human hand, the latter tries more and more to reproduce, or take inspiration by, the nature's approach, by means of hardware and software technology. On this regard, one of the major constraints robotics has to overcome is the real-time processing of a large amounts of data generated by the tactile sensors while grasping, which poses serious problems to the available computational power. In this paper a bio-inspired approach to tactile data processing has been followed in order to design and test a hardware-software robotic architecture that works on the parallel processing of a large amount of tactile sensing signals. The working principle of the architecture bases on the cellular nonlinear/neural network (CNN) paradigm, while using both hand shape and spatial-temporal features obtained from an array of microfabricated force sensors, in order to control the sensory-motor coordination of the robotic system. Prototypical grasping tasks were selected to measure the system performances applied to a computer-interfaced robotic hand. Successful grasps of several objects, completely unknown to the robot, e.g. soft and deformable objects like plastic bottles, soft balls, and Japanese tofu, have been demonstrated.

  8. Semiconductor Devices Inspired By and Integrated With Biology

    Energy Technology Data Exchange (ETDEWEB)

    Rogers, John [University of Illinois

    2012-04-25

    Biology is curved, soft and elastic; silicon wafers are not. Semiconductor technologies that can bridge this gap in form and mechanics will create new opportunities in devices that adopt biologically inspired designs or require intimate integration with the human body. This talk describes the development of ideas for electronics that offer the performance of state-of-the-art, wafer- based systems but with the mechanical properties of a rubber band. We explain the underlying materials science and mechanics of these approaches, and illustrate their use in (1) bio- integrated, ‘tissue-like’ electronics with unique capabilities for mapping cardiac and neural electrophysiology, and (2) bio-inspired, ‘eyeball’ cameras with exceptional imaging properties enabled by curvilinear, Petzval designs.

  9. Phase Diagram of Spiking Neural Networks

    Directory of Open Access Journals (Sweden)

    Hamed eSeyed-Allaei

    2015-03-01

    Full Text Available In computer simulations of spiking neural networks, often it is assumed that every two neurons of the network are connected by a probablilty of 2%, 20% of neurons are inhibitory and 80% are excitatory. These common values are based on experiments, observations. but here, I take a different perspective, inspired by evolution. I simulate many networks, each with a different set of parameters, and then I try to figure out what makes the common values desirable by nature. Networks which are configured according to the common values, have the best dynamic range in response to an impulse and their dynamic range is more robust in respect to synaptic weights. In fact, evolution has favored networks of best dynamic range. I present a phase diagram that shows the dynamic ranges of different networks of different parameteres. This phase diagram gives an insight into the space of parameters -- excitatory to inhibitory ratio, sparseness of connections and synaptic weights. It may serve as a guideline to decide about the values of parameters in a simulation of spiking neural network.

  10. Inspiration: One Percent and Rising

    Science.gov (United States)

    Walling, Donovan R.

    2009-01-01

    Inventor Thomas Edison once famously declared, "Genius is one percent inspiration and ninety-nine percent perspiration." If that's the case, then the students the author witnessed at the International Student Media Festival (ISMF) last November in Orlando, Florida, are geniuses and more. The students in the ISMF pre-conference workshop…

  11. London: An Art Teacher's Inspiration

    Science.gov (United States)

    Guhin, Paula

    2012-01-01

    Often overshadowed in people's minds by Paris, London is truly an artist's jewel. The art and architecture, history, gardens and museums are inspiring, yes, but there's so much more to this ancient city. The performances, attractions and markets are a boon to the creative soul. London can be surprisingly inexpensive to visit. Gazing at statues,…

  12. LEGO-inspired drug design

    DEFF Research Database (Denmark)

    Thanh Tung, Truong; Dao, Trong Tuan; Grifell Junyent, Marta

    2018-01-01

    The fungal plasma membrane H+-ATPase (Pma1p) is a potential target for the discovery of new antifungal agents. Surprisingly, no structure-activity relationship studies for small molecules targeting Pma1p have been reported. Herein, we disclose a LEGO-inspired fragment assembly strategy for design...

  13. Lotus-Inspired Nanotechnology Applications

    Indian Academy of Sciences (India)

    Home; Journals; Resonance – Journal of Science Education; Volume 13; Issue 12. Lotus-Inspired Nanotechnology Applications. B Karthick Ramesh Maheshwari. General Article Volume 13 Issue 12 December 2008 pp 1141-1145. Fulltext. Click here to view fulltext PDF. Permanent link:

  14. Biologically-inspired Learning in Pulsed Neural Networks

    DEFF Research Database (Denmark)

    Lehmann, Torsten; Woodburn, Robin

    1999-01-01

    , explaining what we mean by this term and providing an example of a robust, self-learning design that can solve simple classical-conditioning tasks. We give details of the design of individual circuits to perform component functions, which can then be combined into a network to solve the task. We argue...

  15. Complex-Valued Neural Networks

    CERN Document Server

    Hirose, Akira

    2012-01-01

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

  16. A Novel Single Neuron Perceptron with Universal Approximation and XOR Computation Properties

    Directory of Open Access Journals (Sweden)

    Ehsan Lotfi

    2014-01-01

    Full Text Available We propose a biologically motivated brain-inspired single neuron perceptron (SNP with universal approximation and XOR computation properties. This computational model extends the input pattern and is based on the excitatory and inhibitory learning rules inspired from neural connections in the human brain’s nervous system. The resulting architecture of SNP can be trained by supervised excitatory and inhibitory online learning rules. The main features of proposed single layer perceptron are universal approximation property and low computational complexity. The method is tested on 6 UCI (University of California, Irvine pattern recognition and classification datasets. Various comparisons with multilayer perceptron (MLP with gradient decent backpropagation (GDBP learning algorithm indicate the superiority of the approach in terms of higher accuracy, lower time, and spatial complexity, as well as faster training. Hence, we believe the proposed approach can be generally applicable to various problems such as in pattern recognition and classification.

  17. Application of Artificial Neural Network to Computer-Aided Diagnosis of Coronary Artery Disease in Myocardial SPECT Bull's-eye Images

    National Research Council Canada - National Science Library

    Fujita, Hiroshi; Katafuchi, Tetsuro; Uehara, Toshiisa; Nishimura, Tsunehiko

    1992-01-01

    .... The technique employs a neural network to analyze 201 Tl myocardial SPECT bull's-eye images. This multi-layer feed-forward neural network with a backpropagation algorithm has 256 input units (pattern...

  18. Forecasting the EMU inflation rate: Linear econometric vs. non-linear computational models using genetic neural fuzzy systems

    DEFF Research Database (Denmark)

    Kooths, Stefan; Mitze, Timo Friedel; Ringhut, Eric

    2004-01-01

    This paper compares the predictive power of linear econometric and non-linear computational models for forecasting the inflation rate in the European Monetary Union (EMU). Various models of both types are developed using different monetary and real activity indicators. They are compared according...

  19. Neural Oscillators Programming Simplified

    Directory of Open Access Journals (Sweden)

    Patrick McDowell

    2012-01-01

    Full Text Available The neurological mechanism used for generating rhythmic patterns for functions such as swallowing, walking, and chewing has been modeled computationally by the neural oscillator. It has been widely studied by biologists to model various aspects of organisms and by computer scientists and robotics engineers as a method for controlling and coordinating the gaits of walking robots. Although there has been significant study in this area, it is difficult to find basic guidelines for programming neural oscillators. In this paper, the authors approach neural oscillators from a programmer’s point of view, providing background and examples for developing neural oscillators to generate rhythmic patterns that can be used in biological modeling and robotics applications.

  20. Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms

    Science.gov (United States)

    Samala, Ravi K.; Chan, Heang-Ping; Hadjiiski, Lubomir M.; Helvie, Mark A.; Cha, Kenny H.; Richter, Caleb D.

    2017-12-01

    Transfer learning in deep convolutional neural networks (DCNNs) is an important step in its application to medical imaging tasks. We propose a multi-task transfer learning DCNN with the aim of translating the ‘knowledge’ learned from non-medical images to medical diagnostic tasks through supervised training and increasing the generalization capabilities of DCNNs by simultaneously learning auxiliary tasks. We studied this approach in an important application: classification of malignant and benign breast masses. With Institutional Review Board (IRB) approval, digitized screen-film mammograms (SFMs) and digital mammograms (DMs) were collected from our patient files and additional SFMs were obtained from the Digital Database for Screening Mammography. The data set consisted of 2242 views with 2454 masses (1057 malignant, 1397 benign). In single-task transfer learning, the DCNN was trained and tested on SFMs. In multi-task transfer learning, SFMs and DMs were used to train the DCNN, which was then tested on SFMs. N-fold cross-validation with the training set was used for training and parameter optimization. On the independent test set, the multi-task transfer learning DCNN was found to have significantly (p  =  0.007) higher performance compared to the single-task transfer learning DCNN. This study demonstrates that multi-task transfer learning may be an effective approach for training DCNN in medical imaging applications when training samples from a single modality are limited.

  1. Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms.

    Science.gov (United States)

    Samala, Ravi K; Chan, Heang-Ping; Hadjiiski, Lubomir M; Helvie, Mark A; Cha, Kenny; Richter, Caleb

    2017-10-16

    Transfer learning in deep convolutional neural networks (DCNNs) is an important step in its application to medical imaging tasks. We propose a multi-task transfer learning DCNN with the aims of translating the 'knowledge' learned from non-medical images to medical diagnostic tasks through supervised training and increasing the generalization capabilities of DCNNs by simultaneously learning auxiliary tasks. We studied this approach in an important application: classification of malignant and benign breast masses. With IRB approval, digitized screen-film mammograms (SFMs) and digital mammograms (DMs) were collected from our patient files and additional SFMs were obtained from the Digital Database for Screening Mammography. The data set consisted of 2,242 views with 2,454 masses (1,057 malignant, 1,397 benign). In single-task transfer learning, the DCNN was trained and tested on SFMs. In multi-task transfer learning, SFMs and DMs were used to train the DCNN, which was then tested on SFMs. N-fold cross-validation with the training set was used for training and parameter optimization. On the independent test set, the multi-task transfer learning DCNN was found to have significantly (p=0.007) higher performance compared to the single-task transfer learning DCNN. This study demonstrates that multi-task transfer learning may be an effective approach for training DCNN in medical imaging applications when training samples from a single modality are limited. © 2017 Institute of Physics and Engineering in Medicine.

  2. Computation of the Speed of Four In-Wheel Motors of an Electric Vehicle Using a Radial Basis Neural Network

    Directory of Open Access Journals (Sweden)

    M. Yildirim

    2016-12-01

    Full Text Available This paper presents design and speed estimation for an Electric Vehicle (EV with four in-wheel motors using Radial Basis Neural Network (RBNN. According to the steering angle and the speed of EV, the speeds of all wheels are calculated by equations derived from the Ackermann-Jeantand model using CoDeSys Software Package. The Electronic Differential System (EDS is also simulated by Matlab/Simulink using the mathematical equations. RBNN is used for the estimation of the wheel speeds based on the steering angle and EV speed. Further, different levels of noise are added to the steering angle and the EV speed. The speeds of front wheels calculated by CoDeSys are sent to two Induction Motor (IM drives via a Controller Area Network-Bus (CAN-Bus. These speed values are measured experimentally by a tachometer changing the steering angle and EV speed. RBNN results are verified by CoDeSys, Simulink, and experimental results. As a result, it is observed that RBNN is a good estimator for EDS of an EV with in-wheel motor due to its robustness to different levels of sensor noise.

  3. A Tony Thomas-Inspired Guide to INSPIRE

    Energy Technology Data Exchange (ETDEWEB)

    O' Connell, Heath B.; /Fermilab

    2010-04-01

    The SPIRES database was created in the late 1960s to catalogue the high energy physics preprints received by the SLAC Library. In the early 1990s it became the first database on the web and the first website outside of Europe. Although indispensible to the HEP community, its aging software infrastructure is becoming a serious liability. In a joint project involving CERN, DESY, Fermilab and SLAC, a new database, INSPIRE, is being created to replace SPIRES using CERN's modern, open-source Invenio database software. INSPIRE will maintain the content and functionality of SPIRES plus many new features. I describe this evolution from the birth of SPIRES to the current day, noting that the career of Tony Thomas spans this timeline.

  4. Building Neural Net Software

    OpenAIRE

    Neto, João Pedro; Costa, José Félix

    1999-01-01

    In a recent paper [Neto et al. 97] we showed that programming languages can be translated on recurrent (analog, rational weighted) neural nets. The goal was not efficiency but simplicity. Indeed we used a number-theoretic approach to machine programming, where (integer) numbers were coded in a unary fashion, introducing a exponential slow down in the computations, with respect to a two-symbol tape Turing machine. Implementation of programming languages in neural nets turns to be not only theo...

  5. Neural fields theory and applications

    CERN Document Server

    Graben, Peter; Potthast, Roland; Wright, James

    2014-01-01

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

  6. Training and Validating a Deep Convolutional Neural Network for Computer-Aided Detection and Classification of Abnormalities on Frontal Chest Radiographs.

    Science.gov (United States)

    Cicero, Mark; Bilbily, Alexander; Colak, Errol; Dowdell, Tim; Gray, Bruce; Perampaladas, Kuhan; Barfett, Joseph

    2017-05-01

    Convolutional neural networks (CNNs) are a subtype of artificial neural network that have shown strong performance in computer vision tasks including image classification. To date, there has been limited application of CNNs to chest radiographs, the most frequently performed medical imaging study. We hypothesize CNNs can learn to classify frontal chest radiographs according to common findings from a sufficiently large data set. Our institution's research ethics board approved a single-center retrospective review of 35,038 adult posterior-anterior chest radiographs and final reports performed between 2005 and 2015 (56% men, average age of 56, patient type: 24% inpatient, 39% outpatient, 37% emergency department) with a waiver for informed consent. The GoogLeNet CNN was trained using 3 graphics processing units to automatically classify radiographs as normal (n = 11,702) or into 1 or more of cardiomegaly (n = 9240), consolidation (n = 6788), pleural effusion (n = 7786), pulmonary edema (n = 1286), or pneumothorax (n = 1299). The network's performance was evaluated using receiver operating curve analysis on a test set of 2443 radiographs with the criterion standard being board-certified radiologist interpretation. Using 256 × 256-pixel images as input, the network achieved an overall sensitivity and specificity of 91% with an area under the curve of 0.964 for classifying a study as normal (n = 1203). For the abnormal categories, the sensitivity, specificity, and area under the curve, respectively, were 91%, 91%, and 0.962 for pleural effusion (n = 782), 82%, 82%, and 0.868 for pulmonary edema (n = 356), 74%, 75%, and 0.850 for consolidation (n = 214), 81%, 80%, and 0.875 for cardiomegaly (n = 482), and 78%, 78%, and 0.861 for pneumothorax (n = 167). Current deep CNN architectures can be trained with modest-sized medical data sets to achieve clinically useful performance at detecting and excluding common pathology on chest radiographs.

  7. Improvement of reliability of molecular DNA computing: solution of inverse problem of Raman spectroscopy using artificial neural networks

    Science.gov (United States)

    Dolenko, T. A.; Burikov, S. A.; Vervald, E. N.; Efitorov, A. O.; Laptinskiy, K. A.; Sarmanova, O. E.; Dolenko, S. A.

    2017-02-01

    Elaboration of methods for the control of biochemical reactions with deoxyribonucleic acid (DNA) strands is necessary for the solution of one of the basic problems in the creation of biocomputers—improvement in the reliability of molecular DNA computing. In this paper, the results of the solution of the four-parameter inverse problem of laser Raman spectroscopy—the determination of the type and concentration of each of the DNA nitrogenous bases in multi-component solutions—are presented.

  8. The relationship between the neural computations for speech and music perception is context-dependent: an activation likelihood estimate study

    Directory of Open Access Journals (Sweden)

    Arianna eLaCroix

    2015-08-01

    Full Text Available The relationship between the neurobiology of speech and music has been investigated for more than a century. There remains no widespread agreement regarding how (or to what extent music perception utilizes the neural circuitry that is engaged in speech processing, particularly at the cortical level. Prominent models such as Patel’s Shared Syntactic Integration Resource Hypothesis (SSIRH and Koelsch’s neurocognitive model of music perception suggest a high degree of overlap, particularly in the frontal lobe, but also perhaps more distinct representations in the temporal lobe with hemispheric asymmetries. The present meta-analysis study used activation likelihood estimate analyses to identify the brain regions consistently activated for music as compared to speech across the functional neuroimaging (fMRI and PET literature. Eighty music and 91 speech neuroimaging studies of healthy adult control subjects were analyzed. Peak activations reported in the music and speech studies were divided into four paradigm categories: passive listening, discrimination tasks, error/anomaly detection tasks and memory-related tasks. We then compared activation likelihood estimates within each category for music versus speech, and each music condition with passive listening. We found that listening to music and to speech preferentially activate distinct temporo-parietal bilateral cortical networks. We also found music and speech to have shared resources in the left pars opercularis but speech-specific resources in the left pars triangularis. The extent to which music recruited speech-activated frontal resources was modulated by task. While there are certainly limitations to meta-analysis techniques particularly regarding sensitivity, this work suggests that the extent of shared resources between speech and music may be task-dependent and highlights the need to consider how task effects may be affecting conclusions regarding the neurobiology of speech and music.

  9. The relationship between the neural computations for speech and music perception is context-dependent: an activation likelihood estimate study

    Science.gov (United States)

    LaCroix, Arianna N.; Diaz, Alvaro F.; Rogalsky, Corianne

    2015-01-01

    The relationship between the neurobiology of speech and music has been investigated for more than a century. There remains no widespread agreement regarding how (or to what extent) music perception utilizes the neural circuitry that is engaged in speech processing, particularly at the cortical level. Prominent models such as Patel's Shared Syntactic Integration Resource Hypothesis (SSIRH) and Koelsch's neurocognitive model of music perception suggest a high degree of overlap, particularly in the frontal lobe, but also perhaps more distinct representations in the temporal lobe with hemispheric asymmetries. The present meta-analysis study used activation likelihood estimate analyses to identify the brain regions consistently activated for music as compared to speech across the functional neuroimaging (fMRI and PET) literature. Eighty music and 91 speech neuroimaging studies of healthy adult control subjects were analyzed. Peak activations reported in the music and speech studies were divided into four paradigm categories: passive listening, discrimination tasks, error/anomaly detection tasks and memory-related tasks. We then compared activation likelihood estimates within each category for music vs. speech, and each music condition with passive listening. We found that listening to music and to speech preferentially activate distinct temporo-parietal bilateral cortical networks. We also found music and speech to have shared resources in the left pars opercularis but speech-specific resources in the left pars triangularis. The extent to which music recruited speech-activated frontal resources was modulated by task. While there are certainly limitations to meta-analysis techniques particularly regarding sensitivity, this work suggests that the extent of shared resources between speech and music may be task-dependent and highlights the need to consider how task effects may be affecting conclusions regarding the neurobiology of speech and music. PMID:26321976

  10. The emergence of two anti-phase oscillatory neural populations in a computational model of the Parkinsonian globus pallidus

    Directory of Open Access Journals (Sweden)

    Robert John Merrison-Hort

    2013-12-01

    Full Text Available Experiments in rodent models of Parkinson's Disease have demonstrated a prominent increase of oscillatory firing patterns in neurons within the Parkinsonian globus pallidus (GP which may underlie some of the motor symptoms of the disease. There are two main pathways from the cortex to GP: via the striatum and via the subthalamic nucleus (STN, but it is not known how these inputs sculpt the pathological pallidal firing patterns. To study this we developed a novel neural network model of conductance-based spiking pallidal neurons with cortex-modulated input from STN neurons. Our results support the hypothesis that entrainment occurs primarily via the subthalamic pathway. We find that as a result of the interplay between excitatory input from the STN and mutual inhibitory coupling between GP neurons, a homogeneous population of GP neurons demonstrates a self-organising dynamical behaviour where two groups of neurons emerge: one spiking in-phase with the cortical rhythm and the other in anti-phase. This finding mirrors what is seen in recordings from the GP of rodents that have had Parkinsonism induced via brain lesions. Our model also includes downregulation of Hyperpolarization-activated Cyclic Nucleotide-gated (HCN channels in response to burst firing of GP neurons, since this has been suggested as a possible mechanism for the emergence of Parkinsonian activity. We found that the downregulation of HCN channels provides even better correspondence with experimental data but that it is not essential in order for the two groups of oscillatory neurons to appear. We discuss how the influence of inhibitory striatal input will strengthen our results.

  11. Continuum robot arms inspired by cephalopods

    Science.gov (United States)

    Walker, Ian D.; Dawson, Darren M.; Flash, Tamar; Grasso, Frank W.; Hanlon, Roger T.; Hochner, Binyamin; Kier, William M.; Pagano, Christopher C.; Rahn, Christopher D.; Zhang, Qiming M.

    2005-05-01

    In this paper, we describe our recent results in the development of a new class of soft, continuous backbone ("continuum") robot manipulators. Our work is strongly motivated by the dexterous appendages found in cephalopods, particularly the arms and suckers of octopus, and the arms and tentacles of squid. Our ongoing investigation of these animals reveals interesting and unexpected functional aspects of their structure and behavior. The arrangement and dynamic operation of muscles and connective tissue observed in the arms of a variety of octopus species motivate the underlying design approach for our soft manipulators. These artificial manipulators feature biomimetic actuators, including artificial muscles based on both electro-active polymers (EAP) and pneumatic (McKibben) muscles. They feature a "clean" continuous backbone design, redundant degrees of freedom, and exhibit significant compliance that provides novel operational capacities during environmental interaction and object manipulation. The unusual compliance and redundant degrees of freedom provide strong potential for application to delicate tasks in cluttered and/or unstructured environments. Our aim is to endow these compliant robotic mechanisms with the diverse and dexterous grasping behavior observed in octopuses. To this end, we are conducting fundamental research into the manipulation tactics, sensory biology, and neural control of octopuses. This work in turn leads to novel approaches to motion planning and operator interfaces for the robots. The paper describes the above efforts, along with the results of our development of a series of continuum tentacle-like robots, demonstrating the unique abilities of biologically-inspired design.

  12. Nature-Inspired Chemical Reaction Optimisation Algorithms.

    Science.gov (United States)

    Siddique, Nazmul; Adeli, Hojjat

    2017-01-01

    Nature-inspired meta-heuristic algorithms have dominated the scientific literature in the areas of machine learning and cognitive computing paradigm in the last three decades. Chemical reaction optimisation (CRO) is a population-based meta-heuristic algorithm based on the principles of chemical reaction. A chemical reaction is seen as a process of transforming the reactants (or molecules) through a sequence of reactions into products. This process of transformation is implemented in the CRO algorithm to solve optimisation problems. This article starts with an overview of the chemical reactions and how it is applied to the optimisation problem. A review of CRO and its variants is presented in the paper. Guidelines from the literature on the effective choice of CRO parameters for solution of optimisation problems are summarised.

  13. Photosystem Inspired Peptide Hybrid Catalysts

    Science.gov (United States)

    2017-06-07

    peptide-based hybrid material can be a promising new class of proton conductor. Contrary to the conventional viewpoint on CO2 as a major contributor of... materials (Figure 2) Tyrosine is known to be an important sequence that helps electron transfer and hydrogen ion transfer in the natural world. In...13. SUPPLEMENTARY NOTES 14. ABSTRACT Biological systems can inspire to make new hybrid materials defined at the molecular level. We propose a novel

  14. Temperature based daily incoming solar radiation modeling based on gene expression programming, neuro-fuzzy and neural network computing techniques.

    Science.gov (United States)

    Landeras, G.; López, J. J.; Kisi, O.; Shiri, J.

    2012-04-01

    The correct observation/estimation of surface incoming solar radiation (RS) is very important for many agricultural, meteorological and hydrological related applications. While most weather stations are provided with sensors for air temperature detection, the presence of sensors necessary for the detection of solar radiation is not so habitual and the data quality provided by them is sometimes poor. In these cases it is necessary to estimate this variable. Temperature based modeling procedures are reported in this study for estimating daily incoming solar radiation by using Gene Expression Programming (GEP) for the first time, and other artificial intelligence models such as Artificial Neural Networks (ANNs), and Adaptive Neuro-Fuzzy Inference System (ANFIS). Traditional temperature based solar radiation equations were also included in this study and compared with artificial intelligence based approaches. Root mean square error (RMSE), mean absolute error (MAE) RMSE-based skill score (SSRMSE), MAE-based skill score (SSMAE) and r2 criterion of Nash and Sutcliffe criteria were used to assess the models' performances. An ANN (a four-input multilayer perceptron with ten neurons in the hidden layer) presented the best performance among the studied models (2.93 MJ m-2 d-1 of RMSE). A four-input ANFIS model revealed as an interesting alternative to ANNs (3.14 MJ m-2 d-1 of RMSE). Very limited number of studies has been done on estimation of solar radiation based on ANFIS, and the present one demonstrated the ability of ANFIS to model solar radiation based on temperatures and extraterrestrial radiation. By the way this study demonstrated, for the first time, the ability of GEP models to model solar radiation based on daily atmospheric variables. Despite the accuracy of GEP models was slightly lower than the ANFIS and ANN models the genetic programming models (i.e., GEP) are superior to other artificial intelligence models in giving a simple explicit equation for the

  15. Soft computing model for optimized siRNA design by identifying off target possibilities using artificial neural network model.

    Science.gov (United States)

    Murali, Reena; John, Philips George; Peter S, David

    2015-05-15

    The ability of small interfering RNA (siRNA) to do posttranscriptional gene regulation by knocking down targeted genes is an important research topic in functional genomics, biomedical research and in cancer therapeutics. Many tools had been developed to design exogenous siRNA with high experimental inhibition. Even though considerable amount of work has been done in designing exogenous siRNA, design of effective siRNA sequences is still a challenging work because the target mRNAs must be selected such that their corresponding siRNAs are likely to be efficient against that target and unlikely to accidentally silence other transcripts due to sequence similarity. In some cases, siRNAs may tolerate mismatches with the target mRNA, but knockdown of genes other than the intended target could make serious consequences. Hence to design siRNAs, two important concepts must be considered: the ability in knocking down target genes and the off target possibility on any nontarget genes. So before doing gene silencing by siRNAs, it is essential to analyze their off target effects in addition to their inhibition efficacy against a particular target. Only a few methods have been developed by considering both efficacy and off target possibility of siRNA against a gene. In this paper we present a new design of neural network model with whole stacking energy (ΔG) that enables to identify the efficacy and off target effect of siRNAs against target genes. The tool lists all siRNAs against a particular target with their inhibition efficacy and number of matches or sequence similarity with other genes in the database. We could achieve an excellent performance of Pearson Correlation Coefficient (R=0. 74) and Area Under Curve (AUC=0.906) when the threshold of whole stacking energy is ≥-34.6 kcal/mol. To the best of the author's knowledge, this is one of the best score while considering the "combined efficacy and off target possibility" of siRNA for silencing a gene. The proposed model

  16. Computational vision

    CERN Document Server

    Wechsler, Harry

    1990-01-01

    The book is suitable for advanced courses in computer vision and image processing. In addition to providing an overall view of computational vision, it contains extensive material on topics that are not usually covered in computer vision texts (including parallel distributed processing and neural networks) and considers many real applications.

  17. Multiple Linear Regression Model Based on Neural Network and Its Application in the MBR Simulation

    Directory of Open Access Journals (Sweden)

    Chunqing Li

    2012-01-01

    Full Text Available The computer simulation of the membrane bioreactor MBR has become the research focus of the MBR simulation. In order to compensate for the defects, for example, long test period, high cost, invisible equipment seal, and so forth, on the basis of conducting in-depth study of the mathematical model of the MBR, combining with neural network theory, this paper proposed a three-dimensional simulation system for MBR wastewater treatment, with fast speed, high efficiency, and good visualization. The system is researched and developed with the hybrid programming of VC++ programming language and OpenGL, with a multifactor linear regression model of affecting MBR membrane fluxes based on neural network, applying modeling method of integer instead of float and quad tree recursion. The experiments show that the three-dimensional simulation system, using the above models and methods, has the inspiration and reference for the future research and application of the MBR simulation technology.

  18. COMPUTING

    CERN Multimedia

    M. Kasemann

    Overview In autumn the main focus was to process and handle CRAFT data and to perform the Summer08 MC production. The operational aspects were well covered by regular Computing Shifts, experts on duty and Computing Run Coordination. At the Computing Resource Board (CRB) in October a model to account for service work at Tier 2s was approved. The computing resources for 2009 were reviewed for presentation at the C-RRB. The quarterly resource monitoring is continuing. Facilities/Infrastructure operations Operations during CRAFT data taking ran fine. This proved to be a very valuable experience for T0 workflows and operations. The transfers of custodial data to most T1s went smoothly. A first round of reprocessing started at the Tier-1 centers end of November; it will take about two weeks. The Computing Shifts procedure was tested full scale during this period and proved to be very efficient: 30 Computing Shifts Persons (CSP) and 10 Computing Resources Coordinators (CRC). The shift program for the shut down w...

  19. A Pruning Neural Network Model in Credit Classification Analysis

    Directory of Open Access Journals (Sweden)

    Yajiao Tang

    2018-01-01

    Full Text Available Nowadays, credit classification models are widely applied because they can help financial decision-makers to handle credit classification issues. Among them, artificial neural networks (ANNs have been widely accepted as the convincing methods in the credit industry. In this paper, we propose a pruning neural network (PNN and apply it to solve credit classification problem by adopting the well-known Australian and Japanese credit datasets. The model is inspired by synaptic nonlinearity of a dendritic tree in a biological neural model. And it is trained by an error back-propagation algorithm. The model is capable of realizing a neuronal pruning function by removing the superfluous synapses and useless dendrites and forms a tidy dendritic morphology at the end of learning. Furthermore, we utilize logic circuits (LCs to simulate the dendritic structures successfully which makes PNN be implemented on the hardware effectively. The statistical results of our experiments have verified that PNN obtains superior performance in comparison with other classical algorithms in terms of accuracy and computational efficiency.

  20. COMPUTING

    CERN Multimedia

    M. Kasemann

    Overview During the past three months activities were focused on data operations, testing and re-enforcing shift and operational procedures for data production and transfer, MC production and on user support. Planning of the computing resources in view of the new LHC calendar in ongoing. Two new task forces were created for supporting the integration work: Site Commissioning, which develops tools helping distributed sites to monitor job and data workflows, and Analysis Support, collecting the user experience and feedback during analysis activities and developing tools to increase efficiency. The development plan for DMWM for 2009/2011 was developed at the beginning of the year, based on the requirements from the Physics, Computing and Offline groups (see Offline section). The Computing management meeting at FermiLab on February 19th and 20th was an excellent opportunity discussing the impact and for addressing issues and solutions to the main challenges facing CMS computing. The lack of manpower is particul...

  1. COMPUTING

    CERN Multimedia

    I. Fisk

    2011-01-01

    Introduction CMS distributed computing system performed well during the 2011 start-up. The events in 2011 have more pile-up and are more complex than last year; this results in longer reconstruction times and harder events to simulate. Significant increases in computing capacity were delivered in April for all computing tiers, and the utilisation and load is close to the planning predictions. All computing centre tiers performed their expected functionalities. Heavy-Ion Programme The CMS Heavy-Ion Programme had a very strong showing at the Quark Matter conference. A large number of analyses were shown. The dedicated heavy-ion reconstruction facility at the Vanderbilt Tier-2 is still involved in some commissioning activities, but is available for processing and analysis. Facilities and Infrastructure Operations Facility and Infrastructure operations have been active with operations and several important deployment tasks. Facilities participated in the testing and deployment of WMAgent and WorkQueue+Request...

  2. COMPUTING

    CERN Multimedia

    P. McBride

    The Computing Project is preparing for a busy year where the primary emphasis of the project moves towards steady operations. Following the very successful completion of Computing Software and Analysis challenge, CSA06, last fall, we have reorganized and established four groups in computing area: Commissioning, User Support, Facility/Infrastructure Operations and Data Operations. These groups work closely together with groups from the Offline Project in planning for data processing and operations. Monte Carlo production has continued since CSA06, with about 30M events produced each month to be used for HLT studies and physics validation. Monte Carlo production will continue throughout the year in the preparation of large samples for physics and detector studies ramping to 50 M events/month for CSA07. Commissioning of the full CMS computing system is a major goal for 2007. Site monitoring is an important commissioning component and work is ongoing to devise CMS specific tests to be included in Service Availa...

  3. Biologically-inspired adaptive obstacle negotiation behavior of hexapod robots

    Science.gov (United States)

    Goldschmidt, Dennis; Wörgötter, Florentin; Manoonpong, Poramate

    2014-01-01

    Neurobiological studies have shown that insects are able to adapt leg movements and posture for obstacle negotiation in changing environments. Moreover, the distance to an obstacle where an insect begins to climb is found to be a major parameter for successful obstacle negotiation. Inspired by these findings, we present an adaptive neural control mechanism for obstacle negotiation behavior in hexapod robots. It combines locomotion control, backbone joint control, local leg reflexes, and neural learning. While the first three components generate locomotion including walking and climbing, the neural learning mechanism allows the robot to adapt its behavior for obstacle negotiation with respect to changing conditions, e.g., variable obstacle heights and different walking gaits. By successfully learning the association of an early, predictive signal (conditioned stimulus, CS) and a late, reflex signal (unconditioned stimulus, UCS), both provided by ultrasonic sensors at the front of the robot, the robot can autonomously find an appropriate distance from an obstacle to initiate climbing. The adaptive neural control was developed and tested first on a physical robot simulation, and was then successfully transferred to a real hexapod robot, called AMOS II. The results show that the robot can efficiently negotiate obstacles with a height up to 85% of the robot's leg length in simulation and 75% in a real environment. PMID:24523694

  4. Biologically-Inspired Adaptive Obstacle Negotiation Behavior of Hexapod Robots

    Directory of Open Access Journals (Sweden)

    Dennis eGoldschmidt

    2014-01-01

    Full Text Available Neurobiological studies have shown that insects are able to adapt leg movements and posture for obstacle negotiation in changing environments. Moreover, the distance to an obstacle where an insect begins to climb is found to be a major parameter for successful obstacle negotiation. Inspired by these findings, we present an adaptive neural control mechanism for obstacle negotiation behavior in hexapod robots. It combines locomotion control, backbone joint control, local leg reflexes, and neural learning. While the first three components generate locomotion including walking and climbing, the neural learning mechanism allows the robot to adapt its behavior for obstacle negotiation with respect to changing conditions, e.g., variable obstacle heights and different walking gaits. By successfully learning the association of an early, predictive signal (conditioned stimulus, CS and a late, reflex signal (unconditioned stimulus, UCS, both provided by ultrasonic sensors at the front of the robot, the robot can autonomously find an appropriate distance from an obstacle to initiate climbing. The adaptive neural control was developed and tested first on a physical robot simulation, and was then successfully transferred to a real hexapod robot, called AMOS II. The results show that the robot can efficiently negotiate obstacles with a height up to 85% of the robot's leg length in simulation and 75% in a real environment.

  5. Biologically-inspired adaptive obstacle negotiation behavior of hexapod robots.

    Science.gov (United States)

    Goldschmidt, Dennis; Wörgötter, Florentin; Manoonpong, Poramate

    2014-01-01

    Neurobiological studies have shown that insects are able to adapt leg movements and posture for obstacle negotiation in changing environments. Moreover, the distance to an obstacle where an insect begins to climb is found to be a major parameter for successful obstacle negotiation. Inspired by these findings, we present an adaptive neural control mechanism for obstacle negotiation behavior in hexapod robots. It combines locomotion control, backbone joint control, local leg reflexes, and neural learning. While the first three components generate locomotion including walking and climbing, the neural learning mechanism allows the robot to adapt its behavior for obstacle negotiation with respect to changing conditions, e.g., variable obstacle heights and different walking gaits. By successfully learning the association of an early, predictive signal (conditioned stimulus, CS) and a late, reflex signal (unconditioned stimulus, UCS), both provided by ultrasonic sensors at the front of the robot, the robot can autonomously find an appropriate distance from an obstacle to initiate climbing. The adaptive neural control was developed and tested first on a physical robot simulation, and was then successfully transferred to a real hexapod robot, called AMOS II. The results show that the robot can efficiently negotiate obstacles with a height up to 85% of the robot's leg length in simulation and 75% in a real environment.

  6. Nature–inspired metaheuristic algorithms to find near–OGR sequences for WDM channel allocation and their performance comparison

    Directory of Open Access Journals (Sweden)

    Bansal Shonak

    2017-05-01

    Full Text Available Nowadays, nature–inspired metaheuristic algorithms are most powerful optimizing algorithms for solving the NP–complete problems. This paper proposes three approaches to find near–optimal Golomb ruler sequences based on nature–inspired algorithms in a reasonable time. The optimal Golomb ruler (OGR sequences found their application in channel–allocation method that allows suppression of the crosstalk due to four–wave mixing in optical wavelength division multiplexing systems. The simulation results conclude that the proposed nature–inspired metaheuristic optimization algorithms are superior to the existing conventional and nature–inspired algorithms to find near–OGRs in terms of ruler length, total optical channel bandwidth, computation time, and computational complexity. Based on the simulation results, the performance of proposed different nature–inspired metaheuristic algorithms are being compared by using statistical tests. The statistical test results conclude the superiority of the proposed nature–inspired optimization algorithms.

  7. Feature Fusion Based on Convolutional Neural Network for SAR ATR

    Directory of Open Access Journals (Sweden)

    Chen Shi-Qi

    2017-01-01

    Full Text Available Recent breakthroughs in algorithms related to deep convolutional neural networks (DCNN have stimulated the development of various of signal processing approaches, where the specific application of Automatic Target Recognition (ATR using Synthetic Aperture Radar (SAR data has spurred widely attention. Inspired by the more efficient distributed training such as inception architecture and residual network, a new feature fusion structure which jointly exploits all the merits of each version is proposed to reduce the data dimensions and the complexity of computation. The detailed procedure presented in this paper consists of the fused features, which make the representation of SAR images more distinguishable after the extraction of a set of features from DCNN, followed by a trainable classifier. In particular, the obtained results on the 10-class benchmark data set demonstrate that the presented architecture can achieve remarkable classification performance to the current state-of-the-art methods.

  8. A Computational Analysis of Neural Mechanisms Underlying the Maturation of Multisensory Speech Integration in Neurotypical Children and Those on the Autism Spectrum.

    Science.gov (United States)

    Cuppini, Cristiano; Ursino, Mauro; Magosso, Elisa; Ross, Lars A; Foxe, John J; Molholm, Sophie

    2017-01-01

    Failure to appropriately develop multisensory integration (MSI) of audiovisual speech may affect a child's ability to attain optimal communication. Studies have shown protracted development of MSI into late-childhood and identified deficits in MSI in children with an autism spectrum disorder (ASD). Currently, the neural basis of acquisition of this ability is not well understood. Here, we developed a computational model informed by neurophysiology to analyze possible mechanisms underlying MSI maturation, and its delayed development in ASD. The model posits that strengthening of feedforward and cross-sensory connections, responsible for the alignment of auditory and visual speech sound representations in posterior superior temporal gyrus/sulcus, can explain behavioral data on the acquisition of MSI. This was simulated by a training phase during which the network was exposed to unisensory and multisensory stimuli, and projections were crafted by Hebbian rules of potentiation and depression. In its mature architecture, the network also reproduced the well-known multisensory McGurk speech effect. Deficits in audiovisual speech perception in ASD were well accounted for by fewer multisensory exposures, compatible with a lack of attention, but not by reduced synaptic connectivity or synaptic plasticity.

  9. A Computational Analysis of Neural Mechanisms Underlying the Maturation of Multisensory Speech Integration in Neurotypical Children and Those on the Autism Spectrum

    Directory of Open Access Journals (Sweden)

    Cristiano Cuppini

    2017-10-01

    Full Text Available Failure to appropriately develop multisensory integration (MSI of audiovisual speech may affect a child's ability to attain optimal communication. Studies have shown protracted development of MSI into late-childhood and identified deficits in MSI in children with an autism spectrum disorder (ASD. Currently, the neural basis of acquisition of this ability is not well understood. Here, we developed a computational model informed by neurophysiology to analyze possible mechanisms underlying MSI maturation, and its delayed development in ASD. The model posits that strengthening of feedforward and cross-sensory connections, responsible for the alignment of auditory and visual speech sound representations in posterior superior temporal gyrus/sulcus, can explain behavioral data on the acquisition of MSI. This was simulated by a training phase during which the network was exposed to unisensory and multisensory stimuli, and projections were crafted by Hebbian rules of potentiation and depression. In its mature architecture, the network also reproduced the well-known multisensory McGurk speech effect. Deficits in audiovisual speech perception in ASD were well accounted for by fewer multisensory exposures, compatible with a lack of attention, but not by reduced synaptic connectivity or synaptic plasticity.

  10. Comparative approximations of criticality in a neural and quantum regime.

    Science.gov (United States)

    Bettinger, Jesse Sterling

    2017-12-01

    Under a variety of conditions, stochastic and non-linear systems with many degrees of freedom tend to evolve towards complexity and criticality. Over the last decades, a steady proliferation of models re: far-from-equilibrium thermodynamics of metastable, many-valued systems arose, serving as attributes of a 'critical' attractor landscape. Building off recent data citing trademark aspects of criticality in the brain-including: power-laws, scale-free (1/f) behavior (scale invariance, or scale independence), critical slowing, and avalanches-it has been conjectured that operating at criticality entails functional advantages such as: optimized neural computation and information processing; boosted memory; large dynamical ranges; long-range communication; and an increased ability to react to highly diverse stimuli. In short, critical dynamics provide a necessary condition for neurobiologically significant elements of brain dynamics. Theoretical predictions have been verified in specific models such as Boolean networks, liquid state machines, and neural networks. These findings inspired the neural criticality hypothesis, proposing that the brain operates in a critical state because the associated optimal computational capabilities provide an evolutionarily advantage. This paper develops in three parts: after developing the critical landscape, we will then shift gears to rediscover another inroad to criticality via stochastic quantum field theory and dissipative dynamics. The existence of these two approaches deserves some consideration, given both neural and quantum criticality hypotheses propose specific mechanisms that leverage the same phenomena. This suggests that understanding the quantum approach could help to shed light on brain-based modeling. In the third part, we will turn to Whitehead's actual entities and modes of perception in order to demonstrate a concomitant logic underwriting both models. In the discussion, I briefly motivate a reading of criticality and

  11. AER synthetic generation in hardware for bio-inspired spiking systems

    Science.gov (United States)

    Linares-Barranco, Alejandro; Linares-Barranco, Bernabe; Jimenez-Moreno, Gabriel; Civit-Balcells, Anton

    2005-06-01

    Address Event Representation (AER) is an emergent neuromorphic interchip communication protocol that allows for real-time virtual massive connectivity between huge number neurons located on different chips. By exploiting high speed digital communication circuits (with nano-seconds timings), synaptic neural connections can be time multiplexed, while neural activity signals (with mili-seconds timings) are sampled at low frequencies. Also, neurons generate 'events' according to their activity levels. More active neurons generate more events per unit time, and access the interchip communication channel more frequently, while neurons with low activity consume less communication bandwidth. When building multi-chip muti-layered AER systems it is absolutely necessary to have a computer interface that allows (a) to read AER interchip traffic into the computer and visualize it on screen, and (b) convert conventional frame-based video stream in the computer into AER and inject it at some point of the AER structure. This is necessary for test and debugging of complex AER systems. This paper addresses the problem of converting, in a computer, a conventional frame-based video stream into the spike event based representation AER. There exist several proposed software methods for synthetic generation of AER for bio-inspired systems. This paper presents a hardware implementation for one method, which is based on Linear-Feedback-Shift-Register (LFSR) pseudo-random number generation. The sequence of events generated by this hardware, which follows a Poisson distribution like a biological neuron, has been reconstructed using two AER integrator cells. The error of reconstruction for a set of images that produces different traffic loads of event in the AER bus is used as evaluation criteria. A VHDL description of the method, that includes the Xilinx PCI Core, has been implemented and tested using a general purpose PCI-AER board. This PCI-AER board has been developed by authors, and uses

  12. COMPUTING

    CERN Multimedia

    I. Fisk

    2013-01-01

    Computing activity had ramped down after the completion of the reprocessing of the 2012 data and parked data, but is increasing with new simulation samples for analysis and upgrade studies. Much of the Computing effort is currently involved in activities to improve the computing system in preparation for 2015. Operations Office Since the beginning of 2013, the Computing Operations team successfully re-processed the 2012 data in record time, not only by using opportunistic resources like the San Diego Supercomputer Center which was accessible, to re-process the primary datasets HTMHT and MultiJet in Run2012D much earlier than planned. The Heavy-Ion data-taking period was successfully concluded in February collecting almost 500 T. Figure 3: Number of events per month (data) In LS1, our emphasis is to increase efficiency and flexibility of the infrastructure and operation. Computing Operations is working on separating disk and tape at the Tier-1 sites and the full implementation of the xrootd federation ...

  13. Adaptation and hybridization in computational intelligence

    CERN Document Server

    Jr, Iztok

    2015-01-01

      This carefully edited book takes a walk through recent advances in adaptation and hybridization in the Computational Intelligence (CI) domain. It consists of ten chapters that are divided into three parts. The first part illustrates background information and provides some theoretical foundation tackling the CI domain, the second part deals with the adaptation in CI algorithms, while the third part focuses on the hybridization in CI. This book can serve as an ideal reference for researchers and students of computer science, electrical and civil engineering, economy, and natural sciences that are confronted with solving the optimization, modeling and simulation problems. It covers the recent advances in CI that encompass Nature-inspired algorithms, like Artificial Neural networks, Evolutionary Algorithms and Swarm Intelligence –based algorithms.  

  14. Space as an inspiring context

    Science.gov (United States)

    Stancu, Cristina

    2017-04-01

    Using space as context to inspire science education tapps into the excitement of generations of discovering the unknown resulting in unprecedented public participation. Educators are finding exciting and age appropiate materials for their class that explore science, technology, engineering and mathematics. Possible misconceptions are highlighted so that teachers may plan lessons to facilitate correct conceptual understanding. With a range of hands-on learning experiences, Web materials and online ,opportunities for students, educators are invited to take a closer look to actual science missions. This session leverages resources, materials and expertise to address a wide range of traditional and nontraditional audiences while providing consistent messages and information on various space agencies programs.

  15. Innovations in nature inspired optimization and learning methods

    OpenAIRE

    Corchado Rodríguez, Emilio; Abraham, Ajith P.

    2017-01-01

    The nine papers included in this special issue represent a selection of extended contributions presented at the Third World Congress on Nature and Biologically Inspired Computing (NaBIC2011), held in Salamanca, Spain, October 19–21, 2011. Papers were selected on the basis of fundamental ideas and concepts rather than the direct usage of well-established techniques. This special issue is then aimed at practitioners, researchers and postgraduate students, who are engaged in developing and apply...

  16. New Gromov-Inspired Metrics on Phylogenetic Tree Space.

    Science.gov (United States)

    Liebscher, Volkmar

    2018-03-01

    We present a new class of metrics for unrooted phylogenetic X-trees inspired by the Gromov-Hausdorff distance for (compact) metric spaces. These metrics can be efficiently computed by linear or quadratic programming. They are robust under NNI operations, too. The local behaviour of the metrics shows that they are different from any previously introduced metrics. The performance of the metrics is briefly analysed on random weighted and unweighted trees as well as random caterpillars.

  17. COMPUTING

    CERN Multimedia

    I. Fisk

    2010-01-01

    Introduction It has been a very active quarter in Computing with interesting progress in all areas. The activity level at the computing facilities, driven by both organised processing from data operations and user analysis, has been steadily increasing. The large-scale production of simulated events that has been progressing throughout the fall is wrapping-up and reprocessing with pile-up will continue. A large reprocessing of all the proton-proton data has just been released and another will follow shortly. The number of analysis jobs by users each day, that was already hitting the computing model expectations at the time of ICHEP, is now 33% higher. We are expecting a busy holiday break to ensure samples are ready in time for the winter conferences. Heavy Ion An activity that is still in progress is computing for the heavy-ion program. The heavy-ion events are collected without zero suppression, so the event size is much large at roughly 11 MB per event of RAW. The central collisions are more complex and...

  18. COMPUTING

    CERN Multimedia

    M. Kasemann P. McBride Edited by M-C. Sawley with contributions from: P. Kreuzer D. Bonacorsi S. Belforte F. Wuerthwein L. Bauerdick K. Lassila-Perini M-C. Sawley

    Introduction More than seventy CMS collaborators attended the Computing and Offline Workshop in San Diego, California, April 20-24th to discuss the state of readiness of software and computing for collisions. Focus and priority were given to preparations for data taking and providing room for ample dialog between groups involved in Commissioning, Data Operations, Analysis and MC Production. Throughout the workshop, aspects of software, operating procedures and issues addressing all parts of the computing model were discussed. Plans for the CMS participation in STEP’09, the combined scale testing for all four experiments due in June 2009, were refined. The article in CMS Times by Frank Wuerthwein gave a good recap of the highly collaborative atmosphere of the workshop. Many thanks to UCSD and to the organizers for taking care of this workshop, which resulted in a long list of action items and was definitely a success. A considerable amount of effort and care is invested in the estimate of the comput...

  19. An immune-inspired semi-supervised algorithm for breast cancer diagnosis.

    Science.gov (United States)

    Peng, Lingxi; Chen, Wenbin; Zhou, Wubai; Li, Fufang; Yang, Jin; Zhang, Jiandong

    2016-10-01

    Breast cancer is the most frequently and world widely diagnosed life-threatening cancer, which is the leading cause of cancer death among women. Early accurate diagnosis can be a big plus in treating breast cancer. Researchers have approached this problem using various data mining and machine learning techniques such as support vector machine, artificial neural network, etc. The computer immunology is also an intelligent method inspired by biological immune system, which has been successfully applied in pattern recognition, combination optimization, machine learning, etc. However, most of these diagnosis methods belong to a supervised diagnosis method. It is very expensive to obtain labeled data in biology and medicine. In this paper, we seamlessly integrate the state-of-the-art research on life science with artificial intelligence, and propose a semi-supervised learning algorithm to reduce the need for labeled data. We use two well-known benchmark breast cancer datasets in our study, which are acquired from the UCI machine learning repository. Extensive experiments are conducted and evaluated on those two datasets. Our experimental results demonstrate the effectiveness and efficiency of our proposed algorithm, which proves that our algorithm is a promising automatic diagnosis method for breast cancer. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  20. Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot

    Directory of Open Access Journals (Sweden)

    Eduard eGrinke

    2015-10-01

    Full Text Available Walking animals, like insects, with little neural computing can effectively perform complex behaviors. They can walk around their environment, escape from corners/deadlocks, and avoid or climb over obstacles. While performing all these behaviors, they can also adapt their movements to deal with an unknown situation. As a consequence, they successfully navigate through their complex environment. The versatile and adaptive abilities are the result of an integration of several ingredients embedded in their sensorimotor loop. Biological studies reveal that the ingredients include neural dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a walking robot is a challenging task. In this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural mechanisms with plasticity, sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent network consisting of two fully connected neurons. Online correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors in the network to generate different turning angles with short-term memory for a biomechanical walking robot. The turning information is transmitted as descending steering signals to the locomotion control which translates the signals into motor actions. As a result, the robot can walk around and adapt its turning angle for avoiding obstacles in different situations as well as escaping from sharp corners or deadlocks. Using backbone joint control embedded in the locomotion control allows the robot to climb over small obstacles. Consequently, it can successfully explore and navigate in complex environments.

  1. From biologically-inspired physics to physics-inspired biology From biologically-inspired physics to physics-inspired biology

    Science.gov (United States)

    Kornyshev, Alexei A.

    2010-10-01

    The conference 'From DNA-Inspired Physics to Physics-Inspired Biology' (1-5 June 2009, International Center for Theoretical Physics, Trieste, Italy) that myself and two former presidents of the American Biophysical Society—Wilma Olson (Rutgers University) and Adrian Parsegian (NIH), with the support of an ICTP team (Ralf Gebauer (Local Organizer) and Doreen Sauleek (Conference Secretary)), have organized was intended to establish stronger links between the biology and physics communities on the DNA front. The relationships between them were never easy. In 1997, Adrian published a paper in Physics Today ('Harness the Hubris') summarizing his thoughts about the main obstacles for a successful collaboration. The bottom line of that article was that physicists must seriously learn biology before exploring it and even having an interpreter, a friend or co-worker, who will be cooperating with you and translating the problems of biology into a physical language, may not be enough. He started his story with a joke about a physicist asking a biologist: 'I want to study the brain. Tell me something about it!' Biologist: 'First, the brain consists of two parts, and..' Physicist: 'Stop. You have told me too much.' Adrian listed a few direct avenues where physicists' contributions may be particularly welcome. This gentle and elegantly written paper caused, however, a stormy reaction from Bob Austin (Princeton), published together with Adrian's notes, accusing Adrian of forbidding physicists to attack big questions in biology straightaway. Twelve years have passed and many new developments have taken place in the biologist-physicist interaction. This was something I addressed in my opening conference speech, with my position lying somewhere inbetween Parsegian's and Austin's, which is briefly outlined here. I will first recall certain precepts or 'dogmas' that fly in the air like Valkyries, poisoning those relationships. Since the early seventies when I was a first year Ph

  2. COMPUTING

    CERN Multimedia

    P. McBride

    It has been a very active year for the computing project with strong contributions from members of the global community. The project has focused on site preparation and Monte Carlo production. The operations group has begun processing data from P5 as part of the global data commissioning. Improvements in transfer rates and site availability have been seen as computing sites across the globe prepare for large scale production and analysis as part of CSA07. Preparations for the upcoming Computing Software and Analysis Challenge CSA07 are progressing. Ian Fisk and Neil Geddes have been appointed as coordinators for the challenge. CSA07 will include production tests of the Tier-0 production system, reprocessing at the Tier-1 sites and Monte Carlo production at the Tier-2 sites. At the same time there will be a large analysis exercise at the Tier-2 centres. Pre-production simulation of the Monte Carlo events for the challenge is beginning. Scale tests of the Tier-0 will begin in mid-July and the challenge it...

  3. COMPUTING

    CERN Multimedia

    I. Fisk

    2012-01-01

    Introduction Computing continued with a high level of activity over the winter in preparation for conferences and the start of the 2012 run. 2012 brings new challenges with a new energy, more complex events, and the need to make the best use of the available time before the Long Shutdown. We expect to be resource constrained on all tiers of the computing system in 2012 and are working to ensure the high-priority goals of CMS are not impacted. Heavy ions After a successful 2011 heavy-ion run, the programme is moving to analysis. During the run, the CAF resources were well used for prompt analysis. Since then in 2012 on average 200 job slots have been used continuously at Vanderbilt for analysis workflows. Operations Office As of 2012, the Computing Project emphasis has moved from commissioning to operation of the various systems. This is reflected in the new organisation structure where the Facilities and Data Operations tasks have been merged into a common Operations Office, which now covers everything ...

  4. COMPUTING

    CERN Multimedia

    I. Fisk

    2011-01-01

    Introduction It has been a very active quarter in Computing with interesting progress in all areas. The activity level at the computing facilities, driven by both organised processing from data operations and user analysis, has been steadily increasing. The large-scale production of simulated events that has been progressing throughout the fall is wrapping-up and reprocessing with pile-up will continue. A large reprocessing of all the proton-proton data has just been released and another will follow shortly. The number of analysis jobs by users each day, that was already hitting the computing model expectations at the time of ICHEP, is now 33% higher. We are expecting a busy holiday break to ensure samples are ready in time for the winter conferences. Heavy Ion The Tier 0 infrastructure was able to repack and promptly reconstruct heavy-ion collision data. Two copies were made of the data at CERN using a large CASTOR disk pool, and the core physics sample was replicated ...

  5. COMPUTING

    CERN Multimedia

    M. Kasemann

    Introduction More than seventy CMS collaborators attended the Computing and Offline Workshop in San Diego, California, April 20-24th to discuss the state of readiness of software and computing for collisions. Focus and priority were given to preparations for data taking and providing room for ample dialog between groups involved in Commissioning, Data Operations, Analysis and MC Production. Throughout the workshop, aspects of software, operating procedures and issues addressing all parts of the computing model were discussed. Plans for the CMS participation in STEP’09, the combined scale testing for all four experiments due in June 2009, were refined. The article in CMS Times by Frank Wuerthwein gave a good recap of the highly collaborative atmosphere of the workshop. Many thanks to UCSD and to the organizers for taking care of this workshop, which resulted in a long list of action items and was definitely a success. A considerable amount of effort and care is invested in the estimate of the co...

  6. COMPUTING

    CERN Multimedia

    M. Kasemann

    CCRC’08 challenges and CSA08 During the February campaign of the Common Computing readiness challenges (CCRC’08), the CMS computing team had achieved very good results. The link between the detector site and the Tier0 was tested by gradually increasing the number of parallel transfer streams well beyond the target. Tests covered the global robustness at the Tier0, processing a massive number of very large files and with a high writing speed to tapes.  Other tests covered the links between the different Tiers of the distributed infrastructure and the pre-staging and reprocessing capacity of the Tier1’s: response time, data transfer rate and success rate for Tape to Buffer staging of files kept exclusively on Tape were measured. In all cases, coordination with the sites was efficient and no serious problem was found. These successful preparations prepared the ground for the second phase of the CCRC’08 campaign, in May. The Computing Software and Analysis challen...

  7. COMPUTING

    CERN Multimedia

    I. Fisk

    2010-01-01

    Introduction The first data taking period of November produced a first scientific paper, and this is a very satisfactory step for Computing. It also gave the invaluable opportunity to learn and debrief from this first, intense period, and make the necessary adaptations. The alarm procedures between different groups (DAQ, Physics, T0 processing, Alignment/calibration, T1 and T2 communications) have been reinforced. A major effort has also been invested into remodeling and optimizing operator tasks in all activities in Computing, in parallel with the recruitment of new Cat A operators. The teams are being completed and by mid year the new tasks will have been assigned. CRB (Computing Resource Board) The Board met twice since last CMS week. In December it reviewed the experience of the November data-taking period and could measure the positive improvements made for the site readiness. It also reviewed the policy under which Tier-2 are associated with Physics Groups. Such associations are decided twice per ye...

  8. COMPUTING

    CERN Multimedia

    M. Kasemann

    Introduction During the past six months, Computing participated in the STEP09 exercise, had a major involvement in the October exercise and has been working with CMS sites on improving open issues relevant for data taking. At the same time operations for MC production, real data reconstruction and re-reconstructions and data transfers at large scales were performed. STEP09 was successfully conducted in June as a joint exercise with ATLAS and the other experiments. It gave good indication about the readiness of the WLCG infrastructure with the two major LHC experiments stressing the reading, writing and processing of physics data. The October Exercise, in contrast, was conducted as an all-CMS exercise, where Physics, Computing and Offline worked on a common plan to exercise all steps to efficiently access and analyze data. As one of the major results, the CMS Tier-2s demonstrated to be fully capable for performing data analysis. In recent weeks, efforts were devoted to CMS Computing readiness. All th...

  9. Computational Composites

    DEFF Research Database (Denmark)

    Vallgårda, Anna K. A.

    to understand the computer as a material like any other material we would use for design, like wood, aluminum, or plastic. That as soon as the computer forms a composition with other materials it becomes just as approachable and inspiring as other smart materials. I present a series of investigations of what...... Computational Composite, and Telltale). Through the investigations, I show how the computer can be understood as a material and how it partakes in a new strand of materials whose expressions come to be in context. I uncover some of their essential material properties and potential expressions. I develop a way...

  10. Biologically inspired intelligent decision making

    Science.gov (United States)

    Manning, Timmy; Sleator, Roy D; Walsh, Paul

    2014-01-01

    Artificial neural networks (ANNs) are a class of powerful machine learning models for classification and function approximation which have analogs in nature. An ANN learns to map stimuli to responses through repeated evaluation of exemplars of the mapping. This learning approach results in networks which are recognized for their noise tolerance and ability to generalize meaningful responses for novel stimuli. It is these properties of ANNs which make them appealing for applications to bioinformatics problems where interpretation of data may not always be obvious, and where the domain knowledge required for deductive techniques is incomplete or can cause a combinatorial explosion of rules. In this paper, we provide an introduction to artificial neural network theory and review some interesting recent applications to bioinformatics problems. PMID:24335433

  11. A supervised 'lesion-enhancement' filter by use of a massive-training artificial neural network (MTANN) in computer-aided diagnosis (CAD)

    Science.gov (United States)

    Suzuki, Kenji

    2009-09-01

    Computer-aided diagnosis (CAD) has been an active area of study in medical image analysis. A filter for the enhancement of lesions plays an important role for improving the sensitivity and specificity in CAD schemes. The filter enhances objects similar to a model employed in the filter; e.g. a blob-enhancement filter based on the Hessian matrix enhances sphere-like objects. Actual lesions, however, often differ from a simple model; e.g. a lung nodule is generally modeled as a solid sphere, but there are nodules of various shapes and with internal inhomogeneities such as a nodule with spiculations and ground-glass opacity. Thus, conventional filters often fail to enhance actual lesions. Our purpose in this study was to develop a supervised filter for the enhancement of actual lesions (as opposed to a lesion model) by use of a massive-training artificial neural network (MTANN) in a CAD scheme for detection of lung nodules in CT. The MTANN filter was trained with actual nodules in CT images to enhance actual patterns of nodules. By use of the MTANN filter, the sensitivity and specificity of our CAD scheme were improved substantially. With a database of 69 lung cancers, nodule candidate detection by the MTANN filter achieved a 97% sensitivity with 6.7 false positives (FPs) per section, whereas nodule candidate detection by a difference-image technique achieved a 96% sensitivity with 19.3 FPs per section. Classification-MTANNs were applied for further reduction of the FPs. The classification-MTANNs removed 60% of the FPs with a loss of one true positive; thus, it achieved a 96% sensitivity with 2.7 FPs per section. Overall, with our CAD scheme based on the MTANN filter and classification-MTANNs, an 84% sensitivity with 0.5 FPs per section was achieved. First presented at the Seventh International Conference on Machine Learning and Applications, San Diego, CA, USA, 11-13 December 2008.

  12. Analysis of breast CT lesions using computer-aided diagnosis: an application of neural networks on extracted morphologic and texture features

    Science.gov (United States)

    Ray, Shonket; Prionas, Nicolas D.; Lindfors, Karen K.; Boone, John M.

    2012-03-01

    Dedicated cone-beam breast CT (bCT) scanners have been developed as a potential alternative imaging modality to conventional X-ray mammography in breast cancer diagnosis. As with other modalities, quantitative imaging (QI) analysis can potentially be utilized as a tool to extract useful numeric information concerning diagnosed lesions from high quality 3D tomographic data sets. In this work, preliminary QI analysis was done by designing and implementing a computer-aided diagnosis (CADx) system consisting of image preprocessing, object(s) of interest (i.e. masses, microcalcifications) segmentation, structural analysis of the segmented object(s), and finally classification into benign or malignant disease. Image sets were acquired from bCT patient scans with diagnosed lesions. Iterative watershed segmentation (IWS), a hybridization of the watershed method using observer-set markers and a gradient vector flow (GVF) approach, was used as the lesion segmentation method in 3D. Eight morphologic parameters and six texture features based on gray level co-occurrence matrix (GLCM) calculations were obtained per segmented lesion and combined into multi-dimensional feature input data vectors. Artificial neural network (ANN) classifiers were used by performing cross validation and network parameter optimization to maximize area under the curve (AUC) values of the resulting receiver-operating characteristic (ROC) curves. Within these ANNs, biopsy-proven diagnoses of malignant and benign lesions were recorded as target data while the feature vectors were saved as raw input data. With the image data separated into post-contrast (n = 55) and pre-contrast sets (n = 39), a maximum AUC of 0.70 +/- 0.02 and 0.80 +/- 0.02 were achieved, respectively, for each data set after ANN application.

  13. A two-step convolutional neural network based computer-aided detection scheme for automatically segmenting adipose tissue volume depicting on CT images.

    Science.gov (United States)

    Wang, Yunzhi; Qiu, Yuchen; Thai, Theresa; Moore, Kathleen; Liu, Hong; Zheng, Bin

    2017-06-01

    Accurately assessment of adipose tissue volume inside a human body plays an important role in predicting disease or cancer risk, diagnosis and prognosis. In order to overcome limitation of using only one subjectively selected CT image slice to estimate size of fat areas, this study aims to develop and test a computer-aided detection (CAD) scheme based on deep learning technique to automatically segment subcutaneous fat areas (SFA) and visceral fat areas (VFA) depicting on volumetric CT images. A retrospectively collected CT image dataset was divided into two independent training and testing groups. The proposed CAD framework consisted of two steps with two convolution neural networks (CNNs) namely, Selection-CNN and Segmentation-CNN. The first CNN was trained using 2,240 CT slices to select abdominal CT slices depicting SFA and VFA. The second CNN was trained with 84,000pixel patches and applied to the selected CT slices to identify fat-related pixels and assign them into SFA and VFA classes. Comparing to the manual CT slice selection and fat pixel segmentation results, the accuracy of CT slice selection using the Selection-CNN yielded 95.8%, while the accuracy of fat pixel segmentation using the Segmentation-CNN was 96.8%. This study demonstrated the feasibility of applying a new deep learning based CAD scheme to automatically recognize abdominal section of human body from CT scans and segment SFA and VFA from volumetric CT data with high accuracy or agreement with the manual segmentation results. Copyright © 2017 Elsevier B.V. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    Jennifer L Collinger

    2014-02-01

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

  15. Guard Cell and Tropomyosin Inspired Chemical Sensor

    Directory of Open Access Journals (Sweden)

    Jacquelyn K.S. Nagel

    2013-10-01

    Full Text Available Sensors are an integral part of many engineered products and systems. Biological inspiration has the potential to improve current sensor designs as well as inspire innovative ones. This paper presents the design of an innovative, biologically-inspired chemical sensor that performs “up-front” processing through mechanical means. Inspiration from the physiology (function of the guard cell coupled with the morphology (form and physiology of tropomyosin resulted in two concept variants for the chemical sensor. Applications of the sensor design include environmental monitoring of harmful gases, and a non-invasive approach to detect illnesses including diabetes, liver disease, and cancer on the breath.

  16. Biologically inspired robots elicit a robust fear response in zebrafish

    Science.gov (United States)

    Ladu, Fabrizio; Bartolini, Tiziana; Panitz, Sarah G.; Butail, Sachit; Macrı, Simone; Porfiri, Maurizio

    2015-03-01

    We investigate the behavioral response of zebrafish to three fear-evoking stimuli. In a binary choice test, zebrafish are exposed to a live allopatric predator, a biologically-inspired robot, and a computer-animated image of the live predator. A target tracking algorithm is developed to score zebrafish behavior. Unlike computer-animated images, the robotic and live predator elicit a robust avoidance response. Importantly, the robotic stimulus elicits more consistent inter-individual responses than the live predator. Results from this effort are expected to aid in hypothesis-driven studies on zebrafish fear response, by offering a valuable approach to maximize data-throughput and minimize animal subjects.

  17. A new bio-inspired decision chain for UAV sense-and-avoid applications

    Science.gov (United States)

    Fallavollita, P.; Cimini, F.; Balsi, M.; Esposito, S.; Jankowski, S.

    This work, after a preliminary feasibility study using a Matlab environment simulation, defines the design and the real hardware testing of a new bio-inspired decision chain for UAV sense-and-avoid applications. Relying on a single and cheap visible camera sensor, computer vision, bio-inspired and automatic decision algorithms have been adopted and implemented on a specific ARM embedded platform through C++/OpenCV coding. A first data set processing, really captured on flight, has been presented.

  18. [Nikola Tesla: flashes of inspiration].

    Science.gov (United States)

    Villarejo-Galende, Albero; Herrero-San Martín, Alejandro

    2013-01-16

    Nikola Tesla (1856-1943) was one of the greatest inventors in history and a key player in the revolution that led to the large-scale use of electricity. He also made important contributions to such diverse fields as x-rays, remote control, radio, the theory of consciousness or electromagnetism. In his honour, the international unit of magnetic induction was named after him. Yet, his fame is scarce in comparison with that of other inventors of the time, such as Edison, with whom he had several heated arguments. He was a rather odd, reserved person who lived for his inventions, the ideas for which came to him in moments of inspiration. In his autobiography he relates these flashes with a number of neuropsychiatric manifestations, which can be seen to include migraine auras, synaesthesiae, obsessions and compulsions.

  19. MSSM-inspired multifield inflation

    Science.gov (United States)

    Dubinin, M. N.; Petrova, E. Yu.; Pozdeeva, E. O.; Sumin, M. V.; Vernov, S. Yu.

    2017-12-01

    Despite the fact that experimentally with a high degree of statistical significance only a single Standard Model-like Higgs boson is discovered at the LHC, extended Higgs sectors with multiple scalar fields not excluded by combined fits of the data are more preferable theoretically for internally consistent realistic models of particle physics. We analyze the inflationary scenarios which could be induced by the two-Higgs-doublet potential of the Minimal Supersymmetric Standard Model (MSSM) where five scalar fields have non-minimal couplings to gravity. Observables following from such MSSM-inspired multifield inflation are calculated and a number of consistent inflationary scenarios are constructed. Cosmological evolution with different initial conditions for the multifield system leads to consequences fully compatible with observational data on the spectral index and the tensor-to-scalar ratio. It is demonstrated that the strong coupling approximation is precise enough to describe such inflationary scenarios.

  20. Collide@CERN: sharing inspiration

    CERN Multimedia

    Katarina Anthony

    2012-01-01

    Late last year, Julius von Bismarck was appointed to be CERN's first "artist in residence" after winning the Collide@CERN Digital Arts award. He’ll be spending two months at CERN starting this March but, to get a flavour of what’s in store, he visited the Organization last week for a crash course in its inspiring activities.   Julius von Bismarck, taking a closer look... When we arrive to interview German artist Julius von Bismarck, he’s being given a presentation about antiprotons’ ability to kill cancer cells. The whiteboard in the room contains graphs and equations that might easily send a non-scientist running, yet as Julius puts it, “if I weren’t interested, I’d be asleep”. Given his numerous questions, he must have been fascinated. “This ‘introduction’ week has been exhilarating,” says Julius. “I’ve been able to interact ...