Neurons And Networks
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Neuronal Networks In Brain Function Cns Disorders And Therapeutics
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Author : Carl Faingold
language : en
Publisher: Academic Press
Release Date : 2013-12-26
Neuronal Networks In Brain Function Cns Disorders And Therapeutics written by Carl Faingold and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-12-26 with Medical categories.
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
Neurons Networks And Motor Behavior
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Author : Paul S. G. Stein
language : en
Publisher: MIT Press
Release Date : 1997
Neurons Networks And Motor Behavior written by Paul S. G. Stein and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with Medical categories.
Recent advances in motor behavior research rely on detailed knowledge of the characteristics of the neurons and networks that generate motor behavior. At the cellular level, Neurons, Networks, and Motor Behavior describes the computational characteristics of individual neurons and how these characteristics are modified by neuromodulators. At the network and behavioral levels, the volume discusses how network structure is dynamically modulated to produce adaptive behavior. Comparisons of model systems throughout the animal kingdom provide insights into general principles of motor control. Contributors describe how networks generate such motor behaviors as walking, swimming, flying, scratching, reaching, breathing, feeding, and chewing. An emerging principle of organization is that nervous systems are remarkably efficient in constructing neural networks that control multiple tasks and dynamically adapt to change.The volume contains six sections: selection and initiation of motor patterns; generation and formation of motor patterns: cellular and systems properties; generation and formation of motor patterns: computational approaches; modulation and reconfiguration; short-term modulation of pattern generating circuits; and sensory modification of motor output to control whole body orientation.
Complex Valued Neural Networks With Multi Valued Neurons
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Author : Igor Aizenberg
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-06-24
Complex Valued Neural Networks With Multi Valued Neurons written by Igor Aizenberg and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-06-24 with Computers categories.
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.
Neurons And Networks
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Author : John E. Dowling
language : en
Publisher: Harvard University Press
Release Date : 2001
Neurons And Networks written by John E. Dowling and has been published by Harvard University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Medical categories.
Completely revised and enlarged with six new chapters, the second edition of Neurons and Networks is an introduction not just to neurobiology, but to all of behavioral neuroscience. It is an ideal text for first- or second-year college students with minimal college science exposure.
Weakly Connected Neural Networks
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Author : Frank C. Hoppensteadt
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Weakly Connected Neural Networks written by Frank C. Hoppensteadt and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
This book is devoted to an analysis of general weakly connected neural networks (WCNNs) that can be written in the form (0.1) m Here, each Xi E IR is a vector that summarizes all physiological attributes of the ith neuron, n is the number of neurons, Ii describes the dynam ics of the ith neuron, and gi describes the interactions between neurons. The small parameter € indicates the strength of connections between the neurons. Weakly connected systems have attracted much attention since the sec ond half of seventeenth century, when Christian Huygens noticed that a pair of pendulum clocks synchronize when they are attached to a light weight beam instead of a wall. The pair of clocks is among the first weakly connected systems to have been studied. Systems of the form (0.1) arise in formal perturbation theories developed by Poincare, Liapunov and Malkin, and in averaging theories developed by Bogoliubov and Mitropolsky.
Functional Networks With Applications
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Author : Enrique Castillo
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Functional Networks With Applications written by Enrique Castillo and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Science categories.
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.
Network Neuroscience
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Author : Flavio Fröhlich
language : en
Publisher: Academic Press
Release Date : 2016-09-20
Network Neuroscience written by Flavio Fröhlich and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-20 with Medical categories.
Studying brain networks has become a truly interdisciplinary endeavor, attracting students and seasoned researchers alike from a wide variety of academic backgrounds. What has been lacking is an introductory textbook that brings together the different fields and provides a gentle introduction to the major concepts and findings in the emerging field of network neuroscience. Network Neuroscience is a one-stop-shop that is of equal use to the neurobiologist, who is interested in understanding the quantitative methods employed in network neuroscience, and to the physicist or engineer, who is interested in neuroscience applications of mathematical and engineering tools. The book spans 27 chapters that cover everything from individual cells all the way to complex network disorders such as depression and autism spectrum disorders. An additional 12 toolboxes provide the necessary background for making network neuroscience accessible independent of the reader's background. Dr. Flavio Frohlich wrote this book based on his experience of mentoring dozens of trainees in the Frohlich Lab, from undergraduate students to senior researchers. The Frohlich lab pursues a unique and integrated vision that combines computer simulations, animal model studies, human studies, and clinical trials with the goal of developing novel brain stimulation treatments for psychiatric disorders. The book is based on a course he teaches at UNC that has attracted trainees from many different departments, including neuroscience, biomedical engineering, psychology, cell biology, physiology, neurology, and psychiatry. Dr. Frohlich has consistently received rave reviews for his teaching. With this book he hopes to make his integrated view of neuroscience available to trainees and researchers on a global scale. His goal is to make the book the training manual for the next generation of (network) neuroscientists, who will be fusing biology, engineering, and medicine to unravel the big questions about the brain and to revolutionize psychiatry and neurology. - Easy-to-read, comprehensive introduction to the emerging field of network neuroscience - Includes 27 chapters packed with information on topics from single neurons to complex network disorders such as depression and autism - Features 12 toolboxes serve as primers to provide essential background knowledge in the fields of biology, mathematics, engineering, and physics
An Introduction To Neural Networks
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Author : Kevin Gurney
language : en
Publisher: CRC Press
Release Date : 2018-10-08
An Introduction To Neural Networks written by Kevin Gurney and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-08 with Computers categories.
Though mathematical ideas underpin the study of neural networks, the author presents the fundamentals without the full mathematical apparatus. All aspects of the field are tackled, including artificial neurons as models of their real counterparts; the geometry of network action in pattern space; gradient descent methods, including back-propagation; associative memory and Hopfield nets; and self-organization and feature maps. The traditionally difficult topic of adaptive resonance theory is clarified within a hierarchical description of its operation. The book also includes several real-world examples to provide a concrete focus. This should enhance its appeal to those involved in the design, construction and management of networks in commercial environments and who wish to improve their understanding of network simulator packages. As a comprehensive and highly accessible introduction to one of the most important topics in cognitive and computer science, this volume should interest a wide range of readers, both students and professionals, in cognitive science, psychology, computer science and electrical engineering.
Multivariate Statistical Machine Learning Methods For Genomic Prediction
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Author : Osval Antonio Montesinos López
language : en
Publisher: Springer Nature
Release Date : 2022-02-14
Multivariate Statistical Machine Learning Methods For Genomic Prediction written by Osval Antonio Montesinos López and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-14 with Technology & Engineering categories.
This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.
From Molecules To Networks
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Author : John H. Byrne
language : en
Publisher: Academic Press
Release Date : 2009
From Molecules To Networks written by John H. Byrne and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Medical categories.
An understanding of the nervous system at virtually any level of analysis requires an understanding of its basic building block, the neuron. This book provides the solid foundation of the morphological, biochemical, and biophysical properties of nerve cells. All chapters have been thoroughly revised for this second edition to reflect the significant advances of the past five years. The new edition expands on the network aspects of cellular neurobiology by adding a new chapter, Information Processing in Neural Networks, and on the relation of cell biological processes to various neurological diseases. The new concluding chapter illustrates how the great strides in understanding the biochemical and biophysical properties of nerve cells have led to fundamental insights into important aspects of neurodegenerative disease. Includes two new chapters: Information Processing in Neural Networks - describes the principles of operation of neural networks and the key circuit motifs that are common to many networks in the nervous system. Molecular and Cellular Mechanisms of Neurodegenerative Disease - introduces the progress made in the last 20 years in elucidating the cellular and molecular mechanisms underlying brain disorders, including Amyotrophic Lateral Sclerosis (ALS), Parkinson disease, and Alzheimer's disease.