Conferences related to Neurons

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2019 IEEE 17th International Conference on Industrial Informatics (INDIN)

Industrial information technologies


2019 IEEE 28th International Symposium on Industrial Electronics (ISIE)

The conference will provide a forum for discussions and presentations of advancements inknowledge, new methods and technologies relevant to industrial electronics, along with their applications and future developments.


2019 IEEE Industry Applications Society Annual Meeting

The Annual Meeting is a gathering of experts who work and conduct research in the industrial applications of electrical systems.


2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)

2019 IEEE International Conference on Systems, Man, and Cybernetics (SMC2019) will be held in the south of Europe in Bari, one of the most beautiful and historical cities in Italy. The Bari region’s nickname is “Little California” for its nice weather and Bari's cuisine is one of Italian most traditional , based of local seafood and olive oil. SMC2019 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report up-to-the-minute innovations and developments, summarize state­of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems and cybernetics. Advances have importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience, and thereby improve quality of life.


2019 IEEE Photonics Conference (IPC)

The IEEE Photonics Conference, previously known as the IEEE LEOS Annual Meeting, offers technical presentations by the world’s leading scientists and engineers in the areas of lasers, optoelectronics, optical fiber networks, and associated lightwave technologies and applications. It also features compelling plenary talks on the industry’s most important issues, weekend events aimed at students and young photonics professionals, and a manufacturer’s exhibition.


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Periodicals related to Neurons

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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 Circuits and Systems, IEEE Transactions on

The Transactions on Biomedical Circuits and Systems addresses areas at the crossroads of Circuits and Systems and Life Sciences. The main emphasis is on microelectronic issues in a wide range of applications found in life sciences, physical sciences and engineering. The primary goal of the journal is to bridge the unique scientific and technical activities of the Circuits and Systems ...


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.


Circuits and Systems I: Regular Papers, IEEE Transactions on

Part I will now contain regular papers focusing on all matters related to fundamental theory, applications, analog and digital signal processing. Part II will report on the latest significant results across all of these topic areas.


Circuits and Systems II: Express Briefs, IEEE Transactions on

Part I will now contain regular papers focusing on all matters related to fundamental theory, applications, analog and digital signal processing. Part II will report on the latest significant results across all of these topic areas.


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Most published Xplore authors for Neurons

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Xplore Articles related to Neurons

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RBF networks for density estimation

1996 8th European Signal Processing Conference (EUSIPCO 1996), 1996

A non-parametric probability density function (pdf) estimation technique is presented. The estimation consists in approximating the unknown pdf by a network of Gaussian Radial Basis Functions (GRBFs). Complexity analysis is introduced in order to select the optimal number of GRBFs. Results obtained on real data show the potentiality of this technique.


Design of a solid-state neuron circuit for use in self-organizing systems

1960 IEEE International Solid-State Circuits Conference. Digest of Technical Papers, 1960

The artificial neuron or information-processing cell was designed primarily as a component for experimental neuron-network studies. With minor additions, it could be used for perceptron-type experiments. Each cell has ten exciting and ten inhibiting inputs, and each input has a separate weight associated with it. The operation of the cell depends upon the difference, between the weighted sums of the ...


Considerations underlying the study of sensory elements

1963 IEEE International Solid-State Circuits Conference. Digest of Technical Papers, 1963

None


Nonlinear formant-pitch prediction using Recurrent Neural Networks

1996 8th European Signal Processing Conference (EUSIPCO 1996), 1996

In this study, a parallel structure is proposed for the nonlinear formant and pitch prediction of speech signals using Recurrent Neural Networks (RNN) The well known Real Time Recurrent Learning (RTRL) algorithm is used as the learning algorithm. Its performance is evaluated in terms of the mean-square error and sensitivity to pitch errors through extensive computer simulations and compared to ...


Synchronous Machine Steady-State Stability Annlysis Using An Artificial Neural Network

IEEE Power Engineering Review, 1991

None


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Educational Resources on Neurons

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IEEE-USA E-Books

  • RBF networks for density estimation

    A non-parametric probability density function (pdf) estimation technique is presented. The estimation consists in approximating the unknown pdf by a network of Gaussian Radial Basis Functions (GRBFs). Complexity analysis is introduced in order to select the optimal number of GRBFs. Results obtained on real data show the potentiality of this technique.

  • Design of a solid-state neuron circuit for use in self-organizing systems

    The artificial neuron or information-processing cell was designed primarily as a component for experimental neuron-network studies. With minor additions, it could be used for perceptron-type experiments. Each cell has ten exciting and ten inhibiting inputs, and each input has a separate weight associated with it. The operation of the cell depends upon the difference, between the weighted sums of the exciting and the inhibiting input signals.

  • Considerations underlying the study of sensory elements

    None

  • Nonlinear formant-pitch prediction using Recurrent Neural Networks

    In this study, a parallel structure is proposed for the nonlinear formant and pitch prediction of speech signals using Recurrent Neural Networks (RNN) The well known Real Time Recurrent Learning (RTRL) algorithm is used as the learning algorithm. Its performance is evaluated in terms of the mean-square error and sensitivity to pitch errors through extensive computer simulations and compared to the combined formant-pitch RNN predictor and to the linear predictor.

  • Synchronous Machine Steady-State Stability Annlysis Using An Artificial Neural Network

    None

  • 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.

  • A microprobe with integrated amplifiers for neurophysiology

    A multielectrode probe containing integrated buffer amplifiers, capable of recording the activity of single neurons in the brain, has been fabricated using IC technology, The probe overcomes the stray coupling-noise limitations of conventional microelectrode recording systems.

  • Surface EMG Decomposition

    This chapter provides an overview of surface EMG decomposition techniques, along with their basic assumptions, properties, and limitations. Surface electrodes measure the electrical activity of several nearby muscle fibers that are active during a muscle contraction. The electrical activity of each fiber can be described by a single fiber action potential (SFAP) that propagates from the neuromuscular junction towards the tendons. There is large diversity of decomposition techniques that can roughly be categorized either as template matching or latent component analysis (blind source separation) approaches. Decomposition of surface EMG is a powerful tool enabling noninvasive insight not only into muscle control strategies, but also into peripheral muscle properties. It provides unambiguous information on physiological parameters of individual motor units that can easily be interpreted. The identification of motor units (MUs) discharge patterns from surface EMG signals, acquired during dynamic muscle contractions, needs to be addressed.

  • Design and electrical simulation of on-chip neural learning based on nanocomponents

    None

  • Application of Hopfield neural network for extracting Doppler spectrum from ocean echo

    This paper proposes the method of a Hopfield-type neural network (HNN) for extracting Doppler spectrum from ocean echo. First, it introduces the basic principle of HNN for optimized processing. Second, expanding the principle of utilizing autoregression (AR) to estimate frequency spectrum, we point out how to apply HNN in spectrum estimation. Last, the three methods are utilized to process actual data, that is, the conventional fast Fourier transform method, modern spectrum estimation–AR method, and the spectrum estimation method based on HNN. The results obtained by the three methods prove that the spectrum estimation method based on HNN is feasible for extracting the Doppler spectrum from ocean echo.



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