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| Author | : Wulfram Gerstner |
| Publisher | : Cambridge University Press |
| Total Pages | : 591 |
| Release | : 2014-07-24 |
| Genre | : Computers |
| ISBN | : 1107060834 |
This solid introduction uses the principles of physics and the tools of mathematics to approach fundamental questions of neuroscience.
| Author | : Robert Kozma |
| Publisher | : Academic Press |
| Total Pages | : 398 |
| Release | : 2023-10-11 |
| Genre | : Computers |
| ISBN | : 0323958168 |
Artificial Intelligence in the Age of Neural Networks and Brain Computing, Second Edition demonstrates that present disruptive implications and applications of AI is a development of the unique attributes of neural networks, mainly machine learning, distributed architectures, massive parallel processing, black-box inference, intrinsic nonlinearity, and smart autonomous search engines. The book covers the major basic ideas of "brain-like computing" behind AI, provides a framework to deep learning, and launches novel and intriguing paradigms as possible future alternatives. The present success of AI-based commercial products proposed by top industry leaders, such as Google, IBM, Microsoft, Intel, and Amazon, can be interpreted using the perspective presented in this book by viewing the co-existence of a successful synergism among what is referred to as computational intelligence, natural intelligence, brain computing, and neural engineering. The new edition has been updated to include major new advances in the field, including many new chapters. - Developed from the 30th anniversary of the International Neural Network Society (INNS) and the 2017 International Joint Conference on Neural Networks (IJCNN - Authored by top experts, global field pioneers, and researchers working on cutting-edge applications in signal processing, speech recognition, games, adaptive control and decision-making - Edited by high-level academics and researchers in intelligent systems and neural networks - Includes all new chapters, including topics such as Frontiers in Recurrent Neural Network Research; Big Science, Team Science, Open Science for Neuroscience; A Model-Based Approach for Bridging Scales of Cortical Activity; A Cognitive Architecture for Object Recognition in Video; How Brain Architecture Leads to Abstract Thought; Deep Learning-Based Speech Separation and Advances in AI, Neural Networks
| Author | : Li Deng |
| Publisher | : |
| Total Pages | : 212 |
| Release | : 2014 |
| Genre | : Machine learning |
| ISBN | : 9781601988140 |
Provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks
| Author | : Jose Mira |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 900 |
| Release | : 1999-05-19 |
| Genre | : Computers |
| ISBN | : 9783540660699 |
This book constitutes, together with its compagnion LNCS 1606, the refereed proceedings of the International Work-Conference on Artificial & Neural Networks, IWANN'99, held in Alicante, Spain in June 1999. The 91 revised papers presented were carefully reviewed & selected for inclusion in the book. This volume is devoted to applications of biologically inspired artificial neural networks in various engineering disciplines. The papers are organized in parts on artificial neural nets simulation & implementation, image processing & engineering applications.
| Author | : Jose Mira |
| Publisher | : Springer |
| Total Pages | : 890 |
| Release | : 2006-12-08 |
| Genre | : Computers |
| ISBN | : 3540487719 |
This book constitutes, together with its compagnion LNCS 1607, the refereed proceedings of the International Work-Conference on Artificial and Natural Neural Networks, IWANN'99, held in Alicante, Spain in June 1999. The 89 revised papers presented were carefully reviewed and selected for inclusion in the book. This volume is devoted to foundational issues of neural computation and tools for neural modeling. The papers are organized in parts on neural modeling: biophysical and structural models; plasticity phenomena: maturing, learning, and memory; and artificial intelligence and cognitive neuroscience.
| Author | : Donna L. Hudson |
| Publisher | : John Wiley & Sons |
| Total Pages | : 337 |
| Release | : 1999-10-08 |
| Genre | : Computers |
| ISBN | : 0780334043 |
Using examples drawn from biomedicine and biomedical engineering, this essential reference book brings you comprehensive coverage of all the major techniques currently available to build computer-assisted decision support systems. You will find practical solutions for biomedicine based on current theory and applications of neural networks, artificial intelligence, and other methods for the development of decision aids, including hybrid systems. Neural Networks and Artificial Intelligence for Biomedical Engineering offers students and scientists of biomedical engineering, biomedical informatics, and medical artificial intelligence a deeper understanding of the powerful techniques now in use with a wide range of biomedical applications. Highlighted topics include: Types of neural networks and neural network algorithms Knowledge representation, knowledge acquisition, and reasoning methodologies Chaotic analysis of biomedical time series Genetic algorithms Probability-based systems and fuzzy systems Evaluation and validation of decision support aids
| Author | : Toshinori Munakata |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 248 |
| Release | : 2001-11-14 |
| Genre | : Computers |
| ISBN | : 9780387983028 |
A first course in AI, covering new technologies and their applications. With each topic, the book covers the most essential and widely employed material, particularly as it is used in real-world applications. The prerequisites are minimal: a basic understanding of computer science and mathematics is sufficient, making this suitable for undergraduates coming to the subject for the first time. Professor Munakata is a leading figure in this field and has given courses on this topic extensively. As a result, students and researchers will enjoy this authoritative introduction to the subject, with its emphasis on concise yet clear descriptions of the technical substance.
| Author | : J.W. Donahoe |
| Publisher | : Elsevier |
| Total Pages | : 601 |
| Release | : 1997-09-26 |
| Genre | : Computers |
| ISBN | : 0080537367 |
This internationally authored volume presents major findings, concepts, and methods of behavioral neuroscience coordinated with their simulation via neural networks. A central theme is that biobehaviorally constrained simulations provide a rigorous means to explore the implications of relatively simple processes for the understanding of cognition (complex behavior). Neural networks are held to serve the same function for behavioral neuroscience as population genetics for evolutionary science. The volume is divided into six sections, each of which includes both experimental and simulation research: (1) neurodevelopment and genetic algorithms, (2) synaptic plasticity (LTP), (3) sensory/hippocampal systems, (4) motor systems, (5) plasticity in large neural systems (reinforcement learning), and (6) neural imaging and language. The volume also includes an integrated reference section and a comprehensive index.
| Author | : Martin T. Hagan |
| Publisher | : |
| Total Pages | : |
| Release | : 2003 |
| Genre | : Neural networks (Computer science) |
| ISBN | : 9789812403766 |
| Author | : Kishan Mehrotra |
| Publisher | : MIT Press |
| Total Pages | : 376 |
| Release | : 1997 |
| Genre | : Computers |
| ISBN | : 9780262133289 |
Elements of Artificial Neural Networks provides a clearly organized general introduction, focusing on a broad range of algorithms, for students and others who want to use neural networks rather than simply study them. The authors, who have been developing and team teaching the material in a one-semester course over the past six years, describe most of the basic neural network models (with several detailed solved examples) and discuss the rationale and advantages of the models, as well as their limitations. The approach is practical and open-minded and requires very little mathematical or technical background. Written from a computer science and statistics point of view, the text stresses links to contiguous fields and can easily serve as a first course for students in economics and management. The opening chapter sets the stage, presenting the basic concepts in a clear and objective way and tackling important -- yet rarely addressed -- questions related to the use of neural networks in practical situations. Subsequent chapters on supervised learning (single layer and multilayer networks), unsupervised learning, and associative models are structured around classes of problems to which networks can be applied. Applications are discussed along with the algorithms. A separate chapter takes up optimization methods. The most frequently used algorithms, such as backpropagation, are introduced early on, right after perceptrons, so that these can form the basis for initiating course projects. Algorithms published as late as 1995 are also included. All of the algorithms are presented using block-structured pseudo-code, and exercises are provided throughout. Software implementing many commonly used neural network algorithms is available at the book's website. Transparency masters, including abbreviated text and figures for the entire book, are available for instructors using the text.
