Theoretical Aspects Of Neurocomputing: Selected Papers From The Symposium On Neural Networks And Neurocomputing (Neuronet '90)
Theoretical Aspects Of Neurocomputing: Selected Papers From The Symposium On Neural Networks And Neurocomputing (Neuronet '90)

Author: Novak Mirko

Publisher: World Scientific

Published: 1991-03-15

Total Pages: 300

ISBN-13: 9814569208

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This book contains a selection of both full and extended contributions presented at NEURONET '90. These contributions are predominantly oriented towards the theoretical problems of neurocomputing, and involve a wide scope of aspects — from neurophysiology and cytology to theoretical problems in neural network architectures, mathematical background of neurocomputing and learning strategies.

Information-Theoretic Aspects of Neural Networks
Information-Theoretic Aspects of Neural Networks

Author: P. S. Neelakanta

Publisher: CRC Press

Published: 2020-09-23

Total Pages: 417

ISBN-13: 1000102750

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Information theoretics vis-a-vis neural networks generally embodies parametric entities and conceptual bases pertinent to memory considerations and information storage, information-theoretic based cost-functions, and neurocybernetics and self-organization. Existing studies only sparsely cover the entropy and/or cybernetic aspects of neural information. Information-Theoretic Aspects of Neural Networks cohesively explores this burgeoning discipline, covering topics such as: Shannon information and information dynamics neural complexity as an information processing system memory and information storage in the interconnected neural web extremum (maximum and minimum) information entropy neural network training non-conventional, statistical distance-measures for neural network optimizations symmetric and asymmetric characteristics of information-theoretic error-metrics algorithmic complexity based representation of neural information-theoretic parameters genetic algorithms versus neural information dynamics of neurocybernetics viewed in the information-theoretic plane nonlinear, information-theoretic transfer function of the neural cellular units statistical mechanics, neural networks, and information theory semiotic framework of neural information processing and neural information flow fuzzy information and neural networks neural dynamics conceived through fuzzy information parameters neural information flow dynamics informatics of neural stochastic resonance Information-Theoretic Aspects of Neural Networks acts as an exceptional resource for engineers, scientists, and computer scientists working in the field of artificial neural networks as well as biologists applying the concepts of communication theory and protocols to the functioning of the brain. The information in this book explores new avenues in the field and creates a common platform for analyzing the neural complex as well as artificial neural networks.

Principles of Neurocomputing for Science and Engineering
Principles of Neurocomputing for Science and Engineering

Author: Fredric M. Ham

Publisher: McGraw-Hill Science, Engineering & Mathematics

Published: 2000

Total Pages: 680

ISBN-13:

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Neurocomputing can be applied to problems such as pattern recognition, optimization, event classification, control and identification of nonlinear systems, and statistical analysis - just to name a few. This book is intended for a course in neural networks."--BOOK JACKET.

Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications
Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications

Author: Oscar Castillo

Publisher: Springer

Published: 2018-01-10

Total Pages: 535

ISBN-13: 3319710087

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This book comprises papers on diverse aspects of fuzzy logic, neural networks, and nature-inspired optimization meta-heuristics and their application in various areas such as intelligent control and robotics, pattern recognition, medical diagnosis, time series prediction and optimization of complex problems. The book is organized into seven main parts, each with a collection of papers on a similar subject. The first part presents new concepts and algorithms based on type-2 fuzzy logic for dynamic parameter adaptation in meta-heuristics. The second part discusses network theory and applications, and includes papers describing applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The third part addresses the theory and practice of meta-heuristics in different areas of application, while the fourth part describes diverse fuzzy logic applications in the control area, which can be considered as intelligent controllers. The next two parts explore applications in areas, such as time series prediction, and pattern recognition and new optimization and evolutionary algorithms and their applications respectively. Lastly, the seventh part addresses the design and application of different hybrid intelligent systems.

The Oxford Handbook of Philosophy and Neuroscience
The Oxford Handbook of Philosophy and Neuroscience

Author: John Bickle

Publisher: OUP USA

Published: 2009-07-27

Total Pages: 652

ISBN-13: 0195304780

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This title is a collection of interdisciplinary research from contributors including both philosophers and neuroscientists. Topics covered include the neurobiology of learning and memory perception and sensation, neurocomputational modelling neuroanatomy, neuroethics, and neurology and clinical neuropsychology.

Bio-inspired Neurocomputing
Bio-inspired Neurocomputing

Author: Akash Kumar Bhoi

Publisher: Springer Nature

Published: 2020-07-21

Total Pages: 427

ISBN-13: 9811554951

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This book covers the latest technological advances in neuro-computational intelligence in biological processes where the primary focus is on biologically inspired neuro-computational techniques. The theoretical and practical aspects of biomedical neural computing, brain-inspired computing, bio-computational models, artificial intelligence (AI) and machine learning (ML) approaches in biomedical data analytics are covered along with their qualitative and quantitative features. The contents cover numerous computational applications, methodologies and emerging challenges in the field of bio-soft computing and bio-signal processing. The authors have taken meticulous care in describing the fundamental concepts, identifying the research gap and highlighting the problems with the strategical computational approaches to address the ongoing challenges in bio-inspired models and algorithms. Given the range of topics covered, this book can be a valuable resource for students, researchers as well as practitioners interested in the rapidly evolving field of neurocomputing and biomedical data analytics.

An Information-Theoretic Approach to Neural Computing
An Information-Theoretic Approach to Neural Computing

Author: Gustavo Deco

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 265

ISBN-13: 1461240166

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A detailed formulation of neural networks from the information-theoretic viewpoint. The authors show how this perspective provides new insights into the design theory of neural networks. In particular they demonstrate how these methods may be applied to the topics of supervised and unsupervised learning, including feature extraction, linear and non-linear independent component analysis, and Boltzmann machines. Readers are assumed to have a basic understanding of neural networks, but all the relevant concepts from information theory are carefully introduced and explained. Consequently, readers from varied scientific disciplines, notably cognitive scientists, engineers, physicists, statisticians, and computer scientists, will find this an extremely valuable introduction to this topic.

Advances in Data Mining. Applications and Theoretical Aspects
Advances in Data Mining. Applications and Theoretical Aspects

Author: Petra Perner

Publisher: Springer Science & Business Media

Published: 2009-07-09

Total Pages: 412

ISBN-13: 364203067X

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This volume comprises the proceedings of the Industrial Conference on Data Mining (ICDM 2009) held in Leipzig (www.data-mining-forum.de). For this edition the Program Committee received 130 submissions. After the pe- review process, we accepted 32 high-quality papers for oral presentation that are included in this book. The topics range from theoretical aspects of data mining to app- cations of data mining, such as on multimedia data, in marketing, finance and telec- munication, in medicine and agriculture, and in process control, industry and society. Ten papers were selected for poster presentations that are published in the ICDM Poster Proceedings Volume by ibai-publishing (www.ibai-publishing.org). In conjunction with ICDM two workshops were run focusing on special hot app- cation-oriented topics in data mining. The workshop Data Mining in Marketing DMM 2009 was run for the second time. The papers are published in a separate workshop book “Advances in Data Mining on Markting” by ibai-publishing (www.ibai-publishing.org). The Workshop on Case-Based Reasoning for Multimedia Data CBR-MD ran for the second year. The papers are published in a special issue of the International Journal of Transactios on Case-Based Reasoning (www.ibai-publishing.org/journal/cbr).

RAM-based Neural Networks
RAM-based Neural Networks

Author: James Austin

Publisher: World Scientific

Published: 1998

Total Pages: 256

ISBN-13: 9789810232535

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RAM-based networks are a class of methods for building pattern recognition systems. Unlike other neural network methods, they learn very quickly and as a result are applicable to a wide variety of problems. This important book presents the latest work by the majority of researchers in the field of RAM-based networks.

The Handbook of Brain Theory and Neural Networks
The Handbook of Brain Theory and Neural Networks

Author: Michael A. Arbib

Publisher: MIT Press

Published: 2003

Total Pages: 1328

ISBN-13: 0262011972

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This second edition presents the enormous progress made in recent years in the many subfields related to the two great questions : how does the brain work? and, How can we build intelligent machines? This second edition greatly increases the coverage of models of fundamental neurobiology, cognitive neuroscience, and neural network approaches to language. (Midwest).