Conferences related to Neurons

Back to Top

2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES)

IECBES is a series of bi-annual conference since 2010. The conference will provide excellent platform for knowledge exchange between researchers, scientists, academicians and engineers working in the areas of biomedical engineering. It is open for local and international participants.

  • 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES)

    IECBES is the flagship conference of EMB Malaysia Chapter. Its scope includes new findings in research areas of Biomedical Engineering with keywords indicated above.

  • 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES)

    Biomedical Signal and Image Processing:Cardiovascular, Respiratory, Neural, Biomedical Instrumentation & Devices: Sensor, Micro / Nano / Wearable Technology,Biomaterial, Biomimetics, Rehabilitation andTherapeutic Health System, Biomedical Modeling and Simulation, Bioinformatics, Biomechanics and medical robotics, Ergonomics & Human Factors,Healthcare Information System, Telemedicine, eHealth, myHealth.

  • 2012 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES 2012)

    Biomedical Signal and Image Processing:Cardiovascular, Respiratory, Neural, Biomedical Instrumentation & Devices: Sensor, Micro / Nano / Wearable Technology,Biomaterial, Biomimetics, Rehabilitation and Therapeutic Health System, Biomedical Modeling and Simulation, Bioinformatics, Biomechanics and medical robotics, Ergonomics & Human Factors,Healthcare Information System, Telemedicine, eHealth, myHealth.

  • 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES)

    Biomedical Signal and Image Processing: Cardiovascular, Respiratory, Neural Biomedical Instrumentation & Devices: Sensor, micro/ nano/wearable technology, biomaterial, biomimetic Rehabilitation and Therapeutic Health System Biomedical Modelling and Simulation Bioinformatics Biomechanics and medical robotics Healthcare Information System: Telemedicine, eHealth, mHealth


2017 10th International Symposium on Image and Signal Processing and Analysis (ISPA)

The scope of the conference are theory and applications of signal processing, signal analysis, image processing, and image analysis.


2017 11th European Conference on Antennas and Propagation (EUCAP)

The conference addresses all scientific and application topics in the area of electromagnetic antennas and radio propagation whatever the frequency.


2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)

It covers practice, experience and promising new ideas in the broad area of intelligent systems and knowledge engineering. The topics include: Foundations of Intelligent Systems; Knowledge Engineering and Management; Practical Applications and Systems.


2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) is a premier international forum for scientists and researchers to present the state of the art of data mining and intelligent methods inspired from nature, particularly biological, linguistic, and physical systems, with applications to computers, circuits, systems, control, robotics, communications, and more.

  • 2016 12th International Conference on Natural Computation and 13th Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

    International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) is a premier international forum for scientists and researchers to present the state of the art of data mining and intelligent methods inspired from nature, particularly biological, linguistic, and physical systems, with applications to computers, circuits, systems, control, robotics, communications, and more.

  • 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

    FSKD is an international forum on fuzzy systems and knowledge discovery. Specific topics include fuzzy sets, Bioinformatics and Bio-Medical Informatics, Genomics, Proteomics, Big Data, Databases and Applications, Semi-Structured/Unstructured Data Mining, Multimedia Mining, Web and Text Data Mining, Graphic Model Discovery, Data Warehousing and OLAP, Pattern Recognition and Diagnostics, etc..

  • 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

    FSKD is an international forum on fuzzy systems and knowledge discovery. Specific topics include fuzzy sets, rough sets, Statistical methods, Parallel/ Distributed data mining, KDD Process and human interaction, Knowledge management, Knowledge visualization, Reliability and robustness, Knowledge Discovery in Specific Domains, High dimensional data, Temporal data, Data streaming, Scientific databases, Semi-structured/unstructured data, Multimedia, Text, Web and the Internet, Graphic model discovery, Software warehouse and software mining, Data engineering, Communications and networking, Software engineering, Distributed systems and computer hardware

  • 2013 10th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

    FSKD is an international forum on fuzzy systems and knowledge discovery. Specific topics include fuzzy sets, rough sets, Statistical methods, Parallel/ Distributed data mining, KDD Process and human interaction, Knowledge management, Knowledge visualization, Reliability and robustness, Knowledge Discovery in Specific Domains, High dimensional data, Temporal data, Data streaming, Scientific databases, Semi-structured/unstructured data, Multimedia, Text, Web and the Internet, Graphic model discovery, Software warehouse and software mining, Data engineering, Communications and networking, Software engineering, Distributed systems and computer hardware, etc.

  • 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

    FSKD is an international forum on fuzzy systems and knowledge discovery. Specific topics include fuzzy theory and foundations; stability of fuzzy systems; fuzzy methods and algorithms; fuzzy image, speech and signal processing; multimedia; fuzzy hardware and architectures; data mining.

  • 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

    FSKD is an international forum on fuzzy systems and knowledge discovery. Specific topics include fuzzy theory and foundations; stability of fuzzy systems; fuzzy methods and algorithms; fuzzy image, speech and signal processing; multimedia; fuzzy hardware and architectures; data mining.

  • 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

    FSKD is an international forum on fuzzy systems and knowledge discovery. Specific topics include fuzzy theory and foundations; stability of fuzzy systems; fuzzy methods and algorithms; fuzzy image, speech and signal processing; multimedia; fuzzy hardware and architectures; data mining.

  • 2007 International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

    FSKD '07 covers all aspects of fuzzy systems and knowledge discovery, including recent theoretical advances and interesting applications, for example, fuzzy theory and models, mathematical foundation of fuzzy systems, fuzzy image/signal processing, fuzzy control and robotics, fuzzy hardware and architectures, fuzzy systems and the internet, fuzzy optimization and modeling, fuzzy decision and support, classification, clustering, statistical methods, knowledge etc.


More Conferences

Periodicals related to Neurons

Back to Top

Antennas and Propagation, IEEE Transactions on

Experimental and theoretical advances in antennas including design and development, and in the propagation of electromagnetic waves including scattering, diffraction and interaction with continuous media; and applications pertinent to antennas and propagation, such as remote sensing, applied optics, and millimeter and submillimeter wave techniques.


Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Consumer Electronics, IEEE Transactions on

The design and manufacture of consumer electronics products, components, and related activities, particularly those used for entertainment, leisure, and educational purposes


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 ...


Geoscience and Remote Sensing, IEEE Transactions on

Theory, concepts, and techniques of science and engineering as applied to sensing the earth, oceans, atmosphere, and space; and the processing, interpretation, and dissemination of this information.


More Periodicals


Xplore Articles related to Neurons

Back to Top

Simulate Human Visual Perception Using Expert Neurons

Horatiu Sachelarie 2013 Seventh International Conference on Complex, Intelligent, and Software Intensive Systems, 2013

This paper presents two main concepts. The first one is a new concept of artificial neuron, different from the classic approach. The second one is a proposal of a system that simulates the human visual perception in artificial intelligence systems based on what is known at this date on the human visual system at neurological and cellular levels. After studying ...


A Logic Connectionist Approach To Self-organized Associative Memory

Wing-Kay Kan Proceedings of the 1988 IEEE International Conference on Systems, Man, and Cybernetics, 1988

First Page of the Article ![](/xploreAssets/images/absImages/00754393.png)


3D dynamical networks to emulate complex neural phenomena

M. Bucolo; F. Conti; L. Fortuna; A. Frasca 2005 IEEE International Symposium on Circuits and Systems, 2005

Visual hallucinations have been described several times, but the causes that produces hallucinations are unknown. These kinds of studies are important because these phenomena do not appears only in people affected by mental illnesses; in the last years hallucinations have been reported by astronauts in several space missions. The generation of such phenomena can be dealt with by studying the ...


Performance Analysis Of Neuronal Learning Models

O. M. Omidvar; J. Y. Cheung Proceedings of the 1988 IEEE International Conference on Systems, Man, and Cybernetics, 1988

First Page of the Article ![](/xploreAssets/images/absImages/00754392.png)


Selectable and Unselectable Sets of Neurons in Recurrent Neural Networks With Saturated Piecewise Linear Transfer Function

Lei Zhang; Zhang Yi IEEE Transactions on Neural Networks, 2011

The concepts of selectable and unselectable sets are proposed to describe some interesting dynamical properties of a class of recurrent neural networks (RNNs) with saturated piecewise linear transfer function. A set of neurons is said to be selectable if it can be co-unsaturated at a stable equilibrium point by some external input. A set of neurons is said to be ...


More Xplore Articles

Educational Resources on Neurons

Back to Top

eLearning

Simulate Human Visual Perception Using Expert Neurons

Horatiu Sachelarie 2013 Seventh International Conference on Complex, Intelligent, and Software Intensive Systems, 2013

This paper presents two main concepts. The first one is a new concept of artificial neuron, different from the classic approach. The second one is a proposal of a system that simulates the human visual perception in artificial intelligence systems based on what is known at this date on the human visual system at neurological and cellular levels. After studying ...


A Logic Connectionist Approach To Self-organized Associative Memory

Wing-Kay Kan Proceedings of the 1988 IEEE International Conference on Systems, Man, and Cybernetics, 1988

First Page of the Article ![](/xploreAssets/images/absImages/00754393.png)


3D dynamical networks to emulate complex neural phenomena

M. Bucolo; F. Conti; L. Fortuna; A. Frasca 2005 IEEE International Symposium on Circuits and Systems, 2005

Visual hallucinations have been described several times, but the causes that produces hallucinations are unknown. These kinds of studies are important because these phenomena do not appears only in people affected by mental illnesses; in the last years hallucinations have been reported by astronauts in several space missions. The generation of such phenomena can be dealt with by studying the ...


Performance Analysis Of Neuronal Learning Models

O. M. Omidvar; J. Y. Cheung Proceedings of the 1988 IEEE International Conference on Systems, Man, and Cybernetics, 1988

First Page of the Article ![](/xploreAssets/images/absImages/00754392.png)


Selectable and Unselectable Sets of Neurons in Recurrent Neural Networks With Saturated Piecewise Linear Transfer Function

Lei Zhang; Zhang Yi IEEE Transactions on Neural Networks, 2011

The concepts of selectable and unselectable sets are proposed to describe some interesting dynamical properties of a class of recurrent neural networks (RNNs) with saturated piecewise linear transfer function. A set of neurons is said to be selectable if it can be co-unsaturated at a stable equilibrium point by some external input. A set of neurons is said to be ...


More eLearning Resources

IEEE-USA E-Books

  • Bio‐Inspired Systems

    This chapter contains sections titled: Introduction and overview Swarm Intelligence Artificial Immune System Cellular signaling pathways Conclusion Appendix IV: Bio‐inspired Networking - Further Reading

  • Spike Sorting

    Excitable cells, such as neurons, produce action potentials (APs) that in extracellular recordings are often referred to as spikes. The contributions of each cell must be isolated from the background noise and from those of the other cells. This chapter focuses on state-of-the-art techniques addressing the problem of spike sorting, including the resolution of overlapped action potentials (APs). It proposes the mathematical modeling of multiunit recordings and the complexity in the resolution of overlapped APs. Then, the summarizes state-of-the-art spike sorting algorithms and discusses the advantages and limitations of each and the applicability of these methods for different types of experimental demands.

  • 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.

  • Multilayer Neural Networks and Backpropagation

    A computationally effective method for training the multilayer perceptrons is the backpropagation algorithm, which is regarded as a landmark in the development of neural network. This chapter presents two different learning methods, batch learning and online learning, on the basis of how the supervised learning of the multilayer perceptron is actually performed. The essence of backpropagation learning is to encode an input-output mapping into the synaptic weights and thresholds of a multilayer perceptron. It is hoped that the network becomes well trained so that it learns enough about the past to generalize to the future. The chapter concludes with cross-validation and generalization. Cross-validation is appealing particularly when people have to design a large neural network with good generalization as the goal in different ways. Generalization is assumed that the test data are drawn from the same population used to generate the training data.

  • Self-Organizing Tree Map

    This chapter proposes a new mechanism named the self-organizing tree map (SOTM). The motivation of the new method is twofold: (a) to keep the ART's ability to create new output neurons dynamically while overcoming the Global threshold setting problem; (b) to keep the SOM's property of topology preservation while strengthening the flexibility of adapting to changes in the input distribution and maximally reflecting the distribution of the input patterns. In the SOTM, relationships between the output neurons can be dynamically defined during learning. There are thus, two different levels of adaptation in the SOTM, which involves weight adaptation and structure adaptation. The basic principle underlying competitive learning is vector quantization. The chapter demonstrates dynamic topology and classification capability are two prominent characteristics of the SOTM. Finally, the SOTM model not only enhanced the ART's autonomous category classification and the SOM's topology preservation, but also overcame their weaknesses.

  • Wavelet Denoising and Conditioning of Neural Recordings

    This chapter presents wavelet-based denoising algorithms as a preprocessing stage before spike detection and sorting. The first part of the chapter overviews wavelet-based denoising algorithms. The dyadic wavelet transform is compared with a timeinvariant approach, showing that the latter is best suited to the denoising of neural signals. The second part of the chapter shows a sample application with eletroneurographic (ENG) signals recorded from the sciatic nerve of rabbits while the experimenter stimulated the paw of the animal. The wavelet-based denoising is compared with a traditional band-pass filter in two cases: when followed by spike sorting and when followed by traditional rectified bin integration (RBI). The results illustrate the benefits of wavelet denoising over standard band-pass filtering and demonstrate that there is an even more marked improvement when the subsequent step requires signals with high signal-to-noise ratio (SNR), such as in the case of spike sorting.

  • Neural Networks for Antennas

    This chapter contains sections titled: Introduction Neural Network Concepts Neural Network Model Development Other Neural Network Models Used for Antenna Modeling A Typical Example Antenna Applications Some Issues of Using Neural Networks for Antenna Problems Disadvantages of Using Neural Networks New Trends Summary References

  • Ear Physiology

    This chapter contains sections titled: Introduction Anatomical Pathways From the Ear to the Perception of Sound The Peripheral Auditory System Hair Cell and Auditory Nerve Functions Properties of the Auditory Nerve Summary and Block Diagram of the Peripheral Auditory System Exercises

  • Wireless, Implantable Neuroprostheses: Applying Advanced Technology to Untether the Mind

    This chapter contains sections titled: * Introduction * Context and Motivation * Listening to the Brain: Sensing Dynamic Changes in Neural Activity * The Write Operation: Perturbing the Brain with Light * Summary

  • Self-Organization

    This chapter presents the principles of Self-Organization, and focuses on Adaptive Resonance Theory (ART) and Self-Organizing Map (SOM) neural networks. It investigates the theoretic basis of formulations of these neural networks, and illustrates a few examples. The structures of these networks and their learning algorithms are also thoroughly explored in the chapter. The ART architecture is a specifically designed neural network to overcome the stability-plasticity dilemma. It is described using nonlinear differential equations. In addition to ART and SOM, there are two other fixed approaches that could be considered fundamental, namely, Neural Gas and the Hierarchical Feature Map. While both are strongly related to the SOM in terms of the learning mechanism, they each have spawned a range of newer architectures are introduced in the chapter. The chapter explains other popular architectures to have emerged based on similar principles of Self-Organization, drawing a distinction between static and dynamic architectures.



Standards related to Neurons

Back to Top

No standards are currently tagged "Neurons"


Jobs related to Neurons

Back to Top