Springer Series on Bio- and Neurosystems – serie

This book offers a timely overview of theories and methods developed by an authoritative group of researchers to understand the link between criticality and brain functioning. Cortical information processing in particular and brain function in general rely heavily on the collective dynamics of neurons and networks distributed over many brain areas. A key concept for characterizing and understanding brain dynamics is the idea that networks operate near a critical state, which offers several potential benefits for computation and information processing. However, there is still a large gap between research on criticality and understanding brain function. For example, cortical networks are not homogeneous but highly structured, they are not in a state of spontaneous activation but strongly driven by changing external stimuli, and they process information with respect to behavioral goals. So far the questions relating to how critical dynamics may support computation in this complex setting, and whether they can outperform other information processing schemes remain open. Based on the workshop “Dynamical Network States, Criticality and Cortical Function", held in March 2017 at the Hanse Institute for Advanced Studies (HWK) in Delmenhorst, Germany, the book provides readers with extensive information on these topics, as well as tools and ideas to answer the above-mentioned questions. It is meant for physicists, computational and systems neuroscientists, and biologists.

This book offers a timely overview of theories and methods developed by an authoritative group of researchers to understand the link between criticality and brain functioning. Cortical information processing in particular and brain function in general rely heavily on the collective dynamics of neurons and networks distributed over many brain areas. A key concept for characterizing and understanding brain dynamics is the idea that networks operate near a critical state, which offers several potential benefits for computation and information processing. However, there is still a large gap between research on criticality and understanding brain function. For example, cortical networks are not homogeneous but highly structured, they are not in a state of spontaneous activation but strongly driven by changing external stimuli, and they process information with respect to behavioral goals. So far the questions relating to how critical dynamics may support computation in this complex setting, and whether they can outperform other information processing schemes remain open. Based on the workshop “Dynamical Network States, Criticality and Cortical Function", held in March 2017 at the Hanse Institute for Advanced Studies (HWK) in Delmenhorst, Germany, the book provides readers with extensive information on these topics, as well as tools and ideas to answer the above-mentioned questions. It is meant for physicists, computational and systems neuroscientists, and biologists.

Mobile Brain–Body Imaging and the Neuroscience of Art, Innovation and Creativity is a trans-disciplinary, collective, multimedia collaboration that critically uncovers the challenges and opportunities for transformational and innovative research and performance at the nexus of art, science and engineering. This book addresses a set of universal and timeless questions with a profound impact on the human condition: How do the creative arts and aesthetic experiences engage the brain and mind and promote innovation?How do arts–science collaborations employ aesthetics as a means of problem-solving and thereby create meaning?How can the creative arts and neuroscience advance understanding of individuality and social cognition, improve health and promote life-long learning?How are neurotechnologies changing science and artistic expression?How are the arts and citizen science innovating neuroscience studies, informal learning and outreach in the public sphere?Emerging from the 2016 and 2017 International Conferences on Mobile Brain–Body Imaging and the Neuroscience of Art, Innovation and Creativity held in Cancun, Mexico and Valencia, Spain to explore these topics, this book intertwines disciplines and investigates not only their individual products—art and data—but also something more substantive and unique; the international pool of contributors reveals something larger about humanity by revealing the state of the art in collaboration between arts and sciences and providing an investigational roadmap projected from recent advances.Mobile Brain–Body Imaging and the Neuroscience of Art, Innovation and Creativity is written for academic researchers, professionals working in industrial and clinical centers, independent researchers and artists from the performing arts, and other readers interested in understanding emergent innovations at the nexus of art, science, engineering, medicine and the humanities. The book contains language, design features (illustrations, diagrams) to develop a conversational bridge between the disciplines involved supplemented by access to video, artistic presentations and the results of a hackathon from the MoBI conferences.

Mobile Brain–Body Imaging and the Neuroscience of Art, Innovation and Creativity is a trans-disciplinary, collective, multimedia collaboration that critically uncovers the challenges and opportunities for transformational and innovative research and performance at the nexus of art, science and engineering. This book addresses a set of universal and timeless questions with a profound impact on the human condition: How do the creative arts and aesthetic experiences engage the brain and mind and promote innovation?How do arts–science collaborations employ aesthetics as a means of problem-solving and thereby create meaning?How can the creative arts and neuroscience advance understanding of individuality and social cognition, improve health and promote life-long learning?How are neurotechnologies changing science and artistic expression?How are the arts and citizen science innovating neuroscience studies, informal learning and outreach in the public sphere?Emerging from the 2016 and 2017 International Conferences on Mobile Brain–Body Imaging and the Neuroscience of Art, Innovation and Creativity held in Cancun, Mexico and Valencia, Spain to explore these topics, this book intertwines disciplines and investigates not only their individual products—art and data—but also something more substantive and unique; the international pool of contributors reveals something larger about humanity by revealing the state of the art in collaboration between arts and sciences and providing an investigational roadmap projected from recent advances.Mobile Brain–Body Imaging and the Neuroscience of Art, Innovation and Creativity is written for academic researchers, professionals working in industrial and clinical centers, independent researchers and artists from the performing arts, and other readers interested in understanding emergent innovations at the nexus of art, science, engineering, medicine and the humanities. The book contains language, design features (illustrations, diagrams) to develop a conversational bridge between the disciplines involved supplemented by access to video, artistic presentations and the results of a hackathon from the MoBI conferences.

This book offers the first comprehensive yet critical overview of methods used to evaluate interaction between humans and social robots. It reviews commonly used evaluation methods, and shows that they are not always suitable for this purpose. Using representative case studies, the book identifies good and bad practices for evaluating human-robot interactions and proposes new standardized processes as well as recommendations, carefully developed on the basis of intensive discussions between specialists in various HRI-related disciplines, e.g. psychology, ethology, ergonomics, sociology, ethnography, robotics, and computer science. The book is the result of a close, long-standing collaboration between the editors and the invited contributors, including, but not limited to, their inspiring discussions at the workshop on Evaluation Methods Standardization for Human-Robot Interaction (EMSHRI), which have been organized yearly since 2015. By highlighting and weighing good and bad practices in evaluation design for HRI, the book will stimulate the scientific community to search for better solutions, take advantages of interdisciplinary collaborations, and encourage the development of new standards to accommodate the growing presence of robots in the day-to-day and social lives of human beings.

This book offers the first comprehensive yet critical overview of methods used to evaluate interaction between humans and social robots. It reviews commonly used evaluation methods, and shows that they are not always suitable for this purpose. Using representative case studies, the book identifies good and bad practices for evaluating human-robot interactions and proposes new standardized processes as well as recommendations, carefully developed on the basis of intensive discussions between specialists in various HRI-related disciplines, e.g. psychology, ethology, ergonomics, sociology, ethnography, robotics, and computer science. The book is the result of a close, long-standing collaboration between the editors and the invited contributors, including, but not limited to, their inspiring discussions at the workshop on Evaluation Methods Standardization for Human-Robot Interaction (EMSHRI), which have been organized yearly since 2015. By highlighting and weighing good and bad practices in evaluation design for HRI, the book will stimulate the scientific community to search for better solutions, take advantages of interdisciplinary collaborations, and encourage the development of new standards to accommodate the growing presence of robots in the day-to-day and social lives of human beings.

This book takes the notions of adaptivity and learning from the realm of engineering into the realm of biology and natural processes. It introduces a Hebbian-LMS algorithm, an integration of unsupervised Hebbian learning and supervised LMS learning in neural networks, as a mathematical representation of a general theory for synaptic learning in the brain, and adaptation and functional control of homeostasis in living systems. Written in a language that is able to address students and scientists with different backgrounds, this book accompanies readers on a unique journey through various homeostatic processes in living organisms, such as body temperature control and synaptic plasticity, explaining how the Hebbian-LMS algorithm can help understand them, and suggesting some open questions for future research. It also analyses cell signalling pathways from an unusual perspective, where hormones and hormone receptors are shown to be regulated via the principles of the Hebbian-LMS algorithm. It further discusses addiction and pain, and various kinds of mood disorders alike, showing how they can be modelled with the Hebbian-LMS algorithm.  For the first time, the Hebbian-LMS algorithm, which has been derived from a combination of Hebbian theory from the neuroscience field and the LMS algorithm from the engineering field of adaptive signal processing, becomes a potent model for understanding how biological regulation works. Thus, this book is breaking new ground in neuroscience by providing scientists with a general theory for how nature does control synaptic learning. It then goes beyond that, showing that the same principles apply to hormone-mediated regulation of physiological processes. In turn, the book tackles in more depth the concept of learning. It covers computer simulations and strategies for training neural networks with the Hebbian-LMS algorithm, demonstrating that the resulting algorithms are able to identify relationships between unknown input patterns. It shows how this can translate in useful ideas to understand human memory and design cognitive structures. All in all, this book offers an absolutely, unique, inspiring reading for biologists, physiologists, and engineers, paving the way for future studies on what we could call the nature’s secret learning algorithm.

This book takes the notions of adaptivity and learning from the realm of engineering into the realm of biology and natural processes. It introduces a Hebbian-LMS algorithm, an integration of unsupervised Hebbian learning and supervised LMS learning in neural networks, as a mathematical representation of a general theory for synaptic learning in the brain, and adaptation and functional control of homeostasis in living systems. Written in a language that is able to address students and scientists with different backgrounds, this book accompanies readers on a unique journey through various homeostatic processes in living organisms, such as body temperature control and synaptic plasticity, explaining how the Hebbian-LMS algorithm can help understand them, and suggesting some open questions for future research. It also analyses cell signalling pathways from an unusual perspective, where hormones and hormone receptors are shown to be regulated via the principles of the Hebbian-LMS algorithm. It further discusses addiction and pain, and various kinds of mood disorders alike, showing how they can be modelled with the Hebbian-LMS algorithm.  For the first time, the Hebbian-LMS algorithm, which has been derived from a combination of Hebbian theory from the neuroscience field and the LMS algorithm from the engineering field of adaptive signal processing, becomes a potent model for understanding how biological regulation works. Thus, this book is breaking new ground in neuroscience by providing scientists with a general theory for how nature does control synaptic learning. It then goes beyond that, showing that the same principles apply to hormone-mediated regulation of physiological processes. In turn, the book tackles in more depth the concept of learning. It covers computer simulations and strategies for training neural networks with the Hebbian-LMS algorithm, demonstrating that the resulting algorithms are able to identify relationships between unknown input patterns. It shows how this can translate in useful ideas to understand human memory and design cognitive structures. All in all, this book offers an absolutely, unique, inspiring reading for biologists, physiologists, and engineers, paving the way for future studies on what we could call the nature’s secret learning algorithm.

This book covers the medical condition of diabetic patients, their early symptoms and methods conventionally used for diagnosing and monitoring diabetes. It describes various techniques and technologies used for diabetes detection. The content is built upon moving from regressive technology (invasive) and adapting new-age pain-free technologies (non-invasive), machine learning and artificial intelligence for diabetes monitoring and management. This book details all the popular technologies used in the health care and medical fields for diabetic patients. An entire chapter is dedicated to how the future of this field will be shaping up and the challenges remaining to be conquered. Finally, it shows artificial intelligence and predictions, which can be beneficial for the early detection, dose monitoring and surveillance for patients suffering from diabetes

This book covers the medical condition of diabetic patients, their early symptoms and methods conventionally used for diagnosing and monitoring diabetes. It describes various techniques and technologies used for diabetes detection. The content is built upon moving from regressive technology (invasive) and adapting new-age pain-free technologies (non-invasive), machine learning and artificial intelligence for diabetes monitoring and management. This book details all the popular technologies used in the health care and medical fields for diabetic patients. An entire chapter is dedicated to how the future of this field will be shaping up and the challenges remaining to be conquered. Finally, it shows artificial intelligence and predictions, which can be beneficial for the early detection, dose monitoring and surveillance for patients suffering from diabetes

This book offers a timely review of modern technologies for health, with a special emphasis on wireless and wearable technologies, GIS tools and machine learning methods for managing the impacts of pandemics. It describes new strategies for forecasting evolution of pandemics, optimizing contract tracing, and for detection and diagnosis of diseases, among others. Written by researchers and professionals with different backgrounds, this book offers a extensive information and a source of inspiration for physiologists, engineers, IT scientists and policy makers in the health and technology sector.

This book offers a timely review of modern technologies for health, with a special emphasis on wireless and wearable technologies, GIS tools and machine learning methods for managing the impacts of pandemics. It describes new strategies for forecasting evolution of pandemics, optimizing contract tracing, and for detection and diagnosis of diseases, among others. Written by researchers and professionals with different backgrounds, this book offers a extensive information and a source of inspiration for physiologists, engineers, IT scientists and policy makers in the health and technology sector.

This open access book offers a timely and comprehensive review of the field of neurotronics. Gathering cutting-edge contributions from neuroscientists, biologists, psychologists, as well as physicists, microelectronics engineers and information scientists, it gives extensive information on fundamental information pathways in selected nervous systems. It also highlights their relevance as building blocks for novel computing architectures, such as bio-inspired electronic devices, neuromorphic architectures, memristive devices, adaptive sensors and emergent, pulsed-coupled oscillatory networks.  All in all, this book offers a unique bridge between fundamental research in neuroscience, neural information processing, nonlinear dynamics, and self-organization, and advanced practical applications concerning the fabrication of hardware-oriented computing.

This open access book offers a timely and comprehensive review of the field of neurotronics. Gathering cutting-edge contributions from neuroscientists, biologists, psychologists, as well as physicists, microelectronics engineers and information scientists, it gives extensive information on fundamental information pathways in selected nervous systems. It also highlights their relevance as building blocks for novel computing architectures, such as bio-inspired electronic devices, neuromorphic architectures, memristive devices, adaptive sensors and emergent, pulsed-coupled oscillatory networks.  All in all, this book offers a unique bridge between fundamental research in neuroscience, neural information processing, nonlinear dynamics, and self-organization, and advanced practical applications concerning the fabrication of hardware-oriented computing.

How does human memory work?  How does human pattern recognition work?  The book’s motivation is twofold, to add to knowledge in the field of neuroscience, and to design a highly simplified cognitive memory constructed using software and existing electronic components.  Readers are taken on an inspiring journey through the fundamentals of human memory, how it is constructed, and how it works in everyday life.  The book goes more in-depth into the human side of cognitive memory — how seeing, hearing, walking and speaking works.  Impairments in cognitive memory are also discussed.  Lastly, the book sheds light on how meaning is extracted from sensory inputs and from stored data. This book is not without controversy.  Neuroscientists accept the engrams (or memory traces) model that long-term memory is stored in the brain’s neural networks. The authors believe that long-term human memory is stored digitally, in the DNA of brain cells, and not in analog neural networks.  Further, the authors believe that innate knowledge of humans and animals is inherited, transmitted from parents to offspring at the moment of conception.  The single cell contains the innate knowledge in the DNA of its nucleus.   Memory is stored in DNA.  The brain’s neural networks are for access and retrieval of memory and not for actual storage.  This book offers a unique, inspiring reading to researchers and other readers interested in the science of memory.

How does human memory work?  How does human pattern recognition work?  The book’s motivation is twofold, to add to knowledge in the field of neuroscience, and to design a highly simplified cognitive memory constructed using software and existing electronic components.  Readers are taken on an inspiring journey through the fundamentals of human memory, how it is constructed, and how it works in everyday life.  The book goes more in-depth into the human side of cognitive memory — how seeing, hearing, walking and speaking works.  Impairments in cognitive memory are also discussed.  Lastly, the book sheds light on how meaning is extracted from sensory inputs and from stored data. This book is not without controversy.  Neuroscientists accept the engrams (or memory traces) model that long-term memory is stored in the brain’s neural networks. The authors believe that long-term human memory is stored digitally, in the DNA of brain cells, and not in analog neural networks.  Further, the authors believe that innate knowledge of humans and animals is inherited, transmitted from parents to offspring at the moment of conception.  The single cell contains the innate knowledge in the DNA of its nucleus.   Memory is stored in DNA.  The brain’s neural networks are for access and retrieval of memory and not for actual storage.  This book offers a unique, inspiring reading to researchers and other readers interested in the science of memory.

This book provides a comprehensive overview of modern computer-based techniques for analyzing the structure, properties and dynamics of biomolecules and biomolecular processes. It is organized in four main parts; the first one deals with methodology of molecular simulations; the second one with applications of molecular simulations; the third one introduces bioinformatics methods and the use of experimental information in molecular simulations; the last part reports on selected applications of molecular quantum mechanics. This second edition has been thoroughly revised and updated to include the latest progresses made in the respective field of research.

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.

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.