The Self-Assembling Brain: How Neural Networks Grow Smarter, Hiesinger Peter Robin
Автор: Nikola K. Kasabov Название: Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence ISBN: 3662577135 ISBN-13(EAN): 9783662577134 Издательство: Springer Рейтинг: Цена: 260870.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Spiking neural networks (SNN) are biologically inspired computational models that represent and process information internally as trains of spikes. This monograph book presents the classical theory and applications of SNN, including original author’s contribution to the area. The book introduces for the first time not only deep learning and deep knowledge representation in the human brain and in brain-inspired SNN, but takes that further to develop new types of AI systems, called in the book brain-inspired AI (BI-AI). BI-AI systems are illustrated on: cognitive brain data, including EEG, fMRI and DTI; audio-visual data; brain-computer interfaces; personalized modelling in bio-neuroinformatics; multisensory streaming data modelling in finance, environment and ecology; data compression; neuromorphic hardware implementation. Future directions, such as the integration of multiple modalities, such as quantum-, molecular- and brain information processing, is presented in the last chapter. The book is a research book for postgraduate students, researchers and practitioners across wider areas, including computer and information sciences, engineering, applied mathematics, bio- and neurosciences.
Автор: Ivan Coluzza Название: Design of Self-Assembling Materials ISBN: 3030100790 ISBN-13(EAN): 9783030100797 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Поставка под заказ. Описание:
Brings together the fields of Biology, Chemistry and Physics.
Provides insight into assembling dynamics of proteins.
Summarizes resent developments in the field of polymere science and technology.
Автор: Moo K. Chung Название: Brain Network Analysis ISBN: 110718486X ISBN-13(EAN): 9781107184862 Издательство: Cambridge Academ Рейтинг: Цена: 77090.00 T Наличие на складе: Невозможна поставка. Описание: This tutorial reference provides a coherent overview of statistical and mathematical approaches used in brain network analysis. It goes beyond graph theory to explore different models in network science, regression, and algebraic topology, empowering methodological understanding in a manner immediately usable to both researchers and students.
Artificial Intelligence in the Age of Neural Networks and Brain Computing demonstrates that existing disruptive implications and applications of AI is a development of the unique attributes of neural networks, mainly machine learning, distributed architectures, massive parallel processing, black-box inference, intrinsic nonlinearity and smart autonomous search engines. The book covers the major basic ideas of brain-like computing behind AI, provides a framework to deep learning, and launches novel and intriguing paradigms as future alternatives. The success of AI-based commercial products proposed by top industry leaders, such as Google, IBM, Microsoft, Intel and Amazon can be interpreted using this book.
Developed from the 30th anniversary of the International Neural Network Society (INNS) and the 2017 International Joint Conference on Neural Networks (IJCNN)
Authored by top experts, global field pioneers and researchers working on cutting-edge applications in signal processing, speech recognition, games, adaptive control and decision-making
Edited by high-level academics and researchers in intelligent systems and neural networks
Автор: Nikola K. Kasabov Название: Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence ISBN: 366258607X ISBN-13(EAN): 9783662586075 Издательство: Springer Рейтинг: Цена: 260870.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Spiking neural networks (SNN) are biologically inspired computational models that represent and process information internally as trains of spikes. This monograph book presents the classical theory and applications of SNN, including original author’s contribution to the area. The book introduces for the first time not only deep learning and deep knowledge representation in the human brain and in brain-inspired SNN, but takes that further to develop new types of AI systems, called in the book brain-inspired AI (BI-AI). BI-AI systems are illustrated on: cognitive brain data, including EEG, fMRI and DTI; audio-visual data; brain-computer interfaces; personalized modelling in bio-neuroinformatics; multisensory streaming data modelling in finance, environment and ecology; data compression; neuromorphic hardware implementation. Future directions, such as the integration of multiple modalities, such as quantum-, molecular- and brain information processing, is presented in the last chapter. The book is a research book for postgraduate students, researchers and practitioners across wider areas, including computer and information sciences, engineering, applied mathematics, bio- and neurosciences.
Автор: Udo Seiffert Название: Self-Organizing Neural Networks ISBN: 3662003430 ISBN-13(EAN): 9783662003435 Издательство: Springer Рейтинг: Цена: 81050.00 T Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Benjamin W Y Lo, Hitoshi Fukuda, Yusuke Nishimura, Yee-Yin Wan, Aurora W M Lo Название: Brain-Body Interactions: Contemporary Outcome Prediction in Aneurysmal Subarachnoid Hemorrhage Using Bayesian Neural Networks and Fuzzy Logic ISBN: 1633214168 ISBN-13(EAN): 9781633214163 Издательство: Nova Science Рейтинг: Цена: 89750.00 T Наличие на складе: Невозможна поставка. Описание: This monograph serves as an in-depth guide to the use of the innovative combination of Bayesian analysis, artificial neural networks and fuzzy logic to create an individualized clinical prediction model applicable to many areas in medicine. This guide assumes no prior knowledge of advanced statistics or clinical medicine. Both the applied research scientist and clinician will be able to follow the clinical case of outcome prediction in ruptured brain aneurysms and apply this innovative prognostication model to different areas in medicine. By using Bayesian neural networks with fuzzy logic inferences, the practitioner can create a system that incorporates ones own experience (Bayesian concepts), recognizes unknown areas in medicine (artificial neural networks) and grey zones in diagnoses and prognoses (fuzzy logic inferences). This monograph also profiles contemporary research advances in the diagnosis and treatment of aneurysmal subarachnoid hemorrhage. Application of this clinical prediction modelling system to the case of ruptured brain aneurysms has led to clarification of clinical prognostication in this area, as well as discovery of brain-body interactions that are important in influencing outcome in these patients. The potential impact of such monograph is to demonstrate how to create such clinical outcome prediction model, as well to help find new prognostic factors and brain-body interactions, that when recognized and treated early, can lead to better clinical outcome for the patient.
Автор: Ranjan Sumit, Senthamilarasu S. Название: Applied Deep Learning and Computer Vision for Self-Driving Cars: Build autonomous vehicles using deep neural networks and behavior-cloning techniques ISBN: 1838646302 ISBN-13(EAN): 9781838646301 Издательство: Неизвестно Рейтинг: Цена: 60070.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book teaches you the different techniques and methodologies associated while implementing deep learning solutions in self-driving cars. You will use real-world examples to implement various neural network architectures to develop your own autonomous and automated vehicle using the Python environment.
Автор: Deamer David W. Название: Assembling Life: How Can Life Begin on Earth and Other Habitable Planets? ISBN: 0190646381 ISBN-13(EAN): 9780190646387 Издательство: Oxford Academ Рейтинг: Цена: 41170.00 T Наличие на складе: Поставка под заказ. Описание: Explores the possibilities of how life began on Earth four billion years ago
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