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Neural Networks in Unity: C# Programming For Windows 10 Uwp
by Abhishek Nandy Manisha BiswasLearn the core concepts of neural networks and discover the different types of neural network, using Unity as your platform. In this book you will start by exploring back propagation and unsupervised neural networks with Unity and C#. You’ll then move onto activation functions, such as sigmoid functions, step functions, and so on. The author also explains all the variations of neural networks such as feed forward, recurrent, and radial.Once you’ve gained the basics, you’ll start programming Unity with C#. In this section the author discusses constructing neural networks for unsupervised learning, representing a neural network in terms of data structures in C#, and replicating a neural network in Unity as a simulation. Finally, you’ll define back propagation with Unity C#, before compiling your project.What You'll LearnDiscover the concepts behind neural networksWork with Unity and C# See the difference between fully connected and convolutional neural networksMaster neural network processing for Windows 10 UWPWho This Book Is ForGaming professionals, machine learning and deep learning enthusiasts.
Neural Networks with Discontinuous/Impact Activations
by Marat Akhmet Enes YılmazThis book presents as its main subject new models in mathematical neuroscience. A wide range of neural networks models with discontinuities are discussed, including impulsive differential equations, differential equations with piecewise constant arguments, and models of mixed type. These models involve discontinuities, which are natural because huge velocities and short distances are usually observed in devices modeling the networks. A discussion of the models, appropriate for the proposed applications, is also provided.
Neural Networks with Keras Cookbook: Over 70 recipes leveraging deep learning techniques across image, text, audio, and game bots
by V Kishore AyyadevaraImplement neural network architectures by building them from scratch for multiple real-world applications.Key FeaturesFrom scratch, build multiple neural network architectures such as CNN, RNN, LSTM in KerasDiscover tips and tricks for designing a robust neural network to solve real-world problemsGraduate from understanding the working details of neural networks and master the art of fine-tuning themBook DescriptionThis book will take you from the basics of neural networks to advanced implementations of architectures using a recipe-based approach.We will learn about how neural networks work and the impact of various hyper parameters on a network's accuracy along with leveraging neural networks for structured and unstructured data.Later, we will learn how to classify and detect objects in images. We will also learn to use transfer learning for multiple applications, including a self-driving car using Convolutional Neural Networks.We will generate images while leveraging GANs and also by performing image encoding. Additionally, we will perform text analysis using word vector based techniques. Later, we will use Recurrent Neural Networks and LSTM to implement chatbot and Machine Translation systems.Finally, you will learn about transcribing images, audio, and generating captions and also use Deep Q-learning to build an agent that plays Space Invaders game.By the end of this book, you will have developed the skills to choose and customize multiple neural network architectures for various deep learning problems you might encounter.What you will learnBuild multiple advanced neural network architectures from scratchExplore transfer learning to perform object detection and classificationBuild self-driving car applications using instance and semantic segmentationUnderstand data encoding for image, text and recommender systemsImplement text analysis using sequence-to-sequence learningLeverage a combination of CNN and RNN to perform end-to-end learningBuild agents to play games using deep Q-learningWho this book is forThis intermediate-level book targets beginners and intermediate-level machine learning practitioners and data scientists who have just started their journey with neural networks. This book is for those who are looking for resources to help them navigate through the various neural network architectures; you'll build multiple architectures, with concomitant case studies ordered by the complexity of the problem. A basic understanding of Python programming and a familiarity with basic machine learning are all you need to get started with this book.
Neural Networks with Model Compression (Computational Intelligence Methods and Applications)
by David Doermann Sheng Xu Baochang Zhang Tiancheng WangDeep learning has achieved impressive results in image classification, computer vision and natural language processing. To achieve better performance, deeper and wider networks have been designed, which increase the demand for computational resources. The number of floating-point operations (FLOPs) has increased dramatically with larger networks, and this has become an obstacle for convolutional neural networks (CNNs) being developed for mobile and embedded devices. In this context, our book will focus on CNN compression and acceleration, which are important for the research community. We will describe numerous methods, including parameter quantization, network pruning, low-rank decomposition and knowledge distillation. More recently, to reduce the burden of handcrafted architecture design, neural architecture search (NAS) has been used to automatically build neural networks by searching over a vast architecture space. Our book will also introduce NAS due to its superiority and state-of-the-art performance in various applications, such as image classification and object detection. We also describe extensive applications of compressed deep models on image classification, speech recognition, object detection and tracking. These topics can help researchers better understand the usefulness and the potential of network compression on practical applications. Moreover, interested readers should have basic knowledge about machine learning and deep learning to better understand the methods described in this book.
Neural Networks with R
by Giuseppe Ciaburro Balaji VenkateswaranUncover the power of artificial neural networks by implementing them through R code. About This Book • Develop a strong background in neural networks with R, to implement them in your applications • Build smart systems using the power of deep learning • Real-world case studies to illustrate the power of neural network models Who This Book Is For This book is intended for anyone who has a statistical background with knowledge in R and wants to work with neural networks to get better results from complex data. If you are interested in artificial intelligence and deep learning and you want to level up, then this book is what you need! What You Will Learn • Set up R packages for neural networks and deep learning • Understand the core concepts of artificial neural networks • Understand neurons, perceptrons, bias, weights, and activation functions • Implement supervised and unsupervised machine learning in R for neural networks • Predict and classify data automatically using neural networks • Evaluate and fine-tune the models you build. In Detail Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning. This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you'll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. This book covers various types of neural network including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases. By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book. Style and approach A step-by-step guide filled with real-world practical examples.
Neural Networks with TensorFlow and Keras: Training, Generative Models, and Reinforcement Learning
by Philip HuaExplore the capabilities of machine learning and neural networks. This comprehensive guidebook is tailored for professional programmers seeking to deepen their understanding of neural networks, machine learning techniques, and large language models (LLMs). The book explores the core of machine learning techniques, covering essential topics such as data pre-processing, model selection, and customization. It provides a robust foundation in neural network fundamentals, supplemented by practical case studies and projects. You will explore various network topologies, including Deep Neural Networks (DNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), and Large Language Models (LLMs). Each concept is explained with clear, step-by-step instructions and accompanied by Python code examples using the latest versions of TensorFlow and Keras, ensuring a hands-on learning experience. By the end of this book, you will gain practical skills to apply these techniques to solving problems. Whether you are looking to advance your career or enhance your programming capabilities, this book provides the tools and knowledge needed to excel in the rapidly evolving field of machine learning and neural networks. What You Will Learn Grasp the fundamentals of various neural network topologies, including DNN, RNN, LSTM, VAE, GAN, and LLMs Implement neural networks using the latest versions of TensorFlow and Keras, with detailed Python code examples Know the techniques for data pre-processing, model selection, and customization to optimize machine learning models Apply machine learning and neural network techniques in various professional scenarios Who This Book Is For Data scientists, machine learning enthusiasts, and software developers who wish to deepen their understanding of neural networks and machine learning techniques
Neural Networks, Machine Learning, and Image Processing: Mathematical Modeling and Applications
by Manoj Sahni Ritu Sahni Jose M. MerigoSECTION I Mathematical Modeling and Neural Network’ Mathematical Essence Chapter 1 Mathematical Modeling on Thermoregulation in Sarcopenia1.1. Introduction 1.2. Discretization 1.3. Modeling and Simulation of Basal Metabolic Rate and Skin Layers Thickness 1.4. Mathematical Model and Boundary Conditions 1.5. Solution of the Model 1.6. Numerical Results and discussion 1.7. Conclusion References Chapter 2 Multi-objective University Course Scheduling for Un
Neural Representations of Natural Language (Studies in Computational Intelligence #783)
by Wei Liu Roberto Togneri Lyndon White Mohammed BennamounThis book offers an introduction to modern natural language processing using machine learning, focusing on how neural networks create a machine interpretable representation of the meaning of natural language. Language is crucially linked to ideas – as Webster’s 1923 “English Composition and Literature” puts it: “A sentence is a group of words expressing a complete thought”. Thus the representation of sentences and the words that make them up is vital in advancing artificial intelligence and other “smart” systems currently being developed. Providing an overview of the research in the area, from Bengio et al.’s seminal work on a “Neural Probabilistic Language Model” in 2003, to the latest techniques, this book enables readers to gain an understanding of how the techniques are related and what is best for their purposes. As well as a introduction to neural networks in general and recurrent neural networks in particular, this book details the methods used for representing words, senses of words, and larger structures such as sentences or documents. The book highlights practical implementations and discusses many aspects that are often overlooked or misunderstood. The book includes thorough instruction on challenging areas such as hierarchical softmax and negative sampling, to ensure the reader fully and easily understands the details of how the algorithms function. Combining practical aspects with a more traditional review of the literature, it is directly applicable to a broad readership. It is an invaluable introduction for early graduate students working in natural language processing; a trustworthy guide for industry developers wishing to make use of recent innovations; and a sturdy bridge for researchers already familiar with linguistics or machine learning wishing to understand the other.
Neural Search - From Prototype to Production with Jina: Build deep learning–powered search systems that you can deploy and manage with ease
by Feng Wang Bo Wang Shubham Saboo Cristian Mitroi Susana GuzmanImplement neural search systems on the cloud by leveraging Jina design patternsKey FeaturesIdentify the different search techniques and discover applications of neural searchGain a solid understanding of vector representation and apply your knowledge in neural searchUnlock deeper levels of knowledge of Jina for neural searchBook DescriptionSearch is a big and ever-growing part of the tech ecosystem. Traditional search, however, has limitations that are hard to overcome because of the way it is designed. Neural search is a novel approach that uses the power of machine learning to retrieve information using vector embeddings as first-class citizens, opening up new possibilities of improving the results obtained through traditional search. Although neural search is a powerful tool, it is new and finetuning it can be tedious as it requires you to understand the several components on which it relies. Jina fills this gap by providing an infrastructure that reduces the time and complexity involved in creating deep learning–powered search engines. This book will enable you to learn the fundamentals of neural networks for neural search, its strengths and weaknesses, as well as how to use Jina to build a search engine. With the help of step-by-step explanations, practical examples, and self-assessment questions, you'll become well-versed with the basics of neural search and core Jina concepts, and learn to apply this knowledge to build your own search engine. By the end of this deep learning book, you'll be able to make the most of Jina's neural search design patterns to build an end-to-end search solution for any modality.What you will learnUnderstand how neural search and legacy search workGrasp the machine learning and math fundamentals needed for neural searchGet to grips with the foundation of vector representationExplore the basic components of JinaAnalyze search systems with different modalitiesUncover the capabilities of Jina with the help of practical examplesWho this book is forIf you are a machine learning, deep learning, or artificial intelligence engineer interested in building a search system of any kind (text, QA, image, audio, PDF, 3D models, or others) using modern software architecture, this book is for you. This book is perfect for Python engineers who are interested in building a search system of any kind using state-of-the-art deep learning techniques.
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.