Artificial Intelligence for Neurological Disorders provides a comprehensive resource of state-of-the-art approaches for AI, big data analytics and machine learning-based neurological research. The book discusses many machine learning techniques to detect neurological diseases at the cellular level, as well as other applications such as image segmentation, classification and image indexing, neural networks and image processing methods. Chapters include AI techniques for the early detection of neurological disease and deep learning applications using brain imaging methods like EEG, MEG, fMRI, fNIRS and PET for seizure prediction or neuromuscular rehabilitation. The goal of this book is to provide readers with broad coverage of these methods to encourage an even wider adoption of AI, Machine Learning and Big Data Analytics for problem-solving and stimulating neurological research and therapy advances. - Discusses various AI and ML methods to apply for neurological research - Explores Deep Learning techniques for brain MRI images - Covers AI techniques for the early detection of neurological diseases and seizure prediction - Examines cognitive therapies using AI and Deep Learning methods

The text discusses the techniques of deep learning and machine learning in the field of neuroscience, engineering approaches to study the brain structure and dynamics, convolutional networks for fast, energy-efficient neuromorphic computing, and reinforcement learning in feedback control. It showcases case studies in neural data analysis. Features: Focuses on neuron modeling, development, and direction of neural circuits to explain perception, behavior, and biologically inspired intelligent agents for decision making Showcases important aspects such as human behavior prediction using smart technologies and understanding the modeling of nervous systems Discusses nature-inspired algorithms such as swarm intelligence, ant colony optimization, and multi-agent systems Presents information-theoretic, control-theoretic, and decision-theoretic approaches in neuroscience. Includes case studies in functional magnetic resonance imaging (fMRI) and neural data analysis This reference text addresses different applications of computational neuro-sciences using artificial intelligence, deep learning, and other machine learning techniques to fine-tune the models, thereby solving the real-life problems prominently. It will further discuss important topics such as neural rehabili-tation, brain-computer interfacing, neural control, neural system analysis, and neurobiologically inspired self-monitoring systems. It will serve as an ideal reference text for graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, information technology, and biomedical engineering.

Cognitive neuroscience is the interdisciplinary study of how cognitive and intellectual functions are processed and represented within the brain, which is critical to building understanding of core psychological and behavioural processes such as learning, memory, behaviour, perception, and consciousness. Understanding these processes not only offers relevant fundamental insights into brain-behavioural relations, but may also lead to actionable knowledge that can be applied in the clinical treatment of patients with various brain-related disabilities. This Handbook examines complex cognitive systems through the lens of neuroscience, as well as providing an overview of development and applications within cognitive and systems neuroscience research and beyond. Containing 35 original, state of the art contributions from leading experts in the field, this Handbook is essential reading for researchers and students of cognitive psychology, as well as scholars across the fields of neuroscientific, behavioural and health sciences. Part 1: Attention, Learning and Memory Part 2: Language and Communication Part 3: Emotion and Motivation Part 4: Social Cognition Part 5: Cognitive Control and Decision Making Part 6: Intelligence

Though there have been many developments in sensory/motor prosthetics, they have not yet reached the level of standard and worldwide use like pacemakers and cochlear implants. One challenging issue in motor prosthetics is the large variety of patient situations, which depending on the type of neurological disorder. To improve neuroprosthetic performance beyond the current limited use of such systems, robust bio-signal processing and model-based control involving actual sensory motor state (with biosignal feedback) would bring about new modalities and applications, and could be a breakthrough toward adaptive neuroprosthetics. Recent advances of Brain Computer Interfaces (BCI) now enable patients to transmit their intention of movement. However, the functionality and controllability of motor prosthetics itself can be further improved to take advantage of BCI interfaces. In this Research Topic we welcome contribution of original research articles, computational and experimental studies, review articles, and methodological advances related to biosignal processing that may enhance the functionality of sensory motor neuroprosthetics. The scope of this topic includes, but is not limited to, studies aimed at enhancing: 1) computational biosignal processing in EMG (Electromyography), EEG (Electroencephalography), and other modalities of biofeedback information; 2) the computational method in modeling and control of sensory motor neuroprosthetics; 3) the systematic functionality aiming to provide solutions for specific pathological movement disorders; 4) human interfaces such as BCI - but in the case of BCI study, manuscripts should be experimental studies which are applied to sensory/motor neuroprosthetics in patients with motor disabilities.

Using a consistent Skinnerian perspective, Behavior Analysis and Learning: A Biobehavioral Approach, Sixth Edition provides an advanced introduction to the principles of behavior analysis and learned behaviors, covering a full range of principles from basic respondent and operant conditioning through applied behavior analysis into cultural design. The textbook uses Darwinian, neurophysiological, and biological theories and research to inform B. F. Skinner’s philosophy of radical behaviorism. The sixth edition expands focus on neurophysiological mechanisms and their relation to the experimental analysis of behavior, providing updated studies and references to reflect current expansions and changes in the field of behavior analysis. By bringing together ideas from behavior analysis, neuroscience, and epigenetics under a selectionist framework, this textbook facilitates understanding of behavior at environmental, genetic, and neurophysiological levels. This "grand synthesis" of behavior, neuroscience, and neurobiology roots behavior firmly in biology. The book includes special sections, "New Directions," "Focus On," "Note On," "On the Applied Side," and "Advanced Section," which enhance student learning and provide greater insight on specific topics. This book is a valuable resource for advanced undergraduate and graduate students in psychology or other behavior-based disciplines, especially behavioral neuroscience. For additional resources to use alongside the textbook, consult the Companion Website at www.routledge.com/cw/pierce.

This book contains the proceedings of the 4TH International Conference on Computational Methods in Science and Technology (ICCMST 2024). The proceedings explores research and innovation in the field of Internet of things, Cloud Computing, Machine Learning, Networks, System Design and Methodologies, Big Data Analytics and Applications, ICT for Sustainable Environment, Artificial Intelligence and it provides real time assistance and security for advanced stage learners, researchers and academicians has been presented. This will be a valuable read to researchers, academicians, undergraduate students, postgraduate students, and professionals within the fields of Computer Science, Sustainability and Artificial Intelligence.

The charge of the Army Research Laboratory Technical Assessment Board (ARLTAB) is to provide biennial assessments of the scientific and technical quality of the research, development, and analysis programs at the Army Research Laboratory (ARL). The ARLTAB is assisted by six panels, each of which focuses on the portion of the ARL program conducted by one of ARL's six directorates1. When requested to do so by ARL, the ARLTAB also examines work that cuts across the directorates. For example, during 2011-2012, ARL requested that the ARLTAB examine crosscutting work in the areas of autonomous systems and network science. The overall quality of ARL's technical staff and their work continues to be impressive. Staff continue to demonstrate clear, passionate mindfulness of the importance of transitioning technology to support immediate and longer-term Army needs. Their involvement with the wider scientific and engineering community continues to expand. Such continued involvement and collaboration are fundamentally important for ARL's scientific and technical activities and need to include the essential elements of peer review and interaction through publications and travel to attend professional meetings, including international professional meetings. In general, ARL is working very well within an appropriate research and development niche and has been demonstrating significant accomplishments, as exemplified in the following discussion, which also addresses opportunities and challenges.

Despite substantial progress in the development of neuroimaging methodologies, translational applications of neuroimaging remain scarce. This Research Topic invites article submissions that present promising neuroimaging applications and methods addressing critical needs for improving health outcomes. These may include Original Research, Clinical Trial, Systematic Review or Methods articles that investigate neuroimaging metrics as outcome measures or in combination with neural perturbation techniques (e.g., neurofeedback, neurostimulation), other translational applications (e.g., guiding neurosurgery). To foster debate, we also welcome critical Review, Opinion, and Perspective articles that survey the field and its progress towards clinical utility.