Neural Networks and Analog Computation

The material in this book will be of interest to researchers in a variety of engineering and applied sciences disciplines.

Author: Hava T. Siegelmann

Publisher: Springer Science & Business Media

ISBN: 9781461207078

Category: Computers

Page: 181

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The theoretical foundations of Neural Networks and Analog Computation conceptualize neural networks as a particular type of computer consisting of multiple assemblies of basic processors interconnected in an intricate structure. Examining these networks under various resource constraints reveals a continuum of computational devices, several of which coincide with well-known classical models. On a mathematical level, the treatment of neural computations enriches the theory of computation but also explicated the computational complexity associated with biological networks, adaptive engineering tools, and related models from the fields of control theory and nonlinear dynamics. The material in this book will be of interest to researchers in a variety of engineering and applied sciences disciplines. In addition, the work may provide the base of a graduate-level seminar in neural networks for computer science students.
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Artificial Neural Networks and Machine Learning ICANN 2019 Theoretical Neural Computation

Balcázar, J.L., Gavald`a, R., Siegelmann, H.T.: Computational power of neural networks: A characterization in ... Neural Netw. 61, 85–117 (2015) 10. Siegelmann, H.T.: Neural Networks and Analog Computation: Beyond the Turing Limit.

Author: Igor V. Tetko

Publisher: Springer Nature

ISBN: 9783030304874

Category: Computers

Page: 839

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The proceedings set LNCS 11727, 11728, 11729, 11730, and 11731 constitute the proceedings of the 28th International Conference on Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019. The total of 277 full papers and 43 short papers presented in these proceedings was carefully reviewed and selected from 494 submissions. They were organized in 5 volumes focusing on theoretical neural computation; deep learning; image processing; text and time series; and workshop and special sessions.
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Artificial Neural Networks and Machine Learning ICANN 2014

Cabessa, J., Siegelmann, H.T.: The computational power of interactive recurrent neural networks. Neural Computation 24(4), 996–1019 (2012) 4. Cabessa, J., Villa, A.E.P.: The expressive power of analog recurrent neural networks on ...

Author: Stefan Wermter

Publisher: Springer

ISBN: 9783319111797

Category: Computers

Page: 852

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The book constitutes the proceedings of the 24th International Conference on Artificial Neural Networks, ICANN 2014, held in Hamburg, Germany, in September 2014. The 107 papers included in the proceedings were carefully reviewed and selected from 173 submissions. The focus of the papers is on following topics: recurrent networks; competitive learning and self-organisation; clustering and classification; trees and graphs; human-machine interaction; deep networks; theory; reinforcement learning and action; vision; supervised learning; dynamical models and time series; neuroscience; and applications.
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Limitations and Future Trends in Neural Computation

[ 55 ] I. Parberry , A primer on the complexity theory of neural networks , in : R.B. Banerji , ed . , Formal Techniques ... [ 61 ] H.T. Siegelmann , Neural Networks and Analog Computation : Beyond the Turing Limit , Birkhäuser , Boston ...

Author:

Publisher: IOS Press

ISBN: 1586033247

Category: Neural computers

Page: 245

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Artificial Neural Networks ICANN 2006

They can be used to perform symbolic computations. It has already been proven that every computable function can be exactly calculated by a discrete analog neural network with a piece-wise linear activation function. Neural networks can ...

Author: Stefanos Kollias

Publisher: Springer Science & Business Media

ISBN: 9783540386254

Category: Computers

Page: 1008

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The two-volume set LNCS 4131 and LNCS 4132 constitutes the refereed proceedings of the 16th International Conference on Artificial Neural Networks, ICANN 2006. The set presents 208 revised full papers, carefully reviewed and selected from 475 submissions. This first volume presents 103 papers, organized in topical sections on feature selection and dimension reduction for regression, learning algorithms, advances in neural network learning methods, ensemble learning, hybrid architectures, and more.
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Analog VLSI Neural Networks

Teuvo Kohonen, “An introduction to neural computing,” Neural Networks, Vol. 1, No. 1, pp. 3–16, 1988. Bart Kosko, Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence, Prentice Hall: Englewood Cliffs, ...

Author: Yoshiyasu Takefuji

Publisher: Springer Science & Business Media

ISBN: 9781461535829

Category: Technology & Engineering

Page: 131

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This book brings together in one place important contributions and state-of-the-art research in the rapidly advancing area of analog VLSI neural networks. The book serves as an excellent reference, providing insights into some of the most important issues in analog VLSI neural networks research efforts.
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Artificial Neural Networks

The computational equivalence between so-called rational recurrent neural networks and Turing machines has now become ... study of the computational power of recurrent neural networks from the perspective of analog computation [56].

Author: Petia Koprinkova-Hristova

Publisher: Springer

ISBN: 9783319099033

Category: Technology & Engineering

Page: 488

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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 algorithms for prototype selection, and group structure discovering. Moreover, the book discusses one-class support vector machines for pattern recognition, handwritten digit recognition, time series forecasting and classification, and anomaly identification in data analytics and automated data analysis. By presenting the state-of-the-art and discussing the current challenges in the fields of artificial neural networks, bioinformatics and neuroinformatics, the book is intended to promote the implementation of new methods and improvement of existing ones, and to support advanced students, researchers and professionals in their daily efforts to identify, understand and solve a number of open questions in these fields.
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Theory and Practice of Natural Computing

extra analog units. Nevertheless, the ultimate goal is to prove a proper “natural” hierarchy of neural networks between integer and rational weights similarly as it is known between rational and real weights [2] and possibly, ...

Author: David Fagan

Publisher: Springer

ISBN: 9783030040703

Category: Computers

Page: 474

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This book constitutes the refereed proceedings of the 7th International Conference on Theory and Practice of Natural Computing, TPNC 2017, held in Dublin, Ireland, in December 2018. The 35 full papers presented in this book, together with one invited talk, were carefully reviewed and selected from 69 submissions. The papers are organized around the following topical sections: applications of natural computing as algorithms, bioinformatics, control, cryptography, design, economics. The more theoretical contributions handle with artificial chemistry, artificial immune systems, artificial life, cellular automata, cognitive computing, cognitive engineering, cognitive robotics, collective behaviour, complex systems, computational intelligence, computational social science, computing with words, developmental systems, DNA computing, DNA nanotechnology, evolutionary algorithms, evolutionary computing, evolutionary game theory, fractal geometry, fuzzy control, fuzzy logic, fuzzy sets, fuzzy systems, genetic algorithms, genetic programming, granular computing, heuristics, intelligent agents, intelligent systems, machine intelligence, molecular programming, neural computing, neural networks, quantum communication, quantum computing, rough sets, self-assembly.
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From Natural to Artificial Neural Computation

Analog neural network implementations are faster and smaller than their digital counterparts , but the problem of ... Neurocomputing is the term used to characterise the analog computation based on artificial neural networks that are ...

Author: International Workshop on Artificial Neural Networks

Publisher: Springer Science & Business Media

ISBN: 3540594973

Category: Computers

Page: 1150

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This volume presents the proceedings of the International Workshop on Artificial Neural Networks, IWANN '95, held in Torremolinos near Malaga, Spain in June 1995. The book contains 143 revised papers selected from a wealth of submissions and five invited contributions; it covers all current aspects of neural computation and presents the state of the art of ANN research and applications. The papers are organized in sections on neuroscience, computational models of neurons and neural nets, organization principles, learning, cognitive science and AI, neurosimulators, implementation, neural networks for perception, and neural networks for communication and control.
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