Neural

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The NEURON Book

Nicholas T. Carnevale,Michael L. Hines
The NEURON Book Book PDF

Author : Nicholas T. Carnevale,Michael L. Hines
Publisher : Cambridge University Press
Page : 399 pages
File Size : 51,5 Mb
Release : 2006-01-12
Category : Medical
ISBN : 9781139447836

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The NEURON Book by Nicholas T. Carnevale,Michael L. Hines Pdf

The authoritative reference on NEURON, the simulation environment for modeling biological neurons and neural networks that enjoys wide use in the experimental and computational neuroscience communities. This book shows how to use NEURON to construct and apply empirically based models. Written primarily for neuroscience investigators, teachers, and students, it assumes no previous knowledge of computer programming or numerical methods. Readers with a background in the physical sciences or mathematics, who have some knowledge about brain cells and circuits and are interested in computational modeling, will also find it helpful. The NEURON Book covers material that ranges from the inner workings of this program, to practical considerations involved in specifying the anatomical and biophysical properties that are to be represented in models. It uses a problem-solving approach, with many working examples that readers can try for themselves.

Neural Smithing

Russell Reed,Robert J MarksII
Neural Smithing Book PDF

Author : Russell Reed,Robert J MarksII
Publisher : MIT Press
Page : 359 pages
File Size : 41,9 Mb
Release : 1999-02-17
Category : Computers
ISBN : 9780262181907

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Neural Smithing by Russell Reed,Robert J MarksII Pdf

Artificial neural networks are nonlinear mapping systems whose structure is loosely based on principles observed in the nervous systems of humans and animals. The basic idea is that massive systems of simple units linked together in appropriate ways can generate many complex and interesting behaviors. This book focuses on the subset of feedforward artificial neural networks called multilayer perceptrons (MLP). These are the mostly widely used neural networks, with applications as diverse as finance (forecasting), manufacturing (process control), and science (speech and image recognition). This book presents an extensive and practical overview of almost every aspect of MLP methodology, progressing from an initial discussion of what MLPs are and how they might be used to an in-depth examination of technical factors affecting performance. The book can be used as a tool kit by readers interested in applying networks to specific problems, yet it also presents theory and references outlining the last ten years of MLP research.

Complex-Valued Neural Networks with Multi-Valued Neurons

Igor Aizenberg
Complex Valued Neural Networks with Multi Valued Neurons Book PDF

Author : Igor Aizenberg
Publisher : Springer Science & Business Media
Page : 273 pages
File Size : 51,8 Mb
Release : 2011-06-24
Category : Computers
ISBN : 9783642203527

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Complex-Valued Neural Networks with Multi-Valued Neurons by Igor Aizenberg Pdf

Complex-Valued Neural Networks have higher functionality, learn faster and generalize better than their real-valued counterparts. This book is devoted to the Multi-Valued Neuron (MVN) and MVN-based neural networks. It contains a comprehensive observation of MVN theory, its learning, and applications. MVN is a complex-valued neuron whose inputs and output are located on the unit circle. Its activation function is a function only of argument (phase) of the weighted sum. MVN derivative-free learning is based on the error-correction rule. A single MVN can learn those input/output mappings that are non-linearly separable in the real domain. Such classical non-linearly separable problems as XOR and Parity n are the simplest that can be learned by a single MVN. Another important advantage of MVN is a proper treatment of the phase information. These properties of MVN become even more remarkable when this neuron is used as a basic one in neural networks. The Multilayer Neural Network based on Multi-Valued Neurons (MLMVN) is an MVN-based feedforward neural network. Its backpropagation learning algorithm is derivative-free and based on the error-correction rule. It does not suffer from the local minima phenomenon. MLMVN outperforms many other machine learning techniques in terms of learning speed, network complexity and generalization capability when solving both benchmark and real-world classification and prediction problems. Another interesting application of MVN is its use as a basic neuron in multi-state associative memories. The book is addressed to those readers who develop theoretical fundamentals of neural networks and use neural networks for solving various real-world problems. It should also be very suitable for Ph.D. and graduate students pursuing their degrees in computational intelligence.

Process Neural Networks

Xingui He,Shaohua Xu
Process Neural Networks Book PDF

Author : Xingui He,Shaohua Xu
Publisher : Springer Science & Business Media
Page : 240 pages
File Size : 41,8 Mb
Release : 2010-07-05
Category : Computers
ISBN : 9783540737629

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Process Neural Networks by Xingui He,Shaohua Xu Pdf

For the first time, this book sets forth the concept and model for a process neural network. You’ll discover how a process neural network expands the mapping relationship between the input and output of traditional neural networks and greatly enhances the expression capability of artificial neural networks. Detailed illustrations help you visualize information processing flow and the mapping relationship between inputs and outputs.

Non-Linear Feedback Neural Networks

Mohd. Samar Ansari
Non Linear Feedback Neural Networks Book PDF

Author : Mohd. Samar Ansari
Publisher : Springer
Page : 217 pages
File Size : 47,9 Mb
Release : 2013-09-03
Category : Technology & Engineering
ISBN : 9788132215639

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Non-Linear Feedback Neural Networks by Mohd. Samar Ansari Pdf

This book aims to present a viable alternative to the Hopfield Neural Network (HNN) model for analog computation. It is well known the standard HNN suffers from problems of convergence to local minima, and requirement of a large number of neurons and synaptic weights. Therefore, improved solutions are needed. The non-linear synapse neural network (NoSyNN) is one such possibility and is discussed in detail in this book. This book also discusses the applications in computationally intensive tasks like graph coloring, ranking, and linear as well as quadratic programming. The material in the book is useful to students, researchers and academician working in the area of analog computation.

Neural Codes and Distributed Representations

L. F. Abbott,Terrence Joseph Sejnowski
Neural Codes and Distributed Representations Book PDF

Author : L. F. Abbott,Terrence Joseph Sejnowski
Publisher : MIT Press
Page : 378 pages
File Size : 50,8 Mb
Release : 1999
Category : Coding theory
ISBN : 0262511002

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Neural Codes and Distributed Representations by L. F. Abbott,Terrence Joseph Sejnowski Pdf

Since its founding in 1989 by Terrence Sejnowski, Neural Computation has become the leading journal in the field. Foundations of Neural Computation collects, by topic, the most significant papers that have appeared in the journal over the past nine years. The present volume focuses on neural codes and representations, topics of broad interest to neuroscientists and modelers. The topics addressed are: how neurons encode information through action potential firing patterns, how populations of neurons represent information, and how individual neurons use dendritic processing and biophysical properties of synapses to decode spike trains. The papers encompass a wide range of levels of investigation, from dendrites and neurons to networks and systems.

Neural Networks

Andrew D. Chapman
Neural Networks Book PDF

Author : Andrew D. Chapman
Publisher : The Autodidact’s Toolkit
Page : 457 pages
File Size : 45,5 Mb
Release : 2023-12-06
Category : Computers
ISBN : 8210379456XXX

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Neural Networks by Andrew D. Chapman Pdf

In the rapidly advancing world of AI, neural networks emerge as the driving force behind some of the most groundbreaking innovations. Neural Networks is your essential companion in unraveling the complexities and unlocking the potential of these powerful technologies. Begin with a historical journey, understanding how neural networks evolved from simple models to sophisticated systems driving modern AI. Gain clear insights into fundamental concepts, architectures, and the mechanics that make neural networks tick. Delve into deep learning, comparing it with traditional machine learning, and explore its pivotal role in current AI advancements. Whether you are a beginner in AI, a seasoned professional, or simply an AI enthusiast, this book offers a structured and detailed pathway to understanding neural networks. Each chapter is crafted to provide both theoretical knowledge and practical insights, ensuring a well-rounded grasp of the subject matter. Understand the practical aspects of implementing neural networks, from data collection to model optimization. Delve into critical discussions on ethics, bias, and the societal impacts of AI technologies. Neural Networks is more than just a book. It is a gateway to the future, a tool that prepares you to be a part of, and possibly shape, the next generation of AI innovations. Open its pages and embark on a journey that transcends the boundaries of technology, into a world where neural networks redefine what is possible.

Neural Information Processing

Chu Kiong Loo,Yap Keem Siah,Kok Wai Wong,Andrew Teoh Beng Jin,Kaizhu Huang
Neural Information Processing Book PDF

Author : Chu Kiong Loo,Yap Keem Siah,Kok Wai Wong,Andrew Teoh Beng Jin,Kaizhu Huang
Publisher : Springer
Page : 666 pages
File Size : 48,7 Mb
Release : 2014-10-20
Category : Computers
ISBN : 9783319126371

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Neural Information Processing by Chu Kiong Loo,Yap Keem Siah,Kok Wai Wong,Andrew Teoh Beng Jin,Kaizhu Huang Pdf

The three volume set LNCS 8834, LNCS 8835, and LNCS 8836 constitutes the proceedings of the 21st International Conference on Neural Information Processing, ICONIP 2014, held in Kuching, Malaysia, in November 2014. The 231 full papers presented were carefully reviewed and selected from 375 submissions. The selected papers cover major topics of theoretical research, empirical study, and applications of neural information processing research. The 3 volumes represent topical sections containing articles on cognitive science, neural networks and learning systems, theory and design, applications, kernel and statistical methods, evolutionary computation and hybrid intelligent systems, signal and image processing, and special sessions intelligent systems for supporting decision, making processes, theories and applications, cognitive robotics, and learning systems for social network and web mining.

Neural Information Processing

Masumi Ishikawa,Kenji Doya,Hiroyuki Miyamoto,Takeshi Yamakawa
Neural Information Processing Book PDF

Author : Masumi Ishikawa,Kenji Doya,Hiroyuki Miyamoto,Takeshi Yamakawa
Publisher : Springer Science & Business Media
Page : 1165 pages
File Size : 48,5 Mb
Release : 2008-06-16
Category : Computers
ISBN : 9783540691549

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Neural Information Processing by Masumi Ishikawa,Kenji Doya,Hiroyuki Miyamoto,Takeshi Yamakawa Pdf

The two volume set LNCS 4984 and LNCS 4985 constitutes the thoroughly refereed post-conference proceedings of the 14th International Conference on Neural Information Processing, ICONIP 2007, held in Kitakyushu, Japan, in November 2007, jointly with BRAINIT 2007, the 4th International Conference on Brain-Inspired Information Technology. The 228 revised full papers presented were carefully reviewed and selected from numerous ordinary paper submissions and 15 special organized sessions. The 116 papers of the first volume are organized in topical sections on computational neuroscience, learning and memory, neural network models, supervised/unsupervised/reinforcement learning, statistical learning algorithms, optimization algorithms, novel algorithms, as well as motor control and vision. The second volume contains 112 contributions related to statistical and pattern recognition algorithms, neuromorphic hardware and implementations, robotics, data mining and knowledge discovery, real world applications, cognitive and hybrid intelligent systems, bioinformatics, neuroinformatics, brain-conputer interfaces, and novel approaches.

Artificial Neural Networks and Machine Learning – ICANN 2018

Věra Kůrková,Yannis Manolopoulos,Barbara Hammer,Lazaros Iliadis,Ilias Maglogiannis
Artificial Neural Networks and Machine Learning ICANN 2018 Book PDF

Author : Věra Kůrková,Yannis Manolopoulos,Barbara Hammer,Lazaros Iliadis,Ilias Maglogiannis
Publisher : Springer
Page : 632 pages
File Size : 40,6 Mb
Release : 2018-09-25
Category : Computers
ISBN : 9783030014216

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Artificial Neural Networks and Machine Learning – ICANN 2018 by Věra Kůrková,Yannis Manolopoulos,Barbara Hammer,Lazaros Iliadis,Ilias Maglogiannis Pdf

This three-volume set LNCS 11139-11141 constitutes the refereed proceedings of the 27th International Conference on Artificial Neural Networks, ICANN 2018, held in Rhodes, Greece, in October 2018. The 139 full and 28 short papers as well as 41 full poster papers and 41 short poster papers presented in these volumes was carefully reviewed and selected from total of 360 submissions. They are related to the following thematic topics: AI and Bioinformatics, Bayesian and Echo State Networks, Brain Inspired Computing, Chaotic Complex Models, Clustering, Mining, Exploratory Analysis, Coding Architectures, Complex Firing Patterns, Convolutional Neural Networks, Deep Learning (DL), DL in Real Time Systems, DL and Big Data Analytics, DL and Big Data, DL and Forensics, DL and Cybersecurity, DL and Social Networks, Evolving Systems – Optimization, Extreme Learning Machines, From Neurons to Neuromorphism, From Sensation to Perception, From Single Neurons to Networks, Fuzzy Modeling, Hierarchical ANN, Inference and Recognition, Information and Optimization, Interacting with The Brain, Machine Learning (ML), ML for Bio Medical systems, ML and Video-Image Processing, ML and Forensics, ML and Cybersecurity, ML and Social Media, ML in Engineering, Movement and Motion Detection, Multilayer Perceptrons and Kernel Networks, Natural Language, Object and Face Recognition, Recurrent Neural Networks and Reservoir Computing, Reinforcement Learning, Reservoir Computing, Self-Organizing Maps, Spiking Dynamics/Spiking ANN, Support Vector Machines, Swarm Intelligence and Decision-Making, Text Mining, Theoretical Neural Computation, Time Series and Forecasting, Training and Learning.

Advances in Neural Networks – ISNN 2015

Xiaolin Hu,Yousheng Xia,Yunong Zhang,Dongbin Zhao
Advances in Neural Networks ISNN 2015 Book PDF

Author : Xiaolin Hu,Yousheng Xia,Yunong Zhang,Dongbin Zhao
Publisher : Springer
Page : 510 pages
File Size : 52,8 Mb
Release : 2015-10-14
Category : Computers
ISBN : 9783319253930

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Advances in Neural Networks – ISNN 2015 by Xiaolin Hu,Yousheng Xia,Yunong Zhang,Dongbin Zhao Pdf

The volume LNCS 9377 constitutes the refereed proceedings of the 12th International Symposium on Neural Networks, ISNN 2015, held in Jeju, South Korea in October 2015. The 55 revised full papers presented were carefully reviewed and selected from 97 submissions. These papers cover many topics of neural network-related research including intelligent control, neurodynamic analysis, memristive neurodynamics, computer vision, signal processing, machine learning, and optimization.

Principles of Neural Coding

Rodrigo Quian Quiroga,Stefano Panzeri
Principles of Neural Coding Book PDF

Author : Rodrigo Quian Quiroga,Stefano Panzeri
Publisher : CRC Press
Page : 625 pages
File Size : 44,9 Mb
Release : 2013-05-06
Category : Medical
ISBN : 9781439853313

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Principles of Neural Coding by Rodrigo Quian Quiroga,Stefano Panzeri Pdf

Understanding how populations of neurons encode information is the challenge faced by researchers in the field of neural coding. Focusing on the many mysteries and marvels of the mind has prompted a prominent team of experts in the field to put their heads together and fire up a book on the subject. Simply titled Principles of Neural Coding, this b

Exploring Neural Networks with C#

Ryszard Tadeusiewicz,Rituparna Chaki,Nabendu Chaki
Exploring Neural Networks with C Book PDF

Author : Ryszard Tadeusiewicz,Rituparna Chaki,Nabendu Chaki
Publisher : CRC Press
Page : 302 pages
File Size : 54,9 Mb
Release : 2017-07-27
Category : Computers
ISBN : 9781498760379

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Exploring Neural Networks with C# by Ryszard Tadeusiewicz,Rituparna Chaki,Nabendu Chaki Pdf

The utility of artificial neural network models lies in the fact that they can be used to infer functions from observations making them especially useful in applications where the complexity of data or tasks makes the design of such functions by hand impractical.Exploring Neural Networks with C# presents the important properties of neural networks

Complex-Valued Neural Networks

Akira Hirose
Complex Valued Neural Networks Book PDF

Author : Akira Hirose
Publisher : Springer
Page : 176 pages
File Size : 46,6 Mb
Release : 2007-01-11
Category : Computers
ISBN : 9783540334576

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Complex-Valued Neural Networks by Akira Hirose Pdf

This monograph instructs graduate- and undergraduate-level students in electrical engineering, informatics, control engineering, mechanics, robotics, bioengineering on the concepts of complex-valued neural networks. Emphasizing basic concepts and ways of thinking about neural networks, the author focuses on neural networks that deal with complex numbers; the practical advantages of complex-valued neural networks, and their origins; the development of principal applications? The book uses detailed examples to answer these questions and more.

Neural Computing for Advanced Applications

Haijun Zhang,Zhao Zhang,Zhou Wu,Tianyong Hao
Neural Computing for Advanced Applications Book PDF

Author : Haijun Zhang,Zhao Zhang,Zhou Wu,Tianyong Hao
Publisher : Springer Nature
Page : 542 pages
File Size : 44,9 Mb
Release : 2020-08-12
Category : Computers
ISBN : 9789811576706

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Neural Computing for Advanced Applications by Haijun Zhang,Zhao Zhang,Zhou Wu,Tianyong Hao Pdf

This book presents refereed proceedings of the First International Conference on Neural Computing for Advanced Applications, NCAA 2020, held in July, 2020. Due to the COVID-19 pandemic the conference was held online. The 36 full papers and 7 short papers were thorougly reviewed and selected from a total of 113 qualified submissions. The papers present resent research on such topics as neural network theory, and cognitive sciences, machine learning, data mining, data security & privacy protection, and data-driven applications, computational intelligence, nature-inspired optimizers, and their engineering applications, cloud/edge/fog computing, the Internet of Things/Vehicles (IoT/IoV), and their system optimization, control systems, network synchronization, system integration, and industrial artificial intelligence, fuzzy logic, neuro-fuzzy systems, decision making, and their applications in management sciences, computer vision, image processing, and their industrial applications, and natural language processing, machine translation, knowledge graphs, and their applications.