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Author: Josef Skrzypek
Publisher: Springer Science & Business Media
ISBN: 1461527368
Category : Science
Languages : en
Pages : 263
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Book Description
Neural Network Simulation Environments describes some of the best examples of neural simulation environments. All current neural simulation tools can be classified into four overlapping categories of increasing sophistication in software engineering. The least sophisticated are undocumented and dedicated programs, developed to solve just one specific problem; these tools cannot easily be used by the larger community and have not been included in this volume. The next category is a collection of custom-made programs, some perhaps borrowed from other application domains, and organized into libraries, sometimes with a rudimentary user interface. More recently, very sophisticated programs started to appear that integrate advanced graphical user interface and other data analysis tools. These are frequently dedicated to just one neural architecture/algorithm as, for example, three layers of interconnected artificial `neurons' learning to generalize input vectors using a backpropagation algorithm. Currently, the most sophisticated simulation tools are complete, system-level environments, incorporating the most advanced concepts in software engineering that can support experimentation and model development of a wide range of neural networks. These environments include sophisticated graphical user interfaces as well as an array of tools for analysis, manipulation and visualization of neural data. Neural Network Simulation Environments is an excellent reference for researchers in both academia and industry, and can be used as a text for advanced courses on the subject.
Author: Josef Skrzypek
Publisher: Springer Science & Business Media
ISBN: 1461527368
Category : Science
Languages : en
Pages : 263
Get Book Here
Book Description
Neural Network Simulation Environments describes some of the best examples of neural simulation environments. All current neural simulation tools can be classified into four overlapping categories of increasing sophistication in software engineering. The least sophisticated are undocumented and dedicated programs, developed to solve just one specific problem; these tools cannot easily be used by the larger community and have not been included in this volume. The next category is a collection of custom-made programs, some perhaps borrowed from other application domains, and organized into libraries, sometimes with a rudimentary user interface. More recently, very sophisticated programs started to appear that integrate advanced graphical user interface and other data analysis tools. These are frequently dedicated to just one neural architecture/algorithm as, for example, three layers of interconnected artificial `neurons' learning to generalize input vectors using a backpropagation algorithm. Currently, the most sophisticated simulation tools are complete, system-level environments, incorporating the most advanced concepts in software engineering that can support experimentation and model development of a wide range of neural networks. These environments include sophisticated graphical user interfaces as well as an array of tools for analysis, manipulation and visualization of neural data. Neural Network Simulation Environments is an excellent reference for researchers in both academia and industry, and can be used as a text for advanced courses on the subject.
Author: Alfredo Weitzenfeld
Publisher: MIT Press
ISBN: 9780262731492
Category : Computers
Languages : en
Pages : 466
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Book Description
Simulation in NSL - Modeling in NSL - Schematic Capture System - User Interface and Graphical Windows - The Modeling Language NSLM - The Scripting Language NSLS - Adaptive Resonance Theory - Depth Perception - Retina - Receptive Fields - The Associative Search Network: Landmark Learning and Hill Climbing - A Model of Primate Visual-Motor Conditional Learning - The Modular Design of the Oculomotor System in Monkeys - Crowley-Arbib Saccade Model - A Cerebellar Model of Sensorimotor Adaptation - Learning to Detour - Face Recognition by Dynamic Link Matching - Appendix I : NSLM Methods - NSLJ Extensions - NSLC Extensions - NSLJ and NSLC Differences - NSLJ and NSLC Installation Instructions.
Author: Nicholas T. Carnevale
Publisher: Cambridge University Press
ISBN: 1139447831
Category : Medical
Languages : en
Pages : 399
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Book Description
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.
Author: Alexandros Iosifidis
Publisher: Academic Press
ISBN: 0323885721
Category : Technology & Engineering
Languages : en
Pages : 638
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Book Description
Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end learning point-of-view. The book is suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics, Deep Learning, Robotic Perception and Cognition tasks. - Presents deep learning principles and methodologies - Explains the principles of applying end-to-end learning in robotics applications - Presents how to design and train deep learning models - Shows how to apply deep learning in robot vision tasks such as object recognition, image classification, video analysis, and more - Uses robotic simulation environments for training deep learning models - Applies deep learning methods for different tasks ranging from planning and navigation to biosignal analysis
Author: Management Association, Information Resources
Publisher: IGI Global
ISBN: 1668424096
Category : Computers
Languages : en
Pages : 1575
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Book Description
Artificial neural networks (ANNs) present many benefits in analyzing complex data in a proficient manner. As an effective and efficient problem-solving method, ANNs are incredibly useful in many different fields. From education to medicine and banking to engineering, artificial neural networks are a growing phenomenon as more realize the plethora of uses and benefits they provide. Due to their complexity, it is vital for researchers to understand ANN capabilities in various fields. The Research Anthology on Artificial Neural Network Applications covers critical topics related to artificial neural networks and their multitude of applications in a number of diverse areas including medicine, finance, operations research, business, social media, security, and more. Covering everything from the applications and uses of artificial neural networks to deep learning and non-linear problems, this book is ideal for computer scientists, IT specialists, data scientists, technologists, business owners, engineers, government agencies, researchers, academicians, and students, as well as anyone who is interested in learning more about how artificial neural networks can be used across a wide range of fields.
Author: Subana Shanmuganathan
Publisher: Springer
ISBN: 3319284959
Category : Technology & Engineering
Languages : en
Pages : 468
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Book Description
This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling.
Author: William W. Hsieh
Publisher: Cambridge University Press
ISBN: 0521791928
Category : Computers
Languages : en
Pages : 364
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Book Description
A graduate textbook that provides a unified treatment of machine learning methods and their applications in the environmental sciences.
Author: Zhang, Ming
Publisher: IGI Global
ISBN: 1615207120
Category : Computers
Languages : en
Pages : 660
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Book Description
"This book introduces and explains Higher Order Neural Networks (HONNs) to people working in the fields of computer science and computer engineering, and how to use HONNS in these areas"--Provided by publisher.
Author: Abu-Taieh, Evon M. O.
Publisher: IGI Global
ISBN: 1605667757
Category : Computers
Languages : en
Pages : 609
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Book Description
"This book provides a comprehensive overview of theory and practice in simulation systems focusing on major breakthroughs within the technological arena, with particular concentration on the accelerating principles, concepts and applications"--Provided by publisher.
Author: James M. Bower
Publisher: Springer Science & Business Media
ISBN: 1475798008
Category : Medical
Languages : en
Pages : 897
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Book Description
This volume includes papers presented at the Fifth Annual Computational Neurosci ence meeting (CNS*96) held in Boston, Massachusetts, July 14 - 17, 1996. This collection includes 148 of the 234 papers presented at the meeting. Acceptance for mceting presenta tion was based on the peer review of preliminary papers originally submitted in May of 1996. The papers in this volume represent final versions of this work submitted in January of 1997. As represented by this volume, computational neuroscience continues to expand in quality, size and breadth of focus as increasing numbers of neuroscientists are taking a computational approach to understanding nervous system function. Defining computa tional neuroscience as the exploration of how brains compute, it is clear that there is al most no subject or area of modern neuroscience research that is not appropriate for computational studies. The CNS meetings as well as this volume reflect this scope and di versity.
