Conferences related to Neurons

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2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invited sessions of the latest significant findings and developments in all the major fields of biomedical engineering. Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and poster sessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.

  • 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

    The conference will cover diverse topics ranging from biomedical engineering to healthcare technologies to medical and clinical applications. The conference program will consist of invited plenary lectures, symposia, workshops, invited sessions and oral and poster sessions of unsolicited contributions. All papers will be peer reviewed and accepted papers of up to 4 pages will appear in the Conference Proceedings and be indexed by IEEE Xplore and Medline/PubMed.

  • 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

    The conference program will consist of plenary lectures, symposia, workshops and invited sessions of the latest significant findings and developments in all the major fields of biomedical engineering. Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and poster sessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.

  • 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

    The Annual International Conference of the IEEE Engineering in Medicine and Biology Society covers a broad spectrum of topics from biomedical engineering and physics to medical and clinical applications. The conference program will consist of invited plenary lectures, symposia, workshops, invited sessions, oral and poster sessions of unsolicited contributions. All papers will be peer reviewed and accepted papers of up to 4 pages will appear in the Conference Proceedings and be indexed by PubMed and EI. Prop

  • 2012 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

    The annual conference of EMBS averages 2000 attendees from over 50 countries. The scope of the conference is general in nature to focus on the interdisciplinary fields of biomedical engineering. Themes included but not limited to are: Imaging, Biosignals, Biorobotics, Bioinstrumentation, Neural, Rehabilitation, Bioinformatics, Healthcare IT, Medical Devices, etc

  • 2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

    The annual conference of EMBS averages 2000 attendees from over 50 countries. The scope of the conference is general in nature to focus on the interdisciplinary fields of biomedical engineering. Themes included but not limited to are: Imaging, Biosignals, Biorobotics, Bioinstrumentation, Neural, Rehabilitation, Bioinformatics, Healthcare IT, Medical Devices, etc.

  • 2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

    The annual conference of EMBS averages 2000 attendees from over 50 countries. The scope of the conference is general in nature to focus on the interdisciplinary fields of biomedical engineering. Themes included but not limited to are: Imaging, Biosignals, Biorobotics, Bioinstrumentation, Neural, Rehabilitation, Bioinformatics, Healthcare IT, Medical Devices, etc

  • 2009 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

    The annual conference of EMBS averages 2000 attendees from over 50 countries. The scope of the conference is general in nature to focus on the interdisciplinary fields of biomedical engineering. Themes included but not limited to are: Imaging, Biosignals, Biorobotics, Bioinstrumentation, Neural, Rehabilitation, Bioinformatics, Healthcare IT, Medical Devices, etc


2014 IEEE International Symposium on Circuits and Systems (ISCAS)

The IEEE International Symposium on Circuits and Systems (ISCAS) is the flagship conference of the IEEE Circuits and Systems Society and the world’s premier networking forum in the highly active fields of theory, design and implementation of circuits and systems.ISCAS 2014 will have a special focus on nano/bio circuits and systems applied to enhancing living and lifestyles, and seeks to address multidisciplinary challenges in healthcare and well-being, the environment and climate change.

  • 2013 IEEE International Symposium on Circuits and Systems (ISCAS)

    The Symposium will focus on circuits and systems employing nanodevices (both extremely scaled CMOS and non-CMOS devices) and circuit fabrics (mixture of standard CMOS and evolving nano-structure elements) and their implementation cost, switching speed, energy efficiency, and reliability. The ISCAS 2010 will include oral and poster sessions; tutorials given by experts in state-of-the-art topics; and special sessions, with the aim of complementing the regular program with topics of particular interest to the community that cut across and beyond disciplines traditionally represented at ISCAS.

  • 2012 IEEE International Symposium on Circuits and Systems - ISCAS 2012

    2012 International Symposium on Circuits and Systems (ISCAS 2012) aims at providing the world's premier forum of leading researchers in circuits and systems areas from academia and industries, especially focusing on Convergence of BINET (BioInfoNanoEnviro Tech.) which represents IT, NT and ET and leading Human Life Revolutions. Prospective authors are invited to submit papers of their original works emphasizing contributions beyond the present state of the art. We also welcome proposals on special tuto

  • 2011 IEEE International Symposium on Circuits and Systems (ISCAS)

    The IEEE International Symposium on Circuits and Systems (ISCAS) is the world's premier networking forum of leading researchers in the highly active fields of theory, design and implementation of circuits and systems.

  • 2010 IEEE International Symposium on Circuits and Systems - ISCAS 2010

    ISCAS is a unique conference dealing with circuits and systems. It's the yearly "rendez-vous" of leading researchers, coming both from academia and industry, in the highly active fields of theory, design and implementation of circuits and systems. The Symposium will focus on circuits and systems for high quality life and consumer technologies, including mobile communications, advanced multimedia systems, sensor networks and Nano-Bio Circuit Fabrics and Systems.

  • 2009 IEEE International Symposium on Circuits and Systems - ISCAS 2009

    Analog Signal Processing, Biomedical Circuits and Systems, Blind Signal Processing, Cellular Neural Networks and Array Computing, Circuits and Systems for Communications, Computer-Aided Network Design, Digital Signal Processing, Life-Science Systems and Applications, Multimedia Systems and Applications, Nanoelectronics and Gigascale Systems, Neural Systems and Applications, Nonlinear Circuits and Applications, Power Systems and Power Electronic Circuits, Sensory Systems, Visual Signal Processing and Communi

  • 2008 IEEE International Symposium on Circuits and Systems - ISCAS 2008

  • 2007 IEEE International Symposium on Circuits and Systems - ISCAS 2007

  • 2006 IEEE International Symposium on Circuits and Systems - ISCAS 2006


2013 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL)

We will discuss how intelligent biological and artificial systems develop sensorimotor, cognitive, and social abilities, over extended periods of time, through dynamic interactions with their physical and social environments. This field lies at the intersection of a number of scientific and engineering disciplines including Neuroscience, Developmental Psychology, Developmental Linguistics, Cognitive Science, Computational Neuroscience, Artificial Intelligence, Machine Learning, and Robotics.

  • 2012 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL)

    The ICDL and the Epigenetic Robotics conferences are the premier venues for interdisciplinary research that blends the boundaries between robotics, artificial intelligence, machine learning, developmental psychology, neuroscience, and philosophy. The scope of development and learning covered by this conference includes perceptual, cognitive, motor, behavioral, emotional and other related capabilities that are exhibited by humans, higher animals, artificial systems and robots.

  • 2011 IEEE International Conference on Development and Learning (ICDL)

    This conference covers how intelligent biological and artificial systems develop sensorimotor, cognitive and social abilities, over extended periods of time, through dynamic interactions of their brain and body with their physical and social environments.

  • 2010 IEEE 9th International Conference on Development and Learning (ICDL 2010)

    ICDL is for interdisciplinary research blending between robotics, artificial intelligence, machine learning, developmental psychology, neuroscience, and philosophy. The scope of development and learning covered by this conference includes perceptual, cognitive, motor, behavioral, emotional and other capabilities that are exhibited by humans, higher animals, artificial systems and robots.

  • 2009 IEEE 8th International Conference on Development and Learning (ICDL 2009)

    A multidisciplinary conference pertaining to all subjects related to the development and learning process of natural and artificial systems, including perceptual, cognitive, behavioral, emotional and all other mental capabilities that are exhibited by humans, higher animals, robots.

  • 2008 IEEE 7th International Conference on Development and Learning (ICDL 2008)

    ICDL is a unique interdisciplinary conference that brings together researchers in computational sciences with researchers in biological sciences to focus on issues of development and learning. Submissions often feature collaborative work between computer science, robotics, neuroscience, developmental psychology, and related fields.

  • 2007 IEEE 6th International Conference on Development and Learning (ICDL 2007)

    Computational models of human development and learning; bioinspired mechanisms for robot learning and development

  • 2006 5th International Conference on Development and Learning (ICDL 2006)

  • 2005 4th IEEE International Conference on Development and Learning (ICDL 2005)


2010 5th International Summer School on Emerging Technologies in Biomedicine

The aim is to inform young students and researchers about the latest in High Throughput Communication between Brain and Machines . The lectures will be focused on Quantitative Neuroscience, Modern Methods for BCIs, Dynamic Brain Connectivity Mapping, Neuron Based Motor Control, Neuroprosthetics and the tutorial about online BCI system with demonstration.



Periodicals related to Neurons

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Fuzzy Systems, IEEE Transactions on

Theory and application of fuzzy systems with emphasis on engineering systems and scientific applications. (6) (IEEE Guide for Authors) Representative applications areas include:fuzzy estimation, prediction and control; approximate reasoning; intelligent systems design; machine learning; image processing and machine vision;pattern recognition, fuzzy neurocomputing; electronic and photonic implementation; medical computing applications; robotics and motion control; constraint propagation and optimization; civil, chemical and ...


Neural Networks, IEEE Transactions on

Devoted to the science and technology of neural networks, which disclose significant technical knowledge, exploratory developments, and applications of neural networks from biology to software to hardware. Emphasis is on artificial neural networks.




Xplore Articles related to Neurons

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The Multi-valued Astronomical Image Segmentation Based on Pulse Coupled Neural Networks

Liu Qing; Yang Xiaoping; Ma Xiaoshu 2013 Fourth International Conference on Intelligent Systems Design and Engineering Applications, 2013

In order to solve the problem of multi-valued segmentation in the astronomical image, the astronomical image is iteratively processed by Pulse Coupled Neural Networks. In the processing, the maximum mutual information is taken as the optimization segmentation, and the difference of mutual information is regarded as the classified criterion from the relationship between original image and image segmentation. The experimental ...


A New Camera Calibration Based on Neural Network with Tunable Activation Function in Intelligent Space

Mingxin Yuan; Haixiu Hu; Yafeng Jiang; Sheng Hang 2013 Sixth International Symposium on Computational Intelligence and Design, 2013

In order to solve the camera calibration in intelligent space of mobile robot, a new calibration method based on neural network with tunable activation function (TAF) is presented. In the TAF model, the inner product mode is adopted in the calculation of output signal in synapse model and the S function is adopted in base function. Taking the coordinate in ...


Modeling neural networks on the MPP

J. Hicklin; H. Demuth Proceedings., 2nd Symposium on the Frontiers of Massively Parallel Computation, 1988

A network of fixed-connection-weight neuronlike elements is simulated on the massively parallel processor (MPP) in two ways. First, the square connectivity matrix of a 128-neuron network is mapped onto the square MPP processor array. This allows a highly parallel simulation in which 128 MPP processors were active at all times. Second, a 128-by-128 array of neurons is mapped onto the ...


Fault diagnosis in hydraulic turbine governor based on BP neural network

Yu Xiaohui; Liao Ruijin; Yao Chenguo Electrical Machines and Systems, 2001. ICEMS 2001. Proceedings of the Fifth International Conference on, 2001

This paper describes a new fault diagnosis model of the hydraulic turbine governing system with the advanced BPNN (backpropagation neural network), which consists of three layers: i.e. input layer (17 neurons), hidden layer, output layer (13 neurons). It is proved that the system can rind the faults correctly in GeZhouBa hydroelectric power station, and it can conduct the faults examination ...


The Traffic Accident Prediction Based on Neural Network

Huilin Fu; Yucai Zhou 2011 Second International Conference on Digital Manufacturing & Automation, 2011

The traffic accident prediction play an important role in the integrated planning and management of traffic, the reason which with much randomness about the traffic accident include some nonLinear elements, such as people, car, road, cLimate and so on. The traditional way of Linear analyses can not reveal the really situation since the noise pollution and amount of data are ...


More Xplore Articles

Educational Resources on Neurons

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eLearning

The Multi-valued Astronomical Image Segmentation Based on Pulse Coupled Neural Networks

Liu Qing; Yang Xiaoping; Ma Xiaoshu 2013 Fourth International Conference on Intelligent Systems Design and Engineering Applications, 2013

In order to solve the problem of multi-valued segmentation in the astronomical image, the astronomical image is iteratively processed by Pulse Coupled Neural Networks. In the processing, the maximum mutual information is taken as the optimization segmentation, and the difference of mutual information is regarded as the classified criterion from the relationship between original image and image segmentation. The experimental ...


A New Camera Calibration Based on Neural Network with Tunable Activation Function in Intelligent Space

Mingxin Yuan; Haixiu Hu; Yafeng Jiang; Sheng Hang 2013 Sixth International Symposium on Computational Intelligence and Design, 2013

In order to solve the camera calibration in intelligent space of mobile robot, a new calibration method based on neural network with tunable activation function (TAF) is presented. In the TAF model, the inner product mode is adopted in the calculation of output signal in synapse model and the S function is adopted in base function. Taking the coordinate in ...


Modeling neural networks on the MPP

J. Hicklin; H. Demuth Proceedings., 2nd Symposium on the Frontiers of Massively Parallel Computation, 1988

A network of fixed-connection-weight neuronlike elements is simulated on the massively parallel processor (MPP) in two ways. First, the square connectivity matrix of a 128-neuron network is mapped onto the square MPP processor array. This allows a highly parallel simulation in which 128 MPP processors were active at all times. Second, a 128-by-128 array of neurons is mapped onto the ...


Fault diagnosis in hydraulic turbine governor based on BP neural network

Yu Xiaohui; Liao Ruijin; Yao Chenguo Electrical Machines and Systems, 2001. ICEMS 2001. Proceedings of the Fifth International Conference on, 2001

This paper describes a new fault diagnosis model of the hydraulic turbine governing system with the advanced BPNN (backpropagation neural network), which consists of three layers: i.e. input layer (17 neurons), hidden layer, output layer (13 neurons). It is proved that the system can rind the faults correctly in GeZhouBa hydroelectric power station, and it can conduct the faults examination ...


The Traffic Accident Prediction Based on Neural Network

Huilin Fu; Yucai Zhou 2011 Second International Conference on Digital Manufacturing & Automation, 2011

The traffic accident prediction play an important role in the integrated planning and management of traffic, the reason which with much randomness about the traffic accident include some nonLinear elements, such as people, car, road, cLimate and so on. The traditional way of Linear analyses can not reveal the really situation since the noise pollution and amount of data are ...


More eLearning Resources

IEEE-USA E-Books

  • Introduction and Single-Layer Neural Networks

    Neural networks are potentially massively parallel distributed structures and have the ability to learn and generalize. The neuron is the information processing unit of a neural network and the basis for designing numerous neural networks. The most fundamental network architecture is a single-layer neural network, where the single-layer refers to the output layer of computation neurons. This chapter introduces Rosenblatt's neuron. Rosenblatt's perceptron occupies a special place in the historical development of neural networks. The chapter also considers the performance of the perceptron network and is in a position to introduce the perceptron learning rule. This learning rule is an example of supervised training, in which the learning rule is provided with a set of examples of proper network behavior. Finally the chapter further discusses activation function and its types, including a threshold function, or Heaviside function and sigmoid function.

  • Neural Units: Concepts, Models, and Learning

    Neurons and Threshold Logic: Some Basic Concepts Neural Threshold Logic Synthesis Adaptation and Learning for Neural Threshold Elements Adaptive Linear Element (Adaline) Adaline with Sigmoidal Functions Networks with Multiple Neurons Concluding Remarks Problems

  • Dynamical Systems and Interaction Networks

    This chapter first reviews biological models in the framework of dynamical systems. It then illustrates some basic control-theoretic concepts and definitions used in the study of dynamical systems. The chapter also discusses several dynamical models that are typically used in modeling cellular processes and interactions. It then introduces discrete-time Boolean networks. Artificial neural network (ANN) models were originally used to model the connectivities of neurons in brains. Thus, in contrast to cellular signaling networks, ANN models generally have a less direct one-to-one correspondence to biological data. Piecewise linear (PL) systems are quite powerful in modeling biological systems since they may be used as identification models (by means of piecewise linear approximations), or as controllers for more general systems. An interesting class of biological systems with simpler behaved dynamics are systems with monotone dynamics. The dynamics of a monotone system preserves a specific partial order of its inputs over time.

  • Learning in Multilayer Models

    Most neural network programs for personal computers simply control a set of fixed, canned network-layer algorithms with pulldown menus. This new tutorial offers hands-on neural network experiments with a different approach. A simple matrix language lets users create their own neural networks and combine networks, and this is the only currently available software permitting combined simulation of neural networks together with other dynamic systems such as robots or physiological models. The enclosed student version of DESIRE/NEUNET differs from the full system only in the size of its data area and includes a screen editor, compiler, color graphics, help screens, and ready-to-run examples. Users can also add their own help screens and interactive menus.The book provides an introduction to neural networks and simulation, a tutorial on the software, and many complete programs including several backpropagation schemes, creeping random search, competitive learning with and without adaptive-resonance function and "conscience," counterpropagation, nonlinear Grossberg-type neurons, Hopfield-type and bidirectional associative memories, predictors, function learning, biological clocks, system identification, and more.In addition, the book introduces a simple, integrated environment for programming, displays, and report preparation. Even differential equations are entered in ordinary mathematical notation. Users need not learn C or LISP to program nonlinear neuron models. To permit truly interactive experiments, the extra-fast compilation is unnoticeable, and simulations execute faster than PC FORTRAN.The nearly 90 illustrations include block diagrams, computer programs, and simulation-output graphs.Granino A. Kom has been a Professor of Electrical Engineering at the University of Arizona and has worked in the aerospace industry for a decade. He is the author of ten other engineering texts and handbooks.

  • Methods and Neurons

    This chapter contains sections titled: What is an Object? Which Object Property is Definitely Interesting? Are There Best Techniques? Adaptedness of Techniques

  • A selective attention multi-chip system with dynamic synapses and spiking neurons

    Selective attention is the strategy used by biological sensory systems to solve the problem of limited parallel processing capacity: salient subregions of the input stimuli are serially processed, while non-salient regions are suppressed. We present an mixed mode analog/digital Very Large Scale Integration implementation of a building block for a multi-chip neuromorphic hardware model of selective attention. We describe the chip's architecture and its behavior, when its is part of a multi-chip system with a spiking retina as input, and show how it can be used to implement in real-time flexible models of bottom-up attention.

  • Recurrent Neural Networks

    This chapter considers a class of neural networks that have a recurrent structure, including Grossberg network, Hopfield network, and cellular neural networks. The Hopfield network is a form of recurrent artificial neural network invented by John Hopfield in 1982. It consists of a set of neurons and a corresponding set of unit time delays, formatting a multiple-loop feedback system. There are three components to the Grossberg network: Layer 1, Layer 2, and the adaptive weights. Layer 1 is a rough model of the operation of the retina, while Layer 2 represents the visual cortex. Cellular neural networks contain linear and nonlinear circuit elements, which typically are linear capacitors, linear resistors, linear and nonlinear controlled sources, and independent sources. The chapter also describes the mathematical model of a nonlinear dynamic system, and discusses some of the important issues involved in neurodynamics.

  • No title

    Neural interfaces are one of the most exciting emerging technologies to impact bioengineering and neuroscience because they enable an alternate communication channel linking directly the nervous system with man-made devices. This book reveals the essential engineering principles and signal processing tools for deriving control commands from bioelectric signals in large ensembles of neurons. The topics featured include analysis techniques for determining neural representation, modeling in motor systems, computing with neural spikes, and hardware implementation of neural interfaces. Beginning with an exploration of the historical developments that have led to the decoding of information from neural interfaces, this book compares the theory and performance of new neural engineering approaches for BMIs. Contents: Introduction to Neural Interfaces / Foundations of Neuronal Representations / Input-Outpur BMI Models / Regularization Techniques for BMI Models / Neural Decoding Using Generativ BMI Models / Adaptive Algorithms for Point Processes / BMI Systems

  • Connectionism

    This chapter contains sections titled: Connectionism--The Basic Ideas, Modeling a Neuron, Computation with Formal Neurons, An Exclusive-Or Network, Feedback and Word Recognition, Representation, Virtues of Artificial Neural Networks, Learning by Artificial Neural Networks, Notes

  • Multilayer Feedforward Neural Network with Multi-Valued Neurons for Brain-Computer Interfacing

    This chapter contains sections titled: Brain-Computer Interface (BCI) BCI Based on Steady-State Visual Evoked Potentials EEG Signal Preprocessing Decoding Based on MLMVN for Phase-Coded SSVEP BCI System Validation Discussion



Standards related to Neurons

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Jobs related to Neurons

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