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Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
by Russell D. Reed Robert J. MarksArtificial neural networks are nonlinear mapping systems whose structure is loosely based on principles observed in the nervous systems of humans and animals. The basic idea is that massive systems of simple units linked together in appropriate ways can generate many complex and interesting behaviors. This book focuses on the subset of feedforward artificial neural networks called multilayer perceptrons (MLP). These are the mostly widely used neural networks, with applications as diverse as finance (forecasting), manufacturing (process control), and science (speech and image recognition). This book presents an extensive and practical overview of almost every aspect of MLP methodology, progressing from an initial discussion of what MLPs are and how they might be used to an in-depth examination of technical factors affecting performance. The book can be used as a tool kit by readers interested in applying networks to specific problems, yet it also presents theory and references outlining the last ten years of MLP research.
Neural Text-to-Speech Synthesis (Artificial Intelligence: Foundations, Theory, and Algorithms)
by Xu TanText-to-speech (TTS) aims to synthesize intelligible and natural speech based on the given text. It is a hot topic in language, speech, and machine learning research and has broad applications in industry. This book introduces neural network-based TTS in the era of deep learning, aiming to provide a good understanding of neural TTS, current research and applications, and the future research trend. This book first introduces the history of TTS technologies and overviews neural TTS, and provides preliminary knowledge on language and speech processing, neural networks and deep learning, and deep generative models. It then introduces neural TTS from the perspective of key components (text analyses, acoustic models, vocoders, and end-to-end models) and advanced topics (expressive and controllable, robust, model-efficient, and data-efficient TTS). It also points some future research directions and collects some resources related to TTS. This book is the first to introduce neural TTS in a comprehensive and easy-to-understand way and can serve both academic researchers and industry practitioners working on TTS.
Neural-Network Simulation of Strongly Correlated Quantum Systems (Springer Theses)
by Stefanie CzischekQuantum systems with many degrees of freedom are inherently difficult to describe and simulate quantitatively. The space of possible states is, in general, exponentially large in the number of degrees of freedom such as the number of particles it contains. Standard digital high-performance computing is generally too weak to capture all the necessary details, such that alternative quantum simulation devices have been proposed as a solution. Artificial neural networks, with their high non-local connectivity between the neuron degrees of freedom, may soon gain importance in simulating static and dynamical behavior of quantum systems. Particularly promising candidates are neuromorphic realizations based on analog electronic circuits which are being developed to capture, e.g., the functioning of biologically relevant networks. In turn, such neuromorphic systems may be used to measure and control real quantum many-body systems online. This thesis lays an important foundation for the realization of quantum simulations by means of neuromorphic hardware, for using quantum physics as an input to classical neural nets and, in turn, for using network results to be fed back to quantum systems. The necessary foundations on both sides, quantum physics and artificial neural networks, are described, providing a valuable reference for researchers from these different communities who need to understand the foundations of both.
Neural-Symbolic Learning and Reasoning: 18th International Conference, NeSy 2024, Barcelona, Spain, September 9–12, 2024, Proceedings, Part I (Lecture Notes in Computer Science #14979)
by Tarek R. Besold Ernesto Jimenez-Ruiz Roberto Confalonieri Artur D’Avila Garcez Pranava Madhyastha Benedikt WagnerThis book constitutes the refereed proceedings of the 18th International Conference on Neural-Symbolic Learning and Reasoning, NeSy 2024, held in Barcelona, Spain during September 9-12th, 2024. The 30 full papers and 18 short papers were carefully reviewed and selected from 89 submissions, which presented the latest and ongoing research work on neurosymbolic AI. Neurosymbolic AI aims to build rich computational models and systems by combining neural and symbolic learning and reasoning paradigms. This combination hopes to form synergies among their strengths while overcoming their complementary weaknesses.
Neural-Symbolic Learning and Reasoning: 18th International Conference, NeSy 2024, Barcelona, Spain, September 9–12, 2024, Proceedings, Part II (Lecture Notes in Computer Science #14980)
by Tarek R. Besold Ernesto Jimenez-Ruiz Roberto Confalonieri Artur D’Avila Garcez Pranava Madhyastha Benedikt WagnerThis book constitutes the refereed proceedings of the 18th International Conference on Neural-Symbolic Learning and Reasoning, NeSy 2024, held in Barcelona, Spain during September 9-12th, 2024. The 30 full papers and 18 short papers were carefully reviewed and selected from 89 submissions, which presented the latest and ongoing research work on neurosymbolic AI. Neurosymbolic AI aims to build rich computational models and systems by combining neural and symbolic learning and reasoning paradigms. This combination hopes to form synergies among their strengths while overcoming their complementary weaknesses.
Neuro Design: Neuromarketing Insights to Boost Engagement and Profitability
by Darren BridgerToday, businesses of all sizes generate a great deal of creative graphic media and content, including websites, presentations, videos and social media posts. Most big companies, including Procter & Gamble, Coca-Cola, Tesco and Google, now use neuroscience research and theories to optimise their digital content. Neuro Design opens up this new world of neuromarketing design theories and recommendations, and describes insights from the growing field of neuroaesthetics that will enable readers to enhance customer engagement with their website and boost profitability.
Neuro Symbolic Reasoning and Learning (SpringerBriefs in Computer Science)
by Gerardo I. Simari Paulo Shakarian Chitta Baral Bowen Xi Lahari PokalaThis book provides a broad overview of the key results and frameworks for various NSAI tasks as well as discussing important application areas. This book also covers neuro symbolic reasoning frameworks such as LNN, LTN, and NeurASP and learning frameworks. This would include differential inductive logic programming, constraint learning and deep symbolic policy learning. Additionally, application areas such a visual question answering and natural language processing are discussed as well as topics such as verification of neural networks and symbol grounding. Detailed algorithmic descriptions, example logic programs, and an online supplement that includes instructional videos and slides provide thorough but concise coverage of this important area of AI. Neuro symbolic artificial intelligence (NSAI) encompasses the combination of deep neural networks with symbolic logic for reasoning and learning tasks. NSAI frameworks are now capable of embedding prior knowledge in deep learning architectures, guiding the learning process with logical constraints, providing symbolic explainability, and using gradient-based approaches to learn logical statements. Several approaches are seeing usage in various application areas. This book is designed for researchers and advanced-level students trying to understand the current landscape of NSAI research as well as those looking to apply NSAI research in areas such as natural language processing and visual question answering. Practitioners who specialize in employing machine learning and AI systems for operational use will find this book useful as well.
Neuro-Fuzzy Equalizers for Mobile Cellular Channels
by K.C. RaveendranathanEqualizers are present in all forms of communication systems. Neuro-Fuzzy Equalizers for Mobile Cellular Channels details the modeling of a mobile broadband communication channel and designing of a neuro-fuzzy adaptive equalizer for it. This book focuses on the concept of the simulation of wireless channel equalizers using the adaptive-network-based fuzzy inference system (ANFIS). The book highlights a study of currently existing equalizers for wireless channels. It discusses several techniques for channel equalization, including the type-2 fuzzy adaptive filter (type-2 FAF), compensatory neuro-fuzzy filter (CNFF), and radial basis function (RBF) neural network. Neuro-Fuzzy Equalizers for Mobile Cellular Channels starts with a brief introduction to channel equalizers, and the nature of mobile cellular channels with regard to the frequency reuse and the resulting CCI. It considers the many channel models available for mobile cellular channels, establishes the mobile indoor channel as a Rayleigh fading channel, presents the channel equalization problem, and focuses on various equalizers for mobile cellular channels. The book discusses conventional equalizers like LE and DFE using a simple LMS algorithm and transversal equalizers. It also covers channel equalization with neural networks and fuzzy logic, and classifies various equalizers. This being a fairly new branch of study, the book considers in detail the concept of fuzzy logic controllers in noise cancellation problems and provides the fundamental concepts of neuro-fuzzy. The final chapter offers a recap and explores venues for further research. This book also establishes a common mathematical framework of the equalizers using the RBF model and develops a mathematical model for ultra-wide band (UWB) channels using the channel co-variance matrix (CCM). Introduces the novel concept of the application of adaptive-network-based fuzzy inference system (ANFIS) in the design of wireless channel equalizers Provides model ultra-wide band (UWB) channels using channel co-variance matrix Offers a formulation of a unified radial basis function (RBF) framework for ANFIS-based and fuzzy adaptive filter (FAF) Type II, as well as compensatory neuro-fuzzy equalizers Includes extensive use of MATLAB® as the simulation tool in all the above cases
Neuro-Symbolic Artificial Intelligence: Bridging Logic and Learning (Studies in Computational Intelligence #1176)
by Ravi Tomar Amar Ramdane-Cherif Thipendra P. Singh Bikram Pratim BhuyanThis book highlights and attempts to fill a crucial gap in the existing literature by providing a comprehensive exploration of the emerging field of neuro-symbolic AI. It introduces the concept of neuro-symbolic AI, highlighting its fusion of symbolic reasoning and machine learning. The book covers symbolic AI and knowledge representation, neural networks and deep learning, neuro-symbolic integration approaches, reasoning and inference techniques, applications in healthcare and robotics, as well as challenges and future directions. By combining the power of symbolic logic and knowledge representation with the flexibility of neural networks, neuro-symbolic AI offers the potential for more interpretable and trustworthy AI systems. This book is a valuable resource for researchers, practitioners, and students interested in understanding and applying neuro-symbolic AI.
Neuro-fuzzy Modeling of Multi-field Surface Neuroprostheses for Hand Grasping (Springer Theses)
by Eukene Imatz OjangurenThis thesis presents a novel neuro-fuzzy modeling approach for grasp neuroprostheses. At first, it offers a detailed study of discomfort due to the application of Functional Electrical Stimulation to the upper limb. Then, it discusses briefly previous methods to model hand movements induced by FES with the purpose of introducing the new modeling approach based on intelligent systems. This approach is thoroughly described in the book, together with the proposed application to induce hand and finger movements by means of a surface FES system based on multi-field electrodes. The validation tests, carried out on both healthy and neurologically impaired subjects, demonstrate the efficacy of the proposed modeling method. All in all, the book proposes an innovative system based on fuzzy neural networks that is expected to improve the design and validation of advanced control systems for non-invasive grasp neuroprostheses.
Neuro-inspired Computing Using Resistive Synaptic Devices
by Shimeng YuThis book summarizes the recent breakthroughs in hardware implementation of neuro-inspired computing using resistive synaptic devices. The authors describe how two-terminal solid-state resistive memories can emulate synaptic weights in a neural network. Readers will benefit from state-of-the-art summaries of resistive synaptic devices, from the individual cell characteristics to the large-scale array integration. This book also discusses peripheral neuron circuits design challenges and design strategies. Finally, the authors describe the impact of device non-ideal properties (e.g. noise, variation, yield) and their impact on the learning performance at the system-level, using a device-algorithm co-design methodology.
Neurocognitive Music Therapy: Intersecting Music, Medicine and Technology for Health and Well-Being
by Rafael Ramírez-MeléndezFor thousands of years, music has acted as a powerful medium for evoking emotions, facilitating communication, and nurturing overall well-being. With the advent of new sophisticated neuroimaging technology, human responses to music and music therapy are being viewed through a new lens. As a consequence, new knowledge is being obtained about how music can produce significant improvements in cognitive, social, overt and agitated behaviours. The aim of this book is to provide an overview of neurocognitive music therapy, its impact and implications in the practice of evidence-based music interventions. The book seeks to provide researchers, psychologists, music therapists, musicians and physicians interested in the therapeutic applications of music, with a source of information about current techniques and novel music interventions. It is structured into several chapters, each of them presenting peer-reviewed research and evidence-based procedures carried out in a specific clinical context. Topics covered in the book include: Musical engagement for individuals with motor disabilities Enhancing emotional processing in autism through music Stroke rehabilitation via musical interventions Musical neurofeedback for emotional disorders Emotional modulation with music therapy in palliative care AI-driven personalisation in music interventionsThe book highlights the profound capacity of music-based interventions to facilitate cognitive and emotional processing, enhance communication, and promote motor rehabilitation. At the same time, the book demonstrates how modern technologies offer new opportunities to evaluate, validate, and potentiate music-based interventions, allowing new and innovative possibilities and more personalised interventions. This book aims to contribute to the growing body of knowledge in this field and inspire further research and innovation in the practice of music therapy.
Neurocomputing for Design Automation (Computer Aided Engineering)
by Hyo Seon ParkNeurocomputing for Design Automation provides innovative design theories and computational models with two broad objectives: automation and optimization.This singular book:Presents an introduction to the automation and optimization of engineering design of complex engineering systems using neural network computing Outlines new computational models and paradigms for automating the complex process of design for unique engineering systems, such as steel highrise building structures Applies design theories and models to the solution of structural design problems Integrates three computing paradigms: mathematical optimization, neural network computing, and parallel processingThe applications described are general enough to be applied directly or by extension to other engineering design problems, such as aerospace or mechanical design. Also, the computational models are shown to be stable and robust - particularly suitable for design automation of large systems, such as a 144-story steel super-highrise building structure with more than 20,000 members.The book provides an exceptional framework for the automation and optimization of engineering design, focusing on a new computing paradigm - neural networks computing. It presents the automation of complex systems at a new and higher level never achieved before.
Neuroergonomics: Principles and Practice (Cognitive Science and Technology)
by Chang S. NamThis book sums up key research findings, and theoretical and technological advances having a direct bearing on neuroergonomics. Neuroergonomics is an emerging area whose Neuroergonomics is an emerging area that is collectively defined as the study of human brain function and behaviour in relation to behavioural performance in natural environments and everyday settings. It helps readers to understand neural mechanisms of human cognition in the context of human interaction with complex systems, as well as understanding the change of perception, decision-making and training in humans. The authors give new insights into augmenting human performance, reflecting upon the opportunities provided through neuroergonomics research and development. Computer systems acting on data from behavioural-output, physiological, and neurological sensing technologies are used to determine the user’s cognitive state and adapt the systems to change, support, and monitor human cognition. Various domains and case studies delve into the field of neuroergonomics in detail. These include, but are not limited to:an evaluation of technologies in health, workplace, and education settings, to show the different impacts of neuroergonomics in everyday lives;assessment of real-time cognitive measures;dynamic casual interactions between inhibition and updating functions, through analysis of behavioral, neurophysiological and effective connectivity metrics; and applications in human performance modelling and assessment of mental workload, showing the reader how to train and improve working memory capacity.Neuroergonomics: Principles and Practice provides academic practitioners and graduate students with a single go-to handbook that will be of significant assistance in research associated with human factors and ergonomics, human-computer interaction, human-systems engineering and cognitive neuroscience.
Neuroimaging of Headache Disorders (Headache)
by Igor Petrušić Yonggang WangThis book offers a comprehensive overview of structural and functional neuroimaging findings related to the pathophysiology of primary and secondary headaches. In addition, it provides recommendations for best practice and decision-making in ordering neuroimaging investigation when faced with patients suffering from a vast range of headache types, whether in everyday practice, or in an ambulance or emergency room. Hopefully, this book will promote the adequate use of cutting-edge neuroimaging in headache research and in ongoing clinical trials in major neurology centers in Europe and worldwide. The structure of the book is designed to cover the basic principles of neuroimaging that neurologists should be aware of when making decisions about headache management, scientifically based recommendations for the application of different neuroimaging protocols in the emergency department and in the neurological clinic, the latest findings from advanced neuroimaging related to migraine without and with aura, chronic migraine and medication overuse headache, cluster headache, trigeminal neuralgia and other forms of headache and orofacial pain. Finally, the book contains a chapter on future directions in headache neuroimaging and the implementation of machine and deep learning algorithms in the neuroimaging and classification of headaches and the prediction of treatment outcomes. Neurologists, radiologists and physicians involved in pain medicine will benefit from this book, by having access to comprehensive, state-of-the-art knowledge on research and clinical practice in the field of headache neuroimaging. Furthermore, it could be a compendium for medical students and residents who are usually introduced to headache neuroimaging through multidisciplinary university programmes. Headache patients will also benefit from this book, being helped to better understand their condition from a neuroimaging techniques perspective.
Neuromorphic Computing Principles and Organization
by Abderazek Ben Abdallah Khanh N. DangThis book focuses on neuromorphic computing principles and organization and how to build fault-tolerant scalable hardware for large and medium scale spiking neural networks with learning capabilities. In addition, the book describes in a comprehensive way the organization and how to design a spike-based neuromorphic system to perform network of spiking neurons communication, computing, and adaptive learning for emerging AI applications. The book begins with an overview of neuromorphic computing systems and explores the fundamental concepts of artificial neural networks. Next, we discuss artificial neurons and how they have evolved in their representation of biological neuronal dynamics. Afterward, we discuss implementing these neural networks in neuron models, storage technologies, inter-neuron communication networks, learning, and various design approaches. Then, comes the fundamental design principle to build an efficient neuromorphic system in hardware. The challenges that need to be solved toward building a spiking neural network architecture with many synapses are discussed. Learning in neuromorphic computing systems and the major emerging memory technologies that promise neuromorphic computing are then given.A particular chapter of this book is dedicated to the circuits and architectures used for communication in neuromorphic systems. In particular, the Network-on-Chip fabric is introduced for receiving and transmitting spikes following the Address Event Representation (AER) protocol and the memory accessing method. In addition, the interconnect design principle is covered to help understand the overall concept of on-chip and off-chip communication. Advanced on-chip interconnect technologies, including si-photonic three-dimensional interconnects and fault-tolerant routing algorithms, are also given. The book also covers the main threats of reliability and discusses several recovery methods for multicore neuromorphic systems. This is important for reliable processing in several embedded neuromorphic applications. A reconfigurable design approach that supports multiple target applications via dynamic reconfigurability, network topology independence, and network expandability is also described in the subsequent chapters. The book ends with a case study about a real hardware-software design of a reliable three-dimensional digital neuromorphic processor geared explicitly toward the 3D-ICs biological brain’s three-dimensional structure. The platform enables high integration density and slight spike delay of spiking networks and features a scalable design. We present methods for fault detection and recovery in a neuromorphic system as well.Neuromorphic Computing Principles and Organization is an excellent resource for researchers, scientists, graduate students, and hardware-software engineers dealing with the ever-increasing demands on fault-tolerance, scalability, and low power consumption. It is also an excellent resource for teaching advanced undergraduate and graduate students about the fundamentals concepts, organization, and actual hardware-software design of reliable neuromorphic systems with learning and fault-tolerance capabilities.
Neuromorphic Computing Principles and Organization
by Abderazek Ben Abdallah Khanh N. DangThe second edition of Neuromorphic Computing Principles and Organization delves deeply into neuromorphic computing, focusing on designing fault-tolerant, scalable hardware for spiking neural networks. Each chapter includes exercises to enhance understanding. All existing chapters have been meticulously revised, and a new chapter on advanced neuromorphic prosthesis design serves as a comprehensive case study. The book starts with an overview of neuromorphic systems and fundamental artificial neural network concepts. It explores artificial neurons, neuron models, storage technologies, inter-neuron communication, learning mechanisms, and design approaches. Detailed discussions cover challenges in constructing spiking neural networks and emerging memory technologies. A dedicated chapter addresses circuits and architectures, including Network-on-Chip (NoC) fabric, Address Event Representation (AER), memory access methods, and photonic interconnects. Reliability issues, recovery methods for multicore systems, and reconfigurable designs supporting multiple applications are examined. The book also describes the hardware-software design of a three-dimensional neuromorphic processor, focusing on high integration density, minimal spike delay, and scalable design. The book concludes with a comprehensive review of neuromorphic systems, providing a detailed analysis of the field and an overarching understanding of the key concepts discussed throughout the text.
Neuromorphic Computing and Beyond: Parallel, Approximation, Near Memory, and Quantum
by Khaled Salah MohamedThis book discusses and compares several new trends that can be used to overcome Moore’s law limitations, including Neuromorphic, Approximate, Parallel, In Memory, and Quantum Computing. The author shows how these paradigms are used to enhance computing capability as developers face the practical and physical limitations of scaling, while the demand for computing power keeps increasing. The discussion includes a state-of-the-art overview and the essential details of each of these paradigms.
Neuromorphic Computing: Transforming Disaster Management and Resilience in Civil Engineering (SpringerBriefs in Applied Sciences and Technology)
by Ali Akbar Firoozi Ali Asghar FirooziThis book delves into the transformative potential of neuromorphic computing within the field of civil engineering, specifically focusing on its application to disaster management. With the increasing frequency and severity of natural disasters, traditional disaster management systems face significant challenges in prediction accuracy, response time, and effective resource allocation. Neuromorphic computing, inspired by the neural processes of the human brain, offers a revolutionary approach to addressing these challenges. Through an in-depth exploration, this book outlines the theoretical foundations of neuromorphic computing, its integration into smart infrastructure, and the development of advanced predictive models for natural disasters such as earthquakes, floods, and urban fires. Additionally, it examines the technical, ethical, and social considerations inherent in deploying these technologies, alongside a vision for their future development. The convergence of neuromorphic computing and civil engineering heralds a new era of enhanced resilience, where more informed, rapid, and effective disaster management strategies are not just a possibility but a reality. The book contributes to the discourse on leveraging cutting-edge computing technologies to foster safer, more resilient communities in the face of natural calamities.
Neuromorphic Engineering: The Scientist’s, Algorithms Designer’s and Computer Architect’s Perspectives on Brain-Inspired Computing
by Elishai Ezra TsurThe brain is not a glorified digital computer. It does not store information in registers, and it does not mathematically transform mental representations to establish perception or behavior. The brain cannot be downloaded to a computer to provide immortality, nor can it destroy the world by having its emerged consciousness traveling in cyberspace. However, studying the brain's core computation architecture can inspire scientists, computer architects, and algorithm designers to think fundamentally differently about their craft. Neuromorphic engineers have the ultimate goal of realizing machines with some aspects of cognitive intelligence. They aspire to design computing architectures that could surpass existing digital von Neumann-based computing architectures' performance. In that sense, brain research bears the promise of a new computing paradigm. As part of a complete cognitive hardware and software ecosystem, neuromorphic engineering opens new frontiers for neuro-robotics, artificial intelligence, and supercomputing applications. This book will present neuromorphic engineering from three perspectives: the scientist, the computer architect, and the algorithm designer. We will zoom in and out of the different disciplines, allowing readers with diverse backgrounds to understand and appreciate the field. Overall, the book will cover the basics of neuronal modeling, neuromorphic circuits, neural architectures, event-based communication, and the neural engineering framework. Readers will have the opportunity to understand the different views over the inherently multidisciplinary field of neuromorphic engineering.
Neuromorphic Intelligence: Learning, Architectures and Large-Scale Systems (Synthesis Lectures on Engineering, Science, and Technology)
by Badong Chen Shuangming YangThis book provides a valuable resource on the design of neuromorphic intelligence, which serves as a computational foundation for building compact and low-power brain-inspired intelligent systems. The book introduces novel spiking neural network learning algorithms, including spike-based learning based on the multi-compartment model and spike-based learning with information theory. These offer important insights and academic value for readers to grasp the latest advances in neural-inspired learning. Additionally, the book presents insights and approaches to the design of scalable neuromorphic architectures, which are crucial foundations for achieving highly cognitive and energy-efficient computing systems. Furthermore, the book introduces representative large-scale neuromorphic systems and reviews several recently implemented large-scale digital neuromorphic systems by the authors, providing corresponding application scenarios.
Neuromorphic Solutions for Sensor Fusion and Continual Learning Systems: Applications in Drone Navigation and Radar Sensing
by Francky Catthoor Georges Gielen Ali Safa Lars KeuninckxThis book provides novel theoretical foundations and experimental demonstrations of Spiking Neural Networks (SNNs) in tasks such as radar gesture recognition for IoT devices and autonomous drone navigation using a fusion of retina-inspired event-based camera and radar sensing. The authors describe important new findings about the Spike-Timing-Dependent Plasticity (STDP) learning rule, which is widely believed to be one of the key learning mechanisms taking place in the brain. Readers will be enabled to create novel classes of edge AI and robotics applications, using highly energy- and area-efficient SNNs
Neuromorphic and Brain-Based Robots
by Hiroaki Wagatsuma Jeffrey L. KrichmarNeuromorphic and brain-based robotics have enormous potential for furthering our understanding of the brain. By embodying models of the brain on robotic platforms, researchers can investigate the roots of biological intelligence and work towards the development of truly intelligent machines. This book provides a broad introduction to this groundbreaking area for researchers from a wide range of fields, from engineering to neuroscience. Case studies explore how robots are being used in current research, including a whisker system that allows a robot to sense its environment and neurally inspired navigation systems that show impressive mapping results. Looking to the future, several chapters consider the development of cognitive, or even conscious robots that display the adaptability and intelligence of biological organisms. Finally, the ethical implications of intelligent robots are explored, from morality and Asimov's three laws to the question of whether robots have rights.
Neuronale Modellierung der Sprachverarbeitung und des Sprachlernens: Eine Einführung
by Bernd J. KrögerDieses Buch erläutert die Thematik der Produktion und Wahrnehmung gesprochener Sprache aus neurowissenschaftlicher Sicht. Nach der Darstellung der Grundlagen der Sprachverarbeitung und des Spracherwerbs wird dem Leser ein neurobiologisch basiertes und computerimplementiertes neuronales Simulationsmodell vorgestellt. Diese Einführung in die quantitative und computerimplementierbare Modellierung der Sprachverarbeitung und des Sprachlernens basiert auf einem naturwissenschaftlich orientierten Ansatz zur Beschreibung gesprochener Sprache. Dennoch wird weitgehend auf mathematische Beschreibungen verzichtet.Das Buch spricht Studierende und Wissenschaftler der Bereiche Neurowissenschaften, Informatik, Medizin, Psychologie und Linguistik an, die sich in das Gebiet der Sprachverarbeitung und des Spracherwerbs aus neurowissenschaftlicher Sicht einarbeiten möchten. Es richtet sich aber auch an Anwender, die sich mit der Entwicklung von Software zur Sprachverarbeitung befassen.
Neuronale Netze kompakt: Vom Perceptron zum Deep Learning (IT kompakt)
by Daniel SonnetDaten sind das neue Gold - und neuronale Netze haben bereits einigen Unternehmen geholfen, diesen Schatz auszugraben. Verschaffen Sie sich mit diesem Buch innerhalb kürzester Zeit einen soliden Überblick über neuronale Netze. Nach der Lektüre dieses Buches kennen Sie den historischen Werdegang dieser leistungsfähigen Approximatoren und Sie sind vertraut mit den aktuell wichtigsten Begriffen. Des Weiteren kennen Sie die Möglichkeiten sowie die Grenzen neuronaler Netze. Dieses Buch richtet sich in erster Linie an Praktiker, die einen schnellen Einstieg in das Thema suchen, ohne parallel einen Hochschulkurs in Mathematik und Statistik zu machen.