INSPIRED High School Computing Academies

Science.gov (United States)

Doerschuk, Peggy; Liu, Jiangjiang; Mann, Judith

2011-01-01

If we are to attract more women and minorities to computing we must engage students at an early age. As part of its mission to increase participation of women and underrepresented minorities in computing, the Increasing Student Participation in Research Development Program (INSPIRED) conducts computing academies for high school students. The…

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.

Neural computation and the computational theory of cognition.

Science.gov (United States)

Piccinini, Gualtiero; Bahar, Sonya

2013-04-01

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 generic sense. After that, we reject on empirical grounds the common assimilation of neural computation to either analog or digital computation, concluding that neural computation is sui generis. Analog computation requires continuous signals; digital computation requires strings of digits. But current neuroscientific evidence indicates that typical neural signals, such as spike trains, are graded like continuous signals but are constituted by discrete functional elements (spikes); thus, typical neural signals are neither continuous signals nor strings of digits. It follows that neural computation is sui generis. Finally, we highlight three important consequences of a proper understanding of neural computation for the theory of cognition. First, understanding neural computation requires a specially designed mathematical theory (or theories) rather than the mathematical theories of analog or digital computation. Second, several popular views about neural computation turn out to be incorrect. Third, computational theories of cognition that rely on non-neural notions of computation ought to be replaced or reinterpreted in terms of neural computation. Copyright © 2012 Cognitive Science Society, Inc.

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...

Computationally efficient model predictive control algorithms a neural network approach

CERN Document Server

Ławryńczuk, Maciej

2014-01-01

This book thoroughly discusses computationally efficient (suboptimal) Model Predictive Control (MPC) techniques based on neural models. The subjects treated include: ·         A few types of suboptimal MPC algorithms in which a linear approximation of the model or of the predicted trajectory is successively calculated on-line and used for prediction. ·         Implementation details of the MPC algorithms for feedforward perceptron neural models, neural Hammerstein models, neural Wiener models and state-space neural models. ·         The MPC algorithms based on neural multi-models (inspired by the idea of predictive control). ·         The MPC algorithms with neural approximation with no on-line linearization. ·         The MPC algorithms with guaranteed stability and robustness. ·         Cooperation between the MPC algorithms and set-point optimization. Thanks to linearization (or neural approximation), the presented suboptimal algorithms do not require d...

Computational Intelligence-Assisted Understanding of Nature-Inspired Superhydrophobic Behavior.

Science.gov (United States)

Zhang, Xia; Ding, Bei; Cheng, Ran; Dixon, Sebastian C; Lu, Yao

2018-01-01

In recent years, state-of-the-art computational modeling of physical and chemical systems has shown itself to be an invaluable resource in the prediction of the properties and behavior of functional materials. However, construction of a useful computational model for novel systems in both academic and industrial contexts often requires a great depth of physicochemical theory and/or a wealth of empirical data, and a shortage in the availability of either frustrates the modeling process. In this work, computational intelligence is instead used, including artificial neural networks and evolutionary computation, to enhance our understanding of nature-inspired superhydrophobic behavior. The relationships between experimental parameters (water droplet volume, weight percentage of nanoparticles used in the synthesis of the polymer composite, and distance separating the superhydrophobic surface and the pendant water droplet in adhesive force measurements) and multiple objectives (water droplet contact angle, sliding angle, and adhesive force) are built and weighted. The obtained optimal parameters are consistent with the experimental observations. This new approach to materials modeling has great potential to be applied more generally to aid design, fabrication, and optimization for myriad functional materials.

Biologically-inspired Learning in Pulsed Neural Networks

DEFF Research Database (Denmark)

Lehmann, Torsten; Woodburn, Robin

1999-01-01

Self-learning chips to implement many popular ANN (artificial neural network) algorithms are very difficult to design. We explain why this is so and say what lessons previous work teaches us in the design of self-learning systems. We offer a contribution to the `biologically-inspired' approach......, 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...

Artificial neuron operations and spike-timing-dependent plasticity using memristive devices for brain-inspired computing

Science.gov (United States)

Marukame, Takao; Nishi, Yoshifumi; Yasuda, Shin-ichi; Tanamoto, Tetsufumi

2018-04-01

The use of memristive devices for creating artificial neurons is promising for brain-inspired computing from the viewpoints of computation architecture and learning protocol. We present an energy-efficient multiplier accumulator based on a memristive array architecture incorporating both analog and digital circuitries. The analog circuitry is used to full advantage for neural networks, as demonstrated by the spike-timing-dependent plasticity (STDP) in fabricated AlO x /TiO x -based metal-oxide memristive devices. STDP protocols for controlling periodic analog resistance with long-range stability were experimentally verified using a variety of voltage amplitudes and spike timings.

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.

A biologically inspired neural net for trajectory formation and obstacle avoidance.

Science.gov (United States)

Glasius, R; Komoda, A; Gielen, S C

1996-06-01

In this paper we present a biologically inspired two-layered neural network for trajectory formation and obstacle avoidance. The two topographically ordered neural maps consist of analog neurons having continuous dynamics. The first layer, the sensory map, receives sensory information and builds up an activity pattern which contains the optimal solution (i.e. shortest path without collisions) for any given set of current position, target positions and obstacle positions. Targets and obstacles are allowed to move, in which case the activity pattern in the sensory map will change accordingly. The time evolution of the neural activity in the second layer, the motor map, results in a moving cluster of activity, which can be interpreted as a population vector. Through the feedforward connections between the two layers, input of the sensory map directs the movement of the cluster along the optimal path from the current position of the cluster to the target position. The smooth trajectory is the result of the intrinsic dynamics of the network only. No supervisor is required. The output of the motor map can be used for direct control of an autonomous system in a cluttered environment or for control of the actuators of a biological limb or robot manipulator. The system is able to reach a target even in the presence of an external perturbation. Computer simulations of a point robot and a multi-joint manipulator illustrate the theory.

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.

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.

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.

Coastal 'Big Data' and nature-inspired computation: Prediction potentials, uncertainties, and knowledge derivation of neural networks for an algal metric

Science.gov (United States)

Millie, David F.; Weckman, Gary R.; Young, William A.; Ivey, James E.; Fries, David P.; Ardjmand, Ehsan; Fahnenstiel, Gary L.

2013-07-01

Coastal monitoring has become reliant upon automated sensors for data acquisition. Such a technical commitment comes with a cost; particularly, the generation of large, high-dimensional data streams ('Big Data') that personnel must search through to identify data structures. Nature-inspired computation, inclusive of artificial neural networks (ANNs), affords the unearthing of complex, recurring patterns within sizable data volumes. In 2009, select meteorological and hydrological data were acquired via autonomous instruments in Sarasota Bay, Florida (USA). ANNs estimated continuous chlorophyll (CHL) a concentrations from abiotic predictors, with correlations between measured:modeled concentrations >0.90 and model efficiencies ranging from 0.80 to 0.90. Salinity and water temperature were the principal influences for modeled CHL within the Bay; concentrations steadily increased at temperatures >28° C and were greatest at salinities 6.1 μg CHL L-1 maximized at a salinity of ca. 36.3 and a temperature of ca. 29.5 °C. A 10th-order Chebyshev bivariate polynomial equation was fit (adj. r2 = 0.99, p turbidity, temperature, and salinity (and to lesser degrees, wind speed, wind/current direction, irradiance, and urea-nitrogen) were key variables for quantitative rules in tree formalisms. Taken together, computations enabled knowledge provision for and quantifiable representations of the non-linear relationships between environmental variables and CHL a.

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.

Bio-inspired algorithms applied to molecular docking simulations.

Science.gov (United States)

Heberlé, G; de Azevedo, W F

2011-01-01

Nature as a source of inspiration has been shown to have a great beneficial impact on the development of new computational methodologies. In this scenario, analyses of the interactions between a protein target and a ligand can be simulated by biologically inspired algorithms (BIAs). These algorithms mimic biological systems to create new paradigms for computation, such as neural networks, evolutionary computing, and swarm intelligence. This review provides a description of the main concepts behind BIAs applied to molecular docking simulations. Special attention is devoted to evolutionary algorithms, guided-directed evolutionary algorithms, and Lamarckian genetic algorithms. Recent applications of these methodologies to protein targets identified in the Mycobacterium tuberculosis genome are described.

Traceability investigation in Computed Tomography using industry-inspired workpieces

DEFF Research Database (Denmark)

Kraemer, Alexandra; Stolfi, Alessandro; Schneider, Timm

2017-01-01

This paper concerns an investigation of the accuracy of Computed Tomography (CT) measurements using four industry-inspired workpieces. A total of 16 measurands were selected and calibrated using CMMs. CT measurements on industry-inspired workpieces were carried out using two CTs having different...

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...

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.

Biologically-inspired On-chip Learning in Pulsed Neural Networks

DEFF Research Database (Denmark)

Lehmann, Torsten; Woodburn, Robin

1999-01-01

Self-learning chips to implement many popular ANN (artificial neural network) algorithms are very difficult to design. We explain why this is so and say what lessons previous work teaches us in the design of self-learning systems. We offer a contribution to the "biologically-inspired" approach......, 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...

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.

Modular Neural Tile Architecture for Compact Embedded Hardware Spiking Neural Network

NARCIS (Netherlands)

Pande, Sandeep; Morgan, Fearghal; Cawley, Seamus; Bruintjes, Tom; Smit, Gerardus Johannes Maria; McGinley, Brian; Carrillo, Snaider; Harkin, Jim; McDaid, Liam

2013-01-01

Biologically-inspired packet switched network on chip (NoC) based hardware spiking neural network (SNN) architectures have been proposed as an embedded computing platform for classification, estimation and control applications. Storage of large synaptic connectivity (SNN topology) information in

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.

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.

Novel quantum inspired binary neural network algorithm

Indian Academy of Sciences (India)

This parameter is taken as the threshold of neuron for learning of neural network. This algorithm is tested with three benchmark datasets and ... Author Affiliations. OM PRAKASH PATEL1 ARUNA TIWARI. Department of Computer Science and Engineering, Indian Institute of Technology Indore, Indore 453552, India ...

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

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.

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.

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…

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.

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

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.

Neural correlates and neural computations in posterior parietal cortex during perceptual decision-making

Directory of Open Access Journals (Sweden)

Alexander eHuk

2012-10-01

Full Text Available A recent line of work has found remarkable success in relating perceptual decision-making and the spiking activity in the macaque lateral intraparietal area (LIP. In this review, we focus on questions about the neural computations in LIP that are not answered by demonstrations of neural correlates of psychological processes. We highlight three areas of limitations in our current understanding of the precise neural computations that might underlie neural correlates of decisions: (1 empirical questions not yet answered by existing data; (2 implementation issues related to how neural circuits could actually implement the mechanisms suggested by both physiology and psychology; and (3 ecological constraints related to the use of well-controlled laboratory tasks and whether they provide an accurate window on sensorimotor computation. These issues motivate the adoption of a more general encoding-decoding framework that will be fruitful for more detailed contemplation of how neural computations in LIP relate to the formation of perceptual decisions.

Bio-inspired spiking neural network for nonlinear systems control.

Science.gov (United States)

Pérez, Javier; Cabrera, Juan A; Castillo, Juan J; Velasco, Juan M

2018-08-01

Spiking neural networks (SNN) are the third generation of artificial neural networks. SNN are the closest approximation to biological neural networks. SNNs make use of temporal spike trains to command inputs and outputs, allowing a faster and more complex computation. As demonstrated by biological organisms, they are a potentially good approach to designing controllers for highly nonlinear dynamic systems in which the performance of controllers developed by conventional techniques is not satisfactory or difficult to implement. SNN-based controllers exploit their ability for online learning and self-adaptation to evolve when transferred from simulations to the real world. SNN's inherent binary and temporary way of information codification facilitates their hardware implementation compared to analog neurons. Biological neural networks often require a lower number of neurons compared to other controllers based on artificial neural networks. In this work, these neuronal systems are imitated to perform the control of non-linear dynamic systems. For this purpose, a control structure based on spiking neural networks has been designed. Particular attention has been paid to optimizing the structure and size of the neural network. The proposed structure is able to control dynamic systems with a reduced number of neurons and connections. A supervised learning process using evolutionary algorithms has been carried out to perform controller training. The efficiency of the proposed network has been verified in two examples of dynamic systems control. Simulations show that the proposed control based on SNN exhibits superior performance compared to other approaches based on Neural Networks and SNNs. Copyright © 2018 Elsevier Ltd. All rights reserved.

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.

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....

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.

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.

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.

Hardware implementation of stochastic spiking neural networks.

Science.gov (United States)

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

2012-08-01

Spiking Neural Networks, the last generation of Artificial Neural Networks, are characterized by its bio-inspired nature and by a higher computational capacity with respect to other neural models. In real biological neurons, stochastic processes represent an important mechanism of neural behavior and are responsible of its special arithmetic capabilities. In this work we present a simple hardware implementation of spiking neurons that considers this probabilistic nature. The advantage of the proposed implementation is that it is fully digital and therefore can be massively implemented in Field Programmable Gate Arrays. The high computational capabilities of the proposed model are demonstrated by the study of both feed-forward and recurrent networks that are able to implement high-speed signal filtering and to solve complex systems of linear equations.

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.

Introduction to neural networks

International Nuclear Information System (INIS)

Pavlopoulos, P.

1996-01-01

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

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.

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.

Normalization as a canonical neural computation

Science.gov (United States)

Carandini, Matteo; Heeger, David J.

2012-01-01

There is increasing evidence that the brain relies on a set of canonical neural computations, repeating them across brain regions and modalities to apply similar operations to different problems. A promising candidate for such a computation is normalization, in which the responses of neurons are divided by a common factor that typically includes the summed activity of a pool of neurons. Normalization was developed to explain responses in the primary visual cortex and is now thought to operate throughout the visual system, and in many other sensory modalities and brain regions. Normalization may underlie operations such as the representation of odours, the modulatory effects of visual attention, the encoding of value and the integration of multisensory information. Its presence in such a diversity of neural systems in multiple species, from invertebrates to mammals, suggests that it serves as a canonical neural computation. PMID:22108672

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...

A biologically inspired neural network model to transformation invariant object recognition

Science.gov (United States)

Iftekharuddin, Khan M.; Li, Yaqin; Siddiqui, Faraz

2007-09-01

Transformation invariant image recognition has been an active research area due to its widespread applications in a variety of fields such as military operations, robotics, medical practices, geographic scene analysis, and many others. The primary goal for this research is detection of objects in the presence of image transformations such as changes in resolution, rotation, translation, scale and occlusion. We investigate a biologically-inspired neural network (NN) model for such transformation-invariant object recognition. In a classical training-testing setup for NN, the performance is largely dependent on the range of transformation or orientation involved in training. However, an even more serious dilemma is that there may not be enough training data available for successful learning or even no training data at all. To alleviate this problem, a biologically inspired reinforcement learning (RL) approach is proposed. In this paper, the RL approach is explored for object recognition with different types of transformations such as changes in scale, size, resolution and rotation. The RL is implemented in an adaptive critic design (ACD) framework, which approximates the neuro-dynamic programming of an action network and a critic network, respectively. Two ACD algorithms such as Heuristic Dynamic Programming (HDP) and Dual Heuristic dynamic Programming (DHP) are investigated to obtain transformation invariant object recognition. The two learning algorithms are evaluated statistically using simulated transformations in images as well as with a large-scale UMIST face database with pose variations. In the face database authentication case, the 90° out-of-plane rotation of faces from 20 different subjects in the UMIST database is used. Our simulations show promising results for both designs for transformation-invariant object recognition and authentication of faces. Comparing the two algorithms, DHP outperforms HDP in learning capability, as DHP takes fewer steps to

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.

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

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

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

International Nuclear Information System (INIS)

Nikityuk, N.M.; )

2001-01-01

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

Event-Based Computation of Motion Flow on a Neuromorphic Analog Neural Platform.

Science.gov (United States)

Giulioni, Massimiliano; Lagorce, Xavier; Galluppi, Francesco; Benosman, Ryad B

2016-01-01

Estimating the speed and direction of moving objects is a crucial component of agents behaving in a dynamic world. Biological organisms perform this task by means of the neural connections originating from their retinal ganglion cells. In artificial systems the optic flow is usually extracted by comparing activity of two or more frames captured with a vision sensor. Designing artificial motion flow detectors which are as fast, robust, and efficient as the ones found in biological systems is however a challenging task. Inspired by the architecture proposed by Barlow and Levick in 1965 to explain the spiking activity of the direction-selective ganglion cells in the rabbit's retina, we introduce an architecture for robust optical flow extraction with an analog neuromorphic multi-chip system. The task is performed by a feed-forward network of analog integrate-and-fire neurons whose inputs are provided by contrast-sensitive photoreceptors. Computation is supported by the precise time of spike emission, and the extraction of the optical flow is based on time lag in the activation of nearby retinal neurons. Mimicking ganglion cells our neuromorphic detectors encode the amplitude and the direction of the apparent visual motion in their output spiking pattern. Hereby we describe the architectural aspects, discuss its latency, scalability, and robustness properties and demonstrate that a network of mismatched delicate analog elements can reliably extract the optical flow from a simple visual scene. This work shows how precise time of spike emission used as a computational basis, biological inspiration, and neuromorphic systems can be used together for solving specific tasks.

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.

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.

Quantum-Inspired Multidirectional Associative Memory With a Self-Convergent Iterative Learning.

Science.gov (United States)

Masuyama, Naoki; Loo, Chu Kiong; Seera, Manjeevan; Kubota, Naoyuki

2018-04-01

Quantum-inspired computing is an emerging research area, which has significantly improved the capabilities of conventional algorithms. In general, quantum-inspired hopfield associative memory (QHAM) has demonstrated quantum information processing in neural structures. This has resulted in an exponential increase in storage capacity while explaining the extensive memory, and it has the potential to illustrate the dynamics of neurons in the human brain when viewed from quantum mechanics perspective although the application of QHAM is limited as an autoassociation. We introduce a quantum-inspired multidirectional associative memory (QMAM) with a one-shot learning model, and QMAM with a self-convergent iterative learning model (IQMAM) based on QHAM in this paper. The self-convergent iterative learning enables the network to progressively develop a resonance state, from inputs to outputs. The simulation experiments demonstrate the advantages of QMAM and IQMAM, especially the stability to recall reliability.

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

Inherently stochastic spiking neurons for probabilistic neural computation

KAUST Repository

Al-Shedivat, Maruan; Naous, Rawan; Neftci, Emre; Cauwenberghs, Gert; Salama, Khaled N.

2015-01-01

. 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

Recurrent Neural Network for Computing the Drazin Inverse.

Science.gov (United States)

Stanimirović, Predrag S; Zivković, Ivan S; Wei, Yimin

2015-11-01

This paper presents a recurrent neural network (RNN) for computing the Drazin inverse of a real matrix in real time. This recurrent neural network (RNN) is composed of n independent parts (subnetworks), where n is the order of the input matrix. These subnetworks can operate concurrently, so parallel and distributed processing can be achieved. In this way, the computational advantages over the existing sequential algorithms can be attained in real-time applications. The RNN defined in this paper is convenient for an implementation in an electronic circuit. The number of neurons in the neural network is the same as the number of elements in the output matrix, which represents the Drazin inverse. The difference between the proposed RNN and the existing ones for the Drazin inverse computation lies in their network architecture and dynamics. The conditions that ensure the stability of the defined RNN as well as its convergence toward the Drazin inverse are considered. In addition, illustrative examples and examples of application to the practical engineering problems are discussed to show the efficacy of the proposed neural network.

Quantum neural networks: Current status and prospects for development

Science.gov (United States)

Altaisky, M. V.; Kaputkina, N. E.; Krylov, V. A.

2014-11-01

The idea of quantum artificial neural networks, first formulated in [34], unites the artificial neural network concept with the quantum computation paradigm. Quantum artificial neural networks were first systematically considered in the PhD thesis by T. Menneer (1998). Based on the works of Menneer and Narayanan [42, 43], Kouda, Matsui, and Nishimura [35, 36], Altaisky [2, 68], Zhou [67], and others, quantum-inspired learning algorithms for neural networks were developed, and are now used in various training programs and computer games [29, 30]. The first practically realizable scaled hardware-implemented model of the quantum artificial neural network is obtained by D-Wave Systems, Inc. [33]. It is a quantum Hopfield network implemented on the basis of superconducting quantum interference devices (SQUIDs). In this work we analyze possibilities and underlying principles of an alternative way to implement quantum neural networks on the basis of quantum dots. A possibility of using quantum neural network algorithms in automated control systems, associative memory devices, and in modeling biological and social networks is examined.

Computations Underlying Social Hierarchy Learning: Distinct Neural Mechanisms for Updating and Representing Self-Relevant Information.

Science.gov (United States)

Kumaran, Dharshan; Banino, Andrea; Blundell, Charles; Hassabis, Demis; Dayan, Peter

2016-12-07

Knowledge about social hierarchies organizes human behavior, yet we understand little about the underlying computations. Here we show that a Bayesian inference scheme, which tracks the power of individuals, better captures behavioral and neural data compared with a reinforcement learning model inspired by rating systems used in games such as chess. We provide evidence that the medial prefrontal cortex (MPFC) selectively mediates the updating of knowledge about one's own hierarchy, as opposed to that of another individual, a process that underpinned successful performance and involved functional interactions with the amygdala and hippocampus. In contrast, we observed domain-general coding of rank in the amygdala and hippocampus, even when the task did not require it. Our findings reveal the computations underlying a core aspect of social cognition and provide new evidence that self-relevant information may indeed be afforded a unique representational status in the brain. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Brain architecture: a design for natural computation.

Science.gov (United States)

Kaiser, Marcus

2007-12-15

Fifty years ago, John von Neumann compared the architecture of the brain with that of the computers he invented and which are still in use today. In those days, the organization of computers was based on concepts of brain organization. Here, we give an update on current results on the global organization of neural systems. For neural systems, we outline how the spatial and topological architecture of neuronal and cortical networks facilitates robustness against failures, fast processing and balanced network activation. Finally, we discuss mechanisms of self-organization for such architectures. After all, the organization of the brain might again inspire computer architecture.

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

Integrated evolutionary computation neural network quality controller for automated systems

Energy Technology Data Exchange (ETDEWEB)

Patro, S.; Kolarik, W.J. [Texas Tech Univ., Lubbock, TX (United States). Dept. of Industrial Engineering

1999-06-01

With increasing competition in the global market, more and more stringent quality standards and specifications are being demands at lower costs. Manufacturing applications of computing power are becoming more common. The application of neural networks to identification and control of dynamic processes has been discussed. The limitations of using neural networks for control purposes has been pointed out and a different technique, evolutionary computation, has been discussed. The results of identifying and controlling an unstable, dynamic process using evolutionary computation methods has been presented. A framework for an integrated system, using both neural networks and evolutionary computation, has been proposed to identify the process and then control the product quality, in a dynamic, multivariable system, in real-time.

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.

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...

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

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.

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.

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.

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

SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks.

Science.gov (United States)

Zenke, Friedemann; Ganguli, Surya

2018-04-13

A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spiking neural circuits learn and compute in vivo, as well as how we can instantiate such capabilities in artificial spiking circuits in silico. Here we revisit the problem of supervised learning in temporally coding multilayer spiking neural networks. First, by using a surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based three-factor learning rule capable of training multilayer networks of deterministic integrate-and-fire neurons to perform nonlinear computations on spatiotemporal spike patterns. Second, inspired by recent results on feedback alignment, we compare the performance of our learning rule under different credit assignment strategies for propagating output errors to hidden units. Specifically, we test uniform, symmetric, and random feedback, finding that simpler tasks can be solved with any type of feedback, while more complex tasks require symmetric feedback. In summary, our results open the door to obtaining a better scientific understanding of learning and computation in spiking neural networks by advancing our ability to train them to solve nonlinear problems involving transformations between different spatiotemporal spike time patterns.

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

Science.gov (United States)

Beck, Christoph; Garreau, Guillaume; Georgiou, Julius

2016-01-01

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.

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.

Prediction of Software Reliability using Bio Inspired Soft Computing Techniques.

Science.gov (United States)

Diwaker, Chander; Tomar, Pradeep; Poonia, Ramesh C; Singh, Vijander

2018-04-10

A lot of models have been made for predicting software reliability. The reliability models are restricted to using particular types of methodologies and restricted number of parameters. There are a number of techniques and methodologies that may be used for reliability prediction. There is need to focus on parameters consideration while estimating reliability. The reliability of a system may increase or decreases depending on the selection of different parameters used. Thus there is need to identify factors that heavily affecting the reliability of the system. In present days, reusability is mostly used in the various area of research. Reusability is the basis of Component-Based System (CBS). The cost, time and human skill can be saved using Component-Based Software Engineering (CBSE) concepts. CBSE metrics may be used to assess those techniques which are more suitable for estimating system reliability. Soft computing is used for small as well as large-scale problems where it is difficult to find accurate results due to uncertainty or randomness. Several possibilities are available to apply soft computing techniques in medicine related problems. Clinical science of medicine using fuzzy-logic, neural network methodology significantly while basic science of medicine using neural-networks-genetic algorithm most frequently and preferably. There is unavoidable interest shown by medical scientists to use the various soft computing methodologies in genetics, physiology, radiology, cardiology and neurology discipline. CBSE boost users to reuse the past and existing software for making new products to provide quality with a saving of time, memory space, and money. This paper focused on assessment of commonly used soft computing technique like Genetic Algorithm (GA), Neural-Network (NN), Fuzzy Logic, Support Vector Machine (SVM), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC). This paper presents working of soft computing

Experimental Demonstrations of Optical Neural Computers

OpenAIRE

Hsu, Ken; Brady, David; Psaltis, Demetri

1988-01-01

We describe two experiments in optical neural computing. In the first a closed optical feedback loop is used to implement auto-associative image recall. In the second a perceptron-like learning algorithm is implemented with photorefractive holography.

Computational chaos in massively parallel neural networks

Science.gov (United States)

Barhen, Jacob; Gulati, Sandeep

1989-01-01

A fundamental issue which directly impacts the scalability of current theoretical neural network models to massively parallel embodiments, in both software as well as hardware, is the inherent and unavoidable concurrent asynchronicity of emerging fine-grained computational ensembles and the possible emergence of chaotic manifestations. Previous analyses attributed dynamical instability to the topology of the interconnection matrix, to parasitic components or to propagation delays. However, researchers have observed the existence of emergent computational chaos in a concurrently asynchronous framework, independent of the network topology. Researcher present a methodology enabling the effective asynchronous operation of large-scale neural networks. Necessary and sufficient conditions guaranteeing concurrent asynchronous convergence are established in terms of contracting operators. Lyapunov exponents are computed formally to characterize the underlying nonlinear dynamics. Simulation results are presented to illustrate network convergence to the correct results, even in the presence of large delays.

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…

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.

A Novel Clustering Algorithm Inspired by Membrane Computing

Directory of Open Access Journals (Sweden)

Hong Peng

2015-01-01

Full Text Available P systems are a class of distributed parallel computing models; this paper presents a novel clustering algorithm, which is inspired from mechanism of a tissue-like P system with a loop structure of cells, called membrane clustering algorithm. The objects of the cells express the candidate centers of clusters and are evolved by the evolution rules. Based on the loop membrane structure, the communication rules realize a local neighborhood topology, which helps the coevolution of the objects and improves the diversity of objects in the system. The tissue-like P system can effectively search for the optimal partitioning with the help of its parallel computing advantage. The proposed clustering algorithm is evaluated on four artificial data sets and six real-life data sets. Experimental results show that the proposed clustering algorithm is superior or competitive to k-means algorithm and several evolutionary clustering algorithms recently reported in the literature.

Efficient universal computing architectures for decoding neural activity.

Directory of Open Access Journals (Sweden)

Benjamin I Rapoport

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

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...

Physicists Get INSPIREd: INSPIRE Project and Grid Applications

International Nuclear Information System (INIS)

Klem, Jukka; Iwaszkiewicz, Jan

2011-01-01

INSPIRE is the new high-energy physics scientific information system developed by CERN, DESY, Fermilab and SLAC. INSPIRE combines the curated and trusted contents of SPIRES database with Invenio digital library technology. INSPIRE contains the entire HEP literature with about one million records and in addition to becoming the reference HEP scientific information platform, it aims to provide new kinds of data mining services and metrics to assess the impact of articles and authors. Grid and cloud computing provide new opportunities to offer better services in areas that require large CPU and storage resources including document Optical Character Recognition (OCR) processing, full-text indexing of articles and improved metrics. D4Science-II is a European project that develops and operates an e-Infrastructure supporting Virtual Research Environments (VREs). It develops an enabling technology (gCube) which implements a mechanism for facilitating the interoperation of its e-Infrastructure with other autonomously running data e-Infrastructures. As a result, this creates the core of an e-Infrastructure ecosystem. INSPIRE is one of the e-Infrastructures participating in D4Science-II project. In the context of the D4Science-II project, the INSPIRE e-Infrastructure makes available some of its resources and services to other members of the resulting ecosystem. Moreover, it benefits from the ecosystem via a dedicated Virtual Organization giving access to an array of resources ranging from computing and storage resources of grid infrastructures to data and services.

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

International Nuclear Information System (INIS)

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

2012-01-01

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

Neural computing thermal comfort index for HVAC systems

International Nuclear Information System (INIS)

Atthajariyakul, S.; Leephakpreeda, T.

2005-01-01

The primary purpose of a heating, ventilating and air conditioning (HVAC) system within a building is to make occupants comfortable. Without real time determination of human thermal comfort, it is not feasible for the HVAC system to yield controlled conditions of the air for human comfort all the time. This paper presents a practical approach to determine human thermal comfort quantitatively via neural computing. The neural network model allows real time determination of the thermal comfort index, where it is not practical to compute the conventional predicted mean vote (PMV) index itself in real time. The feed forward neural network model is proposed as an explicit function of the relation of the PMV index to accessible variables, i.e. the air temperature, wet bulb temperature, globe temperature, air velocity, clothing insulation and human activity. An experiment in an air conditioned office room was done to demonstrate the effectiveness of the proposed methodology. The results show good agreement between the thermal comfort index calculated from the neural network model in real time and those calculated from the conventional PMV model

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.

Computational neural network regression model for Host based Intrusion Detection System

Directory of Open Access Journals (Sweden)

Sunil Kumar Gautam

2016-09-01

Full Text Available The current scenario of information gathering and storing in secure system is a challenging task due to increasing cyber-attacks. There exists computational neural network techniques designed for intrusion detection system, which provide security to single machine and entire network's machine. In this paper, we have used two types of computational neural network models, namely, Generalized Regression Neural Network (GRNN model and Multilayer Perceptron Neural Network (MPNN model for Host based Intrusion Detection System using log files that are generated by a single personal computer. The simulation results show correctly classified percentage of normal and abnormal (intrusion class using confusion matrix. On the basis of results and discussion, we found that the Host based Intrusion Systems Model (HISM significantly improved the detection accuracy while retaining minimum false alarm rate.

STICK: Spike Time Interval Computational Kernel, a Framework for General Purpose Computation Using Neurons, Precise Timing, Delays, and Synchrony.

Science.gov (United States)

Lagorce, Xavier; Benosman, Ryad

2015-11-01

There has been significant research over the past two decades in developing new platforms for spiking neural computation. Current neural computers are primarily developed to mimic biology. They use neural networks, which can be trained to perform specific tasks to mainly solve pattern recognition problems. These machines can do more than simulate biology; they allow us to rethink our current paradigm of computation. The ultimate goal is to develop brain-inspired general purpose computation architectures that can breach the current bottleneck introduced by the von Neumann architecture. This work proposes a new framework for such a machine. We show that the use of neuron-like units with precise timing representation, synaptic diversity, and temporal delays allows us to set a complete, scalable compact computation framework. The framework provides both linear and nonlinear operations, allowing us to represent and solve any function. We show usability in solving real use cases from simple differential equations to sets of nonlinear differential equations leading to chaotic attractors.

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.

Connecting Neural Coding to Number Cognition: A Computational Account

Science.gov (United States)

Prather, Richard W.

2012-01-01

The current study presents a series of computational simulations that demonstrate how the neural coding of numerical magnitude may influence number cognition and development. This includes behavioral phenomena cataloged in cognitive literature such as the development of numerical estimation and operational momentum. Though neural research has…

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.

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.

On-chip visual perception of motion: a bio-inspired connectionist model on FPGA.

Science.gov (United States)

Torres-Huitzil, César; Girau, Bernard; Castellanos-Sánchez, Claudio

2005-01-01

Visual motion provides useful information to understand the dynamics of a scene to allow intelligent systems interact with their environment. Motion computation is usually restricted by real time requirements that need the design and implementation of specific hardware architectures. In this paper, the design of hardware architecture for a bio-inspired neural model for motion estimation is presented. The motion estimation is based on a strongly localized bio-inspired connectionist model with a particular adaptation of spatio-temporal Gabor-like filtering. The architecture is constituted by three main modules that perform spatial, temporal, and excitatory-inhibitory connectionist processing. The biomimetic architecture is modeled, simulated and validated in VHDL. The synthesis results on a Field Programmable Gate Array (FPGA) device show the potential achievement of real-time performance at an affordable silicon area.

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.

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.

Neural decoding of collective wisdom with multi-brain computing.

Science.gov (United States)

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

2012-01-02

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

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...

Hybrid computing using a neural network with dynamic external memory.

Science.gov (United States)

Graves, Alex; Wayne, Greg; Reynolds, Malcolm; Harley, Tim; Danihelka, Ivo; Grabska-Barwińska, Agnieszka; Colmenarejo, Sergio Gómez; Grefenstette, Edward; Ramalho, Tiago; Agapiou, John; Badia, Adrià Puigdomènech; Hermann, Karl Moritz; Zwols, Yori; Ostrovski, Georg; Cain, Adam; King, Helen; Summerfield, Christopher; Blunsom, Phil; Kavukcuoglu, Koray; Hassabis, Demis

2016-10-27

Artificial neural networks are remarkably adept at sensory processing, sequence learning and reinforcement learning, but are limited in their ability to represent variables and data structures and to store data over long timescales, owing to the lack of an external memory. Here we introduce a machine learning model called a differentiable neural computer (DNC), which consists of a neural network that can read from and write to an external memory matrix, analogous to the random-access memory in a conventional computer. Like a conventional computer, it can use its memory to represent and manipulate complex data structures, but, like a neural network, it can learn to do so from data. When trained with supervised learning, we demonstrate that a DNC can successfully answer synthetic questions designed to emulate reasoning and inference problems in natural language. We show that it can learn tasks such as finding the shortest path between specified points and inferring the missing links in randomly generated graphs, and then generalize these tasks to specific graphs such as transport networks and family trees. When trained with reinforcement learning, a DNC can complete a moving blocks puzzle in which changing goals are specified by sequences of symbols. Taken together, our results demonstrate that DNCs have the capacity to solve complex, structured tasks that are inaccessible to neural networks without external read-write memory.

Biologically inspired collision avoidance system for unmanned vehicles

Science.gov (United States)

Ortiz, Fernando E.; Graham, Brett; Spagnoli, Kyle; Kelmelis, Eric J.

2009-05-01

In this project, we collaborate with researchers in the neuroscience department at the University of Delaware to develop an Field Programmable Gate Array (FPGA)-based embedded computer, inspired by the brains of small vertebrates (fish). The mechanisms of object detection and avoidance in fish have been extensively studied by our Delaware collaborators. The midbrain optic tectum is a biological multimodal navigation controller capable of processing input from all senses that convey spatial information, including vision, audition, touch, and lateral-line (water current sensing in fish). Unfortunately, computational complexity makes these models too slow for use in real-time applications. These simulations are run offline on state-of-the-art desktop computers, presenting a gap between the application and the target platform: a low-power embedded device. EM Photonics has expertise in developing of high-performance computers based on commodity platforms such as graphic cards (GPUs) and FPGAs. FPGAs offer (1) high computational power, low power consumption and small footprint (in line with typical autonomous vehicle constraints), and (2) the ability to implement massively-parallel computational architectures, which can be leveraged to closely emulate biological systems. Combining UD's brain modeling algorithms and the power of FPGAs, this computer enables autonomous navigation in complex environments, and further types of onboard neural processing in future applications.

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 ...

IR wireless cluster synapses of HYDRA very large neural networks

Science.gov (United States)

Jannson, Tomasz; Forrester, Thomas

2008-04-01

RF/IR wireless (virtual) synapses are critical components of HYDRA (Hyper-Distributed Robotic Autonomy) neural networks, already discussed in two earlier papers. The HYDRA network has the potential to be very large, up to 10 11-neurons and 10 18-synapses, based on already established technologies (cellular RF telephony and IR-wireless LANs). It is organized into almost fully connected IR-wireless clusters. The HYDRA neurons and synapses are very flexible, simple, and low-cost. They can be modified into a broad variety of biologically-inspired brain-like computing capabilities. In this third paper, we focus on neural hardware in general, and on IR-wireless synapses in particular. Such synapses, based on LED/LD-connections, dominate the HYDRA neural cluster.

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.

Gas Classification Using Deep Convolutional Neural Networks

Science.gov (United States)

Peng, Pai; Zhao, Xiaojin; Pan, Xiaofang; Ye, Wenbin

2018-01-01

In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP). PMID:29316723

Gas Classification Using Deep Convolutional Neural Networks.

Science.gov (United States)

Peng, Pai; Zhao, Xiaojin; Pan, Xiaofang; Ye, Wenbin

2018-01-08

In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP).

Integrated Markov-neural reliability computation method: A case for multiple automated guided vehicle system

International Nuclear Information System (INIS)

Fazlollahtabar, Hamed; Saidi-Mehrabad, Mohammad; Balakrishnan, Jaydeep

2015-01-01

This paper proposes an integrated Markovian and back propagation neural network approaches to compute reliability of a system. While states of failure occurrences are significant elements for accurate reliability computation, Markovian based reliability assessment method is designed. Due to drawbacks shown by Markovian model for steady state reliability computations and neural network for initial training pattern, integration being called Markov-neural is developed and evaluated. To show efficiency of the proposed approach comparative analyses are performed. Also, for managerial implication purpose an application case for multiple automated guided vehicles (AGVs) in manufacturing networks is conducted. - Highlights: • Integrated Markovian and back propagation neural network approach to compute reliability. • Markovian based reliability assessment method. • Managerial implication is shown in an application case for multiple automated guided vehicles (AGVs) in manufacturing networks

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.

Jet-images: computer vision inspired techniques for jet tagging

International Nuclear Information System (INIS)

Cogan, Josh; Kagan, Michael; Strauss, Emanuel; Schwarztman, Ariel

2015-01-01

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.

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.

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

Neural networks and their potential application in nuclear power plants

International Nuclear Information System (INIS)

Uhrig, R.E.

1991-01-01

A neural network is a data processing system consisting of a number of simple, highly interconnected processing elements in an architecture inspired by the structure of the cerebral cortex portion of the brain. Hence, neural networks are often capable of doing things which humans or animals do well but which conventional computers often do poorly. Neural networks have emerged in the past few years as an area of unusual opportunity for research, development and application to a variety of real world problems. Indeed, neural networks exhibit characteristics and capabilities not provided by any other technology. Examples include reading Japanese Kanji characters and human handwriting, reading a typewritten manuscript aloud, compensating for alignment errors in robots, interpreting very noise signals (e.g., electroencephalograms), modeling complex systems that cannot be modeled mathematically, and predicting whether proposed loans will be good or fail. This paper presents a brief tutorial on neural networks and describes research on the potential applications to nuclear power plants

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.

Concepts and Relations in Neurally Inspired In Situ Concept-Based Computing

NARCIS (Netherlands)

van der Velde, Frank; van der Velde, Frank

2016-01-01

In situ concept-based computing is based on the notion that conceptual representations in the human brain are “in situ.” In this way, they are grounded in perception and action. Examples are neuronal assemblies, whose connection structures develop over time and are distributed over different brain

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.

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.

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...

Bio-inspired vision

International Nuclear Information System (INIS)

Posch, C

2012-01-01

Nature still outperforms the most powerful computers in routine functions involving perception, sensing and actuation like vision, audition, and motion control, and is, most strikingly, orders of magnitude more energy-efficient than its artificial competitors. The reasons for the superior performance of biological systems are subject to diverse investigations, but it is clear that the form of hardware and the style of computation in nervous systems are fundamentally different from what is used in artificial synchronous information processing systems. Very generally speaking, biological neural systems rely on a large number of relatively simple, slow and unreliable processing elements and obtain performance and robustness from a massively parallel principle of operation and a high level of redundancy where the failure of single elements usually does not induce any observable system performance degradation. In the late 1980's, Carver Mead demonstrated that silicon VLSI technology can be employed in implementing ''neuromorphic'' circuits that mimic neural functions and fabricating building blocks that work like their biological role models. Neuromorphic systems, as the biological systems they model, are adaptive, fault-tolerant and scalable, and process information using energy-efficient, asynchronous, event-driven methods. In this paper, some basics of neuromorphic electronic engineering and its impact on recent developments in optical sensing and artificial vision are presented. It is demonstrated that bio-inspired vision systems have the potential to outperform conventional, frame-based vision acquisition and processing systems in many application fields and to establish new benchmarks in terms of redundancy suppression/data compression, dynamic range, temporal resolution and power efficiency to realize advanced functionality like 3D vision, object tracking, motor control, visual feedback loops, etc. in real-time. It is argued that future artificial vision systems

Real-Time Accumulative Computation Motion Detectors

Directory of Open Access Journals (Sweden)

Saturnino Maldonado-Bascón

2009-12-01

Full Text Available The neurally inspired accumulative computation (AC method and its application to motion detection have been introduced in the past years. This paper revisits the fact that many researchers have explored the relationship between neural networks and finite state machines. Indeed, finite state machines constitute the best characterized computational model, whereas artificial neural networks have become a very successful tool for modeling and problem solving. The article shows how to reach real-time performance after using a model described as a finite state machine. This paper introduces two steps towards that direction: (a A simplification of the general AC method is performed by formally transforming it into a finite state machine. (b A hardware implementation in FPGA of such a designed AC module, as well as an 8-AC motion detector, providing promising performance results. We also offer two case studies of the use of AC motion detectors in surveillance applications, namely infrared-based people segmentation and color-based people tracking, respectively.

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.

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.

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...

Spatiotemporal Dynamics and Reliable Computations in Recurrent Spiking Neural Networks

Science.gov (United States)

Pyle, Ryan; Rosenbaum, Robert

2017-01-01

Randomly connected networks of excitatory and inhibitory spiking neurons provide a parsimonious model of neural variability, but are notoriously unreliable for performing computations. We show that this difficulty is overcome by incorporating the well-documented dependence of connection probability on distance. Spatially extended spiking networks exhibit symmetry-breaking bifurcations and generate spatiotemporal patterns that can be trained to perform dynamical computations under a reservoir computing framework.

Spatiotemporal Dynamics and Reliable Computations in Recurrent Spiking Neural Networks.

Science.gov (United States)

Pyle, Ryan; Rosenbaum, Robert

2017-01-06

Randomly connected networks of excitatory and inhibitory spiking neurons provide a parsimonious model of neural variability, but are notoriously unreliable for performing computations. We show that this difficulty is overcome by incorporating the well-documented dependence of connection probability on distance. Spatially extended spiking networks exhibit symmetry-breaking bifurcations and generate spatiotemporal patterns that can be trained to perform dynamical computations under a reservoir computing framework.

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.

Stochastic resonance in an ensemble of single-electron neuromorphic devices and its application to competitive neural networks

International Nuclear Information System (INIS)

Oya, Takahide; Asai, Tetsuya; Amemiya, Yoshihito

2007-01-01

Neuromorphic computing based on single-electron circuit technology is gaining prominence because of its massively increased computational efficiency and the increasing relevance of computer technology and nanotechnology [Likharev K, Mayr A, Muckra I, Tuerel O. CrossNets: High-performance neuromorphic architectures for CMOL circuits. Molec Electron III: Ann NY Acad Sci 1006;2003:146-63; Oya T, Schmid A, Asai T, Leblebici Y, Amemiya Y. On the fault tolerance of a clustered single-electron neural network for differential enhancement. IEICE Electron Expr 2;2005:76-80]. The maximum impact of these technologies will be strongly felt when single-electron circuits based on fault- and noise-tolerant neural structures can operate at room temperature. In this paper, inspired by stochastic resonance (SR) in an ensemble of spiking neurons [Collins JJ, Chow CC, Imhoff TT. Stochastic resonance without tuning. Nature 1995;376:236-8], we propose our design of a basic single-electron neural component and report how we examined its statistical results on a network

Fusion of neural computing and PLS techniques for load estimation

Energy Technology Data Exchange (ETDEWEB)

Lu, M.; Xue, H.; Cheng, X. [Northwestern Polytechnical Univ., Xi' an (China); Zhang, W. [Xi' an Inst. of Post and Telecommunication, Xi' an (China)

2007-07-01

A method to predict the electric load of a power system in real time was presented. The method is based on neurocomputing and partial least squares (PLS). Short-term load forecasts for power systems are generally determined by conventional statistical methods and Computational Intelligence (CI) techniques such as neural computing. However, statistical modeling methods often require the input of questionable distributional assumptions, and neural computing is weak, particularly in determining topology. In order to overcome the problems associated with conventional techniques, the authors developed a CI hybrid model based on neural computation and PLS techniques. The theoretical foundation for the designed CI hybrid model was presented along with its application in a power system. The hybrid model is suitable for nonlinear modeling and latent structure extracting. It can automatically determine the optimal topology to maximize the generalization. The CI hybrid model provides faster convergence and better prediction results compared to the abductive networks model because it incorporates a load conversion technique as well as new transfer functions. In order to demonstrate the effectiveness of the hybrid model, load forecasting was performed on a data set obtained from the Puget Sound Power and Light Company. Compared with the abductive networks model, the CI hybrid model reduced the forecast error by 32.37 per cent on workday, and by an average of 27.18 per cent on the weekend. It was concluded that the CI hybrid model has a more powerful predictive ability. 7 refs., 1 tab., 3 figs.

A specialized face-processing model inspired by the organization of monkey face patches explains several face-specific phenomena observed in humans.

Science.gov (United States)

Farzmahdi, Amirhossein; Rajaei, Karim; Ghodrati, Masoud; Ebrahimpour, Reza; Khaligh-Razavi, Seyed-Mahdi

2016-04-26

Converging reports indicate that face images are processed through specialized neural networks in the brain -i.e. face patches in monkeys and the fusiform face area (FFA) in humans. These studies were designed to find out how faces are processed in visual system compared to other objects. Yet, the underlying mechanism of face processing is not completely revealed. Here, we show that a hierarchical computational model, inspired by electrophysiological evidence on face processing in primates, is able to generate representational properties similar to those observed in monkey face patches (posterior, middle and anterior patches). Since the most important goal of sensory neuroscience is linking the neural responses with behavioral outputs, we test whether the proposed model, which is designed to account for neural responses in monkey face patches, is also able to predict well-documented behavioral face phenomena observed in humans. We show that the proposed model satisfies several cognitive face effects such as: composite face effect and the idea of canonical face views. Our model provides insights about the underlying computations that transfer visual information from posterior to anterior face patches.

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.

Parallel Processing and Bio-inspired Computing for Biomedical Image Registration

Directory of Open Access Journals (Sweden)

Silviu Ioan Bejinariu

2014-07-01

Full Text Available Image Registration (IR is an optimization problem computing optimal parameters of a geometric transform used to overlay one or more source images to a given model by maximizing a similarity measure. In this paper the use of bio-inspired optimization algorithms in image registration is analyzed. Results obtained by means of three different algorithms are compared: Bacterial Foraging Optimization Algorithm (BFOA, Genetic Algorithm (GA and Clonal Selection Algorithm (CSA. Depending on the images type, the registration may be: area based, which is slow but more precise, and features based, which is faster. In this paper a feature based approach based on the Scale Invariant Feature Transform (SIFT is proposed. Finally, results obtained using sequential and parallel implementations on multi-core systems for area based and features based image registration are compared.

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...

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

A Computational Account of Children's Analogical Reasoning: Balancing Inhibitory Control in Working Memory and Relational Representation

Science.gov (United States)

Morrison, Robert G.; Doumas, Leonidas A. A.; Richland, Lindsey E.

2011-01-01

Theories accounting for the development of analogical reasoning tend to emphasize either the centrality of relational knowledge accretion or changes in information processing capability. Simulations in LISA (Hummel & Holyoak, 1997, 2003), a neurally inspired computer model of analogical reasoning, allow us to explore how these factors may…

THE COMPUTATIONAL INTELLIGENCE TECHNIQUES FOR PREDICTIONS - ARTIFICIAL NEURAL NETWORKS

OpenAIRE

Mary Violeta Bar

2014-01-01

The computational intelligence techniques are used in problems which can not be solved by traditional techniques when there is insufficient data to develop a model problem or when they have errors.Computational intelligence, as he called Bezdek (Bezdek, 1992) aims at modeling of biological intelligence. Artificial Neural Networks( ANNs) have been applied to an increasing number of real world problems of considerable complexity. Their most important advantage is solving problems that are too c...

Computational Models and Emergent Properties of Respiratory Neural Networks

Science.gov (United States)

Lindsey, Bruce G.; Rybak, Ilya A.; Smith, Jeffrey C.

2012-01-01

Computational models of the neural control system for breathing in mammals provide a theoretical and computational framework bringing together experimental data obtained from different animal preparations under various experimental conditions. Many of these models were developed in parallel and iteratively with experimental studies and provided predictions guiding new experiments. This data-driven modeling approach has advanced our understanding of respiratory network architecture and neural mechanisms underlying generation of the respiratory rhythm and pattern, including their functional reorganization under different physiological conditions. Models reviewed here vary in neurobiological details and computational complexity and span multiple spatiotemporal scales of respiratory control mechanisms. Recent models describe interacting populations of respiratory neurons spatially distributed within the Bötzinger and pre-Bötzinger complexes and rostral ventrolateral medulla that contain core circuits of the respiratory central pattern generator (CPG). Network interactions within these circuits along with intrinsic rhythmogenic properties of neurons form a hierarchy of multiple rhythm generation mechanisms. The functional expression of these mechanisms is controlled by input drives from other brainstem components, including the retrotrapezoid nucleus and pons, which regulate the dynamic behavior of the core circuitry. The emerging view is that the brainstem respiratory network has rhythmogenic capabilities at multiple levels of circuit organization. This allows flexible, state-dependent expression of different neural pattern-generation mechanisms under various physiological conditions, enabling a wide repertoire of respiratory behaviors. Some models consider control of the respiratory CPG by pulmonary feedback and network reconfiguration during defensive behaviors such as cough. Future directions in modeling of the respiratory CPG are considered. PMID:23687564

Computing with networks of nonlinear mechanical oscillators.

Directory of Open Access Journals (Sweden)

Jean C Coulombe

Full Text Available As it is getting increasingly difficult to achieve gains in the density and power efficiency of microelectronic computing devices because of lithographic techniques reaching fundamental physical limits, new approaches are required to maximize the benefits of distributed sensors, micro-robots or smart materials. Biologically-inspired devices, such as artificial neural networks, can process information with a high level of parallelism to efficiently solve difficult problems, even when implemented using conventional microelectronic technologies. We describe a mechanical device, which operates in a manner similar to artificial neural networks, to solve efficiently two difficult benchmark problems (computing the parity of a bit stream, and classifying spoken words. The device consists in a network of masses coupled by linear springs and attached to a substrate by non-linear springs, thus forming a network of anharmonic oscillators. As the masses can directly couple to forces applied on the device, this approach combines sensing and computing functions in a single power-efficient device with compact dimensions.

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....

Container-code recognition system based on computer vision and deep neural networks

Science.gov (United States)

Liu, Yi; Li, Tianjian; Jiang, Li; Liang, Xiaoyao

2018-04-01

Automatic container-code recognition system becomes a crucial requirement for ship transportation industry in recent years. In this paper, an automatic container-code recognition system based on computer vision and deep neural networks is proposed. The system consists of two modules, detection module and recognition module. The detection module applies both algorithms based on computer vision and neural networks, and generates a better detection result through combination to avoid the drawbacks of the two methods. The combined detection results are also collected for online training of the neural networks. The recognition module exploits both character segmentation and end-to-end recognition, and outputs the recognition result which passes the verification. When the recognition module generates false recognition, the result will be corrected and collected for online training of the end-to-end recognition sub-module. By combining several algorithms, the system is able to deal with more situations, and the online training mechanism can improve the performance of the neural networks at runtime. The proposed system is able to achieve 93% of overall recognition accuracy.

Computational neuroanatomy: ontology-based representation of neural components and connectivity.

Science.gov (United States)

Rubin, Daniel L; Talos, Ion-Florin; Halle, Michael; Musen, Mark A; Kikinis, Ron

2009-02-05

A critical challenge in neuroscience is organizing, managing, and accessing the explosion in neuroscientific knowledge, particularly anatomic knowledge. We believe that explicit knowledge-based approaches to make neuroscientific knowledge computationally accessible will be helpful in tackling this challenge and will enable a variety of applications exploiting this knowledge, such as surgical planning. We developed ontology-based models of neuroanatomy to enable symbolic lookup, logical inference and mathematical modeling of neural systems. We built a prototype model of the motor system that integrates descriptive anatomic and qualitative functional neuroanatomical knowledge. In addition to modeling normal neuroanatomy, our approach provides an explicit representation of abnormal neural connectivity in disease states, such as common movement disorders. The ontology-based representation encodes both structural and functional aspects of neuroanatomy. The ontology-based models can be evaluated computationally, enabling development of automated computer reasoning applications. Neuroanatomical knowledge can be represented in machine-accessible format using ontologies. Computational neuroanatomical approaches such as described in this work could become a key tool in translational informatics, leading to decision support applications that inform and guide surgical planning and personalized care for neurological disease in the future.

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.

Neural Computations in a Dynamical System with Multiple Time Scales

Directory of Open Access Journals (Sweden)

Yuanyuan Mi

2016-09-01

Full Text Available Neural systems display rich short-term dynamics at various levels, e.g., spike-frequencyadaptation (SFA at single neurons, and short-term facilitation (STF and depression (STDat neuronal synapses. These dynamical features typically covers a broad range of time scalesand exhibit large diversity in different brain regions. It remains unclear what the computationalbenefit for the brain to have such variability in short-term dynamics is. In this study, we proposethat the brain can exploit such dynamical features to implement multiple seemingly contradictorycomputations in a single neural circuit. To demonstrate this idea, we use continuous attractorneural network (CANN as a working model and include STF, SFA and STD with increasing timeconstants in their dynamics. Three computational tasks are considered, which are persistent activity,adaptation, and anticipative tracking. These tasks require conflicting neural mechanisms, andhence cannot be implemented by a single dynamical feature or any combination with similar timeconstants. However, with properly coordinated STF, SFA and STD, we show that the network isable to implement the three computational tasks concurrently. We hope this study will shed lighton the understanding of how the brain orchestrates its rich dynamics at various levels to realizediverse cognitive functions.

Chaotic dynamics in nanoscale NbO2 Mott memristors for analogue computing

Science.gov (United States)

Kumar, Suhas; Strachan, John Paul; Williams, R. Stanley

2017-08-01

At present, machine learning systems use simplified neuron models that lack the rich nonlinear phenomena observed in biological systems, which display spatio-temporal cooperative dynamics. There is evidence that neurons operate in a regime called the edge of chaos that may be central to complexity, learning efficiency, adaptability and analogue (non-Boolean) computation in brains. Neural networks have exhibited enhanced computational complexity when operated at the edge of chaos, and networks of chaotic elements have been proposed for solving combinatorial or global optimization problems. Thus, a source of controllable chaotic behaviour that can be incorporated into a neural-inspired circuit may be an essential component of future computational systems. Such chaotic elements have been simulated using elaborate transistor circuits that simulate known equations of chaos, but an experimental realization of chaotic dynamics from a single scalable electronic device has been lacking. Here we describe niobium dioxide (NbO2) Mott memristors each less than 100 nanometres across that exhibit both a nonlinear-transport-driven current-controlled negative differential resistance and a Mott-transition-driven temperature-controlled negative differential resistance. Mott materials have a temperature-dependent metal-insulator transition that acts as an electronic switch, which introduces a history-dependent resistance into the device. We incorporate these memristors into a relaxation oscillator and observe a tunable range of periodic and chaotic self-oscillations. We show that the nonlinear current transport coupled with thermal fluctuations at the nanoscale generates chaotic oscillations. Such memristors could be useful in certain types of neural-inspired computation by introducing a pseudo-random signal that prevents global synchronization and could also assist in finding a global minimum during a constrained search. We specifically demonstrate that incorporating such

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

Science.gov (United States)

Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong

2014-07-01

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 (Tp: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.

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.

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

International Nuclear Information System (INIS)

Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong

2014-01-01

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 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

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

Advances in photonic reservoir computing

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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.

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.

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

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. .

Distributed computing methodology for training neural networks in an image-guided diagnostic application.

Science.gov (United States)

Plagianakos, V P; Magoulas, G D; Vrahatis, M N

2006-03-01

Distributed computing is a process through which a set of computers connected by a network is used collectively to solve a single problem. In this paper, we propose a distributed computing methodology for training neural networks for the detection of lesions in colonoscopy. Our approach is based on partitioning the training set across multiple processors using a parallel virtual machine. In this way, interconnected computers of varied architectures can be used for the distributed evaluation of the error function and gradient values, and, thus, training neural networks utilizing various learning methods. The proposed methodology has large granularity and low synchronization, and has been implemented and tested. Our results indicate that the parallel virtual machine implementation of the training algorithms developed leads to considerable speedup, especially when large network architectures and training sets are used.

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...

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.

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

The equilibrium of neural firing: A mathematical theory

Energy Technology Data Exchange (ETDEWEB)

Lan, Sizhong, E-mail: lsz@fuyunresearch.org [Fuyun Research, Beijing, 100055 (China)

2014-12-15

Inspired by statistical thermodynamics, we presume that neuron system has equilibrium condition with respect to neural firing. We show that, even with dynamically changeable neural connections, it is inevitable for neural firing to evolve to equilibrium. To study the dynamics between neural firing and neural connections, we propose an extended communication system where noisy channel has the tendency towards fixed point, implying that neural connections are always attracted into fixed points such that equilibrium can be reached. The extended communication system and its mathematics could be useful back in thermodynamics.

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...

Spin-neurons: A possible path to energy-efficient neuromorphic computers

Energy Technology Data Exchange (ETDEWEB)

Sharad, Mrigank; Fan, Deliang; Roy, Kaushik [School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana 47907 (United States)

2013-12-21

Recent years have witnessed growing interest in the field of brain-inspired computing based on neural-network architectures. In order to translate the related algorithmic models into powerful, yet energy-efficient cognitive-computing hardware, computing-devices beyond CMOS may need to be explored. The suitability of such devices to this field of computing would strongly depend upon how closely their physical characteristics match with the essential computing primitives employed in such models. In this work, we discuss the rationale of applying emerging spin-torque devices for bio-inspired computing. Recent spin-torque experiments have shown the path to low-current, low-voltage, and high-speed magnetization switching in nano-scale magnetic devices. Such magneto-metallic, current-mode spin-torque switches can mimic the analog summing and “thresholding” operation of an artificial neuron with high energy-efficiency. Comparison with CMOS-based analog circuit-model of a neuron shows that “spin-neurons” (spin based circuit model of neurons) can achieve more than two orders of magnitude lower energy and beyond three orders of magnitude reduction in energy-delay product. The application of spin-neurons can therefore be an attractive option for neuromorphic computers of future.

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.

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. ...

Neural Global Pattern Similarity Underlies True and False Memories.

Science.gov (United States)

Ye, Zhifang; Zhu, Bi; Zhuang, Liping; Lu, Zhonglin; Chen, Chuansheng; Xue, Gui

2016-06-22

The neural processes giving rise to human memory strength signals remain poorly understood. Inspired by formal computational models that posit a central role of global matching in memory strength, we tested a novel hypothesis that the strengths of both true and false memories arise from the global similarity of an item's neural activation pattern during retrieval to that of all the studied items during encoding (i.e., the encoding-retrieval neural global pattern similarity [ER-nGPS]). We revealed multiple ER-nGPS signals that carried distinct information and contributed differentially to true and false memories: Whereas the ER-nGPS in the parietal regions reflected semantic similarity and was scaled with the recognition strengths of both true and false memories, ER-nGPS in the visual cortex contributed solely to true memory. Moreover, ER-nGPS differences between the parietal and visual cortices were correlated with frontal monitoring processes. By combining computational and neuroimaging approaches, our results advance a mechanistic understanding of memory strength in recognition. What neural processes give rise to memory strength signals, and lead to our conscious feelings of familiarity? Using fMRI, we found that the memory strength of a given item depends not only on how it was encoded during learning, but also on the similarity of its neural representation with other studied items. The global neural matching signal, mainly in the parietal lobule, could account for the memory strengths of both studied and unstudied items. Interestingly, a different global matching signal, originated from the visual cortex, could distinguish true from false memories. The findings reveal multiple neural mechanisms underlying the memory strengths of events registered in the brain. Copyright © 2016 the authors 0270-6474/16/366792-11$15.00/0.

A novel single neuron perceptron with universal approximation and XOR computation properties.

Science.gov (United States)

Lotfi, Ehsan; Akbarzadeh-T, M-R

2014-01-01

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.

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.

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.

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.

iSpike: a spiking neural interface for the iCub robot

International Nuclear Information System (INIS)

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

2012-01-01

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

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...

A Neural Information Field Approach to Computational Cognition

Science.gov (United States)

2016-11-18

effects of distraction during list memory . These distractions include short and long delays before recall, and continuous distraction (forced rehearsal... memory encoding and replay in hippocampus. Computational Neuroscience Society (CNS), p. 166, 2014. D. A. Pinotsis, Neural Field Coding of Short Term ...performance of children learning to count in a SPA model; proposed a new SPA model of cognitive load using the N-back task; developed a new model of the

The scientific study of inspiration in the creative process: Challenges and opportunities

Directory of Open Access Journals (Sweden)

Victoria C. Oleynick

2014-06-01

Full Text Available 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, 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.

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.

Artificial intelligence. Application of the Statistical Neural Networks computer program in nuclear medicine

International Nuclear Information System (INIS)

Stefaniak, B.; Cholewinski, W.; Tarkowska, A.

2005-01-01

Artificial Neural Networks (ANN) may be a tool alternative and complementary to typical statistical analysis. However, in spite of many computer application of various ANN algorithms ready for use, artificial intelligence is relatively rarely applied to data processing. In this paper practical aspects of scientific application of ANN in medicine using the Statistical Neural Networks Computer program, were presented. Several steps of data analysis with the above ANN software package were discussed shortly, from material selection and its dividing into groups to the types of obtained results. The typical problems connected with assessing scintigrams by ANN were also described. (author)

A Pruning Neural Network Model in Credit Classification Analysis

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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.

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.

New Computer Simulations of Macular Neural Functioning

Science.gov (United States)

Ross, Muriel D.; Doshay, D.; Linton, S.; Parnas, B.; Montgomery, K.; Chimento, T.

1994-01-01

We use high performance graphics workstations and supercomputers to study the functional significance of the three-dimensional (3-D) organization of gravity sensors. These sensors have a prototypic architecture foreshadowing more complex systems. Scaled-down simulations run on a Silicon Graphics workstation and scaled-up, 3-D versions run on a Cray Y-MP supercomputer. A semi-automated method of reconstruction of neural tissue from serial sections studied in a transmission electron microscope has been developed to eliminate tedious conventional photography. The reconstructions use a mesh as a step in generating a neural surface for visualization. Two meshes are required to model calyx surfaces. The meshes are connected and the resulting prisms represent the cytoplasm and the bounding membranes. A finite volume analysis method is employed to simulate voltage changes along the calyx in response to synapse activation on the calyx or on calyceal processes. The finite volume method insures that charge is conserved at the calyx-process junction. These and other models indicate that efferent processes act as voltage followers, and that the morphology of some afferent processes affects their functioning. In a final application, morphological information is symbolically represented in three dimensions in a computer. The possible functioning of the connectivities is tested using mathematical interpretations of physiological parameters taken from the literature. Symbolic, 3-D simulations are in progress to probe the functional significance of the connectivities. This research is expected to advance computer-based studies of macular functioning and of synaptic plasticity.

Computer simulation system of neural PID control on nuclear reactor

International Nuclear Information System (INIS)

Chen Yuzhong; Yang Kaijun; Shen Yongping

2001-01-01

Neural network proportional integral differential (PID) controller on nuclear reactor is designed, and the control process is simulated by computer. The simulation result show that neutral network PID controller can automatically adjust its parameter to ideal state, and good control result can be gotten in reactor control process

Phase Diagram of Spiking Neural Networks

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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.

Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data.

Science.gov (United States)

Ching, Travers; Zhu, Xun; Garmire, Lana X

2018-04-01

Artificial neural networks (ANN) are computing architectures with many interconnections of simple neural-inspired computing elements, and have been applied to biomedical fields such as imaging analysis and diagnosis. We have developed a new ANN framework called Cox-nnet to predict patient prognosis from high throughput transcriptomics data. In 10 TCGA RNA-Seq data sets, Cox-nnet achieves the same or better predictive accuracy compared to other methods, including Cox-proportional hazards regression (with LASSO, ridge, and mimimax concave penalty), Random Forests Survival and CoxBoost. Cox-nnet also reveals richer biological information, at both the pathway and gene levels. The outputs from the hidden layer node provide an alternative approach for survival-sensitive dimension reduction. In summary, we have developed a new method for accurate and efficient prognosis prediction on high throughput data, with functional biological insights. The source code is freely available at https://github.com/lanagarmire/cox-nnet.

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.

Neural and Computational Mechanisms of Action Processing: Interaction between Visual and Motor Representations.

Science.gov (United States)

Giese, Martin A; Rizzolatti, Giacomo

2015-10-07

Action recognition has received enormous interest in the field of neuroscience over the last two decades. In spite of this interest, the knowledge in terms of fundamental neural mechanisms that provide constraints for underlying computations remains rather limited. This fact stands in contrast with a wide variety of speculative theories about how action recognition might work. This review focuses on new fundamental electrophysiological results in monkeys, which provide constraints for the detailed underlying computations. In addition, we review models for action recognition and processing that have concrete mathematical implementations, as opposed to conceptual models. We think that only such implemented models can be meaningfully linked quantitatively to physiological data and have a potential to narrow down the many possible computational explanations for action recognition. In addition, only concrete implementations allow judging whether postulated computational concepts have a feasible implementation in terms of realistic neural circuits. Copyright © 2015 Elsevier Inc. All rights reserved.

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...

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...

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

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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

Memristor-Based Analog Computation and Neural Network Classification with a Dot Product Engine.

Science.gov (United States)

Hu, Miao; Graves, Catherine E; Li, Can; Li, Yunning; Ge, Ning; Montgomery, Eric; Davila, Noraica; Jiang, Hao; Williams, R Stanley; Yang, J Joshua; Xia, Qiangfei; Strachan, John Paul

2018-03-01

Using memristor crossbar arrays to accelerate computations is a promising approach to efficiently implement algorithms in deep neural networks. Early demonstrations, however, are limited to simulations or small-scale problems primarily due to materials and device challenges that limit the size of the memristor crossbar arrays that can be reliably programmed to stable and analog values, which is the focus of the current work. High-precision analog tuning and control of memristor cells across a 128 × 64 array is demonstrated, and the resulting vector matrix multiplication (VMM) computing precision is evaluated. Single-layer neural network inference is performed in these arrays, and the performance compared to a digital approach is assessed. Memristor computing system used here reaches a VMM accuracy equivalent of 6 bits, and an 89.9% recognition accuracy is achieved for the 10k MNIST handwritten digit test set. Forecasts show that with integrated (on chip) and scaled memristors, a computational efficiency greater than 100 trillion operations per second per Watt is possible. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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.  

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.

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.

Neural networks

International Nuclear Information System (INIS)

Denby, Bruce; Lindsey, Clark; Lyons, Louis

1992-01-01

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

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

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.

High-Speed Photonic Reservoir Computing Using a Time-Delay-Based Architecture: Million Words per Second Classification

Directory of Open Access Journals (Sweden)

Laurent Larger

2017-02-01

Full Text Available Reservoir computing, originally referred to as an echo state network or a liquid state machine, is a brain-inspired paradigm for processing temporal information. It involves learning a “read-out” interpretation for nonlinear transients developed by high-dimensional dynamics when the latter is excited by the information signal to be processed. This novel computational paradigm is derived from recurrent neural network and machine learning techniques. It has recently been implemented in photonic hardware for a dynamical system, which opens the path to ultrafast brain-inspired computing. We report on a novel implementation involving an electro-optic phase-delay dynamics designed with off-the-shelf optoelectronic telecom devices, thus providing the targeted wide bandwidth. Computational efficiency is demonstrated experimentally with speech-recognition tasks. State-of-the-art speed performances reach one million words per second, with very low word error rate. Additionally, to record speed processing, our investigations have revealed computing-efficiency improvements through yet-unexplored temporal-information-processing techniques, such as simultaneous multisample injection and pitched sampling at the read-out compared to information “write-in”.

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

Science.gov (United States)

Chande, Ruchi D; Hargraves, Rosalyn Hobson; Ortiz-Robinson, Norma; Wayne, Jennifer S

2017-01-01

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.

Learning text representation using recurrent convolutional neural network with highway layers

OpenAIRE

Wen, Ying; Zhang, Weinan; Luo, Rui; Wang, Jun

2016-01-01

Recently, the rapid development of word embedding and neural networks has brought new inspiration to various NLP and IR tasks. In this paper, we describe a staged hybrid model combining Recurrent Convolutional Neural Networks (RCNN) with highway layers. The highway network module is incorporated in the middle takes the output of the bi-directional Recurrent Neural Network (Bi-RNN) module in the first stage and provides the Convolutional Neural Network (CNN) module in the last stage with the i...

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...

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

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

Science.gov (United States)

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

2014-12-01

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

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

Deep neural networks rival the representation of primate IT cortex for core visual object recognition.

Directory of Open Access Journals (Sweden)

Charles F Cadieu

2014-12-01

Full Text Available The primate visual system achieves remarkable visual object recognition performance even in brief presentations, and under changes to object exemplar, geometric transformations, and background variation (a.k.a. core visual object recognition. This remarkable performance is mediated by the representation formed in inferior temporal (IT cortex. In parallel, recent advances in machine learning have led to ever higher performing models of object recognition using artificial deep neural networks (DNNs. It remains unclear, however, whether the representational performance of DNNs rivals that of the brain. To accurately produce such a comparison, a major difficulty has been a unifying metric that accounts for experimental limitations, such as the amount of noise, the number of neural recording sites, and the number of trials, and computational limitations, such as the complexity of the decoding classifier and the number of classifier training examples. In this work, we perform a direct comparison that corrects for these experimental limitations and computational considerations. As part of our methodology, we propose an extension of "kernel analysis" that measures the generalization accuracy as a function of representational complexity. Our evaluations show that, unlike previous bio-inspired models, the latest DNNs rival the representational performance of IT cortex on this visual object recognition task. Furthermore, we show that models that perform well on measures of representational performance also perform well on measures of representational similarity to IT, and on measures of predicting individual IT multi-unit responses. Whether these DNNs rely on computational mechanisms similar to the primate visual system is yet to be determined, but, unlike all previous bio-inspired models, that possibility cannot be ruled out merely on representational performance grounds.

Distributed Recurrent Neural Forward Models with Neural Control for Complex Locomotion in Walking Robots

DEFF Research Database (Denmark)

Dasgupta, Sakyasingha; Goldschmidt, Dennis; Wörgötter, Florentin

2015-01-01

here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures) with the underlying neural mechanisms. The neural mechanisms consist of (1) central pattern generator based control for generating basic rhythmic patterns and coordinated......Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental...... conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain...

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.

Fast computation with spikes in a recurrent neural network

International Nuclear Information System (INIS)

Jin, Dezhe Z.; Seung, H. Sebastian

2002-01-01

Neural networks with recurrent connections are sometimes regarded as too slow at computation to serve as models of the brain. Here we analytically study a counterexample, a network consisting of N integrate-and-fire neurons with self excitation, all-to-all inhibition, instantaneous synaptic coupling, and constant external driving inputs. When the inhibition and/or excitation are large enough, the network performs a winner-take-all computation for all possible external inputs and initial states of the network. The computation is done very quickly: As soon as the winner spikes once, the computation is completed since no other neurons will spike. For some initial states, the winner is the first neuron to spike, and the computation is done at the first spike of the network. In general, there are M potential winners, corresponding to the top M external inputs. When the external inputs are close in magnitude, M tends to be larger. If M>1, the selection of the actual winner is strongly influenced by the initial states. If a special relation between the excitation and inhibition is satisfied, the network always selects the neuron with the maximum external input as the winner

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...

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.

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.…

Neural networks and their potential application to nuclear power plants

International Nuclear Information System (INIS)

Uhrig, R.E.

1991-01-01

A network of artificial neurons, usually called an artificial neural network is a data processing system consisting of a number of highly interconnected processing elements in an architecture inspired by the structure of the cerebral cortex portion of the brain. Hence, neural networks are often capable of doing things which humans or animals do well but which conventional computers often do poorly. Neural networks exhibit characteristics and capabilities not provided by any other technology. Neural networks may be designed so as to classify an input pattern as one of several predefined types or to create, as needed, categories or classes of system states which can be interpreted by a human operator. Neural networks have the ability to recognize patterns, even when the information comprising these patterns is noisy, sparse, or incomplete. Thus, systems of artificial neural networks show great promise for use in environments in which robust, fault-tolerant pattern recognition is necessary in a real-time mode, and in which the incoming data may be distorted or noisy. The application of neural networks, a rapidly evolving technology used extensively in defense applications, alone or in conjunction with other advanced technologies, to some of the problems of operating nuclear power plants has the potential to enhance the safety, reliability and operability of nuclear power plants. The potential applications of neural networking include, but are not limited to diagnosing specific abnormal conditions, identification of nonlinear dynamics and transients, detection of the change of mode of operation, control of temperature and pressure during start-up, signal validation, plant-wide monitoring using autoassociative neural networks, monitoring of check valves, modeling of the plant thermodynamics, emulation of core reload calculations, analysis of temporal sequences in NRC's ''licensee event reports,'' and monitoring of plant parameters

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

Science.gov (United States)

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

2010-10-01

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

Reservoir-based Online Adaptive Forward Models with Neural Control for Complex Locomotion in a Hexapod Robot

DEFF Research Database (Denmark)

Manoonpong, Poramate; Dasgupta, Sakyasingha; Goldschmidt, Dennis

2014-01-01

Walking animals show fascinating locomotor abilities and complex behaviors. Biological study has revealed that such complex behaviors is a result of a combination of biomechanics and neural mechanisms. While biomechanics allows for flexibility and a variety of movements, neural mechanisms generate...... locomotion, make predictions, and provide adaptation. Inspired by this finding, we present here an artificial bio-inspired walking system which combines biomechanics (in terms of its body and leg structures) and neural mechanisms. The neural mechanisms consist of 1) central pattern generator-based control...... for generating basic rhythmic patterns and coordinated movements, 2) reservoir-based adaptive forward models with efference copies for sensory prediction as well as state estimation, and 3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental...

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 that modula......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...

Neural Computation Scheme of Compound Control: Tacit Learning for Bipedal Locomotion

Science.gov (United States)

Shimoda, Shingo; Kimura, Hidenori

The growing need for controlling complex behaviors of versatile robots working in unpredictable environment has revealed the fundamental limitation of model-based control strategy that requires precise models of robots and environments before their operations. This difficulty is fundamental and has the same root with the well-known frame problem in artificial intelligence. It has been a central long standing issue in advanced robotics, as well as machine intelligence, to find a prospective clue to attack this fundamental difficulty. The general consensus shared by many leading researchers in the related field is that the body plays an important role in acquiring intelligence that can conquer unknowns. In particular, purposeful behaviors emerge during body-environment interactions with the help of an appropriately organized neural computational scheme that can exploit what the environment can afford. Along this line, we propose a new scheme of neural computation based on compound control which represents a typical feature of biological controls. This scheme is based on classical neuron models with local rules that can create macroscopic purposeful behaviors. This scheme is applied to a bipedal robot and generates the rhythm of walking without any model of robot dynamics and environments.

Artificial neural network model for prediction of safety performance indicators goals in nuclear plants

Energy Technology Data Exchange (ETDEWEB)

Souto, Kelling C.; Nunes, Wallace W. [Instituto Federal de Educacao, Ciencia e Tecnologia do Rio de Janeiro, Nilopolis, RJ (Brazil). Lab. de Aplicacoes Computacionais; Machado, Marcelo D., E-mail: dornemd@eletronuclear.gov.b [ELETROBRAS Termonuclear S.A. (ELETRONUCLEAR), Rio de Janeiro, RJ (Brazil). Gerencia de Combustivel Nuclear - GCN.T

2011-07-01

Safety performance indicators have been developed to provide a quantitative indication of the performance and safety in various industry sectors. These indexes can provide assess to aspects ranging from production, design, and human performance up to management issues in accordance with policy, objectives and goals of the company. The use of safety performance indicators in nuclear power plants around the world is a reality. However, it is necessary to periodically set goal values. Such goals are targets relating to each of the indicators to be achieved by the plant over a predetermined period of operation. The current process of defining these goals is carried out by experts in a subjective way, based on actual data from the plant, and comparison with global indices. Artificial neural networks are computational techniques that present a mathematical model inspired by the neural structure of intelligent organisms that acquire knowledge through experience. This paper proposes an artificial neural network model aimed at predicting values of goals to be used in the evaluation of safety performance indicators for nuclear power plants. (author)

Artificial neural network model for prediction of safety performance indicators goals in nuclear plants

International Nuclear Information System (INIS)

Souto, Kelling C.; Nunes, Wallace W.; Machado, Marcelo D.

2011-01-01

Safety performance indicators have been developed to provide a quantitative indication of the performance and safety in various industry sectors. These indexes can provide assess to aspects ranging from production, design, and human performance up to management issues in accordance with policy, objectives and goals of the company. The use of safety performance indicators in nuclear power plants around the world is a reality. However, it is necessary to periodically set goal values. Such goals are targets relating to each of the indicators to be achieved by the plant over a predetermined period of operation. The current process of defining these goals is carried out by experts in a subjective way, based on actual data from the plant, and comparison with global indices. Artificial neural networks are computational techniques that present a mathematical model inspired by the neural structure of intelligent organisms that acquire knowledge through experience. This paper proposes an artificial neural network model aimed at predicting values of goals to be used in the evaluation of safety performance indicators for nuclear power plants. (author)

Linking Inflammation, Cardiorespiratory Variability, and Neural Control in Acute Inflammation via Computational Modeling.

Science.gov (United States)

Dick, Thomas E; Molkov, Yaroslav I; Nieman, Gary; Hsieh, Yee-Hsee; Jacono, Frank J; Doyle, John; Scheff, Jeremy D; Calvano, Steve E; Androulakis, Ioannis P; An, Gary; Vodovotz, Yoram

2012-01-01

Acute inflammation leads to organ failure by engaging catastrophic feedback loops in which stressed tissue evokes an inflammatory response and, in turn, inflammation damages tissue. Manifestations of this maladaptive inflammatory response include cardio-respiratory dysfunction that may be reflected in reduced heart rate and ventilatory pattern variabilities. We have developed signal-processing algorithms that quantify non-linear deterministic characteristics of variability in biologic signals. Now, coalescing under the aegis of the NIH Computational Biology Program and the Society for Complexity in Acute Illness, two research teams performed iterative experiments and computational modeling on inflammation and cardio-pulmonary dysfunction in sepsis as well as on neural control of respiration and ventilatory pattern variability. These teams, with additional collaborators, have recently formed a multi-institutional, interdisciplinary consortium, whose goal is to delineate the fundamental interrelationship between the inflammatory response and physiologic variability. Multi-scale mathematical modeling and complementary physiological experiments will provide insight into autonomic neural mechanisms that may modulate the inflammatory response to sepsis and simultaneously reduce heart rate and ventilatory pattern variabilities associated with sepsis. This approach integrates computational models of neural control of breathing and cardio-respiratory coupling with models that combine inflammation, cardiovascular function, and heart rate variability. The resulting integrated model will provide mechanistic explanations for the phenomena of respiratory sinus-arrhythmia and cardio-ventilatory coupling observed under normal conditions, and the loss of these properties during sepsis. This approach holds the potential of modeling cross-scale physiological interactions to improve both basic knowledge and clinical management of acute inflammatory diseases such as sepsis and trauma.

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

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.

Diverse spike-timing-dependent plasticity based on multilevel HfO x memristor for neuromorphic computing

Science.gov (United States)

Lu, Ke; Li, Yi; He, Wei-Fan; Chen, Jia; Zhou, Ya-Xiong; Duan, Nian; Jin, Miao-Miao; Gu, Wei; Xue, Kan-Hao; Sun, Hua-Jun; Miao, Xiang-Shui

2018-06-01

Memristors have emerged as promising candidates for artificial synaptic devices, serving as the building block of brain-inspired neuromorphic computing. In this letter, we developed a Pt/HfO x /Ti memristor with nonvolatile multilevel resistive switching behaviors due to the evolution of the conductive filaments and the variation in the Schottky barrier. Diverse state-dependent spike-timing-dependent-plasticity (STDP) functions were implemented with different initial resistance states. The measured STDP forms were adopted as the learning rule for a three-layer spiking neural network which achieves a 75.74% recognition accuracy for MNIST handwritten digit dataset. This work has shown the capability of memristive synapse in spiking neural networks for pattern recognition application.

Brain Inspired Cognitive Model with Attention for Self-Driving Cars

OpenAIRE

Chen, Shitao; Zhang, Songyi; Shang, Jinghao; Chen, Badong; Zheng, Nanning

2017-01-01

Perception-driven approach and end-to-end system are two major vision-based frameworks for self-driving cars. However, it is difficult to introduce attention and historical information of autonomous driving process, which are the essential factors for achieving human-like driving into these two methods. In this paper, we propose a novel model for self-driving cars named brain-inspired cognitive model with attention (CMA). This model consists of three parts: a convolutional neural network for ...

Persistent Memory in Single Node Delay-Coupled Reservoir Computing.

Science.gov (United States)

Kovac, André David; Koall, Maximilian; Pipa, Gordon; Toutounji, Hazem

2016-01-01

Delays are ubiquitous in biological systems, ranging from genetic regulatory networks and synaptic conductances, to predator/pray population interactions. The evidence is mounting, not only to the presence of delays as physical constraints in signal propagation speed, but also to their functional role in providing dynamical diversity to the systems that comprise them. The latter observation in biological systems inspired the recent development of a computational architecture that harnesses this dynamical diversity, by delay-coupling a single nonlinear element to itself. This architecture is a particular realization of Reservoir Computing, where stimuli are injected into the system in time rather than in space as is the case with classical recurrent neural network realizations. This architecture also exhibits an internal memory which fades in time, an important prerequisite to the functioning of any reservoir computing device. However, fading memory is also a limitation to any computation that requires persistent storage. In order to overcome this limitation, the current work introduces an extended version to the single node Delay-Coupled Reservoir, that is based on trained linear feedback. We show by numerical simulations that adding task-specific linear feedback to the single node Delay-Coupled Reservoir extends the class of solvable tasks to those that require nonfading memory. We demonstrate, through several case studies, the ability of the extended system to carry out complex nonlinear computations that depend on past information, whereas the computational power of the system with fading memory alone quickly deteriorates. Our findings provide the theoretical basis for future physical realizations of a biologically-inspired ultrafast computing device with extended functionality.

Inspired Responses

Science.gov (United States)

Steele, Carol Frederick

2011-01-01

In terms of teacher quality, Steele believes the best teachers have reached a stage she terms inspired, and that teachers move progressively through the stages of unaware, aware, and capable until the most reflective teachers finally reach the inspired level. Inspired teachers have a wide repertoire of teaching and class management techniques and…

Topology and computational performance of attractor neural networks

International Nuclear Information System (INIS)

McGraw, Patrick N.; Menzinger, Michael

2003-01-01

To explore the relation between network structure and function, we studied the computational performance of Hopfield-type attractor neural nets with regular lattice, random, small-world, and scale-free topologies. The random configuration is the most efficient for storage and retrieval of patterns by the network as a whole. However, in the scale-free case retrieval errors are not distributed uniformly among the nodes. The portion of a pattern encoded by the subset of highly connected nodes is more robust and efficiently recognized than the rest of the pattern. The scale-free network thus achieves a very strong partial recognition. The implications of these findings for brain function and social dynamics are suggestive

Population coding and decoding in a neural field: a computational study.

Science.gov (United States)

Wu, Si; Amari, Shun-Ichi; Nakahara, Hiroyuki

2002-05-01

This study uses a neural field model to investigate computational aspects of population coding and decoding when the stimulus is a single variable. A general prototype model for the encoding process is proposed, in which neural responses are correlated, with strength specified by a gaussian function of their difference in preferred stimuli. Based on the model, we study the effect of correlation on the Fisher information, compare the performances of three decoding methods that differ in the amount of encoding information being used, and investigate the implementation of the three methods by using a recurrent network. This study not only rediscovers main results in existing literatures in a unified way, but also reveals important new features, especially when the neural correlation is strong. As the neural correlation of firing becomes larger, the Fisher information decreases drastically. We confirm that as the width of correlation increases, the Fisher information saturates and no longer increases in proportion to the number of neurons. However, we prove that as the width increases further--wider than (sqrt)2 times the effective width of the turning function--the Fisher information increases again, and it increases without limit in proportion to the number of neurons. Furthermore, we clarify the asymptotic efficiency of the maximum likelihood inference (MLI) type of decoding methods for correlated neural signals. It shows that when the correlation covers a nonlocal range of population (excepting the uniform correlation and when the noise is extremely small), the MLI type of method, whose decoding error satisfies the Cauchy-type distribution, is not asymptotically efficient. This implies that the variance is no longer adequate to measure decoding accuracy.

Cardiac dosimetric evaluation of deep inspiration breath-hold level variances using computed tomography scans generated from deformable image registration displacement vectors

International Nuclear Information System (INIS)

Harry, Taylor; Rahn, Doug; Semenov, Denis; Gu, Xuejun; Yashar, Catheryn; Einck, John; Jiang, Steve; Cerviño, Laura

2016-01-01

There is a reduction in cardiac dose for left-sided breast radiotherapy during treatment with deep inspiration breath-hold (DIBH) when compared with treatment with free breathing (FB). Various levels of DIBH may occur for different treatment fractions. Dosimetric effects due to this and other motions are a major component of uncertainty in radiotherapy in this setting. Recent developments in deformable registration techniques allow displacement vectors between various temporal and spatial patient representations to be digitally quantified. We propose a method to evaluate the dosimetric effect to the heart from variable reproducibility of DIBH by using deformable registration to create new anatomical computed tomography (CT) scans. From deformable registration, 3-dimensional deformation vectors are generated with FB and DIBH. The obtained deformation vectors are scaled to 75%, 90%, and 110% and are applied to the reference image to create new CT scans at these inspirational levels. The scans are then imported into the treatment planning system and dose calculations are performed. The average mean dose to the heart was 2.5 Gy (0.7 to 9.6 Gy) at FB, 1.2 Gy (0.6 to 3.8 Gy, p < 0.001) at 75% inspiration, 1.1 Gy (0.6 to 3.1 Gy, p = 0.004) at 90% inspiration, 1.0 Gy (0.6 to 3.0 Gy) at 100% inspiration or DIBH, and 1.0 Gy (0.6 to 2.8 Gy, p = 0.019) at 110% inspiration. The average mean dose to the left anterior descending artery (LAD) was 19.9 Gy (2.4 to 46.4 Gy), 8.6 Gy (2.0 to 43.8 Gy, p < 0.001), 7.2 Gy (1.9 to 40.1 Gy, p = 0.035), 6.5 Gy (1.8 to 34.7 Gy), and 5.3 Gy (1.5 to 31.5 Gy, p < 0.001), correspondingly. This novel method enables numerous anatomical situations to be mimicked and quantifies the dosimetric effect they have on a treatment plan.

Cardiac dosimetric evaluation of deep inspiration breath-hold level variances using computed tomography scans generated from deformable image registration displacement vectors

Energy Technology Data Exchange (ETDEWEB)

Harry, Taylor [Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA (United States); Department of Radiation Medicine, Oregon Health and Science University, Portland, OR (United States); Department of Nuclear Engineering and Radiation Health Physics, Oregon State University, Corvallis, OR (United States); Rahn, Doug; Semenov, Denis [Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA (United States); Gu, Xuejun [Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX (United States); Yashar, Catheryn; Einck, John [Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA (United States); Jiang, Steve [Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX (United States); Cerviño, Laura, E-mail: lcervino@ucsd.edu [Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA (United States)

2016-04-01

There is a reduction in cardiac dose for left-sided breast radiotherapy during treatment with deep inspiration breath-hold (DIBH) when compared with treatment with free breathing (FB). Various levels of DIBH may occur for different treatment fractions. Dosimetric effects due to this and other motions are a major component of uncertainty in radiotherapy in this setting. Recent developments in deformable registration techniques allow displacement vectors between various temporal and spatial patient representations to be digitally quantified. We propose a method to evaluate the dosimetric effect to the heart from variable reproducibility of DIBH by using deformable registration to create new anatomical computed tomography (CT) scans. From deformable registration, 3-dimensional deformation vectors are generated with FB and DIBH. The obtained deformation vectors are scaled to 75%, 90%, and 110% and are applied to the reference image to create new CT scans at these inspirational levels. The scans are then imported into the treatment planning system and dose calculations are performed. The average mean dose to the heart was 2.5 Gy (0.7 to 9.6 Gy) at FB, 1.2 Gy (0.6 to 3.8 Gy, p < 0.001) at 75% inspiration, 1.1 Gy (0.6 to 3.1 Gy, p = 0.004) at 90% inspiration, 1.0 Gy (0.6 to 3.0 Gy) at 100% inspiration or DIBH, and 1.0 Gy (0.6 to 2.8 Gy, p = 0.019) at 110% inspiration. The average mean dose to the left anterior descending artery (LAD) was 19.9 Gy (2.4 to 46.4 Gy), 8.6 Gy (2.0 to 43.8 Gy, p < 0.001), 7.2 Gy (1.9 to 40.1 Gy, p = 0.035), 6.5 Gy (1.8 to 34.7 Gy), and 5.3 Gy (1.5 to 31.5 Gy, p < 0.001), correspondingly. This novel method enables numerous anatomical situations to be mimicked and quantifies the dosimetric effect they have on a treatment plan.

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

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

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

Science.gov (United States)

Will, Geert-Jan; Rutledge, Robb B; Moutoussis, Michael; Dolan, Raymond J

2017-10-24

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.

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.

Unification of behavioural, computational and neural accounts of word production errors in post-stroke aphasia

Directory of Open Access Journals (Sweden)

Marija Tochadse

Full Text Available Neuropsychological assessment, brain imaging and computational modelling have augmented our understanding of the multifaceted functional deficits in people with language disorders after stroke. Despite the volume of research using each technique, no studies have attempted to assimilate all three approaches in order to generate a unified behavioural-computational-neural model of post-stroke aphasia.The present study included data from 53 participants with chronic post-stroke aphasia and merged: aphasiological profiles based on a detailed neuropsychological assessment battery which was analysed with principal component and correlational analyses; measures of the impairment taken from Dell's computational model of word production; and the neural correlates of both behavioural and computational accounts analysed by voxel-based correlational methodology.As a result, all three strands coincide with the separation of semantic and phonological stages of aphasic naming, revealing the prominence of these dimensions for the explanation of aphasic performance. Over and above three previously described principal components (phonological ability, semantic ability, executive-demand, we observed auditory working memory as a novel factor. While the phonological Dell parameter was uniquely related to phonological errors/factor, the semantic parameter was less clear-cut, being related to both semantic errors and omissions, and loading heavily with semantic ability and auditory working memory factors. The close relationship between the semantic Dell parameter and omission errors recurred in their high lesion-correlate overlap in the anterior middle temporal gyrus. In addition, the simultaneous overlap of the lesion correlate of omission errors with more dorsal temporal regions, associated with the phonological parameter, highlights the multiple drivers that underpin this error type. The novel auditory working memory factor was located along left superior

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 ...

Nature-Inspired and Energy Efficient Route Planning

DEFF Research Database (Denmark)

Schlichtkrull, Anders; Christensen, J. B. S.; Feld, T.

2015-01-01

Cars are responsible for substantial CO2 emission worldwide. Computers can help solve this problem by computing shortest routes on maps. A good example of this is the popular Google Maps service. However, such services often require the order of the stops on the route to be fixed. By not enforcing....... The app is aimed at private persons and small businesses. The app works by using a nature-inspired algorithm called Ant Colony Optimization....

A Dynamic Connectome Supports the Emergence of Stable Computational Function of Neural Circuits through Reward-Based Learning.

Science.gov (United States)

Kappel, David; Legenstein, Robert; Habenschuss, Stefan; Hsieh, Michael; Maass, Wolfgang

2018-01-01

Synaptic connections between neurons in the brain are dynamic because of continuously ongoing spine dynamics, axonal sprouting, and other processes. In fact, it was recently shown that the spontaneous synapse-autonomous component of spine dynamics is at least as large as the component that depends on the history of pre- and postsynaptic neural activity. These data are inconsistent with common models for network plasticity and raise the following questions: how can neural circuits maintain a stable computational function in spite of these continuously ongoing processes, and what could be functional uses of these ongoing processes? Here, we present a rigorous theoretical framework for these seemingly stochastic spine dynamics and rewiring processes in the context of reward-based learning tasks. We show that spontaneous synapse-autonomous processes, in combination with reward signals such as dopamine, can explain the capability of networks of neurons in the brain to configure themselves for specific computational tasks, and to compensate automatically for later changes in the network or task. Furthermore, we show theoretically and through computer simulations that stable computational performance is compatible with continuously ongoing synapse-autonomous changes. After reaching good computational performance it causes primarily a slow drift of network architecture and dynamics in task-irrelevant dimensions, as observed for neural activity in motor cortex and other areas. On the more abstract level of reinforcement learning the resulting model gives rise to an understanding of reward-driven network plasticity as continuous sampling of network configurations.

Computed tomography of x-ray images using neural networks

Science.gov (United States)

Allred, Lloyd G.; Jones, Martin H.; Sheats, Matthew J.; Davis, Anthony W.

2000-03-01

Traditional CT reconstruction is done using the technique of Filtered Backprojection. While this technique is widely employed in industrial and medical applications, it is not generally understood that FB has a fundamental flaw. Gibbs phenomena states any Fourier reconstruction will produce errors in the vicinity of all discontinuities, and that the error will equal 28 percent of the discontinuity. A number of years back, one of the authors proposed a biological perception model whereby biological neural networks perceive 3D images from stereo vision. The perception model proports an internal hard-wired neural network which emulates the external physical process. A process is repeated whereby erroneous unknown internal values are used to generate an emulated signal with is compared to external sensed data, generating an error signal. Feedback from the error signal is then sued to update the erroneous internal values. The process is repeated until the error signal no longer decrease. It was soon realized that the same method could be used to obtain CT from x-rays without having to do Fourier transforms. Neural networks have the additional potential for handling non-linearities and missing data. The technique has been applied to some coral images, collected at the Los Alamos high-energy x-ray facility. The initial images show considerable promise, in some instances showing more detail than the FB images obtained from the same data. Although routine production using this new method would require a massively parallel computer, the method shows promise, especially where refined detail is required.

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

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Mehmet eKocaturk

2015-08-01

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

Quality-of-service sensitivity to bio-inspired/evolutionary computational methods for intrusion detection in wireless ad hoc multimedia sensor networks

Science.gov (United States)

Hortos, William S.

2012-06-01

In the author's previous work, a cross-layer protocol approach to wireless sensor network (WSN) intrusion detection an identification is created with multiple bio-inspired/evolutionary computational methods applied to the functions of the protocol layers, a single method to each layer, to improve the intrusion-detection performance of the protocol over that of one method applied to only a single layer's functions. The WSN cross-layer protocol design embeds GAs, anti-phase synchronization, ACO, and a trust model based on quantized data reputation at the physical, MAC, network, and application layer, respectively. The construct neglects to assess the net effect of the combined bioinspired methods on the quality-of-service (QoS) performance for "normal" data streams, that is, streams without intrusions. Analytic expressions of throughput, delay, and jitter, coupled with simulation results for WSNs free of intrusion attacks, are the basis for sensitivity analyses of QoS metrics for normal traffic to the bio-inspired methods.

ECO INVESTMENT PROJECT MANAGEMENT THROUGH TIME APPLYING ARTIFICIAL NEURAL NETWORKS

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Tamara Gvozdenović

2007-06-01

Full Text Available he concept of project management expresses an indispensable approach to investment projects. Time is often the most important factor in these projects. The artificial neural network is the paradigm of data processing, which is inspired by the one used by the biological brain, and it is used in numerous, different fields, among which is the project management. This research is oriented to application of artificial neural networks in managing time of investment project. The artificial neural networks are used to define the optimistic, the most probable and the pessimistic time in PERT method. The program package Matlab: Neural Network Toolbox is used in data simulation. The feed-forward back propagation network is chosen.

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. PMID:24782659

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

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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.

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

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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.

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

Science.gov (United States)

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. PMID:26977450

The impact of natural aging on computational and neural indices of perceptual decision making: A review.

Science.gov (United States)

Dully, Jessica; McGovern, David P; O'Connell, Redmond G

2018-02-10

It is well established that natural aging negatively impacts on a wide variety of cognitive functions and research has sought to identify core neural mechanisms that may account for these disparate changes. A central feature of any cognitive task is the requirement to translate sensory information into an appropriate action - a process commonly known as perceptual decision making. While computational, psychophysical, and neurophysiological research has made substantial progress in establishing the key computations and neural mechanisms underpinning decision making, it is only relatively recently that this knowledge has begun to be applied to research on aging. The purpose of this review is to provide an overview of this work which is beginning to offer new insights into the core psychological processes that mediate age-related cognitive decline in adults aged 65 years and over. Mathematical modelling studies have consistently reported that older adults display longer non-decisional processing times and implement more conservative decision policies than their younger counterparts. However, there are limits on what we can learn from behavioural modeling alone and neurophysiological analyses can play an essential role in empirically validating model predictions and in pinpointing the precise neural mechanisms that are impacted by aging. Although few studies to date have explicitly examined correspondences between computational models and neural data with respect to cognitive aging, neurophysiological studies have already highlighted age-related changes at multiple levels of the sensorimotor hierarchy that are likely to be consequential for decision making behaviour. Here, we provide an overview of this literature and suggest some future directions for the field. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Computational Design of Multi-component Bio-Inspired Bilayer Membranes

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Evan Koufos

2014-04-01

Full Text Available Our investigation is motivated by the need to design bilayer membranes with tunable interfacial and mechanical properties for use in a range of applications, such as targeted drug delivery, sensing and imaging. We draw inspiration from biological cell membranes and focus on their principal constituents. In this paper, we present our results on the role of molecular architecture on the interfacial, structural and dynamical properties of bio-inspired membranes. We focus on four lipid architectures with variations in the head group shape and the hydrocarbon tail length. Each lipid species is composed of a hydrophilic head group and two hydrophobic tails. In addition, we study a model of the Cholesterol molecule to understand the interfacial properties of a bilayer membrane composed of rigid, single-tail molecular species. We demonstrate the properties of the bilayer membranes to be determined by the molecular architecture and rigidity of the constituent species. Finally, we demonstrate the formation of a stable mixed bilayer membrane composed of Cholesterol and one of the phospholipid species. Our approach can be adopted to design multi-component bilayer membranes with tunable interfacial and mechanical properties. We use a Molecular Dynamics-based mesoscopic simulation technique called Dissipative Particle Dynamics that resolves the molecular details of the components through soft-sphere coarse-grained models and reproduces the hydrodynamic behavior of the system over extended time scales.

Influence of extracellular oscillations on neural communication: a computational perspective

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Zoran eTiganj

2014-02-01

Full Text Available Neural communication generates oscillations of electric potential in the extracellular medium. In feedback, these oscillations affect the electrochemical processes within the neurons, influencing the timing and the number of action potentials. It is unclear whether this influence should be considered only as noise or it has some functional role in neural communication. Through computer simulations we investigated the effect of various sinusoidal extracellular oscillations on the timing and number of action potentials. Each simulation is based on a multicompartment model of a single neuron, which is stimulated through spatially distributed synaptic activations. A thorough analysis is conducted on a large number of simulations with different models of CA3 and CA1 pyramidal neurons which are modeled using realistic morphologies and active ion conductances. We demonstrated that the influence of the weak extracellular oscillations, which are commonly present in the brain, is rather stochastic and modest. We found that the stronger fields, which are spontaneously present in the brain only in some particular cases (e.g. during seizures or that can be induced externally, could significantly modulate spike timings.

Persistent Memory in Single Node Delay-Coupled Reservoir Computing.

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André David Kovac

Full Text Available Delays are ubiquitous in biological systems, ranging from genetic regulatory networks and synaptic conductances, to predator/pray population interactions. The evidence is mounting, not only to the presence of delays as physical constraints in signal propagation speed, but also to their functional role in providing dynamical diversity to the systems that comprise them. The latter observation in biological systems inspired the recent development of a computational architecture that harnesses this dynamical diversity, by delay-coupling a single nonlinear element to itself. This architecture is a particular realization of Reservoir Computing, where stimuli are injected into the system in time rather than in space as is the case with classical recurrent neural network realizations. This architecture also exhibits an internal memory which fades in time, an important prerequisite to the functioning of any reservoir computing device. However, fading memory is also a limitation to any computation that requires persistent storage. In order to overcome this limitation, the current work introduces an extended version to the single node Delay-Coupled Reservoir, that is based on trained linear feedback. We show by numerical simulations that adding task-specific linear feedback to the single node Delay-Coupled Reservoir extends the class of solvable tasks to those that require nonfading memory. We demonstrate, through several case studies, the ability of the extended system to carry out complex nonlinear computations that depend on past information, whereas the computational power of the system with fading memory alone quickly deteriorates. Our findings provide the theoretical basis for future physical realizations of a biologically-inspired ultrafast computing device with extended functionality.

Biologically-Inspired Adaptive Obstacle Negotiation Behavior of Hexapod Robots

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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.

Energy Scaling Advantages of Resistive Memory Crossbar Based Computation and its Application to Sparse Coding

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Sapan eAgarwal

2016-01-01

Full Text Available The exponential increase in data over the last decade presents a significant challenge to analytics efforts that seek to process and interpret such data for various applications. Neural-inspired computing approaches are being developed in order to leverage the computational advantages of the analog, low-power data processing observed in biological systems. Analog resistive memory crossbars can perform a parallel read or a vector-matrix multiplication as well as a parallel write or a rank-1 update with high computational efficiency. For an NxN crossbar, these two kernels are at a minimum O(N more energy efficient than a digital memory-based architecture. If the read operation is noise limited, the energy to read a column can be independent of the crossbar size (O(1. These two kernels form the basis of many neuromorphic algorithms such as image, text, and speech recognition. For instance, these kernels can be applied to a neural sparse coding algorithm to give an O(N reduction in energy for the entire algorithm. Sparse coding is a rich problem with a host of applications including computer vision, object tracking, and more generally unsupervised learning.

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…

A GIS-based multi-criteria seismic vulnerability assessment using the integration of granular computing rule extraction and artificial neural networks

NARCIS (Netherlands)

Sheikhian, Hossein; Delavar, Mahmoud Reza; Stein, Alfred

2017-01-01

This study proposes multi‐criteria group decision‐making to address seismic physical vulnerability assessment. Granular computing rule extraction is combined with a feed forward artificial neural network to form a classifier capable of training a neural network on the basis of the rules provided by

Multiple Linear Regression Model Based on Neural Network and Its Application in the MBR Simulation

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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.

Retina-Inspired Filter.

Science.gov (United States)

Doutsi, Effrosyni; Fillatre, Lionel; Antonini, Marc; Gaulmin, Julien

2018-07-01

This paper introduces a novel filter, which is inspired by the human retina. The human retina consists of three different layers: the Outer Plexiform Layer (OPL), the inner plexiform layer, and the ganglionic layer. Our inspiration is the linear transform which takes place in the OPL and has been mathematically described by the neuroscientific model "virtual retina." This model is the cornerstone to derive the non-separable spatio-temporal OPL retina-inspired filter, briefly renamed retina-inspired filter, studied in this paper. This filter is connected to the dynamic behavior of the retina, which enables the retina to increase the sharpness of the visual stimulus during filtering before its transmission to the brain. We establish that this retina-inspired transform forms a group of spatio-temporal Weighted Difference of Gaussian (WDoG) filters when it is applied to a still image visible for a given time. We analyze the spatial frequency bandwidth of the retina-inspired filter with respect to time. It is shown that the WDoG spectrum varies from a lowpass filter to a bandpass filter. Therefore, while time increases, the retina-inspired filter enables to extract different kinds of information from the input image. Finally, we discuss the benefits of using the retina-inspired filter in image processing applications such as edge detection and compression.

Optic flow estimation on trajectories generated by bio-inspired closed-loop flight.

Science.gov (United States)

Shoemaker, Patrick A; Hyslop, Andrew M; Humbert, J Sean

2011-05-01

We generated panoramic imagery by simulating a fly-like robot carrying an imaging sensor, moving in free flight through a virtual arena bounded by walls, and containing obstructions. Flight was conducted under closed-loop control by a bio-inspired algorithm for visual guidance with feedback signals corresponding to the true optic flow that would be induced on an imager (computed by known kinematics and position of the robot relative to the environment). The robot had dynamics representative of a housefly-sized organism, although simplified to two-degree-of-freedom flight to generate uniaxial (azimuthal) optic flow on the retina in the plane of travel. Surfaces in the environment contained images of natural and man-made scenes that were captured by the moving sensor. Two bio-inspired motion detection algorithms and two computational optic flow estimation algorithms were applied to sequences of image data, and their performance as optic flow estimators was evaluated by estimating the mutual information between outputs and true optic flow in an equatorial section of the visual field. Mutual information for individual estimators at particular locations within the visual field was surprisingly low (less than 1 bit in all cases) and considerably poorer for the bio-inspired algorithms that the man-made computational algorithms. However, mutual information between weighted sums of these signals and comparable sums of the true optic flow showed significant increases for the bio-inspired algorithms, whereas such improvement did not occur for the computational algorithms. Such summation is representative of the spatial integration performed by wide-field motion-sensitive neurons in the third optic ganglia of flies.

A computational relationship between thalamic sensory neural responses and contrast perception.

Science.gov (United States)

Jiang, Yaoguang; Purushothaman, Gopathy; Casagrande, Vivien A

2015-01-01

Uncovering the relationship between sensory neural responses and perceptual decisions remains a fundamental problem in neuroscience. Decades of experimental and modeling work in the sensory cortex have demonstrated that a perceptual decision pool is usually composed of tens to hundreds of neurons, the responses of which are significantly correlated not only with each other, but also with the behavioral choices of an animal. Few studies, however, have measured neural activity in the sensory thalamus of awake, behaving animals. Therefore, it remains unclear how many thalamic neurons are recruited and how the information from these neurons is pooled at subsequent cortical stages to form a perceptual decision. In a previous study we measured neural activity in the macaque lateral geniculate nucleus (LGN) during a two alternative forced choice (2AFC) contrast detection task, and found that single LGN neurons were significantly correlated with the monkeys' behavioral choices, despite their relatively poor contrast sensitivity and a lack of overall interneuronal correlations. We have now computationally tested a number of specific hypotheses relating these measured LGN neural responses to the contrast detection behavior of the animals. We modeled the perceptual decisions with different numbers of neurons and using a variety of pooling/readout strategies, and found that the most successful model consisted of about 50-200 LGN neurons, with individual neurons weighted differentially according to their signal-to-noise ratios (quantified as d-primes). These results supported the hypothesis that in contrast detection the perceptual decision pool consists of multiple thalamic neurons, and that the response fluctuations in these neurons can influence contrast perception, with the more sensitive thalamic neurons likely to exert a greater influence.

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

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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.

Objects Classification by Learning-Based Visual Saliency Model and Convolutional Neural Network.

Science.gov (United States)

Li, Na; Zhao, Xinbo; Yang, Yongjia; Zou, Xiaochun

2016-01-01

Humans can easily classify different kinds of objects whereas it is quite difficult for computers. As a hot and difficult problem, objects classification has been receiving extensive interests with broad prospects. Inspired by neuroscience, deep learning concept is proposed. Convolutional neural network (CNN) as one of the methods of deep learning can be used to solve classification problem. But most of deep learning methods, including CNN, all ignore the human visual information processing mechanism when a person is classifying objects. Therefore, in this paper, inspiring the completed processing that humans classify different kinds of objects, we bring forth a new classification method which combines visual attention model and CNN. Firstly, we use the visual attention model to simulate the processing of human visual selection mechanism. Secondly, we use CNN to simulate the processing of how humans select features and extract the local features of those selected areas. Finally, not only does our classification method depend on those local features, but also it adds the human semantic features to classify objects. Our classification method has apparently advantages in biology. Experimental results demonstrated that our method made the efficiency of classification improve significantly.

Design and FPGA-implementation of multilayer neural networks with on-chip learning

International Nuclear Information System (INIS)

Haggag, S.S.M.Y

2008-01-01

Artificial Neural Networks (ANN) is used in many applications in the industry because of their parallel structure, high speed, and their ability to give easy solution to complicated problems. For example identifying the orange and apple in the sorting machine with neural network is easier than using image processing techniques to do the same thing. There are different software for designing, training, and testing the ANN, but in order to use the ANN in the industry, it should be implemented on hardware outside the computer. Neural networks are artificial systems inspired on the brain's cognitive behavior, which can learn tasks with some degree of complexity, such as signal processing, diagnosis, robotics, image processing, and pattern recognition. Many applications demand a high computing power and the traditional software implementation are not sufficient.This thesis presents design and FPGA implementation of Multilayer Neural Networks with On-chip learning in re-configurable hardware. Hardware implementation of neural network algorithm is very interesting due their high performance and they can easily be made parallel. The architecture proposed herein takes advantage of distinct data paths for the forward and backward propagation stages and a pipelined adaptation of the on- line backpropagation algorithm to significantly improve the performance of the learning phase. The architecture is easily scalable and able to cope with arbitrary network sizes with the same hardware. The implementation is targeted diagnosis of the Research Reactor accidents to avoid the risk of occurrence of a nuclear accident. The proposed designed circuits are implemented using Xilinx FPGA Chip XC40150xv and occupied 73% of Chip CLBs. It achieved 10.8 μs to take decision in the forward propagation compared with current software implemented of RPS which take 24 ms. The results show that the proposed architecture leads to significant speed up comparing to high end software solutions. On

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.

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.

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.

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...

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.

Artificial Intelligence, Evolutionary Computing and Metaheuristics In the Footsteps of Alan Turing

CERN Document Server

2013-01-01

Alan Turing pioneered many research areas such as artificial intelligence, computability, heuristics and pattern formation.  Nowadays at the information age, it is hard to imagine how the world would be without computers and the Internet. Without Turing's work, especially the core concept of Turing Machine at the heart of every computer, mobile phone and microchip today, so many things on which we are so dependent would be impossible. 2012 is the Alan Turing year -- a centenary celebration of the life and work of Alan Turing. To celebrate Turing's legacy and follow the footsteps of this brilliant mind, we take this golden opportunity to review the latest developments in areas of artificial intelligence, evolutionary computation and metaheuristics, and all these areas can be traced back to Turing's pioneer work. Topics include Turing test, Turing machine, artificial intelligence, cryptography, software testing, image processing, neural networks, nature-inspired algorithms such as bat algorithm and cuckoo sear...

Recurrent Neural Network for Computing Outer Inverse.

Science.gov (United States)

Živković, Ivan S; Stanimirović, Predrag S; Wei, Yimin

2016-05-01

Two linear recurrent neural networks for generating outer inverses with prescribed range and null space are defined. Each of the proposed recurrent neural networks is based on the matrix-valued differential equation, a generalization of dynamic equations proposed earlier for the nonsingular matrix inversion, the Moore-Penrose inversion, as well as the Drazin inversion, under the condition of zero initial state. The application of the first approach is conditioned by the properties of the spectrum of a certain matrix; the second approach eliminates this drawback, though at the cost of increasing the number of matrix operations. The cases corresponding to the most common generalized inverses are defined. The conditions that ensure stability of the proposed neural network are presented. Illustrative examples present the results of numerical simulations.

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.

The quest for a Quantum Neural Network

OpenAIRE

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

2014-01-01

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

A Federal Vision for Future Computing: A Nanotechnology-Inspired Grand Challenge

Science.gov (United States)

2016-07-29

fault-tolerant system that consumes less power than an incandescent light bulb. Recent progress in developing novel, low-power methods of sensing and...computation—including neuromorphic, magneto-electronic, and analog systems—combined with dramatic advances in neuroscience and cognitive sciences...enable ready-to-fabricate designs and specifications. 4. Brain-Inspired Approaches Neuroscience research suggests that the brain is a complex, high

A TLD dose algorithm using artificial neural networks

International Nuclear Information System (INIS)

Moscovitch, M.; Rotunda, J.E.; Tawil, R.A.; Rathbone, B.A.

1995-01-01

An artificial neural network was designed and used to develop a dose algorithm for a multi-element thermoluminescence dosimeter (TLD). The neural network architecture is based on the concept of functional links network (FLN). Neural network is an information processing method inspired by the biological nervous system. A dose algorithm based on neural networks is fundamentally different as compared to conventional algorithms, as it has the capability to learn from its own experience. The neural network algorithm is shown the expected dose values (output) associated with given responses of a multi-element dosimeter (input) many times. The algorithm, being trained that way, eventually is capable to produce its own unique solution to similar (but not exactly the same) dose calculation problems. For personal dosimetry, the output consists of the desired dose components: deep dose, shallow dose and eye dose. The input consists of the TL data obtained from the readout of a multi-element dosimeter. The neural network approach was applied to the Harshaw Type 8825 TLD, and was shown to significantly improve the performance of this dosimeter, well within the U.S. accreditation requirements for personnel dosimeters

Computer-assisted detection of colonic polyps with CT colonography using neural networks and binary classification trees

International Nuclear Information System (INIS)

Jerebko, Anna K.; Summers, Ronald M.; Malley, James D.; Franaszek, Marek; Johnson, C. Daniel

2003-01-01

Detection of colonic polyps in CT colonography is problematic due to complexities of polyp shape and the surface of the normal colon. Published results indicate the feasibility of computer-aided detection of polyps but better classifiers are needed to improve specificity. In this paper we compare the classification results of two approaches: neural networks and recursive binary trees. As our starting point we collect surface geometry information from three-dimensional reconstruction of the colon, followed by a filter based on selected variables such as region density, Gaussian and average curvature and sphericity. The filter returns sites that are candidate polyps, based on earlier work using detection thresholds, to which the neural nets or the binary trees are applied. A data set of 39 polyps from 3 to 25 mm in size was used in our investigation. For both neural net and binary trees we use tenfold cross-validation to better estimate the true error rates. The backpropagation neural net with one hidden layer trained with Levenberg-Marquardt algorithm achieved the best results: sensitivity 90% and specificity 95% with 16 false positives per study

Topology Optimization of Lightweight Lattice Structural Composites Inspired by Cuttlefish Bone

Science.gov (United States)

Hu, Zhong; Gadipudi, Varun Kumar; Salem, David R.

2018-03-01

Lattice structural composites are of great interest to various industries where lightweight multifunctionality is important, especially aerospace. However, strong coupling among the composition, microstructure, porous topology, and fabrication of such materials impedes conventional trial-and-error experimental development. In this work, a discontinuous carbon fiber reinforced polymer matrix composite was adopted for structural design. A reliable and robust design approach for developing lightweight multifunctional lattice structural composites was proposed, inspired by biomimetics and based on topology optimization. Three-dimensional periodic lattice blocks were initially designed, inspired by the cuttlefish bone microstructure. The topologies of the three-dimensional periodic blocks were further optimized by computer modeling, and the mechanical properties of the topology optimized lightweight lattice structures were characterized by computer modeling. The lattice structures with optimal performance were identified.

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.

Binary Factorization in Hopfield-Like Neural Networks: Single-Step Approximation and Computer Simulations

Czech Academy of Sciences Publication Activity Database

Frolov, A. A.; Sirota, A.M.; Húsek, Dušan; Muraviev, I. P.

2004-01-01

Roč. 14, č. 2 (2004), s. 139-152 ISSN 1210-0552 R&D Projects: GA ČR GA201/01/1192 Grant - others:BARRANDE(EU) 99010-2/99053; Intellectual computer Systems(EU) Grant 2.45 Institutional research plan: CEZ:AV0Z1030915 Keywords : nonlinear binary factor analysis * feature extraction * recurrent neural network * Single-Step approximation * neurodynamics simulation * attraction basins * Hebbian learning * unsupervised learning * neuroscience * brain function modeling Subject RIV: BA - General Mathematics

Use of artificial neural networks (computer analysis) in the diagnosis of microcalcifications on mammography

International Nuclear Information System (INIS)

Markopoulos, Christos; Kouskos, Efstratios; Koufopoulos, Konstantinos; Kyriakou, Vasiliki; Gogas, John

2001-01-01

Introduction/objective: the purpose of this study was to evaluate a computer based method for differentiating malignant from benign clustered microcalcifications, comparing it with the performance of three physicians. Methods and material: materials for the study are 240 suspicious microcalcifications on mammograms from 220 female patients who underwent breast biopsy, following hook wire localization under mammographic guidance. The histologic findings were malignant in 108 cases (45%) and benign in 132 cases (55%). Those clusters were analyzed by a computer program and eight features of the calcifications (density, number, area, brightness, diameter average, distance average, proximity average, perimeter compacity average) were quantitatively estimated by a specific artificial neural network. Human input was limited to initial identification of the calcifications. Three physicians-observers were also evaluated for the malignant or benign nature of the clustered microcalcifications. Results: the performance of the artificial network was evaluated by receiver operating characteristics (ROC) curves. ROC curves were also generated for the performance of each observer and for the three observers as a group. The ROC curves for the computer and for the physicians were compared and the results are:area under the curve (AUC) value for computer is 0.937, for physician-1 is 0.746, for physician-2 is 0.785, for physician-3 is 0.835 and for physicians as a group is 0.810. The results of the Student's t-test for paired data showed statistically significant difference between the artificial neural network and the physicians' performance, independently and as a group. Discussion and conclusion: our study showed that computer analysis achieves statistically significantly better performance than that of physicians in the classification of malignant and benign calcifications. This method, after further evaluation and improvement, may help radiologists and breast surgeons in better

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.

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...... prior to the experiment and with a properly trained ANN it is no problem to obtain accurate simulations much faster than real time-without any need for large computational capacity. The present study demonstrates how this hybrid method can be applied to the active truncated experiments yielding a system...

Smart Nacre-inspired Nanocomposites.

Science.gov (United States)

Peng, Jingsong; Cheng, Qunfeng

2018-03-15

Nacre-inspired nanocomposites with excellent mechanical properties have achieved remarkable attention in the past decades. The high performance of nacre-inspired nanocomposites is a good basis for the further application of smart devices. Recently, some smart nanocomposites inspired by nacre have demonstrated good mechanical properties as well as effective and stable stimuli-responsive functions. In this Concept, we summarize the recent development of smart nacre-inspired nanocomposites, including 1D fibers, 2D films and 3D bulk nanocomposites, in response to temperature, moisture, light, strain, and so on. We show that diverse smart nanocomposites could be designed by combining various conventional fabrication methods of nacre-inspired nanocomposites with responsive building blocks and interface interactions. The nacre-inspired strategy is versatile for different kinds of smart nanocomposites in extensive applications, such as strain sensors, displays, artificial muscles, robotics, and so on, and may act as an effective roadmap for designing smart nanocomposites in the future. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Hand-Eye Calibration and Inverse Kinematics of Robot Arm using Neural Network

DEFF Research Database (Denmark)

Wu, Haiyan; Tizzano, Walter; Andersen, Thomas Timm

2013-01-01

Traditional technologies for solving hand-eye calibration and inverse kinematics are cumbersome and time consuming due to the high nonlinearity in the models. An alternative to the traditional approaches is the articial neural network inspired by the remarkable abilities of the animals in dierent...

Trainable hardware for dynamical computing using error backpropagation through physical media.

Science.gov (United States)

Hermans, Michiel; Burm, Michaël; Van Vaerenbergh, Thomas; Dambre, Joni; Bienstman, Peter

2015-03-24

Neural networks are currently implemented on digital Von Neumann machines, which do not fully leverage their intrinsic parallelism. We demonstrate how to use a novel class of reconfigurable dynamical systems for analogue information processing, mitigating this problem. Our generic hardware platform for dynamic, analogue computing consists of a reciprocal linear dynamical system with nonlinear feedback. Thanks to reciprocity, a ubiquitous property of many physical phenomena like the propagation of light and sound, the error backpropagation-a crucial step for tuning such systems towards a specific task-can happen in hardware. This can potentially speed up the optimization process significantly, offering important benefits for the scalability of neuro-inspired hardware. In this paper, we show, using one experimentally validated and one conceptual example, that such systems may provide a straightforward mechanism for constructing highly scalable, fully dynamical analogue computers.

End-to-End Multimodal Emotion Recognition Using Deep Neural Networks

Science.gov (United States)

Tzirakis, Panagiotis; Trigeorgis, George; Nicolaou, Mihalis A.; Schuller, Bjorn W.; Zafeiriou, Stefanos

2017-12-01

Automatic affect recognition is a challenging task due to the various modalities emotions can be expressed with. Applications can be found in many domains including multimedia retrieval and human computer interaction. In recent years, deep neural networks have been used with great success in determining emotional states. Inspired by this success, we propose an emotion recognition system using auditory and visual modalities. To capture the emotional content for various styles of speaking, robust features need to be extracted. To this purpose, we utilize a Convolutional Neural Network (CNN) to extract features from the speech, while for the visual modality a deep residual network (ResNet) of 50 layers. In addition to the importance of feature extraction, a machine learning algorithm needs also to be insensitive to outliers while being able to model the context. To tackle this problem, Long Short-Term Memory (LSTM) networks are utilized. The system is then trained in an end-to-end fashion where - by also taking advantage of the correlations of the each of the streams - we manage to significantly outperform the traditional approaches based on auditory and visual handcrafted features for the prediction of spontaneous and natural emotions on the RECOLA database of the AVEC 2016 research challenge on emotion recognition.

Computer-aided diagnosis of prostate cancer using a deep convolutional neural network from multiparametric MRI.

Science.gov (United States)

Song, Yang; Zhang, Yu-Dong; Yan, Xu; Liu, Hui; Zhou, Minxiong; Hu, Bingwen; Yang, Guang

2018-04-16

Deep learning is the most promising methodology for automatic computer-aided diagnosis of prostate cancer (PCa) with multiparametric MRI (mp-MRI). To develop an automatic approach based on deep convolutional neural network (DCNN) to classify PCa and noncancerous tissues (NC) with mp-MRI. Retrospective. In all, 195 patients with localized PCa were collected from a PROSTATEx database. In total, 159/17/19 patients with 444/48/55 observations (215/23/23 PCas and 229/25/32 NCs) were randomly selected for training/validation/testing, respectively. T 2 -weighted, diffusion-weighted, and apparent diffusion coefficient images. A radiologist manually labeled the regions of interest of PCas and NCs and estimated the Prostate Imaging Reporting and Data System (PI-RADS) scores for each region. Inspired by VGG-Net, we designed a patch-based DCNN model to distinguish between PCa and NCs based on a combination of mp-MRI data. Additionally, an enhanced prediction method was used to improve the prediction accuracy. The performance of DCNN prediction was tested using a receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Moreover, the predicted result was compared with the PI-RADS score to evaluate its clinical value using decision curve analysis. Two-sided Wilcoxon signed-rank test with statistical significance set at 0.05. The DCNN produced excellent diagnostic performance in distinguishing between PCa and NC for testing datasets with an AUC of 0.944 (95% confidence interval: 0.876-0.994), sensitivity of 87.0%, specificity of 90.6%, PPV of 87.0%, and NPV of 90.6%. The decision curve analysis revealed that the joint model of PI-RADS and DCNN provided additional net benefits compared with the DCNN model and the PI-RADS scheme. The proposed DCNN-based model with enhanced prediction yielded high performance in statistical analysis, suggesting

Biomaterials and computation: a strategic alliance to investigate emergent responses of neural cells.

Science.gov (United States)

Sergi, Pier Nicola; Cavalcanti-Adam, Elisabetta Ada

2017-03-28

Topographical and chemical cues drive migration, outgrowth and regeneration of neurons in different and crucial biological conditions. In the natural extracellular matrix, their influences are so closely coupled that they result in complex cellular responses. As a consequence, engineered biomaterials are widely used to simplify in vitro conditions, disentangling intricate in vivo behaviours, and narrowing the investigation on particular emergent responses. Nevertheless, how topographical and chemical cues affect the emergent response of neural cells is still unclear, thus in silico models are used as additional tools to reproduce and investigate the interactions between cells and engineered biomaterials. This work aims at presenting the synergistic use of biomaterials-based experiments and computation as a strategic way to promote the discovering of complex neural responses as well as to allow the interactions between cells and biomaterials to be quantitatively investigated, fostering a rational design of experiments.

Antenna analysis using neural networks

Science.gov (United States)

Smith, William T.

1992-01-01

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

The Use of Neural Network to Recognize the Parts of the Computer Motherboard

OpenAIRE

Abbas M. Ali; S. D. Gore; Musaab AL-Sarierah

2005-01-01

This study suggests a new approach of learning which utilizes the techniques of computer vision to recognize the parts inside the motherboard. The main thrust is to identify different parts of the motherboard using a Hopfield Neural Network. The outcome of the net is compared with the objects stored in the database. The proposed scheme is implemented using bottom -up approach, where steps like edge detection, spatial filtering, image masking..etc are performed in sequence. the scheme is simul...

Neural Network Optimization of Ligament Stiffnesses for the Enhanced Predictive Ability of a Patient-Specific, Computational Foot/Ankle Model.

Science.gov (United States)

Chande, Ruchi D; Wayne, Jennifer S

2017-09-01

Computational models of diarthrodial joints serve to inform the biomechanical function of these structures, and as such, must be supplied appropriate inputs for performance that is representative of actual joint function. Inputs for these models are sourced from both imaging modalities as well as literature. The latter is often the source of mechanical properties for soft tissues, like ligament stiffnesses; however, such data are not always available for all the soft tissues nor is it known for patient-specific work. In the current research, a method to improve the ligament stiffness definition for a computational foot/ankle model was sought with the greater goal of improving the predictive ability of the computational model. Specifically, the stiffness values were optimized using artificial neural networks (ANNs); both feedforward and radial basis function networks (RBFNs) were considered. Optimal networks of each type were determined and subsequently used to predict stiffnesses for the foot/ankle model. Ultimately, the predicted stiffnesses were considered reasonable and resulted in enhanced performance of the computational model, suggesting that artificial neural networks can be used to optimize stiffness inputs.

Training program for radiologic technologists for performing chest X-rays at inspiration in uncooperative children

International Nuclear Information System (INIS)

Langen, Heinz Jakob; Muras, S.; Kohlhauser-Vollmuth, C.; Stenzel, M.; Beer, M.

2009-01-01

A computer program was created to train technologists to perform chest X-rays in crying infants at maximum inspiration. Videos of 4 children were used. Using a computer program, the moment of deepest inspiration was determined in the video in the single frame view. During the normal running video, 14 technologists (3 with significant experience, 3 with little experience and 8 with very little experience in pediatric radiography) simulated a chest radiograph by pushing a button. The computer program stopped the video and the period of time to the optimal moment for a chest x-ray was calculated. Every technologist simulated 10 chest X-rays in each of the 4 video clips. The technologists then trained themselves to perform chest X-rays at optimal inspiration like playing a computer game. After training, the test was repeated. Changes were evaluated by t-test for unpaired samples (level of significance p < 0.05). Although the differences improved in all children, minimal deviation from the optimal moment for taking an X-ray at inspiration occurred in the periodically crying child (0.21 sec before and 0.13 sec after training). In a non-periodically crying infant, the largest differences were shown. The values improved significantly from 0.29 sec to 0.22 sec. The group with substantial experience in pediatric radiology improved significantly from 0.22 sec to 0.15 sec. The group with very little experience in pediatric radiology showed worse results (improvement from 0.29 sec to 0.21 sec). (orig.)

Biomimicry as an approach for bio-inspired structure with the aid of compu

Directory of Open Access Journals (Sweden)

Moheb Sabry Aziz

2016-03-01

Full Text Available Biomimicry is the study of emulating and mimicking nature, where it has been used by designers to help in solving human problems. From centuries ago designers and architects looked at nature as a huge source of inspiration. Biomimicry argues that nature is the best, most influencing and the guaranteed source of innovation for the designers as a result of nature’s 3.85 billion years of evolution, as it holds a gigantic experience of solving problems of the environment and its inhabitants. The biomimicry emerging field deals with new technologies honed from bio-inspired engineering at the micro and macro scale levels. Architects have been searching for answers from nature to their complex questions about different kinds of structures, and they have mimicked a lot of forms from nature to create better and more efficient structures for different architectural purposes. Without computers these complex ways and forms of structures couldn’t been mimicked and thus using computers had risen the way of mimicking and taking inspiration from nature because it is considered a very sophisticated and accurate tool for simulation and computing, as a result designers can imitate different nature’s models in spite of its complexity.

Computational speech segregation based on an auditory-inspired modulation analysis

DEFF Research Database (Denmark)

May, Tobias; Dau, Torsten

2014-01-01

A monaural speech segregation system is presented that estimates the ideal binary mask from noisy speech based on the supervised learning of amplitude modulation spectrogram (AMS) features. Instead of using linearly scaled modulation filters with constant absolute bandwidth, an auditory- inspired...... about speech activity present in neighboring time-frequency units. In order to evaluate the generalization performance of the system to unseen acoustic conditions, the speech segregation system is trained with a limited set of low signal-to-noise ratio (SNR) conditions, but tested over a wide range...

Classification of remotely sensed data using OCR-inspired neural network techniques. [Optical Character Recognition

Science.gov (United States)

Kiang, Richard K.

1992-01-01

Neural networks have been applied to classifications of remotely sensed data with some success. To improve the performance of this approach, an examination was made of how neural networks are applied to the optical character recognition (OCR) of handwritten digits and letters. A three-layer, feedforward network, along with techniques adopted from OCR, was used to classify Landsat-4 Thematic Mapper data. Good results were obtained. To overcome the difficulties that are characteristic of remote sensing applications and to attain significant improvements in classification accuracy, a special network architecture may be required.

Advanced Applications of Neural Networks and Artificial Intelligence: A Review

OpenAIRE

Koushal Kumar; Gour Sundar Mitra Thakur

2012-01-01

Artificial Neural Network is a branch of Artificial intelligence and has been accepted as a new computing technology in computer science fields. This paper reviews the field of Artificial intelligence and focusing on recent applications which uses Artificial Neural Networks (ANN’s) and Artificial Intelligence (AI). It also considers the integration of neural networks with other computing methods Such as fuzzy logic to enhance the interpretation ability of data. Artificial Neural Networks is c...

Crew exploration vehicle (CEV) attitude control using a neural-immunology/memory network

Science.gov (United States)

Weng, Liguo; Xia, Min; Wang, Wei; Liu, Qingshan

2015-01-01

This paper addresses the problem of the crew exploration vehicle (CEV) attitude control. CEVs are NASA's next-generation human spaceflight vehicles, and they use reaction control system (RCS) jet engines for attitude adjustment, which calls for control algorithms for firing the small propulsion engines mounted on vehicles. In this work, the resultant CEV dynamics combines both actuation and attitude dynamics. Therefore, it is highly nonlinear and even coupled with significant uncertainties. To cope with this situation, a neural-immunology/memory network is proposed. It is inspired by the human memory and immune systems. The control network does not rely on precise system dynamics information. Furthermore, the overall control scheme has a simple structure and demands much less computation as compared with most existing methods, making it attractive for real-time implementation. The effectiveness of this approach is also verified via simulation.

Brain inspired hardware architectures - Can they be used for particle physics ?

CERN Multimedia

CERN. Geneva

2016-01-01

After their inception in the 1940s and several decades of moderate success, artificial neural networks have recently demonstrated impressive achievements in analysing big data volumes. Wide and deep network architectures can now be trained using high performance computing systems, graphics card clusters in particular. Despite their successes these state-of-the-art approaches suffer from very long training times and huge energy consumption, in particular during the training phase. The biological brain can perform similar and superior classification tasks in the space and time domains, but at the same time exhibits very low power consumption, rapid unsupervised learning capabilities and fault tolerance. In the talk the differences between classical neural networks and neural circuits in the brain will be presented. Recent hardware implementations of neuromorphic computing systems and their applications will be shown. Finally, some initial ideas to use accelerated neural architectures as trigger processors i...

Program Helps Simulate Neural Networks

Science.gov (United States)

Villarreal, James; Mcintire, Gary

1993-01-01

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

Computer aided detection of ureteral stones in thin slice computed tomography volumes using Convolutional Neural Networks.

Science.gov (United States)

Längkvist, Martin; Jendeberg, Johan; Thunberg, Per; Loutfi, Amy; Lidén, Mats

2018-06-01

Computed tomography (CT) is the method of choice for diagnosing ureteral stones - kidney stones that obstruct the ureter. The purpose of this study is to develop a computer aided detection (CAD) algorithm for identifying a ureteral stone in thin slice CT volumes. The challenge in CAD for urinary stones lies in the similarity in shape and intensity of stones with non-stone structures and how to efficiently deal with large high-resolution CT volumes. We address these challenges by using a Convolutional Neural Network (CNN) that works directly on the high resolution CT volumes. The method is evaluated on a large data base of 465 clinically acquired high-resolution CT volumes of the urinary tract with labeling of ureteral stones performed by a radiologist. The best model using 2.5D input data and anatomical information achieved a sensitivity of 100% and an average of 2.68 false-positives per patient on a test set of 88 scans. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Generalized Net Model of the Cognitive and Neural Algorithm for Adaptive Resonance Theory 1

Directory of Open Access Journals (Sweden)

Todor Petkov

2013-12-01

Full Text Available The artificial neural networks are inspired by biological properties of human and animal brains. One of the neural networks type is called ART [4]. The abbreviation of ART stands for Adaptive Resonance Theory that has been invented by Stephen Grossberg in 1976 [5]. ART represents a family of Neural Networks. It is a cognitive and neural theory that describes how the brain autonomously learns to categorize, recognize and predict objects and events in the changing world. In this paper we introduce a GN model that represent ART1 Neural Network learning algorithm [1]. The purpose of this model is to explain when the input vector will be clustered or rejected among all nodes by the network. It can also be used for explanation and optimization of ART1 learning algorithm.

Neuromorphic implementations of neurobiological learning algorithms for spiking neural networks.

Science.gov (United States)

Walter, Florian; Röhrbein, Florian; Knoll, Alois

2015-12-01

The application of biologically inspired methods in design and control has a long tradition in robotics. Unlike previous approaches in this direction, the emerging field of neurorobotics not only mimics biological mechanisms at a relatively high level of abstraction but employs highly realistic simulations of actual biological nervous systems. Even today, carrying out these simulations efficiently at appropriate timescales is challenging. Neuromorphic chip designs specially tailored to this task therefore offer an interesting perspective for neurorobotics. Unlike Von Neumann CPUs, these chips cannot be simply programmed with a standard programming language. Like real brains, their functionality is determined by the structure of neural connectivity and synaptic efficacies. Enabling higher cognitive functions for neurorobotics consequently requires the application of neurobiological learning algorithms to adjust synaptic weights in a biologically plausible way. In this paper, we therefore investigate how to program neuromorphic chips by means of learning. First, we provide an overview over selected neuromorphic chip designs and analyze them in terms of neural computation, communication systems and software infrastructure. On the theoretical side, we review neurobiological learning techniques. Based on this overview, we then examine on-die implementations of these learning algorithms on the considered neuromorphic chips. A final discussion puts the findings of this work into context and highlights how neuromorphic hardware can potentially advance the field of autonomous robot systems. The paper thus gives an in-depth overview of neuromorphic implementations of basic mechanisms of synaptic plasticity which are required to realize advanced cognitive capabilities with spiking neural networks. Copyright © 2015 Elsevier Ltd. All rights reserved.

[Computer aided diagnosis model for lung tumor based on ensemble convolutional neural network].

Science.gov (United States)

Wang, Yuanyuan; Zhou, Tao; Lu, Huiling; Wu, Cuiying; Yang, Pengfei

2017-08-01

The convolutional neural network (CNN) could be used on computer-aided diagnosis of lung tumor with positron emission tomography (PET)/computed tomography (CT), which can provide accurate quantitative analysis to compensate for visual inertia and defects in gray-scale sensitivity, and help doctors diagnose accurately. Firstly, parameter migration method is used to build three CNNs (CT-CNN, PET-CNN, and PET/CT-CNN) for lung tumor recognition in CT, PET, and PET/CT image, respectively. Then, we aimed at CT-CNN to obtain the appropriate model parameters for CNN training through analysis the influence of model parameters such as epochs, batchsize and image scale on recognition rate and training time. Finally, three single CNNs are used to construct ensemble CNN, and then lung tumor PET/CT recognition was completed through relative majority vote method and the performance between ensemble CNN and single CNN was compared. The experiment results show that the ensemble CNN is better than single CNN on computer-aided diagnosis of lung tumor.

Performing Chest X-Rays at Inspiration in Uncooperative Children: The Effect of Exercises with a Training Program for Radiology Technicians

International Nuclear Information System (INIS)

Langen, H.J.; Sengenberger, C.; Bielmeier, J.; Jocher, R.; Kohlhauser-Vollmuth, Ch.; Eschmann, M.

2014-01-01

It is difficult to acquire a chest X-ray of a crying infant at maximum inspiration. A computer program was developed for technician training. Method. Video clips of 3 babies were used and the moment of deepest inspiration was determined in the single-frame view. 12 technicians simulated chest radiographs at normal video speed by pushing a button. The computer program stopped the video and calculated the period of time to the optimal instant for a chest X-ray. Demonstration software can be tested at website online. Every technician simulated 10 chest X-rays for each of the 3 video clips. The technicians then spent 40 minutes practicing performing chest X-rays at optimal inspiration. The test was repeated after 5, 20, and 40 minutes of practice. Results. 6 participants showed a significant improvement after exercises (collective 1). Deviation from the optimal instant for taking an X-ray at inspiration decreased from 0.39 to 0.22 s after 40 min of practice. 6 technicians showed no significant improvement (collective 2). Deviation decreased from a low starting value of 0.25 s to 0.21 s. Conclusion. The tested computer program improves the ability of radiology technicians to take a chest X-ray at optimal inspiration in a crying child

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.

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.

Physicists get INSPIREd

CERN Multimedia

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...

A novel angle computation and calibration algorithm of bio-inspired sky-light polarization navigation sensor.

Science.gov (United States)

Xian, Zhiwen; Hu, Xiaoping; Lian, Junxiang; Zhang, Lilian; Cao, Juliang; Wang, Yujie; Ma, Tao

2014-09-15

Navigation plays a vital role in our daily life. As traditional and commonly used navigation technologies, Inertial Navigation System (INS) and Global Navigation Satellite System (GNSS) can provide accurate location information, but suffer from the accumulative error of inertial sensors and cannot be used in a satellite denied environment. The remarkable navigation ability of animals shows that the pattern of the polarization sky can be used for navigation. A bio-inspired POLarization Navigation Sensor (POLNS) is constructed to detect the polarization of skylight. Contrary to the previous approach, we utilize all the outputs of POLNS to compute input polarization angle, based on Least Squares, which provides optimal angle estimation. In addition, a new sensor calibration algorithm is presented, in which the installation angle errors and sensor biases are taken into consideration. Derivation and implementation of our calibration algorithm are discussed in detail. To evaluate the performance of our algorithms, simulation and real data test are done to compare our algorithms with several exiting algorithms. Comparison results indicate that our algorithms are superior to the others and are more feasible and effective in practice.

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 ...

Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot

DEFF Research Database (Denmark)

Grinke, Eduard; Tetzlaff, Christian; Wörgötter, Florentin

2015-01-01

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 walking...... dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a many degrees-of-freedom (DOFs) walking robot is a challenging task. Thus, in this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural...... mechanisms with plasticity, exteroceptive 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 neural network consisting of two fully connected neurons. Online...

Neural Control and Adaptive Neural Forward Models for Insect-like, Energy-Efficient, and Adaptable Locomotion of Walking Machines

DEFF Research Database (Denmark)

Manoonpong, Poramate; Parlitz, Ulrich; Wörgötter, Florentin

2013-01-01

such natural properties with artificial legged locomotion systems by using different approaches including machine learning algorithms, classical engineering control techniques, and biologically-inspired control mechanisms. However, their levels of performance are still far from the natural ones. By contrast...... on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models...... allow the machine to autonomously adapt its locomotion to deal with a change of terrain, losing of ground contact during stance phase, stepping on or hitting an obstacle during swing phase, leg damage, and even to promote cockroach-like climbing behavior. Thus, the results presented here show...

Distributed Recurrent Neural Forward Models with Synaptic Adaptation and CPG-based control for Complex Behaviors of Walking Robots

Directory of Open Access Journals (Sweden)

Sakyasingha eDasgupta

2015-09-01

Full Text Available Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures with the underlying neural mechanisms. The neural mechanisms consist of 1 central pattern generator based control for generating basic rhythmic patterns and coordinated movements, 2 distributed (at each leg recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and 3 searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Using simulations we show that this bio-inspired approach with adaptive internal models allows the walking robot to perform complex locomotive behaviors as observed in insects, including walking on undulated terrains, crossing large gaps as well as climbing over high obstacles. Furthermore we demonstrate that the newly developed recurrent network based approach to sensorimotor prediction outperforms the previous state of the art adaptive neuron

Computational optical tomography using 3-D deep convolutional neural networks

Science.gov (United States)

Nguyen, Thanh; Bui, Vy; Nehmetallah, George

2018-04-01

Deep convolutional neural networks (DCNNs) offer a promising performance for many image processing areas, such as super-resolution, deconvolution, image classification, denoising, and segmentation, with outstanding results. Here, we develop for the first time, to our knowledge, a method to perform 3-D computational optical tomography using 3-D DCNN. A simulated 3-D phantom dataset was first constructed and converted to a dataset of phase objects imaged on a spatial light modulator. For each phase image in the dataset, the corresponding diffracted intensity image was experimentally recorded on a CCD. We then experimentally demonstrate the ability of the developed 3-D DCNN algorithm to solve the inverse problem by reconstructing the 3-D index of refraction distributions of test phantoms from the dataset from their corresponding diffraction patterns.

Classical Conditioning with Pulsed Integrated Neural Networks: Circuits and System

DEFF Research Database (Denmark)

Lehmann, Torsten

1998-01-01

In this paper we investigate on-chip learning for pulsed, integrated neural networks. We discuss the implementational problems the technology imposes on learning systems and we find that abiologically inspired approach using simple circuit structures is most likely to bring success. We develop a ...... chip to solve simple classical conditioning tasks, thus verifying the design methodologies put forward in the paper....

Improved quantum-inspired evolutionary algorithm with diversity information applied to economic dispatch problem with prohibited operating zones

International Nuclear Information System (INIS)

Vianna Neto, Julio Xavier; Andrade Bernert, Diego Luis de; Santos Coelho, Leandro dos

2011-01-01

The objective of the economic dispatch problem (EDP) of electric power generation, whose characteristics are complex and highly nonlinear, is to schedule the committed generating unit outputs so as to meet the required load demand at minimum operating cost while satisfying all unit and system equality and inequality constraints. Recently, as an alternative to the conventional mathematical approaches, modern meta-heuristic optimization techniques have been given much attention by many researchers due to their ability to find an almost global optimal solution in EDPs. Research on merging evolutionary computation and quantum computation has been started since late 1990. Inspired on the quantum computation, this paper presented an improved quantum-inspired evolutionary algorithm (IQEA) based on diversity information of population. A classical quantum-inspired evolutionary algorithm (QEA) and the IQEA were implemented and validated for a benchmark of EDP with 15 thermal generators with prohibited operating zones. From the results for the benchmark problem, it is observed that the proposed IQEA approach provides promising results when compared to various methods available in the literature.

Improved quantum-inspired evolutionary algorithm with diversity information applied to economic dispatch problem with prohibited operating zones

Energy Technology Data Exchange (ETDEWEB)

Vianna Neto, Julio Xavier, E-mail: julio.neto@onda.com.b [Pontifical Catholic University of Parana, PUCPR, Undergraduate Program at Mechatronics Engineering, Imaculada Conceicao, 1155, Zip code 80215-901, Curitiba, Parana (Brazil); Andrade Bernert, Diego Luis de, E-mail: dbernert@gmail.co [Pontifical Catholic University of Parana, PUCPR, Industrial and Systems Engineering Graduate Program, LAS/PPGEPS, Imaculada Conceicao, 1155, Zip code 80215-901, Curitiba, Parana (Brazil); Santos Coelho, Leandro dos, E-mail: leandro.coelho@pucpr.b [Pontifical Catholic University of Parana, PUCPR, Industrial and Systems Engineering Graduate Program, LAS/PPGEPS, Imaculada Conceicao, 1155, Zip code 80215-901, Curitiba, Parana (Brazil)

2011-01-15

The objective of the economic dispatch problem (EDP) of electric power generation, whose characteristics are complex and highly nonlinear, is to schedule the committed generating unit outputs so as to meet the required load demand at minimum operating cost while satisfying all unit and system equality and inequality constraints. Recently, as an alternative to the conventional mathematical approaches, modern meta-heuristic optimization techniques have been given much attention by many researchers due to their ability to find an almost global optimal solution in EDPs. Research on merging evolutionary computation and quantum computation has been started since late 1990. Inspired on the quantum computation, this paper presented an improved quantum-inspired evolutionary algorithm (IQEA) based on diversity information of population. A classical quantum-inspired evolutionary algorithm (QEA) and the IQEA were implemented and validated for a benchmark of EDP with 15 thermal generators with prohibited operating zones. From the results for the benchmark problem, it is observed that the proposed IQEA approach provides promising results when compared to various methods available in the literature.

Improved quantum-inspired evolutionary algorithm with diversity information applied to economic dispatch problem with prohibited operating zones

Energy Technology Data Exchange (ETDEWEB)

Neto, Julio Xavier Vianna [Pontifical Catholic University of Parana, PUCPR, Undergraduate Program at Mechatronics Engineering, Imaculada Conceicao, 1155, Zip code 80215-901, Curitiba, Parana (Brazil); Bernert, Diego Luis de Andrade; Coelho, Leandro dos Santos [Pontifical Catholic University of Parana, PUCPR, Industrial and Systems Engineering Graduate Program, LAS/PPGEPS, Imaculada Conceicao, 1155, Zip code 80215-901, Curitiba, Parana (Brazil)

2011-01-15

The objective of the economic dispatch problem (EDP) of electric power generation, whose characteristics are complex and highly nonlinear, is to schedule the committed generating unit outputs so as to meet the required load demand at minimum operating cost while satisfying all unit and system equality and inequality constraints. Recently, as an alternative to the conventional mathematical approaches, modern meta-heuristic optimization techniques have been given much attention by many researchers due to their ability to find an almost global optimal solution in EDPs. Research on merging evolutionary computation and quantum computation has been started since late 1990. Inspired on the quantum computation, this paper presented an improved quantum-inspired evolutionary algorithm (IQEA) based on diversity information of population. A classical quantum-inspired evolutionary algorithm (QEA) and the IQEA were implemented and validated for a benchmark of EDP with 15 thermal generators with prohibited operating zones. From the results for the benchmark problem, it is observed that the proposed IQEA approach provides promising results when compared to various methods available in the literature. (author)

Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot.

Science.gov (United States)

Grinke, Eduard; Tetzlaff, Christian; Wörgötter, Florentin; Manoonpong, Poramate

2015-01-01

Walking animals, like insects, with little neural computing can effectively perform complex behaviors. For example, 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 many degrees-of-freedom (DOFs) walking robot is a challenging task. Thus, in this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural mechanisms with plasticity, exteroceptive 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 neural 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 walking robot. The turning information is transmitted as descending steering signals to the neural 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. The adaptation also enables the robot to effectively escape from sharp corners or deadlocks. Using backbone joint control embedded in the the locomotion control allows the robot to climb over small obstacles

Inspiration from britain?

DEFF Research Database (Denmark)

Vagnby, Bo

2008-01-01

Danish housing policy needs a dose of renewed social concern - and could find new inspiration in Britain's housing and urban planning policies, says Bo Vagnby. Udgivelsesdato: November......Danish housing policy needs a dose of renewed social concern - and could find new inspiration in Britain's housing and urban planning policies, says Bo Vagnby. Udgivelsesdato: November...

On the Reduction of Computational Complexity of Deep Convolutional Neural Networks

Directory of Open Access Journals (Sweden)

Partha Maji

2018-04-01

Full Text Available Deep convolutional neural networks (ConvNets, which are at the heart of many new emerging applications, achieve remarkable performance in audio and visual recognition tasks. Unfortunately, achieving accuracy often implies significant computational costs, limiting deployability. In modern ConvNets it is typical for the convolution layers to consume the vast majority of computational resources during inference. This has made the acceleration of these layers an important research area in academia and industry. In this paper, we examine the effects of co-optimizing the internal structures of the convolutional layers and underlying implementation of fundamental convolution operation. We demonstrate that a combination of these methods can have a big impact on the overall speedup of a ConvNet, achieving a ten-fold increase over baseline. We also introduce a new class of fast one-dimensional (1D convolutions for ConvNets using the Toom–Cook algorithm. We show that our proposed scheme is mathematically well-grounded, robust, and does not require any time-consuming retraining, while still achieving speedups solely from convolutional layers with no loss in baseline accuracy.

Neuromorphic computing with nanoscale spintronic oscillators.

Science.gov (United States)

Torrejon, Jacob; Riou, Mathieu; Araujo, Flavio Abreu; Tsunegi, Sumito; Khalsa, Guru; Querlioz, Damien; Bortolotti, Paolo; Cros, Vincent; Yakushiji, Kay; Fukushima, Akio; Kubota, Hitoshi; Yuasa, Shinji; Stiles, Mark D; Grollier, Julie

2017-07-26

Neurons in the brain behave as nonlinear oscillators, which develop rhythmic activity and interact to process information. Taking inspiration from this behaviour to realize high-density, low-power neuromorphic computing will require very large numbers of nanoscale nonlinear oscillators. A simple estimation indicates that to fit 10 8 oscillators organized in a two-dimensional array inside a chip the size of a thumb, the lateral dimension of each oscillator must be smaller than one micrometre. However, nanoscale devices tend to be noisy and to lack the stability that is required to process data in a reliable way. For this reason, despite multiple theoretical proposals and several candidates, including memristive and superconducting oscillators, a proof of concept of neuromorphic computing using nanoscale oscillators has yet to be demonstrated. Here we show experimentally that a nanoscale spintronic oscillator (a magnetic tunnel junction) can be used to achieve spoken-digit recognition with an accuracy similar to that of state-of-the-art neural networks. We also determine the regime of magnetization dynamics that leads to the greatest performance. These results, combined with the ability of the spintronic oscillators to interact with each other, and their long lifetime and low energy consumption, open up a path to fast, parallel, on-chip computation based on networks of oscillators.

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.

Single-site neural tube closure in human embryos revisited.

Science.gov (United States)

de Bakker, Bernadette S; Driessen, Stan; Boukens, Bastiaan J D; van den Hoff, Maurice J B; Oostra, Roelof-Jan

2017-10-01

Since the multi-site closure theory was first proposed in 1991 as explanation for the preferential localizations of neural tube defects, the closure of the neural tube has been debated. Although the multi-site closure theory is much cited in clinical literature, single-site closure is most apparent in literature concerning embryology. Inspired by Victor Hamburgers (1900-2001) statement that "our real teacher has been and still is the embryo, who is, incidentally, the only teacher who is always right", we decided to critically review both theories of neural tube closure. To verify the theories of closure, we studied serial histological sections of 10 mouse embryos between 8.5 and 9.5 days of gestation and 18 human embryos of the Carnegie collection between Carnegie stage 9 (19-21 days) and 13 (28-32 days). Neural tube closure was histologically defined by the neuroepithelial remodeling of the two adjoining neural fold tips in the midline. We did not observe multiple fusion sites in neither mouse nor human embryos. A meta-analysis of case reports on neural tube defects showed that defects can occur at any level of the neural axis. Our data indicate that the human neural tube fuses at a single site and, therefore, we propose to reinstate the single-site closure theory for neural tube closure. We showed that neural tube defects are not restricted to a specific location, thereby refuting the reasoning underlying the multi-site closure theory. Clin. Anat. 30:988-999, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

El Sistema-inspired ensemble music training is associated with changes in children's neurocognitive functional integration: preliminary ERP evidence.

Science.gov (United States)

Hedayati, Nina; Schibli, Kylie; D'Angiulli, Amedeo

2016-12-01

Children (aged 9-12) training in an El Sistema-inspired program (OrKidstra) and a matched comparison group participated in an auditory Go/No-Go task while event-related potentials (ERPs) were recorded. Entire-sweep waveform patterns correlated with known ERP peaks associated with executive and other cognitive functions and indicated that the spread of neural activity in the initial 250 ms of executive attention processing (pre-P300) showed higher level of topographical overlap in OrKidstra children. In these children, late potentials (post-P300) concurrent with response control were more widely distributed and temporally coordinated. Intensive ensemble music training, we suggest, may be associated with neuroplastic changes facilitating integration of neural information.

Nature inspires sensors to do more with less.

Science.gov (United States)

Mulvaney, Shawn P; Sheehan, Paul E

2014-10-28

The world is filled with widely varying chemical, physical, and biological stimuli. Over millennia, organisms have refined their senses to cope with these diverse stimuli, becoming virtuosos in differentiating closely related antigens, handling extremes in concentration, resetting the spent sensing mechanisms, and processing the multiple data streams being generated. Nature successfully deals with both repeating and new stimuli, demonstrating great adaptability when confronted with the latter. Interestingly, nature accomplishes these feats using a fairly simple toolbox. The sensors community continues to draw inspiration from nature's example: just look at the antibodies used as biosensor capture agents or the neural networks that process multivariate data streams. Indeed, many successful sensors have been built by simply mimicking natural systems. However, some of the most exciting breakthroughs occur when the community moves beyond mimicking nature and learns to use nature's tools in innovative ways.

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.

A Tony Thomas-Inspired Guide to INSPIRE

International Nuclear Information System (INIS)

O'Connell, Heath B.

2010-01-01

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.

Quantum computing

OpenAIRE

Burba, M.; Lapitskaya, T.

2017-01-01

This article gives an elementary introduction to quantum computing. It is a draft for a book chapter of the "Handbook of Nature-Inspired and Innovative Computing", Eds. A. Zomaya, G.J. Milburn, J. Dongarra, D. Bader, R. Brent, M. Eshaghian-Wilner, F. Seredynski (Springer, Berlin Heidelberg New York, 2006).

Nonlinear neural network for hemodynamic model state and input estimation using fMRI data

KAUST Repository

Karam, Ayman M.

2014-11-01

Originally inspired by biological neural networks, artificial neural networks (ANNs) are powerful mathematical tools that can solve complex nonlinear problems such as filtering, classification, prediction and more. This paper demonstrates the first successful implementation of ANN, specifically nonlinear autoregressive with exogenous input (NARX) networks, to estimate the hemodynamic states and neural activity from simulated and measured real blood oxygenation level dependent (BOLD) signals. Blocked and event-related BOLD data are used to test the algorithm on real experiments. The proposed method is accurate and robust even in the presence of signal noise and it does not depend on sampling interval. Moreover, the structure of the NARX networks is optimized to yield the best estimate with minimal network architecture. The results of the estimated neural activity are also discussed in terms of their potential use.

Learning by stimulation avoidance: A principle to control spiking neural networks dynamics.

Science.gov (United States)

Sinapayen, Lana; Masumori, Atsushi; Ikegami, Takashi

2017-01-01

Learning based on networks of real neurons, and learning based on biologically inspired models of neural networks, have yet to find general learning rules leading to widespread applications. In this paper, we argue for the existence of a principle allowing to steer the dynamics of a biologically inspired neural network. Using carefully timed external stimulation, the network can be driven towards a desired dynamical state. We term this principle "Learning by Stimulation Avoidance" (LSA). We demonstrate through simulation that the minimal sufficient conditions leading to LSA in artificial networks are also sufficient to reproduce learning results similar to those obtained in biological neurons by Shahaf and Marom, and in addition explains synaptic pruning. We examined the underlying mechanism by simulating a small network of 3 neurons, then scaled it up to a hundred neurons. We show that LSA has a higher explanatory power than existing hypotheses about the response of biological neural networks to external simulation, and can be used as a learning rule for an embodied application: learning of wall avoidance by a simulated robot. In other works, reinforcement learning with spiking networks can be obtained through global reward signals akin simulating the dopamine system; we believe that this is the first project demonstrating sensory-motor learning with random spiking networks through Hebbian learning relying on environmental conditions without a separate reward system.

Computer Graphics 2: More of the Best Computer Art and Design.

Science.gov (United States)

1994

This collection of computer generated images aims to present media tools and processes, stimulate ideas, and inspire artists and art students working in computer-related design. The images are representative of state-of-the-art editorial, broadcast, packaging, fine arts, and graphic techniques possible through computer generation. Each image is…

Computational Models of Neuron-Astrocyte Interactions Lead to Improved Efficacy in the Performance of Neural Networks

Science.gov (United States)

Alvarellos-González, Alberto; Pazos, Alejandro; Porto-Pazos, Ana B.

2012-01-01

The importance of astrocytes, one part of the glial system, for information processing in the brain has recently been demonstrated. Regarding information processing in multilayer connectionist systems, it has been shown that systems which include artificial neurons and astrocytes (Artificial Neuron-Glia Networks) have well-known advantages over identical systems including only artificial neurons. Since the actual impact of astrocytes in neural network function is unknown, we have investigated, using computational models, different astrocyte-neuron interactions for information processing; different neuron-glia algorithms have been implemented for training and validation of multilayer Artificial Neuron-Glia Networks oriented toward classification problem resolution. The results of the tests performed suggest that all the algorithms modelling astrocyte-induced synaptic potentiation improved artificial neural network performance, but their efficacy depended on the complexity of the problem. PMID:22649480

Classifier for gravitational-wave inspiral signals in nonideal single-detector data

Science.gov (United States)

Kapadia, S. J.; Dent, T.; Dal Canton, T.

2017-11-01

We describe a multivariate classifier for candidate events in a templated search for gravitational-wave (GW) inspiral signals from neutron-star-black-hole (NS-BH) binaries, in data from ground-based detectors where sensitivity is limited by non-Gaussian noise transients. The standard signal-to-noise ratio (SNR) and chi-squared test for inspiral searches use only properties of a single matched filter at the time of an event; instead, we propose a classifier using features derived from a bank of inspiral templates around the time of each event, and also from a search using approximate sine-Gaussian templates. The classifier thus extracts additional information from strain data to discriminate inspiral signals from noise transients. We evaluate a random forest classifier on a set of single-detector events obtained from realistic simulated advanced LIGO data, using simulated NS-BH signals added to the data. The new classifier detects a factor of 1.5-2 more signals at low false positive rates as compared to the standard "reweighted SNR" statistic, and does not require the chi-squared test to be computed. Conversely, if only the SNR and chi-squared values of single-detector events are available, random forest classification performs nearly identically to the reweighted SNR.

A novel role for visual perspective cues in the neural computation of depth.

Science.gov (United States)

Kim, HyungGoo R; Angelaki, Dora E; DeAngelis, Gregory C

2015-01-01

As we explore a scene, our eye movements add global patterns of motion to the retinal image, complicating visual motion produced by self-motion or moving objects. Conventionally, it has been assumed that extraretinal signals, such as efference copy of smooth pursuit commands, are required to compensate for the visual consequences of eye rotations. We consider an alternative possibility: namely, that the visual system can infer eye rotations from global patterns of image motion. We visually simulated combinations of eye translation and rotation, including perspective distortions that change dynamically over time. We found that incorporating these 'dynamic perspective' cues allowed the visual system to generate selectivity for depth sign from motion parallax in macaque cortical area MT, a computation that was previously thought to require extraretinal signals regarding eye velocity. Our findings suggest neural mechanisms that analyze global patterns of visual motion to perform computations that require knowledge of eye rotations.

Optimal Formation of Multirobot Systems Based on a Recurrent Neural Network.

Science.gov (United States)

Wang, Yunpeng; Cheng, Long; Hou, Zeng-Guang; Yu, Junzhi; Tan, Min

2016-02-01

The optimal formation problem of multirobot systems is solved by a recurrent neural network in this paper. The desired formation is described by the shape theory. This theory can generate a set of feasible formations that share the same relative relation among robots. An optimal formation means that finding one formation from the feasible formation set, which has the minimum distance to the initial formation of the multirobot system. Then, the formation problem is transformed into an optimization problem. In addition, the orientation, scale, and admissible range of the formation can also be considered as the constraints in the optimization problem. Furthermore, if all robots are identical, their positions in the system are exchangeable. Then, each robot does not necessarily move to one specific position in the formation. In this case, the optimal formation problem becomes a combinational optimization problem, whose optimal solution is very hard to obtain. Inspired by the penalty method, this combinational optimization problem can be approximately transformed into a convex optimization problem. Due to the involvement of the Euclidean norm in the distance, the objective function of these optimization problems are nonsmooth. To solve these nonsmooth optimization problems efficiently, a recurrent neural network approach is employed, owing to its parallel computation ability. Finally, some simulations and experiments are given to validate the effectiveness and efficiency of the proposed optimal formation approach.

Super-pixel extraction based on multi-channel pulse coupled neural network

Science.gov (United States)

Xu, GuangZhu; Hu, Song; Zhang, Liu; Zhao, JingJing; Fu, YunXia; Lei, BangJun

2018-04-01

Super-pixel extraction techniques group pixels to form over-segmented image blocks according to the similarity among pixels. Compared with the traditional pixel-based methods, the image descripting method based on super-pixel has advantages of less calculation, being easy to perceive, and has been widely used in image processing and computer vision applications. Pulse coupled neural network (PCNN) is a biologically inspired model, which stems from the phenomenon of synchronous pulse release in the visual cortex of cats. Each PCNN neuron can correspond to a pixel of an input image, and the dynamic firing pattern of each neuron contains both the pixel feature information and its context spatial structural information. In this paper, a new color super-pixel extraction algorithm based on multi-channel pulse coupled neural network (MPCNN) was proposed. The algorithm adopted the block dividing idea of SLIC algorithm, and the image was divided into blocks with same size first. Then, for each image block, the adjacent pixels of each seed with similar color were classified as a group, named a super-pixel. At last, post-processing was adopted for those pixels or pixel blocks which had not been grouped. Experiments show that the proposed method can adjust the number of superpixel and segmentation precision by setting parameters, and has good potential for super-pixel extraction.

Goal-directed behaviour and instrumental devaluation: a neural system-level computational model

Directory of Open Access Journals (Sweden)

Francesco Mannella

2016-10-01

Full Text Available Devaluation is the key experimental paradigm used to demonstrate the presence of instrumental behaviours 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 the 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 integrates the results of different 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 behaviour.

Neural Networks

International Nuclear Information System (INIS)

Smith, Patrick I.

2003-01-01

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

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…

Implementation of a pulse coupled neural network in FPGA.

Science.gov (United States)

Waldemark, J; Millberg, M; Lindblad, T; Waldemark, K; Becanovic, V

2000-06-01

The Pulse Coupled neural network, PCNN, is a biologically inspired neural net and it can be used in various image analysis applications, e.g. time-critical applications in the field of image pre-processing like segmentation, filtering, etc. a VHDL implementation of the PCNN targeting FPGA was undertaken and the results presented here. The implementation contains many interesting features. By pipelining the PCNN structure a very high throughput of 55 million neuron iterations per second could be achieved. By making the coefficients re-configurable during operation, a complete recognition system could be implemented on one, or maybe two, chip(s). Reconsidering the ranges and resolutions of the constants may save a lot of hardware, since the higher resolution requires larger multipliers, adders, memories etc.

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

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...

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.

The application of artificial neural networks to TLD dose algorithm

International Nuclear Information System (INIS)

Moscovitch, M.

1997-01-01

We review the application of feed forward neural networks to multi element thermoluminescence dosimetry (TLD) dose algorithm development. A Neural Network is an information processing method inspired by the biological nervous system. A dose algorithm based on a neural network is a fundamentally different approach from conventional algorithms, as it has the capability to learn from its own experience. The neural network algorithm is shown the expected dose values (output) associated with a given response of a multi-element dosimeter (input) many times.The algorithm, being trained that way, eventually is able to produce its own unique solution to similar (but not exactly the same) dose calculation problems. For personnel dosimetry, the output consists of the desired dose components: deep dose, shallow dose, and eye dose. The input consists of the TL data obtained from the readout of a multi-element dosimeter. For this application, a neural network architecture was developed based on the concept of functional links network (FLN). The FLN concept allowed an increase in the dimensionality of the input space and construction of a neural network without any hidden layers. This simplifies the problem and results in a relatively simple and reliable dose calculation algorithm. Overall, the neural network dose algorithm approach has been shown to significantly improve the precision and accuracy of dose calculations. (authors)

A Novel Real-coded Quantum-inspired Genetic Algorithm and Its Application in Data Reconciliation

Directory of Open Access Journals (Sweden)

Gao Lin

2012-06-01

Full Text Available Traditional quantum-inspired genetic algorithm (QGA has drawbacks such as premature convergence, heavy computational cost, complicated coding and decoding process etc. In this paper, a novel real-coded quantum-inspired genetic algorithm is proposed based on interval division thinking. Detailed comparisons with some similar approaches for some standard benchmark functions test validity of the proposed algorithm. Besides, the proposed algorithm is used in two typical nonlinear data reconciliation problems (distilling process and extraction process and simulation results show its efficiency in nonlinear data reconciliation problems.

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.

Development of composite pipelines by filament winding: an study using neural networks; Desenvolvimento de dutos compositos por filament winding: um estudo atraves de redes neurais

Energy Technology Data Exchange (ETDEWEB)

Contant, Sheila [Universidade Estadual de Campinas, SP (Brazil); Lona, Liliane M.F. [Universidade Estadual de Campinas, SP (Brazil). Faculdade de Engenharia Quimica; Calado, Veronica M.A. [Universidade Federal, Rio de Janeiro, RJ (Brazil). Escola de Quimica

2003-07-01

The application of composite materials on pipeline systems for transportation of petroleum and natural gas is being pointed as one alternative to conventional materials, improving safety and reliability and reducing costs. Polymeric composite pipes can be manufactured by filament winding, a method that shows several advantages over other manufacturing processes such as low cost, high production rates and ability to produce high specific strength parts. Because of the many parameters involved in this process, among others aspects, mathematical modeling of filament winding process through conventional methods is complex task. In this work the process has been studied using neural networks, a computational technique inspired in human brain that presents several advantages when compared to conventional methods like a reduced processing time. Neural networks have been applied to prediction of mechanical properties of composite tubes and also to prediction of the thermal behavior of the parts during cure step. Results showed the efficacy of the proposed methodology. (author)

Attacks and Intrusion Detection in Cloud Computing Using Neural Networks and Particle Swarm Optimization Algorithms

Directory of Open Access Journals (Sweden)

Ahmad Shokuh Saljoughi

2018-01-01

Full Text Available Today, cloud computing has become popular among users in organizations and companies. Security and efficiency are the two major issues facing cloud service providers and their customers. Since cloud computing is a virtual pool of resources provided in an open environment (Internet, cloud-based services entail security risks. Detection of intrusions and attacks through unauthorized users is one of the biggest challenges for both cloud service providers and cloud users. In the present study, artificial intelligence techniques, e.g. MLP Neural Network sand particle swarm optimization algorithm, were used to detect intrusion and attacks. The methods were tested for NSL-KDD, KDD-CUP datasets. The results showed improved accuracy in detecting attacks and intrusions by unauthorized users.

An Attractor-Based Complexity Measurement for Boolean Recurrent Neural Networks

Science.gov (United States)

Cabessa, Jérémie; Villa, Alessandro E. P.

2014-01-01

We provide a novel refined attractor-based complexity measurement for Boolean recurrent neural networks that represents an assessment of their computational power in terms of the significance of their attractor dynamics. This complexity measurement is achieved by first proving a computational equivalence between Boolean recurrent neural networks and some specific class of -automata, and then translating the most refined classification of -automata to the Boolean neural network context. As a result, a hierarchical classification of Boolean neural networks based on their attractive dynamics is obtained, thus providing a novel refined attractor-based complexity measurement for Boolean recurrent neural networks. These results provide new theoretical insights to the computational and dynamical capabilities of neural networks according to their attractive potentialities. An application of our findings is illustrated by the analysis of the dynamics of a simplified model of the basal ganglia-thalamocortical network simulated by a Boolean recurrent neural network. This example shows the significance of measuring network complexity, and how our results bear new founding elements for the understanding of the complexity of real brain circuits. PMID:24727866

A high-speed analog neural processor

NARCIS (Netherlands)

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

1994-01-01

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

Ion track based tunable device as humidity sensor: a neural network approach

Science.gov (United States)

Sharma, Mamta; Sharma, Anuradha; Bhattacherjee, Vandana

2013-01-01

Artificial Neural Network (ANN) has been applied in statistical model development, adaptive control system, pattern recognition in data mining, and decision making under uncertainty. The nonlinear dependence of any sensor output on the input physical variable has been the motivation for many researchers to attempt unconventional modeling techniques such as neural networks and other machine learning approaches. Artificial neural network (ANN) is a computational tool inspired by the network of neurons in biological nervous system. It is a network consisting of arrays of artificial neurons linked together with different weights of connection. The states of the neurons as well as the weights of connections among them evolve according to certain learning rules.. In the present work we focus on the category of sensors which respond to electrical property changes such as impedance or capacitance. Recently, sensor materials have been embedded in etched tracks due to their nanometric dimensions and high aspect ratio which give high surface area available for exposure to sensing material. Various materials can be used for this purpose to probe physical (light intensity, temperature etc.), chemical (humidity, ammonia gas, alcohol etc.) or biological (germs, hormones etc.) parameters. The present work involves the application of TEMPOS structures as humidity sensors. The sample to be studied was prepared using the polymer electrolyte (PEO/NH4ClO4) with CdS nano-particles dispersed in the polymer electrolyte. In the present research we have attempted to correlate the combined effects of voltage and frequency on impedance of humidity sensors using a neural network model and results have indicated that the mean absolute error of the ANN Model for the training data was 3.95% while for the validation data it was 4.65%. The corresponding values for the LR model were 8.28% and 8.35% respectively. It was also demonstrated the percentage improvement of the ANN Model with respect to the

Brain inspired high performance electronics on flexible silicon

KAUST Repository

Sevilla, Galo T.

2014-06-01

Brain\\'s stunning speed, energy efficiency and massive parallelism makes it the role model for upcoming high performance computation systems. Although human brain components are a million times slower than state of the art silicon industry components [1], they can perform 1016 operations per second while consuming less power than an electrical light bulb. In order to perform the same amount of computation with today\\'s most advanced computers, the output of an entire power station would be needed. In that sense, to obtain brain like computation, ultra-fast devices with ultra-low power consumption will have to be integrated in extremely reduced areas, achievable only if brain folded structure is mimicked. Therefore, to allow brain-inspired computation, flexible and transparent platform will be needed to achieve foldable structures and their integration on asymmetric surfaces. In this work, we show a new method to fabricate 3D and planar FET architectures in flexible and semitransparent silicon fabric without comprising performance and maintaining cost/yield advantage offered by silicon-based electronics.

Kinds of inspiration in interaction design

DEFF Research Database (Denmark)

Halskov, Kim

2010-01-01

In this paper, we explore the role of sources of inspiration in interaction design. We identify four strategies for relating sources of inspiration to emerging ideas: selection; adaptation; translation; and combination. As our starting point, we argue that sources of inspiration are a form...... of knowledge crucial to creativity. Our research is based on empirical findings arising from the use of Inspiration Card Workshops, which are collaborative design events in which domain and technology insight are combined to create design concepts. In addition to the systematically introduced sources...... of inspiration that form part of the workshop format, a number of spontaneous sources of inspiration emerged during these workshops....

Artificial neural networks in NDT

International Nuclear Information System (INIS)

Abdul Aziz Mohamed

2001-01-01

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

Phase diagram of spiking neural networks.

Science.gov (United States)

Seyed-Allaei, Hamed

2015-01-01

In computer simulations of spiking neural networks, often it is assumed that every two neurons of the network are connected by a probability of 2%, 20% of neurons are inhibitory and 80% are excitatory. These common values are based on experiments, observations, and trials and errors, but here, I take a different perspective, inspired by evolution, I systematically simulate many networks, each with a different set of parameters, and then I try to figure out what makes the common values desirable. I stimulate networks with pulses and then measure their: dynamic range, dominant frequency of population activities, total duration of activities, maximum rate of population and the occurrence time of maximum rate. The results are organized in phase diagram. This phase diagram gives an insight into the space of parameters - excitatory to inhibitory ratio, sparseness of connections and synaptic weights. This phase diagram can be used to decide the parameters of a model. The phase diagrams show that networks which are configured according to the common values, have a good dynamic range in response to an impulse and their dynamic range is robust in respect to synaptic weights, and for some synaptic weights they oscillates in α or β frequencies, independent of external stimuli.

Deep learning for computational chemistry

Energy Technology Data Exchange (ETDEWEB)

Goh, Garrett B. [Advanced Computing, Mathematics, and Data Division, Pacific Northwest National Laboratory, 902 Battelle Blvd Richland Washington 99354; Hodas, Nathan O. [Advanced Computing, Mathematics, and Data Division, Pacific Northwest National Laboratory, 902 Battelle Blvd Richland Washington 99354; Vishnu, Abhinav [Advanced Computing, Mathematics, and Data Division, Pacific Northwest National Laboratory, 902 Battelle Blvd Richland Washington 99354

2017-03-08

The rise and fall of artificial neural networks is well documented in the scientific literature of both the fields of computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on “deep” neural networks. Within the last few years, we have seen the transformative impact of deep learning the computer science domain, notably in speech recognition and computer vision, to the extent that the majority of practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. In this review, we provide an introductory overview into the theory of deep neural networks and their unique properties as compared to traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including QSAR, virtual screening, protein structure modeling, QM calculations, materials synthesis and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against non neural networks state-of-the-art models across disparate research topics, and deep neural network based models often exceeded the “glass ceiling” expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a useful tool and may grow into a pivotal role for various challenges in the computational chemistry field.

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

Directory of Open Access Journals (Sweden)

Bryan C. Daniels

2017-06-01

Full Text Available 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.

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.

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.

Single instruction computer architecture and its application in image processing

Science.gov (United States)

Laplante, Phillip A.

1992-03-01

A single processing computer system using only half-adder circuits is described. In addition, it is shown that only a single hard-wired instruction is needed in the control unit to obtain a complete instruction set for this general purpose computer. Such a system has several advantages. First it is intrinsically a RISC machine--in fact the 'ultimate RISC' machine. Second, because only a single type of logic element is employed the entire computer system can be easily realized on a single, highly integrated chip. Finally, due to the homogeneous nature of the computer's logic elements, the computer has possible implementations as an optical or chemical machine. This in turn suggests possible paradigms for neural computing and artificial intelligence. After showing how we can implement a full-adder, min, max and other operations using the half-adder, we use an array of such full-adders to implement the dilation operation for two black and white images. Next we implement the erosion operation of two black and white images using a relative complement function and the properties of erosion and dilation. This approach was inspired by papers by van der Poel in which a single instruction is used to furnish a complete set of general purpose instructions and by Bohm- Jacopini where it is shown that any problem can be solved using a Turing machine with one entry and one exit.

Kernel Temporal Differences for Neural Decoding

Science.gov (United States)

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

2015-01-01

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

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…

Experimental modal analysis of fractal-inspired multi-frequency structures for piezoelectric energy converters

International Nuclear Information System (INIS)

Castagnetti, D

2012-01-01

An important issue in the field of energy harvesting through piezoelectric materials is the design of simple and efficient structures which are multi-frequency in the ambient vibration range. This paper deals with the experimental assessment of four fractal-inspired multi-frequency structures for piezoelectric energy harvesting. These structures, thin plates of square shape, were proposed in a previous work by the author and their modal response numerically analysed. The present work has two aims. First, to assess the modal response of these structures through an experimental investigation. Second, to evaluate, through computational simulation, the performance of a piezoelectric converter relying on one of these fractal-inspired structures. The four fractal-inspired structures are examined in the range between 0 and 100 Hz, with regard to both eigenfrequencies and eigenmodes. In the same frequency range, the modal response and power output of the piezoelectric converter are investigated. (paper)

Transform a Simple Sketch to a Chinese Painting by a Multiscale Deep Neural Network

Directory of Open Access Journals (Sweden)

Daoyu Lin

2018-01-01

Full Text Available Recently, inspired by the power of deep learning, convolution neural networks can produce fantastic images at the pixel level. However, a significant limiting factor for previous approaches is that they focus on some simple datasets such as faces and bedrooms. In this paper, we propose a multiscale deep neural network to transform sketches into Chinese paintings. To synthesize more realistic imagery, we train the generative network by using both L1 loss and adversarial loss. Additionally, users can control the process of the synthesis since the generative network is feed-forward. This network can also be treated as neural style transfer by adding an edge detector. Furthermore, additional experiments on image colorization and image super-resolution demonstrate the universality of our proposed approach.

Computational Model of Primary Visual Cortex Combining Visual Attention for Action Recognition.

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Na Shu

Full Text Available Humans can easily understand other people's actions through visual systems, while computers cannot. Therefore, a new bio-inspired computational model is proposed in this paper aiming for automatic action recognition. The model focuses on dynamic properties of neurons and neural networks in the primary visual cortex (V1, and simulates the procedure of information processing in V1, which consists of visual perception, visual attention and representation of human action. In our model, a family of the three-dimensional spatial-temporal correlative Gabor filters is used to model the dynamic properties of the classical receptive field of V1 simple cell tuned to different speeds and orientations in time for detection of spatiotemporal information from video sequences. Based on the inhibitory effect of stimuli outside the classical receptive field caused by lateral connections of spiking neuron networks in V1, we propose surround suppressive operator to further process spatiotemporal information. Visual attention model based on perceptual grouping is integrated into our model to filter and group different regions. Moreover, in order to represent the human action, we consider the characteristic of the neural code: mean motion map based on analysis of spike trains generated by spiking neurons. The experimental evaluation on some publicly available action datasets and comparison with the state-of-the-art approaches demonstrate the superior performance of the proposed model.

INSPIRE 2012 da Istanbul a Firenze

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Mauro Salvemini

2012-09-01

Full Text Available DURING THE CONFERENCE HELD IN  ISTANBUL IN  2012 INSPIRE  THE  NEWS  THAT  MOST  IMPRESSED ITALIANS PRESENT,  EVEN THOSE IN THE PUBLIC ADMINISTRATION , WAS THAT THE NEXT  INSPIRE CONFERENCE WILL TAKE PLACE IN  FLORENCEDurante la conferenza INSPIRE 2012 svoltasi ad Istanbul la notizia che ha maggiormente colpito gli italiani presenti, anche quelli della pubblica amministrazione , è stata che la prossima Conferenza INSPIRE si svolgerà a Firenze dal 23 al 27 giugno 2013.

INSPIRE 2012 da Istanbul a Firenze

Directory of Open Access Journals (Sweden)

Mauro Salvemini

2012-09-01

Full Text Available DURING THE CONFERENCE HELD IN  ISTANBUL IN  2012 INSPIRE  THE  NEWS  THAT  MOST  IMPRESSED ITALIANS PRESENT,  EVEN THOSE IN THE PUBLIC ADMINISTRATION , WAS THAT THE NEXT  INSPIRE CONFERENCE WILL TAKE PLACE IN  FLORENCE Durante la conferenza INSPIRE 2012 svoltasi ad Istanbul la notizia che ha maggiormente colpito gli italiani presenti, anche quelli della pubblica amministrazione , è stata che la prossima Conferenza INSPIRE si svolgerà a Firenze dal 23 al 27 giugno 2013.

Multiple Time Series Forecasting Using Quasi-Randomized Functional Link Neural Networks

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Thierry Moudiki

2018-03-01

Full Text Available We are interested in obtaining forecasts for multiple time series, by taking into account the potential nonlinear relationships between their observations. For this purpose, we use a specific type of regression model on an augmented dataset of lagged time series. Our model is inspired by dynamic regression models (Pankratz 2012, with the response variable’s lags included as predictors, and is known as Random Vector Functional Link (RVFL neural networks. The RVFL neural networks have been successfully applied in the past, to solving regression and classification problems. The novelty of our approach is to apply an RVFL model to multivariate time series, under two separate regularization constraints on the regression parameters.

Premonitory urges and tics in Tourette syndrome: computational mechanisms and neural correlates.

Science.gov (United States)

Conceição, Vasco A; Dias, Ângelo; Farinha, Ana C; Maia, Tiago V

2017-10-01

Tourette syndrome is characterized by open motor behaviors - tics - but another crucial aspect of the disorder is the presence of premonitory urges: uncomfortable sensations that typically precede tics and are temporarily alleviated by tics. We review the evidence implicating the somatosensory cortices and the insula in premonitory urges and the motor cortico-basal ganglia-thalamo-cortical loop in tics. We consider how these regions interact during tic execution, suggesting that the insula plays an important role as a nexus linking the sensory and emotional character of premonitory urges with their translation into tics. We also consider how these regions interact during tic learning, integrating the neural evidence with a computational perspective on how premonitory-urge alleviation reinforces tics. Copyright © 2017 Elsevier Ltd. All rights reserved.

Hierarchical modeling of molecular energies using a deep neural network

Science.gov (United States)

Lubbers, Nicholas; Smith, Justin S.; Barros, Kipton

2018-06-01

We introduce the Hierarchically Interacting Particle Neural Network (HIP-NN) to model molecular properties from datasets of quantum calculations. Inspired by a many-body expansion, HIP-NN decomposes properties, such as energy, as a sum over hierarchical terms. These terms are generated from a neural network—a composition of many nonlinear transformations—acting on a representation of the molecule. HIP-NN achieves the state-of-the-art performance on a dataset of 131k ground state organic molecules and predicts energies with 0.26 kcal/mol mean absolute error. With minimal tuning, our model is also competitive on a dataset of molecular dynamics trajectories. In addition to enabling accurate energy predictions, the hierarchical structure of HIP-NN helps to identify regions of model uncertainty.

A highly efficient sharp-interface immersed boundary method with adaptive mesh refinement for bio-inspired flow simulations

Science.gov (United States)

Deng, Xiaolong; Dong, Haibo

2017-11-01

Developing a high-fidelity, high-efficiency numerical method for bio-inspired flow problems with flow-structure interaction is important for understanding related physics and developing many bio-inspired technologies. To simulate a fast-swimming big fish with multiple finlets or fish schooling, we need fine grids and/or a big computational domain, which are big challenges for 3-D simulations. In current work, based on the 3-D finite-difference sharp-interface immersed boundary method for incompressible flows (Mittal et al., JCP 2008), we developed an octree-like Adaptive Mesh Refinement (AMR) technique to enhance the computational ability and increase the computational efficiency. The AMR is coupled with a multigrid acceleration technique and a MPI +OpenMP hybrid parallelization. In this work, different AMR layers are treated separately and the synchronization is performed in the buffer regions and iterations are performed for the convergence of solution. Each big region is calculated by a MPI process which then uses multiple OpenMP threads for further acceleration, so that the communication cost is reduced. With these acceleration techniques, various canonical and bio-inspired flow problems with complex boundaries can be simulated accurately and efficiently. This work is supported by the MURI Grant Number N00014-14-1-0533 and NSF Grant CBET-1605434.

Color encoding in biologically-inspired convolutional neural networks.

Science.gov (United States)

Rafegas, Ivet; Vanrell, Maria

2018-05-11

Convolutional Neural Networks have been proposed as suitable frameworks to model biological vision. Some of these artificial networks showed representational properties that rival primate performances in object recognition. In this paper we explore how color is encoded in a trained artificial network. It is performed by estimating a color selectivity index for each neuron, which allows us to describe the neuron activity to a color input stimuli. The index allows us to classify whether they are color selective or not and if they are of a single or double color. We have determined that all five convolutional layers of the network have a large number of color selective neurons. Color opponency clearly emerges in the first layer, presenting 4 main axes (Black-White, Red-Cyan, Blue-Yellow and Magenta-Green), but this is reduced and rotated as we go deeper into the network. In layer 2 we find a denser hue sampling of color neurons and opponency is reduced almost to one new main axis, the Bluish-Orangish coinciding with the dataset bias. In layers 3, 4 and 5 color neurons are similar amongst themselves, presenting different type of neurons that detect specific colored objects (e.g., orangish faces), specific surrounds (e.g., blue sky) or specific colored or contrasted object-surround configurations (e.g. blue blob in a green surround). Overall, our work concludes that color and shape representation are successively entangled through all the layers of the studied network, revealing certain parallelisms with the reported evidences in primate brains that can provide useful insight into intermediate hierarchical spatio-chromatic representations. Copyright © 2018 Elsevier Ltd. All rights reserved.

Nostalgia-Evoked Inspiration: Mediating Mechanisms and Motivational Implications.

Science.gov (United States)

Stephan, Elena; Sedikides, Constantine; Wildschut, Tim; Cheung, Wing-Yee; Routledge, Clay; Arndt, Jamie

2015-10-01

Six studies examined the nostalgia-inspiration link and its motivational implications. In Study 1, nostalgia proneness was positively associated with inspiration frequency and intensity. In Studies 2 and 3, the recollection of nostalgic (vs. ordinary) experiences increased both general inspiration and specific inspiration to engage in exploratory activities. In Study 4, serial mediational analyses supported a model in which nostalgia increases social connectedness, which subsequently fosters self-esteem, which then boosts inspiration. In Study 5, a rigorous evaluation of this serial mediational model (with a novel nostalgia induction controlling for positive affect) reinforced the idea that nostalgia-elicited social connectedness increases self-esteem, which then heightens inspiration. Study 6 extended the serial mediational model by demonstrating that nostalgia-evoked inspiration predicts goal pursuit (intentions to pursue an important goal). Nostalgia spawns inspiration via social connectedness and attendant self-esteem. In turn, nostalgia-evoked inspiration bolsters motivation. © 2015 by the Society for Personality and Social Psychology, Inc.

Effect of inspiration on airway dimensions measured in maximal inspiration CT images of subjects without airflow limitation

DEFF Research Database (Denmark)

Petersen, Jens; Wille, Mathilde M.W.; Raket, Lars Lau

2014-01-01

. Automated software was utilized to segment lungs and airways, identify segmental bronchi, and match airway branches in all images of the same subject. Inspiration level was defined as segmented total lung volume (TLV) divided by predicted total lung capacity (pTLC). Mixed-effects models were used to predict......OBJECTIVES: To study the effect of inspiration on airway dimensions measured in voluntary inspiration breath-hold examinations. METHODS: 961 subjects with normal spirometry were selected from the Danish Lung Cancer Screening Trial. Subjects were examined annually for five years with low-dose CT...... • The effect of inspiration is greater in higher-generation (more peripheral) airways • Airways of generation 5 and beyond are as distensible as lung parenchyma • Airway dimensions measured from CT should be adjusted for inspiration level....

Desktop Grid Computing with BOINC and its Use for Solving the RND telecommunication Problem

International Nuclear Information System (INIS)

Vega-Rodriguez, M. A.; Vega-Perez, D.; Gomez-Pulido, J. A.; Sanchez-Perez, J. M.

2007-01-01

An important problem in mobile/cellular technology is trying to cover a certain geographical area by using the smallest number of radio antennas, and looking for the biggest cover rate. This is the well known Telecommunication problem identified as Radio Network Design (RND). This optimization problem can be solved by bio-inspired algorithms, among other options. In this work we use the PBIL (Population-Based Incremental Learning) algorithm, that has been little studied in this field but we have obtained very good results with it. PBIL is based on genetic algorithms and competitive learning (typical in neural networks), being a population evolution model based on probabilistic models. Due to the high number of configuration parameters of the PBIL, and because we want to test the RND problem with numerous variants, we have used grid computing with BOINC (Berkeley Open Infrastructure for Network Computing). In this way, we have been able to execute thousands of experiments in few days using around 100 computers at the same time. In this paper we present the most interesting results from our work. (Author)

An Efficient Neural-Network-Based Microseismic Monitoring Platform for Hydraulic Fracture on an Edge Computing Architecture

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Xiaopu Zhang

2018-06-01

Full Text Available Microseismic monitoring is one of the most critical technologies for hydraulic fracturing in oil and gas production. To detect events in an accurate and efficient way, there are two major challenges. One challenge is how to achieve high accuracy due to a poor signal-to-noise ratio (SNR. The other one is concerned with real-time data transmission. Taking these challenges into consideration, an edge-computing-based platform, namely Edge-to-Center LearnReduce, is presented in this work. The platform consists of a data center with many edge components. At the data center, a neural network model combined with convolutional neural network (CNN and long short-term memory (LSTM is designed and this model is trained by using previously obtained data. Once the model is fully trained, it is sent to edge components for events detection and data reduction. At each edge component, a probabilistic inference is added to the neural network model to improve its accuracy. Finally, the reduced data is delivered to the data center. Based on experiment results, a high detection accuracy (over 96% with less transmitted data (about 90% was achieved by using the proposed approach on a microseismic monitoring system. These results show that the platform can simultaneously improve the accuracy and efficiency of microseismic monitoring.

An Efficient Neural-Network-Based Microseismic Monitoring Platform for Hydraulic Fracture on an Edge Computing Architecture.

Science.gov (United States)

Zhang, Xiaopu; Lin, Jun; Chen, Zubin; Sun, Feng; Zhu, Xi; Fang, Gengfa

2018-06-05

Microseismic monitoring is one of the most critical technologies for hydraulic fracturing in oil and gas production. To detect events in an accurate and efficient way, there are two major challenges. One challenge is how to achieve high accuracy due to a poor signal-to-noise ratio (SNR). The other one is concerned with real-time data transmission. Taking these challenges into consideration, an edge-computing-based platform, namely Edge-to-Center LearnReduce, is presented in this work. The platform consists of a data center with many edge components. At the data center, a neural network model combined with convolutional neural network (CNN) and long short-term memory (LSTM) is designed and this model is trained by using previously obtained data. Once the model is fully trained, it is sent to edge components for events detection and data reduction. At each edge component, a probabilistic inference is added to the neural network model to improve its accuracy. Finally, the reduced data is delivered to the data center. Based on experiment results, a high detection accuracy (over 96%) with less transmitted data (about 90%) was achieved by using the proposed approach on a microseismic monitoring system. These results show that the platform can simultaneously improve the accuracy and efficiency of microseismic monitoring.

A neutron spectrum unfolding computer code based on artificial neural networks

International Nuclear Information System (INIS)

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

2014-01-01

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

Neural Networks for Modeling and Control of Particle Accelerators

Science.gov (United States)

Edelen, A. L.; Biedron, S. G.; Chase, B. E.; Edstrom, D.; Milton, S. V.; Stabile, P.

2016-04-01

Particle accelerators are host to myriad nonlinear and complex physical phenomena. They often involve a multitude of interacting systems, are subject to tight performance demands, and should be able to run for extended periods of time with minimal interruptions. Often times, traditional control techniques cannot fully meet these requirements. One promising avenue is to introduce machine learning and sophisticated control techniques inspired by artificial intelligence, particularly in light of recent theoretical and practical advances in these fields. Within machine learning and artificial intelligence, neural networks are particularly well-suited to modeling, control, and diagnostic analysis of complex, nonlinear, and time-varying systems, as well as systems with large parameter spaces. Consequently, the use of neural network-based modeling and control techniques could be of significant benefit to particle accelerators. For the same reasons, particle accelerators are also ideal test-beds for these techniques. Many early attempts to apply neural networks to particle accelerators yielded mixed results due to the relative immaturity of the technology for such tasks. The purpose of this paper is to re-introduce neural networks to the particle accelerator community and report on some work in neural network control that is being conducted as part of a dedicated collaboration between Fermilab and Colorado State University (CSU). We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.

MapReduce Based Parallel Neural Networks in Enabling Large Scale Machine Learning.

Science.gov (United States)

Liu, Yang; Yang, Jie; Huang, Yuan; Xu, Lixiong; Li, Siguang; Qi, Man

2015-01-01

Artificial neural networks (ANNs) have been widely used in pattern recognition and classification applications. However, ANNs are notably slow in computation especially when the size of data is large. Nowadays, big data has received a momentum from both industry and academia. To fulfill the potentials of ANNs for big data applications, the computation process must be speeded up. For this purpose, this paper parallelizes neural networks based on MapReduce, which has become a major computing model to facilitate data intensive applications. Three data intensive scenarios are considered in the parallelization process in terms of the volume of classification data, the size of the training data, and the number of neurons in the neural network. The performance of the parallelized neural networks is evaluated in an experimental MapReduce computer cluster from the aspects of accuracy in classification and efficiency in computation.

Deep learning for computational chemistry.

Science.gov (United States)

Goh, Garrett B; Hodas, Nathan O; Vishnu, Abhinav

2017-06-15

The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on multilayer neural networks. Within the last few years, we have seen the transformative impact of deep learning in many domains, particularly in speech recognition and computer vision, to the extent that the majority of expert practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. In this review, we provide an introductory overview into the theory of deep neural networks and their unique properties that distinguish them from traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including quantitative structure activity relationship, virtual screening, protein structure prediction, quantum chemistry, materials design, and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against non-neural networks state-of-the-art models across disparate research topics, and deep neural network-based models often exceeded the "glass ceiling" expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a valuable tool for computational chemistry. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Supervised learning in spiking neural networks with FORCE training.

Science.gov (United States)

Nicola, Wilten; Clopath, Claudia

2017-12-20

Populations of neurons display an extraordinary diversity in the behaviors they affect and display. Machine learning techniques have recently emerged that allow us to create networks of model neurons that display behaviors of similar complexity. Here we demonstrate the direct applicability of one such technique, the FORCE method, to spiking neural networks. We train these networks to mimic dynamical systems, classify inputs, and store discrete sequences that correspond to the notes of a song. Finally, we use FORCE training to create two biologically motivated model circuits. One is inspired by the zebra finch and successfully reproduces songbird singing. The second network is motivated by the hippocampus and is trained to store and replay a movie scene. FORCE trained networks reproduce behaviors comparable in complexity to their inspired circuits and yield information not easily obtainable with other techniques, such as behavioral responses to pharmacological manipulations and spike timing statistics.

Neural networks - Potential appplication in the nuclear industry

International Nuclear Information System (INIS)

Yiftah, S.

1989-01-01

Neural networks are an emerging technology which is perceived to have potential for solving complex computation problems which cannot be solved by standard computational methods. One such example is the inverse kinematics problem which is considered to be the most difficult problem in robotics. In 1986, only one neural network modelling tool was available, now there are about twenty offered commercially by various companies in North America

Computing Generalized Matrix Inverse on Spiking Neural Substrate

Directory of Open Access Journals (Sweden)

Rohit Shukla

2018-03-01

Full Text Available Emerging neural hardware substrates, such as IBM's TrueNorth Neurosynaptic System, can provide an appealing platform for deploying numerical algorithms. For example, a recurrent Hopfield neural network can be used to find the Moore-Penrose generalized inverse of a matrix, thus enabling a broad class of linear optimizations to be solved efficiently, at low energy cost. However, deploying numerical algorithms on hardware platforms that severely limit the range and precision of representation for numeric quantities can be quite challenging. This paper discusses these challenges and proposes a rigorous mathematical framework for reasoning about range and precision on such substrates. The paper derives techniques for normalizing inputs and properly quantizing synaptic weights originating from arbitrary systems of linear equations, so that solvers for those systems can be implemented in a provably correct manner on hardware-constrained neural substrates. The analytical model is empirically validated on the IBM TrueNorth platform, and results show that the guarantees provided by the framework for range and precision hold under experimental conditions. Experiments with optical flow demonstrate the energy benefits of deploying a reduced-precision and energy-efficient generalized matrix inverse engine on the IBM TrueNorth platform, reflecting 10× to 100× improvement over FPGA and ARM core baselines.

Computing Generalized Matrix Inverse on Spiking Neural Substrate

Science.gov (United States)

Shukla, Rohit; Khoram, Soroosh; Jorgensen, Erik; Li, Jing; Lipasti, Mikko; Wright, Stephen

2018-01-01

Emerging neural hardware substrates, such as IBM's TrueNorth Neurosynaptic System, can provide an appealing platform for deploying numerical algorithms. For example, a recurrent Hopfield neural network can be used to find the Moore-Penrose generalized inverse of a matrix, thus enabling a broad class of linear optimizations to be solved efficiently, at low energy cost. However, deploying numerical algorithms on hardware platforms that severely limit the range and precision of representation for numeric quantities can be quite challenging. This paper discusses these challenges and proposes a rigorous mathematical framework for reasoning about range and precision on such substrates. The paper derives techniques for normalizing inputs and properly quantizing synaptic weights originating from arbitrary systems of linear equations, so that solvers for those systems can be implemented in a provably correct manner on hardware-constrained neural substrates. The analytical model is empirically validated on the IBM TrueNorth platform, and results show that the guarantees provided by the framework for range and precision hold under experimental conditions. Experiments with optical flow demonstrate the energy benefits of deploying a reduced-precision and energy-efficient generalized matrix inverse engine on the IBM TrueNorth platform, reflecting 10× to 100× improvement over FPGA and ARM core baselines. PMID:29593483

Reconstruction of sub-surface archaeological remains from magnetic data using neural computing.

Science.gov (United States)

Bescoby, D. J.; Cawley, G. C.; Chroston, P. N.

2003-04-01

The remains of a former Roman colonial settlement, once part of the classical city of Butrint in southern Albania have been the subject of a high resolution magnetic survey using a caesium-vapour magnetometer. The survey revealed the surviving remains of an extensive planned settlement and a number of outlying buildings, today buried beneath over 0.5 m of alluvial deposits. The aim of the current research is to derive a sub-surface model from the magnetic survey measurements, allowing an enhanced archaeological interpretation of the data. Neural computing techniques are used to perform the non-linear mapping between magnetic data and corresponding sub-surface model parameters. The adoption of neural computing paradigms potentially holds several advantages over other modelling techniques, allowing fast solutions for complex data, while having a high tolerance to noise. A multi-layer perceptron network with a feed-forward architecture is trained to estimate the shape and burial depth of wall foundations using a series of representative models as training data. Parameters used to forward model the training data sets are derived from a number of trial trench excavations targeted over features identified by the magnetic survey. The training of the network was optimized by first applying it to synthetic test data of known source parameters. Pre-processing of the network input data, including the use of a rotationally invariant transform, enhanced network performance and the efficiency of the training data. The approach provides good results when applied to real magnetic data, accurately predicting the depths and layout of wall foundations within the former settlement, verified by subsequent excavation. The resulting sub-surface model is derived from the averaged outputs of a ‘committee’ of five networks, trained with individualized training sets. Fuzzy logic inference has also been used to combine individual network outputs through correlation with data from a second

Inspiral waveforms for spinning compact binaries in a new precessing convention

International Nuclear Information System (INIS)

Gupta, Anuradha; Gopakumar, Achamveedu

2016-01-01

It is customary to use a precessing convention, based on Newtonian orbital angular momentum L N , to model inspiral gravitational waves from generic spinning compact binaries. A key feature of such a precessing convention is its ability to remove all spin precession induced modulations from the orbital phase evolution. However, this convention usually employs a postNewtonian (PN) accurate precessional equation, appropriate for the PN accurate orbital angular momentum L , to evolve the L N -based precessing source frame. This motivated us to develop inspiral waveforms for spinning compact binaries in a precessing convention that explicitly use L to describe the binary orbits. Our approach introduces certain additional 3PN order terms in the orbital phase and frequency evolution equations with respect to the usual L N -based implementation of the precessing convention. The implications of these additional terms are explored by computing the match between inspiral waveforms that employ L and L N -based precessing conventions. We found that the match estimates are smaller than the optimal value, namely 0.97, for a non-negligible fraction of unequal mass spinning compact binaries. (paper)

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...

La maturità di INSPIRE

Directory of Open Access Journals (Sweden)

Mauro Salvemini

2010-03-01

Full Text Available INPIRE's maturityThe INSPIRE Conference 2010 took place from 23 to 25 June 2010 in Kraków, Poland. On 22 June pre-conference workshops have been organized. The theme of this year’s edition has been "INSPIRE as a Framework for Cooperation".The INSPIRE Conference has been organised through a series of plenary sessions addressing common policy issues, and parallel sessions focusing in particular on applications and implementations of SDIs, research issues and new and evolvingtechnologies and applications and poster presentations.

MapReduce Based Parallel Neural Networks in Enabling Large Scale Machine Learning

Directory of Open Access Journals (Sweden)

Yang Liu

2015-01-01

Full Text Available Artificial neural networks (ANNs have been widely used in pattern recognition and classification applications. However, ANNs are notably slow in computation especially when the size of data is large. Nowadays, big data has received a momentum from both industry and academia. To fulfill the potentials of ANNs for big data applications, the computation process must be speeded up. For this purpose, this paper parallelizes neural networks based on MapReduce, which has become a major computing model to facilitate data intensive applications. Three data intensive scenarios are considered in the parallelization process in terms of the volume of classification data, the size of the training data, and the number of neurons in the neural network. The performance of the parallelized neural networks is evaluated in an experimental MapReduce computer cluster from the aspects of accuracy in classification and efficiency in computation.

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

Science.gov (United States)

Tam, Wing-Kin; Yang, Zhi

2018-05-01

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

The feasibility of using neural networks to obtain cross sections from electron swarm data

International Nuclear Information System (INIS)

Morgan, W.L.

1991-01-01

This paper reports that although still more a curiosity than an accepted technique in computational modeling, the very new field of neural computing is beginning to find applications in physics. Presented in some background on neural computing and a discussion on the use of neural networks to obtain electron-impact cross sections from measured drift velocities, characteristic energies, and other swarm data. This is what is known as an inverse problem, a class of problems for which neural networks may be frequently superior to other numerical algorithms. Momentum transfer cross sections obtained for a model problem and for xenon using a neural network are presented

Type-2 fuzzy neural networks and their applications

CERN Document Server

Aliev, Rafik Aziz

2014-01-01

This book deals with the theory, design principles, and application of hybrid intelligent systems using type-2 fuzzy sets in combination with other paradigms of Soft Computing technology such as Neuro-Computing and Evolutionary Computing. It provides a self-contained exposition of the foundation of type-2 fuzzy neural networks and presents a vast compendium of its applications to control, forecasting, decision making, system identification and other real problems. Type-2 Fuzzy Neural Networks and Their Applications is helpful for teachers and students of universities and colleges, for scientis

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

Scaling strength distributions in quasi-brittle materials from micro-to macro-scales: A computational approach to modeling Nature-inspired structural ceramics

International Nuclear Information System (INIS)

Genet, Martin; Couegnat, Guillaume; Tomsia, Antoni P.; Ritchie, Robert O.

2014-01-01

This paper presents an approach to predict the strength distribution of quasi-brittle materials across multiple length-scales, with emphasis on Nature-inspired ceramic structures. It permits the computation of the failure probability of any structure under any mechanical load, solely based on considerations of the microstructure and its failure properties by naturally incorporating the statistical and size-dependent aspects of failure. We overcome the intrinsic limitations of single periodic unit-based approaches by computing the successive failures of the material components and associated stress redistributions on arbitrary numbers of periodic units. For large size samples, the microscopic cells are replaced by a homogenized continuum with equivalent stochastic and damaged constitutive behavior. After establishing the predictive capabilities of the method, and illustrating its potential relevance to several engineering problems, we employ it in the study of the shape and scaling of strength distributions across differing length-scales for a particular quasi-brittle system. We find that the strength distributions display a Weibull form for samples of size approaching the periodic unit; however, these distributions become closer to normal with further increase in sample size before finally reverting to a Weibull form for macroscopic sized samples. In terms of scaling, we find that the weakest link scaling applies only to microscopic, and not macroscopic scale, samples. These findings are discussed in relation to failure patterns computed at different size-scales. (authors)

Sound Classification in Hearing Aids Inspired by Auditory Scene Analysis

Science.gov (United States)

Büchler, Michael; Allegro, Silvia; Launer, Stefan; Dillier, Norbert

2005-12-01

A sound classification system for the automatic recognition of the acoustic environment in a hearing aid is discussed. The system distinguishes the four sound classes "clean speech," "speech in noise," "noise," and "music." A number of features that are inspired by auditory scene analysis are extracted from the sound signal. These features describe amplitude modulations, spectral profile, harmonicity, amplitude onsets, and rhythm. They are evaluated together with different pattern classifiers. Simple classifiers, such as rule-based and minimum-distance classifiers, are compared with more complex approaches, such as Bayes classifier, neural network, and hidden Markov model. Sounds from a large database are employed for both training and testing of the system. The achieved recognition rates are very high except for the class "speech in noise." Problems arise in the classification of compressed pop music, strongly reverberated speech, and tonal or fluctuating noises.

Towards enhancement of performance of K-means clustering using nature-inspired optimization algorithms.

Science.gov (United States)

Fong, Simon; Deb, Suash; Yang, Xin-She; Zhuang, Yan

2014-01-01

Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario.

Human-inspired feedback synergies for environmental interaction with a dexterous robotic hand.

Science.gov (United States)

Kent, Benjamin A; Engeberg, Erik D

2014-11-07

Effortless control of the human hand is mediated by the physical and neural couplings inherent in the structure of the hand. This concept was explored for environmental interaction tasks with the human hand, and a novel human-inspired feedback synergy (HFS) controller was developed for a robotic hand which synchronized position and force feedback signals to mimic observed human hand motions. This was achieved by first recording the finger joint motion profiles of human test subjects, where it was observed that the subjects would extend their fingers to maintain a natural hand posture when interacting with different surfaces. The resulting human joint angle data were used as inspiration to develop the HFS controller for the anthropomorphic robotic hand, which incorporated finger abduction and force feedback in the control laws for finger extension. Experimental results showed that by projecting a broader view of the tasks at hand to each specific joint, the HFS controller produced hand motion profiles that closely mimic the observed human responses and allowed the robotic manipulator to interact with the surfaces while maintaining a natural hand posture. Additionally, the HFS controller enabled the robotic hand to autonomously traverse vertical step discontinuities without prior knowledge of the environment, visual feedback, or traditional trajectory planning techniques.

Human-inspired feedback synergies for environmental interaction with a dexterous robotic hand

International Nuclear Information System (INIS)

Kent, Benjamin A; Engeberg, Erik D

2014-01-01

Effortless control of the human hand is mediated by the physical and neural couplings inherent in the structure of the hand. This concept was explored for environmental interaction tasks with the human hand, and a novel human-inspired feedback synergy (HFS) controller was developed for a robotic hand which synchronized position and force feedback signals to mimic observed human hand motions. This was achieved by first recording the finger joint motion profiles of human test subjects, where it was observed that the subjects would extend their fingers to maintain a natural hand posture when interacting with different surfaces. The resulting human joint angle data were used as inspiration to develop the HFS controller for the anthropomorphic robotic hand, which incorporated finger abduction and force feedback in the control laws for finger extension. Experimental results showed that by projecting a broader view of the tasks at hand to each specific joint, the HFS controller produced hand motion profiles that closely mimic the observed human responses and allowed the robotic manipulator to interact with the surfaces while maintaining a natural hand posture. Additionally, the HFS controller enabled the robotic hand to autonomously traverse vertical step discontinuities without prior knowledge of the environment, visual feedback, or traditional trajectory planning techniques. (paper)

Geo-inspired model: Agents vectors naturals inspired by the environmental management (AVNG of water tributaries

Directory of Open Access Journals (Sweden)

Edwin Eduardo Millán Rojas

2018-02-01

Full Text Available Context: Management to care for the environment and the Earth (geo can be source of inspiration for developing models that allow addressing complexity issues; the objective of this research was to develop an additional aspect of the inspired models. The geoinspired model has two features, the first covering aspects related to environmental management and the behavior of natural resources, and the second has a component of spatial location associated with existing objects on the Earth's surface. Method: The approach developed in the research is descriptive and its main objective is the representation or characterization of a case study within a particular context. Results: The result was the design of a model to emulate the natural behavior of the water tributaries of the Amazon foothills, in order to extend the application of the inspired models and allow the use of elements such as geo-referencing and environmental management. The proposed geoinspired model is called “natural vectors agents inspired in environmental management”. Conclusions: The agents vectors naturals inspired by the environmental are polyform elements that can assume the behavior of environmental entities, which makes it possible to achieve progress in other fields of environmental management (use of soil, climate, flora, fauna, and link environmental issues with the structure of the proposed model.

Network model of chemical-sensing system inspired by mouse taste buds.

Science.gov (United States)

Tateno, Katsumi; Igarashi, Jun; Ohtubo, Yoshitaka; Nakada, Kazuki; Miki, Tsutomu; Yoshii, Kiyonori

2011-07-01

Taste buds endure extreme changes in temperature, pH, osmolarity, so on. Even though taste bud cells are replaced in a short span, they contribute to consistent taste reception. Each taste bud consists of about 50 cells whose networks are assumed to process taste information, at least preliminarily. In this article, we describe a neural network model inspired by the taste bud cells of mice. It consists of two layers. In the first layer, the chemical stimulus is transduced into an irregular spike train. The synchronization of the output impulses is induced by the irregular spike train at the second layer. These results show that the intensity of the chemical stimulus is encoded as the degree of the synchronization of output impulses. The present algorithms for signal processing result in a robust chemical-sensing system.

Emerging phenomena in neural networks with dynamic synapses and their computational implications

Directory of Open Access Journals (Sweden)

Joaquin J. eTorres

2013-04-01

Full Text Available In this paper we review our research on the effect and computational role of dynamical synapses on feed-forward and recurrent neural networks. Among others, we report on the appearance of a new class of dynamical memories which result from the destabilisation of learned memory attractors. This has important consequences for dynamic information processing allowing the system to sequentially access the information stored in the memories under changing stimuli. Although storage capacity of stable memories also decreases, our study demonstrated the positive effect of synaptic facilitation to recover maximum storage capacity and to enlarge the capacity of the system for memory recall in noisy conditions. Possibly, the new dynamical behaviour can be associated with the voltage transitions between up and down states observed in cortical areas in the brain. We investigated the conditions for which the permanence times in the up state are power-law distributed, which is a sign for criticality, and concluded that the experimentally observed large variability of permanence times could be explained as the result of noisy dynamic synapses with large recovery times. Finally, we report how short-term synaptic processes can transmit weak signals throughout more than one frequency range in noisy neural networks, displaying a kind of stochastic multi-resonance. This effect is due to competition between activity-dependent synaptic fluctuations (due to dynamic synapses and the existence of neuron firing threshold which adapts to the incoming mean synaptic input.

Introduction to Concepts in Artificial Neural Networks

Science.gov (United States)

Niebur, Dagmar

1995-01-01

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

Adaptive Fuzzy-Lyapunov Controller Using Biologically Inspired Swarm Intelligence

Directory of Open Access Journals (Sweden)

Alejandro Carrasco Elizalde

2008-01-01

Full Text Available The collective behaviour of swarms produces smarter actions than those achieved by a single individual. Colonies of ants, flocks of birds and fish schools are examples of swarms interacting with their environment to achieve a common goal. This cooperative biological intelligence is the inspiration for an adaptive fuzzy controller developed in this paper. Swarm intelligence is used to adjust the parameters of the membership functions used in the adaptive fuzzy controller. The rules of the controller are designed using a computing-with-words approach called Fuzzy-Lyapunov synthesis to improve the stability and robustness of an adaptive fuzzy controller. Computing-with-words provides a powerful tool to manipulate numbers and symbols, like words in a natural language.

Norsk inspiration til uddannelse og job

DEFF Research Database (Denmark)

Skovhus, Randi Boelskifte; Thomsen, Rie; Buhl, Rita

2017-01-01

Anmeldelse af bog om det norske fag Utdanningsvalg - inspiration til arbejde med uddannelse og job......Anmeldelse af bog om det norske fag Utdanningsvalg - inspiration til arbejde med uddannelse og job...

Feeling Is Believing: Inspiration Encourages Belief in God.

Science.gov (United States)

Critcher, Clayton R; Lee, Chan Jean

2018-05-01

Even without direct evidence of God's existence, about half of the world's population believes in God. Although previous research has found that people arrive at such beliefs intuitively instead of analytically, relatively little research has aimed to understand what experiences encourage or legitimate theistic belief systems. Using cross-cultural correlational and experimental methods, we investigated whether the experience of inspiration encourages a belief in God. Participants who dispositionally experience more inspiration, were randomly assigned to relive or have an inspirational experience, or reported such experiences to be more inspirational all showed stronger belief in God. These effects were specific to inspiration (instead of adjacent affective experiences) and a belief in God (instead of other empirically unverifiable claims). Being inspired by someone or something (but not inspired to do something) offers a spiritually transcendent experience that elevates belief in God, in part because it makes people feel connected to something beyond themselves.

Random noise effects in pulse-mode digital multilayer neural networks.

Science.gov (United States)

Kim, Y C; Shanblatt, M A

1995-01-01

A pulse-mode digital multilayer neural network (DMNN) based on stochastic computing techniques is implemented with simple logic gates as basic computing elements. The pulse-mode signal representation and the use of simple logic gates for neural operations lead to a massively parallel yet compact and flexible network architecture, well suited for VLSI implementation. Algebraic neural operations are replaced by stochastic processes using pseudorandom pulse sequences. The distributions of the results from the stochastic processes are approximated using the hypergeometric distribution. Synaptic weights and neuron states are represented as probabilities and estimated as average pulse occurrence rates in corresponding pulse sequences. A statistical model of the noise (error) is developed to estimate the relative accuracy associated with stochastic computing in terms of mean and variance. Computational differences are then explained by comparison to deterministic neural computations. DMNN feedforward architectures are modeled in VHDL using character recognition problems as testbeds. Computational accuracy is analyzed, and the results of the statistical model are compared with the actual simulation results. Experiments show that the calculations performed in the DMNN are more accurate than those anticipated when Bernoulli sequences are assumed, as is common in the literature. Furthermore, the statistical model successfully predicts the accuracy of the operations performed in the DMNN.

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.

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

Directory of Open Access Journals (Sweden)

Wilfredo Blanco

2017-09-01

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

A Quantum Implementation Model for Artificial Neural Networks

OpenAIRE

Ammar Daskin

2018-01-01

The learning process for multilayered neural networks with many nodes makes heavy demands on computational resources. In some neural network models, the learning formulas, such as the Widrow–Hoff formula, do not change the eigenvectors of the weight matrix while flatting the eigenvalues. In infinity, these iterative formulas result in terms formed by the principal components of the weight matrix, namely, the eigenvectors corresponding to the non-zero eigenvalues. In quantum computing, the pha...

Deep learning with coherent nanophotonic circuits

Science.gov (United States)

Shen, Yichen; Harris, Nicholas C.; Skirlo, Scott; Prabhu, Mihika; Baehr-Jones, Tom; Hochberg, Michael; Sun, Xin; Zhao, Shijie; Larochelle, Hugo; Englund, Dirk; Soljačić, Marin

2017-07-01

Artificial neural networks are computational network models inspired by signal processing in the brain. These models have dramatically improved performance for many machine-learning tasks, including speech and image recognition. However, today's computing hardware is inefficient at implementing neural networks, in large part because much of it was designed for von Neumann computing schemes. Significant effort has been made towards developing electronic architectures tuned to implement artificial neural networks that exhibit improved computational speed and accuracy. Here, we propose a new architecture for a fully optical neural network that, in principle, could offer an enhancement in computational speed and power efficiency over state-of-the-art electronics for conventional inference tasks. We experimentally demonstrate the essential part of the concept using a programmable nanophotonic processor featuring a cascaded array of 56 programmable Mach-Zehnder interferometers in a silicon photonic integrated circuit and show its utility for vowel recognition.

Application of neural network to CT

International Nuclear Information System (INIS)

Ma, Xiao-Feng; Takeda, Tatsuoki

1999-01-01

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

Breath-hold times in patients undergoing radiological examinations. Comparison of expiration and inspiration with and without hyperventilation

International Nuclear Information System (INIS)

Groell, R.; Schaffler, G.J.; Schloffer, S.

2001-01-01

Background. Breath-holding is necessary for imaging studies of the thorax and abdomen using computed tomography, magnetic resonance imaging or ultrasound examinations. The purpose of this study was to compare the breath-hold times in expiration and inspiration and to evaluate the effects of hyperventilation. Patients and methods. Thirty patients and 19 healthy volunteers participated in this study after informed consent was obtained in all. The breath-hold times were measured in expiration and inspiration before and after hyperventilation. Results. The mean breath-hold times in expiration (patients: 24±9 sec, volunteers: 27±7 sec) were significantly shorter than those in inspiration (patients: 41±20 sec, p<0.001; volunteers: 62±18 sec, p<0.001). Additional hyperventilation resulted in a significant increase (range: 40-60%, p≤0.005) of the mean breathhold times either in expiration and in inspiration and for both patients and volunteers. Conclusions. Although breath-holding in expiration is recommended for various imaging studies particularly of the thorax and of the abdomen, suspending respiration in inspiration enables the patient a considerable longer breath-hold time. (author)

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).

Synaptic energy drives the information processing mechanisms in spiking neural networks.

Science.gov (United States)

El Laithy, Karim; Bogdan, Martin

2014-04-01

Flow of energy and free energy minimization underpins almost every aspect of naturally occurring physical mechanisms. Inspired by this fact this work establishes an energy-based framework that spans the multi-scale range of biological neural systems and integrates synaptic dynamic, synchronous spiking activity and neural states into one consistent working paradigm. Following a bottom-up approach, a hypothetical energy function is proposed for dynamic synaptic models based on the theoretical thermodynamic principles and the Hopfield networks. We show that a synapse exposes stable operating points in terms of its excitatory postsynaptic potential as a function of its synaptic strength. We postulate that synapses in a network operating at these stable points can drive this network to an internal state of synchronous firing. The presented analysis is related to the widely investigated temporal coherent activities (cell assemblies) over a certain range of time scales (binding-by-synchrony). This introduces a novel explanation of the observed (poly)synchronous activities within networks regarding the synaptic (coupling) functionality. On a network level the transitions from one firing scheme to the other express discrete sets of neural states. The neural states exist as long as the network sustains the internal synaptic energy.

Understanding the Implications of Neural Population Activity on Behavior

Science.gov (United States)

Briguglio, John

Learning how neural activity in the brain leads to the behavior we exhibit is one of the fundamental questions in Neuroscience. In this dissertation, several lines of work are presented to that use principles of neural coding to understand behavior. In one line of work, we formulate the efficient coding hypothesis in a non-traditional manner in order to test human perceptual sensitivity to complex visual textures. We find a striking agreement between how variable a particular texture signal is and how sensitive humans are to its presence. This reveals that the efficient coding hypothesis is still a guiding principle for neural organization beyond the sensory periphery, and that the nature of cortical constraints differs from the peripheral counterpart. In another line of work, we relate frequency discrimination acuity to neural responses from auditory cortex in mice. It has been previously observed that optogenetic manipulation of auditory cortex, in addition to changing neural responses, evokes changes in behavioral frequency discrimination. We are able to account for changes in frequency discrimination acuity on an individual basis by examining the Fisher information from the neural population with and without optogenetic manipulation. In the third line of work, we address the question of what a neural population should encode given that its inputs are responses from another group of neurons. Drawing inspiration from techniques in machine learning, we train Deep Belief Networks on fake retinal data and show the emergence of Garbor-like filters, reminiscent of responses in primary visual cortex. In the last line of work, we model the state of a cortical excitatory-inhibitory network during complex adaptive stimuli. Using a rate model with Wilson-Cowan dynamics, we demonstrate that simple non-linearities in the signal transferred from inhibitory to excitatory neurons can account for real neural recordings taken from auditory cortex. This work establishes and tests

Online Signature Verification using Recurrent Neural Network and Length-normalized Path Signature

OpenAIRE

Lai, Songxuan; Jin, Lianwen; Yang, Weixin

2017-01-01

Inspired by the great success of recurrent neural networks (RNNs) in sequential modeling, we introduce a novel RNN system to improve the performance of online signature verification. The training objective is to directly minimize intra-class variations and to push the distances between skilled forgeries and genuine samples above a given threshold. By back-propagating the training signals, our RNN network produced discriminative features with desired metrics. Additionally, we propose a novel d...

All-memristive neuromorphic computing with level-tuned neurons

Science.gov (United States)

Pantazi, Angeliki; Woźniak, Stanisław; Tuma, Tomas; Eleftheriou, Evangelos

2016-09-01

In the new era of cognitive computing, systems will be able to learn and interact with the environment in ways that will drastically enhance the capabilities of current processors, especially in extracting knowledge from vast amount of data obtained from many sources. Brain-inspired neuromorphic computing systems increasingly attract research interest as an alternative to the classical von Neumann processor architecture, mainly because of the coexistence of memory and processing units. In these systems, the basic components are neurons interconnected by synapses. The neurons, based on their nonlinear dynamics, generate spikes that provide the main communication mechanism. The computational tasks are distributed across the neural network, where synapses implement both the memory and the computational units, by means of learning mechanisms such as spike-timing-dependent plasticity. In this work, we present an all-memristive neuromorphic architecture comprising neurons and synapses realized by using the physical properties and state dynamics of phase-change memristors. The architecture employs a novel concept of interconnecting the neurons in the same layer, resulting in level-tuned neuronal characteristics that preferentially process input information. We demonstrate the proposed architecture in the tasks of unsupervised learning and detection of multiple temporal correlations in parallel input streams. The efficiency of the neuromorphic architecture along with the homogenous neuro-synaptic dynamics implemented with nanoscale phase-change memristors represent a significant step towards the development of ultrahigh-density neuromorphic co-processors.

All-memristive neuromorphic computing with level-tuned neurons.

Science.gov (United States)

Pantazi, Angeliki; Woźniak, Stanisław; Tuma, Tomas; Eleftheriou, Evangelos

2016-09-02

In the new era of cognitive computing, systems will be able to learn and interact with the environment in ways that will drastically enhance the capabilities of current processors, especially in extracting knowledge from vast amount of data obtained from many sources. Brain-inspired neuromorphic computing systems increasingly attract research interest as an alternative to the classical von Neumann processor architecture, mainly because of the coexistence of memory and processing units. In these systems, the basic components are neurons interconnected by synapses. The neurons, based on their nonlinear dynamics, generate spikes that provide the main communication mechanism. The computational tasks are distributed across the neural network, where synapses implement both the memory and the computational units, by means of learning mechanisms such as spike-timing-dependent plasticity. In this work, we present an all-memristive neuromorphic architecture comprising neurons and synapses realized by using the physical properties and state dynamics of phase-change memristors. The architecture employs a novel concept of interconnecting the neurons in the same layer, resulting in level-tuned neuronal characteristics that preferentially process input information. We demonstrate the proposed architecture in the tasks of unsupervised learning and detection of multiple temporal correlations in parallel input streams. The efficiency of the neuromorphic architecture along with the homogenous neuro-synaptic dynamics implemented with nanoscale phase-change memristors represent a significant step towards the development of ultrahigh-density neuromorphic co-processors.

PREDICTING CUSTOMER CHURN IN BANKING INDUSTRY USING NEURAL NETWORKS

Directory of Open Access Journals (Sweden)

Alisa Bilal Zorić

2016-03-01

Full Text Available The aim of this article is to present a case study of usage of one of the data mining methods, neural network, in knowledge discovery from databases in the banking industry. Data mining is automated process of analysing, organization or grouping a large set of data from different perspectives and summarizing it into useful information using special algorithms. Data mining can help to resolve banking problems by finding some regularity, causality and correlation to business information which are not visible at first sight because they are hidden in large amounts of data. In this paper, we used one of the data mining methods, neural network, within the software package Alyuda NeuroInteligence to predict customer churn in bank. The focus on customer churn is to determinate the customers who are at risk of leaving and analysing whether those customers are worth retaining. Neural network is statistical learning model inspired by biological neural and it is used to estimate or approximate functions that can depend on a large number of inputs which are generally unknown. Although the method itself is complicated, there are tools that enable the use of neural networks without much prior knowledge of how they operate. The results show that clients who use more bank services (products are more loyal, so bank should focus on those clients who use less than three products, and offer them products according to their needs. Similar results are obtained for different network topologies.

Hardware for soft computing and soft computing for hardware

CERN Document Server

Nedjah, Nadia

2014-01-01

Single and Multi-Objective Evolutionary Computation (MOEA),  Genetic Algorithms (GAs), Artificial Neural Networks (ANNs), Fuzzy Controllers (FCs), Particle Swarm Optimization (PSO) and Ant colony Optimization (ACO) are becoming omnipresent in almost every intelligent system design. Unfortunately, the application of the majority of these techniques is complex and so requires a huge computational effort to yield useful and practical results. Therefore, dedicated hardware for evolutionary, neural and fuzzy computation is a key issue for designers. With the spread of reconfigurable hardware such as FPGAs, digital as well as analog hardware implementations of such computation become cost-effective. The idea behind this book is to offer a variety of hardware designs for soft computing techniques that can be embedded in any final product. Also, to introduce the successful application of soft computing technique to solve many hard problem encountered during the design of embedded hardware designs. Reconfigurable em...

A cellular automata based FPGA realization of a new metaheuristic bat-inspired algorithm

Science.gov (United States)

Progias, Pavlos; Amanatiadis, Angelos A.; Spataro, William; Trunfio, Giuseppe A.; Sirakoulis, Georgios Ch.

2016-10-01

Optimization algorithms are often inspired by processes occuring in nature, such as animal behavioral patterns. The main concern with implementing such algorithms in software is the large amounts of processing power they require. In contrast to software code, that can only perform calculations in a serial manner, an implementation in hardware, exploiting the inherent parallelism of single-purpose processors, can prove to be much more efficient both in speed and energy consumption. Furthermore, the use of Cellular Automata (CA) in such an implementation would be efficient both as a model for natural processes, as well as a computational paradigm implemented well on hardware. In this paper, we propose a VHDL implementation of a metaheuristic algorithm inspired by the echolocation behavior of bats. More specifically, the CA model is inspired by the metaheuristic algorithm proposed earlier in the literature, which could be considered at least as efficient than other existing optimization algorithms. The function of the FPGA implementation of our algorithm is explained in full detail and results of our simulations are also demonstrated.

Paradigms for biologically inspired design

DEFF Research Database (Denmark)

Lenau, T. A.; Metzea, A.-L.; Hesselberg, T.

2018-01-01

engineering, medical engineering, nanotechnology, photonics,environmental protection and agriculture. However, a major obstacle for the wider use of biologically inspired design isthe knowledge barrier that exist between the application engineers that have insight into how to design suitable productsand......Biologically inspired design is attracting increasing interest since it offers access to a huge biological repository of wellproven design principles that can be used for developing new and innovative products. Biological phenomena can inspireproduct innovation in as diverse areas as mechanical...... the biologists with detailed knowledge and experience in understanding how biological organisms function in theirenvironment. The biologically inspired design process can therefore be approached using different design paradigmsdepending on the dominant opportunities, challenges and knowledge characteristics...

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...

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.

A Quantum Implementation Model for Artificial Neural Networks

OpenAIRE

Daskin, Ammar

2016-01-01

The learning process for multi layered neural networks with many nodes makes heavy demands on computational resources. In some neural network models, the learning formulas, such as the Widrow-Hoff formula, do not change the eigenvectors of the weight matrix while flatting the eigenvalues. In infinity, this iterative formulas result in terms formed by the principal components of the weight matrix: i.e., the eigenvectors corresponding to the non-zero eigenvalues. In quantum computing, the phase...

Prediction of the Thermal Conductivity of Refrigerants by Computational Methods and Artificial Neural Network.

Science.gov (United States)

Ghaderi, Forouzan; Ghaderi, Amir H; Ghaderi, Noushin; Najafi, Bijan

2017-01-01

Background: The thermal conductivity of fluids can be calculated by several computational methods. However, these methods are reliable only at the confined levels of density, and there is no specific computational method for calculating thermal conductivity in the wide ranges of density. Methods: In this paper, two methods, an Artificial Neural Network (ANN) approach and a computational method established upon the Rainwater-Friend theory, were used to predict the value of thermal conductivity in all ranges of density. The thermal conductivity of six refrigerants, R12, R14, R32, R115, R143, and R152 was predicted by these methods and the effectiveness of models was specified and compared. Results: The results show that the computational method is a usable method for predicting thermal conductivity at low levels of density. However, the efficiency of this model is considerably reduced in the mid-range of density. It means that this model cannot be used at density levels which are higher than 6. On the other hand, the ANN approach is a reliable method for thermal conductivity prediction in all ranges of density. The best accuracy of ANN is achieved when the number of units is increased in the hidden layer. Conclusion: The results of the computational method indicate that the regular dependence between thermal conductivity and density at higher densities is eliminated. It can develop a nonlinear problem. Therefore, analytical approaches are not able to predict thermal conductivity in wide ranges of density. Instead, a nonlinear approach such as, ANN is a valuable method for this purpose.

Parallel consensual neural networks.

Science.gov (United States)

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

1997-01-01

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

Potential usefulness of an artificial neural network for assessing ventricular size

International Nuclear Information System (INIS)

Fukuda, Haruyuki; Nakajima, Hideyuki; Usuki, Noriaki; Saiwai, Shigeo; Miyamoto, Takeshi; Inoue, Yuichi; Onoyama, Yasuto.

1995-01-01

An artificial neural network approach was applied to assess ventricular size from computed tomograms. Three layer, feed-forward neural networks with a back propagation algorithm were designed to distinguish between three degree of enlargement of the ventricles on the basis of patient's age and six items of computed tomographic information. Data for training and testing the neural network were created with computed tomograms of the brains selected at random from daily examinations. Four radiologists decided by mutual consent subjectively based on their experience whether the ventricles were within normal limits, slightly enlarged, or enlarged for the patient's age. The data for training was obtained from 38 patients. The data for testing was obtained from 47 other patients. The performance of the neural network trained using the data for training was evaluated by the rate of correct answers to the data for testing. The valid solution ratio to response of the test data obtained from the trained neural networks was more than 90% for all conditions in this study. The solutions were completely valid in the neural networks with two or three units at the hidden layer with 2,200 learning iterations, and with two units at the hidden layer with 11,000 learning iterations. The squared error decreased remarkably in the range from 0 to 500 learning iterations, and was close to a contrast over two thousand learning iterations. The neural network with a hidden layer having two or three units showed high decision performance. The preliminary results strongly suggest that the neural network approach has potential utility in computer-aided estimation of enlargement of the ventricles. (author)

Iris Data Classification Using Quantum Neural Networks

International Nuclear Information System (INIS)

Sahni, Vishal; Patvardhan, C.

2006-01-01

Quantum computing is a novel paradigm that promises to be the future of computing. The performance of quantum algorithms has proved to be stunning. ANN within the context of classical computation has been used for approximation and classification tasks with some success. This paper presents an idea of quantum neural networks along with the training algorithm and its convergence property. It synergizes the unique properties of quantum bits or qubits with the various techniques in vogue in neural networks. An example application of Fisher's Iris data set, a benchmark classification problem has also been presented. The results obtained amply demonstrate the classification capabilities of the quantum neuron and give an idea of their promising capabilities

22nd Italian Workshop on Neural Nets

CERN Document Server

Bassis, Simone; Esposito, Anna; Morabito, Francesco

2013-01-01

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

Towards Enhancement of Performance of K-Means Clustering Using Nature-Inspired Optimization Algorithms

Directory of Open Access Journals (Sweden)

Simon Fong

2014-01-01

Full Text Available Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario.

Communication analysis for feedback control of civil infrastructure using cochlea-inspired sensing nodes

Science.gov (United States)

Peckens, Courtney A.; Cook, Ireana; Lynch, Jerome P.

2016-04-01

Wireless sensor networks (WSNs) have emerged as a reliable, low-cost alternative to the traditional wired sensing paradigm. While such networks have made significant progress in the field of structural monitoring, significantly less development has occurred for feedback control applications. Previous work in WSNs for feedback control has highlighted many of the challenges of using this technology including latency in the wireless communication channel and computational inundation at the individual sensing nodes. This work seeks to overcome some of those challenges by drawing inspiration from the real-time sensing and control techniques employed by the biological central nervous system and in particular the mammalian cochlea. A novel bio-inspired wireless sensor node was developed that employs analog filtering techniques to perform time-frequency decomposition of a sensor signal, thus encompassing the functionality of the cochlea. The node then utilizes asynchronous sampling of the filtered signal to compress the signal prior to communication. This bio-inspired sensing architecture is extended to a feedback control application in order to overcome the traditional challenges currently faced by wireless control. In doing this, however, the network experiences high bandwidths of low-significance information exchange between nodes, resulting in some lost data. This study considers the impact of this lost data on the control capabilities of the bio-inspired control architecture and finds that it does not significantly impact the effectiveness of control.

Towards Enhancement of Performance of K-Means Clustering Using Nature-Inspired Optimization Algorithms

Science.gov (United States)

Deb, Suash; Yang, Xin-She

2014-01-01

Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario. PMID:25202730

Follow-up of CT-derived airway wall thickness : Correcting for changes in inspiration level improves reliability

NARCIS (Netherlands)

Pompe, Esther; van Rikxoort, Eva M; Mets, Onno M; Charbonnier, Jean-Paul; Kuhnigk, Jan-Martin; de Koning, Harry J; Oudkerk, Matthijs; Vliegenthart, Rozemarijn; Zanen, Pieter; Lammers, Jan-Willem J; van Ginneken, Bram; de Jong, Pim A; Mohamed Hoesein, Firdaus A A

2016-01-01

OBJECTIVES: Airway wall thickness (AWT) is affected by changes in lung volume. This study evaluated whether correcting AWT on computed tomography (CT) for differences in inspiration level improves measurement agreement, reliability, and power to detect changes over time. METHODS: Participants of the

Follow-up of CT-derived airway wall thickness : Correcting for changes in inspiration level improves reliability

NARCIS (Netherlands)

Pompe, Esther; van Rikxoort, Eva M.; Mets, Onno M.; Charbonnier, Jean-Paul; Kuhnigk, Jan-Martin; de Koning, Harry J.; Oudkerk, Matthijs; Vliegenthart, Rozemarijn; Zanen, Pieter; Lammers, Jan-Willem J.; van Ginneken, Bram; de Jong, Pim A.; Hoesein, Firdaus A. A. Mohamed

2016-01-01

Objectives: Airway wall thickness (AWT) is affected by changes in lung volume. This study evaluated whether correcting AWT on computed tomography (CT) for differences in inspiration level improves measurement agreement, reliability, and power to detect changes over time. Methods: Participants of the

A Neurocomputational Model of Goal-Directed Navigation in Insect-Inspired Artificial Agents.

Science.gov (United States)

Goldschmidt, Dennis; Manoonpong, Poramate; Dasgupta, Sakyasingha

2017-01-01

Despite their small size, insect brains are able to produce robust and efficient navigation in complex environments. Specifically in social insects, such as ants and bees, these navigational capabilities are guided by orientation directing vectors generated by a process called path integration. During this process, they integrate compass and odometric cues to estimate their current location as a vector, called the home vector for guiding them back home on a straight path. They further acquire and retrieve path integration-based vector memories globally to the nest or based on visual landmarks. Although existing computational models reproduced similar behaviors, a neurocomputational model of vector navigation including the acquisition of vector representations has not been described before. Here we present a model of neural mechanisms in a modular closed-loop control-enabling vector navigation in artificial agents. The model consists of a path integration mechanism, reward-modulated global learning, random search, and action selection. The path integration mechanism integrates compass and odometric cues to compute a vectorial representation of the agent's current location as neural activity patterns in circular arrays. A reward-modulated learning rule enables the acquisition of vector memories by associating the local food reward with the path integration state. A motor output is computed based on the combination of vector memories and random exploration. In simulation, we show that the neural mechanisms enable robust homing and localization, even in the presence of external sensory noise. The proposed learning rules lead to goal-directed navigation and route formation performed under realistic conditions. Consequently, we provide a novel approach for vector learning and navigation in a simulated, situated agent linking behavioral observations to their possible underlying neural substrates.

Event- and Time-Driven Techniques Using Parallel CPU-GPU Co-processing for Spiking Neural Networks.

Science.gov (United States)

Naveros, Francisco; Garrido, Jesus A; Carrillo, Richard R; Ros, Eduardo; Luque, Niceto R

2017-01-01

Modeling and simulating the neural structures which make up our central neural system is instrumental for deciphering the computational neural cues beneath. Higher levels of biological plausibility usually impose higher levels of complexity in mathematical modeling, from neural to behavioral levels. This paper focuses on overcoming the simulation problems (accuracy and performance) derived from using higher levels of mathematical complexity at a neural level. This study proposes different techniques for simulating neural models that hold incremental levels of mathematical complexity: leaky integrate-and-fire (LIF), adaptive exponential integrate-and-fire (AdEx), and Hodgkin-Huxley (HH) neural models (ranged from low to high neural complexity). The studied techniques are classified into two main families depending on how the neural-model dynamic evaluation is computed: the event-driven or the time-driven families. Whilst event-driven techniques pre-compile and store the neural dynamics within look-up tables, time-driven techniques compute the neural dynamics iteratively during the simulation time. We propose two modifications for the event-driven family: a look-up table recombination to better cope with the incremental neural complexity together with a better handling of the synchronous input activity. Regarding the time-driven family, we propose a modification in computing the neural dynamics: the bi-fixed-step integration method. This method automatically adjusts the simulation step size to better cope with the stiffness of the neural model dynamics running in CPU platforms. One version of this method is also implemented for hybrid CPU-GPU platforms. Finally, we analyze how the performance and accuracy of these modifications evolve with increasing levels of neural complexity. We also demonstrate how the proposed modifications which constitute the main contribution of this study systematically outperform the traditional event- and time-driven techniques under

Neural components of altruistic punishment

Directory of Open Access Journals (Sweden)

Emily eDu

2015-02-01

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

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.