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Author: Enrique Castillo
Publisher: Springer Science & Business Media
ISBN: 1461556015
Category : Science
Languages : en
Pages : 307
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Book Description
Artificial neural networks have been recognized as a powerful tool to learn and reproduce systems in various fields of applications. Neural net works are inspired by the brain behavior and consist of one or several layers of neurons, or computing units, connected by links. Each artificial neuron receives an input value from the input layer or the neurons in the previ ous layer. Then it computes a scalar output from a linear combination of the received inputs using a given scalar function (the activation function), which is assumed the same for all neurons. One of the main properties of neural networks is their ability to learn from data. There are two types of learning: structural and parametric. Structural learning consists of learning the topology of the network, that is, the number of layers, the number of neurons in each layer, and what neurons are connected. This process is done by trial and error until a good fit to the data is obtained. Parametric learning consists of learning the weight values for a given topology of the network. Since the neural functions are given, this learning process is achieved by estimating the connection weights based on the given information. To this aim, an error function is minimized using several well known learning methods, such as the backpropagation algorithm. Unfortunately, for these methods: (a) The function resulting from the learning process has no physical or engineering interpretation. Thus, neural networks are seen as black boxes.
Author: Enrique Castillo
Publisher: Springer Science & Business Media
ISBN: 1461556015
Category : Science
Languages : en
Pages : 307
Get Book Here
Book Description
Artificial neural networks have been recognized as a powerful tool to learn and reproduce systems in various fields of applications. Neural net works are inspired by the brain behavior and consist of one or several layers of neurons, or computing units, connected by links. Each artificial neuron receives an input value from the input layer or the neurons in the previ ous layer. Then it computes a scalar output from a linear combination of the received inputs using a given scalar function (the activation function), which is assumed the same for all neurons. One of the main properties of neural networks is their ability to learn from data. There are two types of learning: structural and parametric. Structural learning consists of learning the topology of the network, that is, the number of layers, the number of neurons in each layer, and what neurons are connected. This process is done by trial and error until a good fit to the data is obtained. Parametric learning consists of learning the weight values for a given topology of the network. Since the neural functions are given, this learning process is achieved by estimating the connection weights based on the given information. To this aim, an error function is minimized using several well known learning methods, such as the backpropagation algorithm. Unfortunately, for these methods: (a) The function resulting from the learning process has no physical or engineering interpretation. Thus, neural networks are seen as black boxes.
Author: Carl Faingold
Publisher: Academic Press
ISBN: 0124158641
Category : Medical
Languages : en
Pages : 537
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Book Description
Neuronal Networks in Brain Function, CNS Disorders, and Therapeutics, edited by two leaders in the field, offers a current and complete review of what we know about neural networks. How the brain accomplishes many of its more complex tasks can only be understood via study of neuronal network control and network interactions. Large networks can undergo major functional changes, resulting in substantially different brain function and affecting everything from learning to the potential for epilepsy. With chapters authored by experts in each topic, this book advances the understanding of: - How the brain carries out important tasks via networks - How these networks interact in normal brain function - Major mechanisms that control network function - The interaction of the normal networks to produce more complex behaviors - How brain disorders can result from abnormal interactions - How therapy of disorders can be advanced through this network approach This book will benefit neuroscience researchers and graduate students with an interest in networks, as well as clinicians in neuroscience, pharmacology, and psychiatry dealing with neurobiological disorders. - Utilizes perspectives and tools from various neuroscience subdisciplines (cellular, systems, physiologic), making the volume broadly relevant - Chapters explore normal network function and control mechanisms, with an eye to improving therapies for brain disorders - Reflects predominant disciplinary shift from an anatomical to a functional perspective of the brain - Edited work with chapters authored by leaders in the field around the globe – the broadest, most expert coverage available
Author: Mingxia Liu
Publisher: Frontiers Media SA
ISBN: 2832520014
Category : Science
Languages : en
Pages : 534
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Book Description
Author: Ramachandran Ramani
Publisher: Oxford University Press
ISBN: 019029776X
Category : Medical
Languages : en
Pages : 265
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Book Description
Functional MRI: Basic Principles and Emerging Clinical Applications provides an overview of the basic principles of fMRI for clinicians with minimal knowledge of the imaging technique and its research potential and clinical applications. The text is divided into two parts, with Section I covering the primary signal measured in fMRI (BOLD), the correlation between neuronal activity and the BOLD signal, and how the data is analyzed and interpreted in fMRI. Section II explores applications of fMRI in cognitive neuroscience and common psychiatric disorders, surgical planning in neurosurgery, anesthesia and the intensive care unit, and more. Timely and highly accessible, this book is a valuable resource for researchers and clinicians interested in understanding what fMRI is, how it works, and its applications.
Author: De-Shuang Huang
Publisher: Springer Science & Business Media
ISBN: 3642148301
Category : Computers
Languages : en
Pages : 593
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Book Description
The International Conference on Intelligent Computing (ICIC) was formed to provide an annual forum dedicated to the emerging and challenging topics in artificial intel- gence, machine learning, pattern recognition, image processing, bioinformatics, and computational biology. It aims to bring together researchers and practitioners from both academia and industry to share ideas, problems, and solutions related to the m- tifaceted aspects of intelligent computing. ICIC 2010, held in Changsha, China, August 18-21, 2010, constituted the 6th - ternational Conference on Intelligent Computing. It built upon the success of ICIC 2009, ICIC 2008, ICIC 2007, ICIC 2006, and ICIC 2005, that were held in Ulsan, Korea, Shanghai, Qingdao, Kunming and Hefei, China, respectively. This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligent computing. Its aim was to unify the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. Therefore, the theme for this conference was “Advanced Intelligent Computing Technology and Applications.” Papers focusing on this theme were solicited, addressing theories, methodologies, and applications in science and technology.
Author: Enrique Castillo
Publisher: Elsevier
ISBN: 0080477917
Category : Mathematics
Languages : en
Pages : 410
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Book Description
The book provides the reader with the different types of functional equations that s/he can find in practice, showing, step by step, how they can be solved.A general methodology for solving functional equations is provided in Chapter 2. The different types of functional equations are described and solved in Chapters 3 to 8. Many examples, coming from different fields, as geometry, science, engineering, economics, probability, statistics, etc, help the reader to change his/her mind in order to state problems as functional equations as an alternative to differential equations, and to state new problems in terms of functional equations or systems.An interesting feature of the book is that it deals with functional networks, a powerful generalization of neural networks that allows solving many practical problems. The second part of the book, Chapters 9 to 13, is devoted to the applications of this important paradigm.The book contains many examples and end of chapter exercises, that facilitates the understanding of the concepts and applications.· A general methodology for solving functional equations is provided in Chapter 2.· It deals with functional networks, a powerful generalization of neural networks.· Many examples, coming from different fields, as geometry, science, engineering, economics, probability, statistics, etc, illustrate the concept of functional equation.· Functional equations are presented as a powerful alternative to differential equations. · The book contains end of chapter exercises.
Author: Alex Fornito
Publisher: Academic Press
ISBN: 0124081185
Category : Medical
Languages : en
Pages : 496
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Book Description
Fundamentals of Brain Network Analysis is a comprehensive and accessible introduction to methods for unraveling the extraordinary complexity of neuronal connectivity. From the perspective of graph theory and network science, this book introduces, motivates and explains techniques for modeling brain networks as graphs of nodes connected by edges, and covers a diverse array of measures for quantifying their topological and spatial organization. It builds intuition for key concepts and methods by illustrating how they can be practically applied in diverse areas of neuroscience, ranging from the analysis of synaptic networks in the nematode worm to the characterization of large-scale human brain networks constructed with magnetic resonance imaging. This text is ideally suited to neuroscientists wanting to develop expertise in the rapidly developing field of neural connectomics, and to physical and computational scientists wanting to understand how these quantitative methods can be used to understand brain organization. - Winner of the 2017 PROSE Award in Biomedicine & Neuroscience and the 2017 British Medical Association (BMA) Award in Neurology - Extensively illustrated throughout by graphical representations of key mathematical concepts and their practical applications to analyses of nervous systems - Comprehensively covers graph theoretical analyses of structural and functional brain networks, from microscopic to macroscopic scales, using examples based on a wide variety of experimental methods in neuroscience - Designed to inform and empower scientists at all levels of experience, and from any specialist background, wanting to use modern methods of network science to understand the organization of the brain
Author: Wenbin Guo
Publisher: Frontiers Media SA
ISBN: 2889661954
Category : Science
Languages : en
Pages : 71
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Book Description
This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.
Author: Luis M. Colon-Perez
Publisher: Frontiers Media SA
ISBN: 288945472X
Category :
Languages : en
Pages : 104
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Book Description
Throughout the history of neuroscience, technological advances are the drivers behind many major advances in our understanding of the nervous system. Investigations of the structure and function of the brain take place on multiple scales, including macroscale at the level of brain regions, mesoscale at the level of neuronal populations, and microscale at the level of single neurons and neuron to neuron interactions. Integration of knowledge over these scales requires novel techniques and interpretations. In this research topic, we highlight nine articles that integrate structural and functional approaches to study brain networks.
Author: Robert J. Howlett
Publisher: Springer Science & Business Media
ISBN: 9783790813685
Category : Computers
Languages : en
Pages : 392
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Book Description
The Radial Basis Function (RBF) neural network has gained in popularity over recent years because of its rapid training and its desirable properties in classification and functional approximation applications. RBF network research has focused on enhanced training algorithms and variations on the basic architecture to improve the performance of the network. In addition, the RBF network is proving to be a valuable tool in a diverse range of application areas, for example, robotics, biomedical engineering, and the financial sector. The two volumes provide a comprehensive survey of the latest developments in this area. Volume 2 contains a wide range of applications in the laboratory and case studies describing current industrial use. Both volumes will prove extremely useful to practitioners in the field, engineers, reserachers, students and technically accomplished managers.
