Conferences related to Neurodynamics

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2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invitedsessions 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 postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE


2020 59th IEEE Conference on Decision and Control (CDC)

The CDC is the premier conference dedicated to the advancement of the theory and practice of systems and control. The CDC annually brings together an international community of researchers and practitioners in the field of automatic control to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, automatic control, and related areas.


2020 IEEE International Conference on Image Processing (ICIP)

The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.


2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.


2020 IEEE International Symposium on Circuits and Systems (ISCAS)

The International Symposium on Circuits and Systems (ISCAS) is the flagship conference of the IEEE Circuits and Systems (CAS) Society and the world’s premier networking and exchange forum for researchers in the highly active fields of theory, design and implementation of circuits and systems. ISCAS2020 focuses on the deployment of CASS knowledge towards Society Grand Challenges and highlights the strong foundation in methodology and the integration of multidisciplinary approaches which are the distinctive features of CAS contributions. The worldwide CAS community is exploiting such CASS knowledge to change the way in which devices and circuits are understood, optimized, and leveraged in a variety of systems and applications.


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Periodicals related to Neurodynamics

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Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


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.


Communications, IEEE Transactions on

Telephone, telegraphy, facsimile, and point-to-point television, by electromagnetic propagation, including radio; wire; aerial, underground, coaxial, and submarine cables; waveguides, communication satellites, and lasers; in marine, aeronautical, space and fixed station services; repeaters, radio relaying, signal storage, and regeneration; telecommunication error detection and correction; multiplexing and carrier techniques; communication switching systems; data communications; and communication theory. In addition to the above, ...


Computational Biology and Bioinformatics, IEEE/ACM Transactions on

Specific topics of interest include, but are not limited to, sequence analysis, comparison and alignment methods; motif, gene and signal recognition; molecular evolution; phylogenetics and phylogenomics; determination or prediction of the structure of RNA and Protein in two and three dimensions; DNA twisting and folding; gene expression and gene regulatory networks; deduction of metabolic pathways; micro-array design and analysis; proteomics; ...


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Most published Xplore authors for Neurodynamics

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Xplore Articles related to Neurodynamics

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Table of Contents

2019 China-Qatar International Workshop on Artificial Intelligence and Applications to Intelligent Manufacturing (AIAIM), 2019

Table of Contents


Construction of Irredundant Multilevel Switching Functions

IEEE Transactions on Electronic Computers, 1964

None


Effect of refractoriness on learning performance of a pattern sequence

2009 International Joint Conference on Neural Networks, 2009

The primary purpose of this study is to reveal the effects of refractoriness on learning performance. We simulated that Elman network, which consists of chaotic neurons, learns a pattern sequence using the back-propagation algorithm. Consequently, the learning speed was accelerated about 46% compared with that of the network consisting of integrate-and-fire model neurons. In addition, we analyzed the required number ...


On noise induced resonances in neurodynamic models

2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), 2004

This work aims at studying dynamical models of neural networks, which exhibit transitions between quasistable states of various complexities. We use the biologically motivated KIII model, which is a high-dimensional dynamical system with extremely fragmented boundaries between limit cycles, tori, fixed points, and chaotic attractors. We study the role of additive noise in the development of itinerant trajectories. Noise broadens ...


Dynamical properties of higher order random neural networks

Proceedings Eighth IEEE International Conference on Tools with Artificial Intelligence, 1996

The authors have previously shown dynamical properties-dynamics of the activities for states-for higher order random neural networks, which use the weighted sum of products of input variables, with the digital state {1-,1} model. The paper describes dynamical properties for higher order random neural networks with the analog state models and the digital state (0,1) model.


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Educational Resources on Neurodynamics

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IEEE.tv Videos

No IEEE.tv Videos are currently tagged "Neurodynamics"

IEEE-USA E-Books

  • Table of Contents

    Table of Contents

  • Construction of Irredundant Multilevel Switching Functions

    None

  • Effect of refractoriness on learning performance of a pattern sequence

    The primary purpose of this study is to reveal the effects of refractoriness on learning performance. We simulated that Elman network, which consists of chaotic neurons, learns a pattern sequence using the back-propagation algorithm. Consequently, the learning speed was accelerated about 46% compared with that of the network consisting of integrate-and-fire model neurons. In addition, we analyzed the required number of hidden neurons, asynchronous activities of hidden neurons' refractoriness. These results suggested that the refractoriness contributes to efficient encoding in the hidden layer of Elman network.

  • On noise induced resonances in neurodynamic models

    This work aims at studying dynamical models of neural networks, which exhibit transitions between quasistable states of various complexities. We use the biologically motivated KIII model, which is a high-dimensional dynamical system with extremely fragmented boundaries between limit cycles, tori, fixed points, and chaotic attractors. We study the role of additive noise in the development of itinerant trajectories. Noise broadens the region of the dominance of chaotic attractors. This result is especially useful in the application of KIII and makes it possible to select parameter regions where KIII can operate as a robust dynamic system and associative memory device.

  • Dynamical properties of higher order random neural networks

    The authors have previously shown dynamical properties-dynamics of the activities for states-for higher order random neural networks, which use the weighted sum of products of input variables, with the digital state {1-,1} model. The paper describes dynamical properties for higher order random neural networks with the analog state models and the digital state (0,1) model.

  • Output Tracking of Time-Varying Linear and Nonlinear Systems Using ZN and ZG Controllers with Pseudo Division-by-Zero Phenomena Shown

    This paper introduces Zhang neurodynamics (ZN) method and Zhang-gradient (ZG) method used to design ZN and ZG controllers for the output tracking of systems, especially time-varying nonlinear system. Thereinto, ZG method is composed of the ZN method and gradient neurodynamics (GN) method. By applying the ZN method and the ZG method, respectively, to the output tracking control problems of time-varying linear (TVL) system and time-varying nonlinear (TVN) system, specific ZN controllers and ZG controllers are obtained. Particularly, TVL system and TVN system discussed in this paper may both have pseudo division-by-zero phenomena. From the numerical results, though ZN controllers and ZG controllers perform the output tracking of TVL system and TVN system well, the infinite value of the former and the finite value of the latter at singular instants verify that the ZG method is more effective in dealing with pseudo division-by-zero problem.

  • An autonomously controlled chaos neural network

    We propose a simple chaos neural network model applied to autonomous chaotic wandering and the autoassociation models. The present artificial neuron model is properly characterized in terms of a sinusoidal activation function to involve chaotic dynamics as the ability of dynamic memory retrievals beyond the conventional chaotic model with a monotonous mapping such as a sigmoid function.

  • Quantization effects of Hebbian-type associative memories

    The effects of quantization strategies in Hebbian-type associative memories are explored. The quantization strategies considered include two-level, three- level with a cut-off threshold, and linear quantizations. The two-level strategy is to clip positive interconnections into +1 and negative interconnections into -1. The three-level quantization uses the same strategy in turning the interconnections into +1 or -1, except that it is applied only to those interconnections having their values larger than a cut-off threshold. Those interconnections within the cutoff threshold are then set to zero. The results indicate that three-level quantization with a properly selected cut- off threshold gives higher network performance than two-level quantization. The performance of a network with linear quantization is also compared with a network with three-level quantization. It is found that the linear quantization does not significantly enhance network performance compared with the three-level quantization.<<ETX>>

  • On Barron's Self-Organizing Control

    Self-organizing control (SOC) is analyzed as a conventional controller and the evolution of adaptive SOC from a simple linear system is shown. The development intentionally avoids any reference to the bionics concepts which Barron uses to describe his system.

  • Neurodynamic interface circuits for a multichannel, wireless sensor IC operating in saltwater

    This paper presents the amplifier and stimulator circuits, as well as 50-Omega antenna driver, that augment a multichannel, wireless sensor interface platform IC for use in neurodynamic studies of a sea animal, Aplysia Californica. The prototype chip, including the platform and 2-channel neurodynamic interface circuits, has been fabricated in a 0.5-mum CMOS process, occupies an active area of just 0.7 mm<sup>2 </sup> and consumes 3.56 mW. This would allow 120 hours of recording using 160mAh lithium ion battery. Test results of the interface circuits are presented and exhibit the desired performance



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