neural networks computational models (24 resultados)

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Softcover. Condición: New. Estado de la sobrecubierta: no dj. First. The human brain is a complex organ that is comprised of intricate neural networks that govern our thoughts, feelings, and behaviors. Computational Models of Neural Networks offers a cutting-edge exploration of the ways in which computational models can be used to simulate and understand the behavior of neural networks. By examining the various computational models that have been developed to simulate neural networks, the author provides a comprehensive understanding of the intricate mechanisms that govern brain function and behavior. From the development of cognitive abilities to the regulation of emotions, this book reveals the profound implications of computational models for our understanding of the brain and nervous system. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.

Softcover. Condición: New. Estado de la sobrecubierta: no dj. First. Computational Models of Neural Networks in Addiction Pathways provides an in-depth analysis of how artificial neural network models simulate the complex neural interactions involved in addiction. The book discusses various computational approaches to understanding craving, reward processing, and relapse. It emphasizes the importance of modeling neural circuitry to predict behavioral outcomes and develop targeted interventions. This resource combines theoretical frameworks with practical applications, offering a sophisticated perspective on the neural underpinnings of addiction. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.

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Softcover. Condición: New. Estado de la sobrecubierta: no dj. First. Computational Models of Neural Networks and Behavior is a cutting-edge exploration of the complex relationships between neural networks, behavior, and computational modeling. This book examines the ways in which computational models can be used to understand and simulate neural networks, offering insights into the neural mechanisms that underlie cognition, emotion, and behavior. By examining the intricate relationships between neural networks, behavior, and computational modeling, Computational Models of Neural Networks and Behavior provides a nuanced understanding of the challenges and opportunities facing the field of neuroscience and offers actionable advice for developing effective computational models of neural networks and behavior. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.

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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Neural Networks: Computational Models and Applications covers a wealth of important theoretical and practical issues in neural networks, including the learning algorithms of feed-forward neural networks, various dynamical properties of recurrent neural networks, winner-take-all networks and their applications in broad manifolds of computational intelligence: pattern recognition, uniform approximation, constrained optimization, NP-hard problems, and image segmentation. By presenting various computational models, this book is developed to provide readers with a quick but insightful understanding of the broad and rapidly growing areas in the neural networks domain.Besides laying down fundamentals on artificial neural networks, this book also studies biologically inspired neural networks. Some typical computational models are discussed, and subsequently applied to objection recognition, scene analysis and associative memory. The studies of bio-inspired models have important implications in computer vision and robotic navigation, as well as new efficient algorithms for image analysis. Another significant feature of the book is that it begins with fundamental dynamical problems in presenting the mathematical techniques extensively used in analyzing neurodynamics, thus allowing non-mathematicians to develop and apply these analytical techniques easily. Written for a wide readership, engineers, computer scientists and mathematicians interested in machine learning, data mining and neural networks modeling will find this book of value. This book will also act as a helpful reference for graduate students studying neural networks and complex dynamical systems.

Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Neural Networks: Computational Models and Applications covers a wealth of important theoretical and practical issues in neural networks, including the learning algorithms of feed-forward neural networks, various dynamical properties of recurrent neural networks, winner-take-all networks and their applications in broad manifolds of computational intelligence: pattern recognition, uniform approximation, constrained optimization, NP-hard problems, and image segmentation. By presenting various computational models, this book is developed to provide readers with a quick but insightful understanding of the broad and rapidly growing areas in the neural networks domain.Besides laying down fundamentals on artificial neural networks, this book also studies biologically inspired neural networks. Some typical computational models are discussed, and subsequently applied to objection recognition, scene analysis and associative memory. The studies of bio-inspired models have important implications in computer vision and robotic navigation, as well as new efficient algorithms for image analysis. Another significant feature of the book is that it begins with fundamental dynamical problems in presenting the mathematical techniques extensively used in analyzing neurodynamics, thus allowing non-mathematicians to develop and apply these analytical techniques easily. Written for a wide readership, engineers, computer scientists and mathematicians interested in machine learning, data mining and neural networks modeling will find this book of value. This book will also act as a helpful reference for graduate students studying neural networks and complex dynamical systems.

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Buch. Condición: Neu. Neuware -Neural Networks: Computational Models and Applications covers a wealth of important theoretical and practical issues in neural networks, including the learning algorithms of feed-forward neural networks, various dynamical properties of recurrent neural networks, winner-take-all networks and their applications in broad manifolds of computational intelligence: pattern recognition, uniform approximation, constrained optimization, NP-hard problems, and image segmentation. By presenting various computational models, this book is developed to provide readers with a quick but insightful understanding of the broad and rapidly growing areas in the neural networks domain.Besides laying down fundamentals on artificial neural networks, this book also studies biologically inspired neural networks. Some typical computational models are discussed, and subsequently applied to objection recognition, scene analysis and associative memory. The studies of bio-inspired models have important implications in computer vision and robotic navigation, as well as new efficient algorithms for image analysis. Another significant feature of the book is that it begins with fundamental dynamical problems in presenting the mathematical techniques extensively used in analyzing neurodynamics, thus allowing non-mathematicians to develop and apply these analytical techniques easily.Written for a wide readership, engineers, computer scientists and mathematicians interested in machine learning, data mining and neural networks modeling will find this book of value. This book will also act as a helpful reference for graduate students studying neural networks and complex dynamical systems.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 324 pp. Englisch.

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Paperback. Condición: Brand New. 300 pages. 9.00x6.00x0.75 inches. In Stock.

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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Latest research on computational models and applications of neural networksNeural Networks: Computational Models and Applications presents important theoretical and practical issues in neural networks, including the learning algorithms of feed.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Neural Networks: Computational Models and Applications presents important theoretical and practical issues in neural networks, including the learning algorithms of feed-forward neural networks, various dynamical properties of recurrent neural networks, winner-take-all networks and their applications in broad manifolds of computational intelligence: pattern recognition, uniform approximation, constrained optimization, NP-hard problems, and image segmentation. The book offers a compact, insightful understanding of the broad and rapidly growing neural networks domain. 324 pp. Englisch.

Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Neural Networks: Computational Models and Applications presents important theoretical and practical issues in neural networks, including the learning algorithms of feed-forward neural networks, various dynamical properties of recurrent neural networks, winner-take-all networks and their applications in broad manifolds of computational intelligence: pattern recognition, uniform approximation, constrained optimization, NP-hard problems, and image segmentation. The book offers a compact, insightful understanding of the broad and rapidly growing neural networks domain. 324 pp. Englisch.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Neural Networks: Computational Models and Applications covers a wealth of important theoretical and practical issues in neural networks, including the learning algorithms of feed-forward neural networks, various dynamical properties of recurrent neural networks, winner-take-all networks and their applications in broad manifolds of computational intelligence: pattern recognition, uniform approximation, constrained optimization, NP-hard problems, and image segmentation. By presenting various computational models, this book is developed to provide readers with a quick but insightful understanding of the broad and rapidly growing areas in the neural networks domain.Besides laying down fundamentals on artificial neural networks, this book also studies biologically inspired neural networks. Some typical computational models are discussed, and subsequently applied to objection recognition, scene analysis and associative memory. The studies of bio-inspired models have important implications in computer vision and robotic navigation, as well as new efficient algorithms for image analysis. Another significant feature of the book is that it begins with fundamental dynamical problems in presenting the mathematical techniques extensively used in analyzing neurodynamics, thus allowing non-mathematicians to develop and apply these analytical techniques easily.Written for a wide readership, engineers, computer scientists and mathematicians interested in machine learning, data mining and neural networks modeling will find this book of value. This book will also act as a helpful reference for graduate students studying neural networks and complex dynamical systems.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 324 pp. Englisch.