Springer Series in Cognitive and Neural Systems – serie

The perception-action cycle is the circular flow of information that takes place between the organism and its environment in the course of a sensory-guided sequence of behaviour towards a goal. Each action causes changes in the environment that are analyzed bottom-up through the perceptual hierarchy and lead to the processing of further action, top-down through the executive hierarchy, toward motor effectors. These actions cause new changes that are analyzed and lead to new action, and so the cycle continues. The Perception-action cycle: Models, architectures and hardware book provides focused and easily accessible reviews of various aspects of the perception-action cycle. It is an unparalleled resource of information that will be an invaluable companion to anyone in constructing and developing models, algorithms and hardware implementations of autonomous machines empowered with cognitive capabilities. The book is divided into three main parts. In the first part, leading computational neuroscientists present brain-inspired models of perception, attention, cognitive control, decision making, conflict resolution and monitoring, knowledge representation and reasoning, learning and memory, planning and action, and consciousness grounded on experimental data. In the second part, architectures, algorithms, and systems with cognitive capabilities and minimal guidance from the brain, are discussed. These architectures, algorithms, and systems are inspired from the areas of cognitive science, computer vision, robotics, information theory, machine learning, computer agents and artificial intelligence. In the third part, the analysis, design and implementation of hardware systems with robust cognitive abilities from the areas of mechatronics, sensing technology, sensor fusion, smart sensor networks, control rules, controllability, stability, model/knowledge representation, and reasoning are discussed.

The study of the brain and behavior is illuminated with the discovery of invariances. Experimental brain research uncovers constancies amidst variation, with respect to interventions and transformations prescribed by experimental paradigms. Place cells, mirror neurons, event related potentials and areas differentially active in fMRI, all illustrate the pervasive role of invariances in neural systems in relation to their function.

A significant amount of effort in neural modeling is directed towards understanding the representation of information in various parts of the brain, such as cortical maps [6], and the paths along which sensory information is processed. Though the time domain is integral an integral aspect of the functioning of biological systems, it has proven very challenging to incorporate the time domain effectively in neural network models. A promising path that is being explored is to study the importance of synchronization in biological systems. Synchronization plays a critical role in the interactions between neurons in the brain, giving rise to perceptual phenomena, and explaining multiple effects such as visual contour integration, and the separation of superposed inputs.The purpose of this book is to provide a unified view of how the time domain can be effectively employed in neural network models. A first direction to consider is to deploy oscillators that model temporal firing patterns of a neuron or a group of neurons. There is a growing body of research on the use of oscillatory neural networks, and their ability to synchronize under the right conditions. Such networks of synchronizing elements have been shown to be effective in image processing and segmentation tasks, and also in solving the binding problem, which is of great significance in the field of neuroscience. The oscillatory neural models can be employed at multiple scales of abstraction, ranging from individual neurons, to groups of neurons using Wilson-Cowan modeling techniques and eventually to the behavior of entire brain regions as revealed in oscillations observed in EEG recordings. A second interesting direction to consider is to understand the effect of different neural network topologies on their ability to create the desired synchronization. A third direction of interest is the extraction of temporal signaling patterns from brain imaging data such as EEG and fMRI. Hence this Special Session is of emerging interest in the brain sciences, as imaging techniques are able to resolve sufficient temporal detail to provide an insight into how the time domain is deployed in cognitive function.The following broad topics will be covered in the book: Synchronization, phase-locking behavior, image processing, image segmentation, temporal pattern analysis, EEG analysis, fMRI analyis, network topology and synchronizability, cortical interactions involving synchronization, and oscillatory neural networks.This book will benefit readers interested in the topics of computational neuroscience, applying neural network models to understand brain function, extracting temporal information from brain imaging data, and emerging techniques for image segmentation using oscillatory networks

The study of the brain and behavior is illuminated with the discovery of invariances. Experimental brain research uncovers constancies amidst variation, with respect to interventions and transformations prescribed by experimental paradigms. Place cells, mirror neurons, event related potentials and areas differentially active in fMRI, all illustrate the pervasive role of invariances in neural systems in relation to their function.

A significant amount of effort in neural modeling is directed towards understanding the representation of information in various parts of the brain, such as cortical maps [6], and the paths along which sensory information is processed. Though the time domain is integral an integral aspect of the functioning of biological systems, it has proven very challenging to incorporate the time domain effectively in neural network models. A promising path that is being explored is to study the importance of synchronization in biological systems. Synchronization plays a critical role in the interactions between neurons in the brain, giving rise to perceptual phenomena, and explaining multiple effects such as visual contour integration, and the separation of superposed inputs.The purpose of this book is to provide a unified view of how the time domain can be effectively employed in neural network models. A first direction to consider is to deploy oscillators that model temporal firing patterns of a neuron or a group of neurons. There is a growing body of research on the use of oscillatory neural networks, and their ability to synchronize under the right conditions. Such networks of synchronizing elements have been shown to be effective in image processing and segmentation tasks, and also in solving the binding problem, which is of great significance in the field of neuroscience. The oscillatory neural models can be employed at multiple scales of abstraction, ranging from individual neurons, to groups of neurons using Wilson-Cowan modeling techniques and eventually to the behavior of entire brain regions as revealed in oscillations observed in EEG recordings. A second interesting direction to consider is to understand the effect of different neural network topologies on their ability to create the desired synchronization. A third direction of interest is the extraction of temporal signaling patterns from brain imaging data such as EEG and fMRI. Hence this Special Session is of emerging interest in the brain sciences, as imaging techniques are able to resolve sufficient temporal detail to provide an insight into how the time domain is deployed in cognitive function.The following broad topics will be covered in the book: Synchronization, phase-locking behavior, image processing, image segmentation, temporal pattern analysis, EEG analysis, fMRI analyis, network topology and synchronizability, cortical interactions involving synchronization, and oscillatory neural networks.This book will benefit readers interested in the topics of computational neuroscience, applying neural network models to understand brain function, extracting temporal information from brain imaging data, and emerging techniques for image segmentation using oscillatory networks

This book models  an idealized neuron as being driven by basic electrical elements, the goal being to systematically characterize the logical properties of neural pulses.  In order to constitute a system, neurons as pulsating devices may be represented using novel circuit elements as delineated in this book.  A plausible brain system is implied by the delineated elements and logically follows from known and likely properties of a neuron. New to electrical science are novel pulse-related circuit elements involving recursive neurons.  A recursive neuron, when properly excited, produces a self-sustaining pulse train that when sampled,  provides a true output with a specified probability, and a false output with complementary probability.  Because of its similarity to the qubits of quantum mechanics, the recursive pulsating neuron is termed a simulated qubit.  Recursive neurons easily function as controlled toggle devices and so are capable of massively parallel calculations, this being a new dimension in brain functioning as described in this book.   Simulated qubits and their possibilities are compared to the qubits of quantum physics.  Included in the book are suggested neural circuits for associative memory search via a randomized process of cue selection, and neural circuits for priority calculations.  These serve to select returns from long term memory, which in turn determines one's next conscious thought or action based on past memorized experiences. The book reports on proposals involving electron tunneling between synapses, and quantum computations within neurons.  Although not a textbook, there are easy exercises at the ends of chapters, and in the appendix there are twelve simulation experiments concerning neurons.​

This book models  an idealized neuron as being driven by basic electrical elements, the goal being to systematically characterize the logical properties of neural pulses.  In order to constitute a system, neurons as pulsating devices may be represented using novel circuit elements as delineated in this book.  A plausible brain system is implied by the delineated elements and logically follows from known and likely properties of a neuron. New to electrical science are novel pulse-related circuit elements involving recursive neurons.  A recursive neuron, when properly excited, produces a self-sustaining pulse train that when sampled,  provides a true output with a specified probability, and a false output with complementary probability.  Because of its similarity to the qubits of quantum mechanics, the recursive pulsating neuron is termed a simulated qubit.  Recursive neurons easily function as controlled toggle devices and so are capable of massively parallel calculations, this being a new dimension in brain functioning as described in this book.   Simulated qubits and their possibilities are compared to the qubits of quantum physics.  Included in the book are suggested neural circuits for associative memory search via a randomized process of cue selection, and neural circuits for priority calculations.  These serve to select returns from long term memory, which in turn determines one's next conscious thought or action based on past memorized experiences. The book reports on proposals involving electron tunneling between synapses, and quantum computations within neurons.  Although not a textbook, there are easy exercises at the ends of chapters, and in the appendix there are twelve simulation experiments concerning neurons.​

The perception-action cycle is the circular flow of information that takes place between the organism and its environment in the course of a sensory-guided sequence of behaviour towards a goal. Each action causes changes in the environment that are analyzed bottom-up through the perceptual hierarchy and lead to the processing of further action, top-down through the executive hierarchy, toward motor effectors. These actions cause new changes that are analyzed and lead to new action, and so the cycle continues. The Perception-action cycle: Models, architectures and hardware book provides focused and easily accessible reviews of various aspects of the perception-action cycle. It is an unparalleled resource of information that will be an invaluable companion to anyone in constructing and developing models, algorithms and hardware implementations of autonomous machines empowered with cognitive capabilities. The book is divided into three main parts. In the first part, leading computational neuroscientists present brain-inspired models of perception, attention, cognitive control, decision making, conflict resolution and monitoring, knowledge representation and reasoning, learning and memory, planning and action, and consciousness grounded on experimental data. In the second part, architectures, algorithms, and systems with cognitive capabilities and minimal guidance from the brain, are discussed. These architectures, algorithms, and systems are inspired from the areas of cognitive science, computer vision, robotics, information theory, machine learning, computer agents and artificial intelligence. In the third part, the analysis, design and implementation of hardware systems with robust cognitive abilities from the areas of mechatronics, sensing technology, sensor fusion, smart sensor networks, control rules, controllability, stability, model/knowledge representation, and reasoning are discussed.

This both accessible and exhaustive book will help to improve modeling of attention and to inspire innovations in industry. It introduces the study of attention and focuses on attention modeling, addressing such themes as saliency models, signal detection and different types of signals, as well as real-life applications. The book is truly multi-disciplinary, collating work from psychology, neuroscience, engineering and computer science, amongst other disciplines.What is attention? We all pay attention every single moment of our lives. Attention is how the brain selects and prioritizes information. The study of attention has become incredibly complex and divided: this timely volume assists the reader by drawing together work on the computational aspects of attention from across the disciplines. Those working in the field as engineers will benefit from this book’s introduction to the psychological and biological approaches to attention, and neuroscientists can learn about engineering work on attention. The work features practical reviews and chapters that are quick and easy to read, as well as chapters which present deeper, more complex knowledge. Everyone whose work relates to human perception, to image, audio and video processing will find something of valuein this book, from students to researchers and those in industry.

This edited volume is about how unprejudiced approaches to real human cognition can improve the design of AI. It covers many aspects of human cognition and across 12 chapters the reader can explore multiple approaches about the complexities of human cognitive skills and reasoning, always guided by experts from different but complimentary academic fields.A central concept is explained: blended cognition, the natural skill of human beings for combining constantly different heuristics during their several task-solving activities. Something that was sometimes observed like a problem as “bad reasoning”, is now the central key for the understanding of the richness, adaptability and creativity of human cognition. The topic of this book connects in a significant way with the disciplines of psychology, neurology, anthropology, philosophy, logics, engineering, logics, and AI. In a nutshell: understanding better humans for designing better machines.Any person with interests on natural and artificial reasoning should read this book as a primary source of inspiration and a way to achieve a critical thinking on these topics.

This book focuses on our current understanding of brain dynamics in various brain disorders (e.g. epilepsy, Alzheimer’s and Parkinson’s disease) and how the multi-scale, multi-level tools of computational neuroscience can enhance this understanding.In recent years, there have been significant advances in the study of the dynamics of the disordered brain at both the microscopic and the macroscopic levels. This understanding can be furthered by the application of multi-scale computational models as integrative principles that may link single neuron dynamics and the dynamics of local and distant brain regions observed using human EEG, ERPs, MEG, LFPs and fMRI.Focusing on the computational models that are used to study movement, memory and cognitive disorders as well as epilepsy and consciousness related diseases, the book brings together physiologists and anatomists investigating cortical circuits; cognitive neuroscientists studying brain dynamics and behaviorby means of EEG and functional magnetic resonance imaging (fMRI); and computational neuroscientists using neural modeling techniques to explore local and large-scale disordered brain dynamics.Covering topics that have a significant impact on the field of medicine, neuroscience and computer science, the book appeals to a diverse group of investigators.

This book focuses on our current understanding of brain dynamics in various brain disorders (e.g. epilepsy, Alzheimer’s and Parkinson’s disease) and how the multi-scale, multi-level tools of computational neuroscience can enhance this understanding.In recent years, there have been significant advances in the study of the dynamics of the disordered brain at both the microscopic and the macroscopic levels. This understanding can be furthered by the application of multi-scale computational models as integrative principles that may link single neuron dynamics and the dynamics of local and distant brain regions observed using human EEG, ERPs, MEG, LFPs and fMRI.Focusing on the computational models that are used to study movement, memory and cognitive disorders as well as epilepsy and consciousness related diseases, the book brings together physiologists and anatomists investigating cortical circuits; cognitive neuroscientists studying brain dynamics and behaviorby means of EEG and functional magnetic resonance imaging (fMRI); and computational neuroscientists using neural modeling techniques to explore local and large-scale disordered brain dynamics.Covering topics that have a significant impact on the field of medicine, neuroscience and computer science, the book appeals to a diverse group of investigators.

This book covers recent advances in the understanding of brain structure, function and disorders based on the fundamental principles of physics.  It covers a broad range of physical phenomena occurring in the brain circuits for perception, cognition, emotion and action, representing the building blocks of the mind.  It provides novel insights into the devastating brain disorders of the mind such as schizophrenia, dementia, autism, aging or addictions, as well as into the new devices for brain repair. The book is aimed at basic researchers in the fields of neuroscience, physics, biophysics and clinicians in the fields of neurology, neurosurgery, psychology, psychiatry.

This book covers recent advances in the understanding of brain structure, function and disorders based on the fundamental principles of physics.  It covers a broad range of physical phenomena occurring in the brain circuits for perception, cognition, emotion and action, representing the building blocks of the mind.  It provides novel insights into the devastating brain disorders of the mind such as schizophrenia, dementia, autism, aging or addictions, as well as into the new devices for brain repair. The book is aimed at basic researchers in the fields of neuroscience, physics, biophysics and clinicians in the fields of neurology, neurosurgery, psychology, psychiatry.

Physical implementation of the memristor at industrial scale sparked the interest from various disciplines, ranging from physics, nanotechnology, electrical engineering, neuroscience, to intelligent robotics. As any promising new technology, it has raised hopes and questions; it is an extremely challenging task to live up to the high expectations and to devise revolutionary and feasible future applications for memristive devices. The possibility of gathering prominent scientists in the heart of the Silicon Valley given by the 2011 International Joint Conference on Neural Networks held in San Jose, CA, has offered us the unique opportunity of organizing a series of special events on the present status and future perspectives in neuromorphic memristor science. This book presents a selection of the remarkable contributions given by the leaders of the field and it may serve as inspiration and future reference to all researchers that want to explore the extraordinary possibilities given bythis revolutionary concept.

The book explains how to understand cognition in terms of brain anatomy, physiology and chemistry, using an approach adapted from techniques for understanding complex electronic systems. These techniques create hierarchies of information process based descriptions on different levels of detail, where higher levels contain less information and can therefore describe complete cognitive phenomena, but are more approximate. The nature of the approximations are well understood, and more approximate higher level descriptions can therefore be mapped to more precise detailed descriptions of any part of a phenomenon as required. Cognitive phenomena, the anatomy and connectivity of major brain structures, neuron physiology, and cellular chemistry are reviewed. Various cognitive tasks are described in terms of information processes performed by different major anatomical structures. These higher level descriptions are selectively mapped to more detailed physiological and chemical levels.

This book details a model of consciousness supported by scientific experimental data from the human brain. It presents how the Corollary Discharge of Attention Movement (CODAM) neural network model allows for a scientific understanding of consciousness as well as provides a solution to the Mind-Body problem. The book provides readers with a general approach to consciousness that is powerful enough to lead to the inner self and its ramifications for the vast range of human experiences. It also offers an approach to the evolution of human consciousness and features chapters on mental disease (especially schizophrenia) and on meditative states (including drug-induced states of mind). Solving the Mind-Body Problem bridges the gap that exists between philosophers of mind and the neuroscience community, allowing the enormous weight of theorizing on the nature of mind to be brought to earth and put under the probing gaze of the scientific facts of life and mind.

The purpose of the book is to advance in the understanding of brain function by defining a general framework for representation based on category theory. The idea is to bring this mathematical formalism into the domain of neural representation of physical spaces, setting the basis for a theory of mental representation, able to relate empirical findings, uniting them into a sound theoretical corpus.The innovative approach presented in the book provides a horizon of interdisciplinary collaboration that aims to set up a common agenda that synthesizes mathematical formalization and empirical procedures in a systemic way. Category theory has been successfully applied to qualitative analysis, mainly in theoretical computer science to deal with programming language semantics. Nevertheless, the potential of category theoretic tools for quantitative analysis of networks has not been tackled so far. Statistical methods to investigate graph structure typically rely on network parameters. Category theory can be seen as an abstraction of graph theory. Thus, new categorical properties can be added into network analysis and graph theoretic constructs can be accordingly extended in more fundamental basis. By generalizing networks using category theory we can address questions and elaborate answers in a more fundamental way without waiving graph theoretic tools. The vital issue is to establish a new framework for quantitative analysis of networks using the theory of categories, in which computational neuroscientists and network theorists may tackle in more efficient ways the dynamics of brain cognitive networks.The intended audience of the book is researchers who wish to explore the validity of mathematical principles in the understanding of cognitive systems. All the actors in cognitive science: philosophers, engineers, neurobiologists, cognitive psychologists, computer scientists etc. are akin to discover along its pages new unforeseen connections through the development of concepts and formal theories described in the book. Practitioners of both pure and applied mathematics e.g., network theorists, will be delighted with the mapping of abstract mathematical concepts in the terra incognita of cognition.

The book explains how to understand cognition in terms of brain anatomy, physiology and chemistry, using an approach adapted from techniques for understanding complex electronic systems. These techniques create hierarchies of information process based descriptions on different levels of detail, where higher levels contain less information and can therefore describe complete cognitive phenomena, but are more approximate. The nature of the approximations are well understood, and more approximate higher level descriptions can therefore be mapped to more precise detailed descriptions of any part of a phenomenon as required. Cognitive phenomena, the anatomy and connectivity of major brain structures, neuron physiology, and cellular chemistry are reviewed. Various cognitive tasks are described in terms of information processes performed by different major anatomical structures. These higher level descriptions are selectively mapped to more detailed physiological and chemical levels.

The purpose of the book is to advance in the understanding of brain function by defining a general framework for representation based on category theory. The idea is to bring this mathematical formalism into the domain of neural representation of physical spaces, setting the basis for a theory of mental representation, able to relate empirical findings, uniting them into a sound theoretical corpus.The innovative approach presented in the book provides a horizon of interdisciplinary collaboration that aims to set up a common agenda that synthesizes mathematical formalization and empirical procedures in a systemic way. Category theory has been successfully applied to qualitative analysis, mainly in theoretical computer science to deal with programming language semantics. Nevertheless, the potential of category theoretic tools for quantitative analysis of networks has not been tackled so far. Statistical methods to investigate graph structure typically rely on network parameters. Category theory can be seen as an abstraction of graph theory. Thus, new categorical properties can be added into network analysis and graph theoretic constructs can be accordingly extended in more fundamental basis. By generalizing networks using category theory we can address questions and elaborate answers in a more fundamental way without waiving graph theoretic tools. The vital issue is to establish a new framework for quantitative analysis of networks using the theory of categories, in which computational neuroscientists and network theorists may tackle in more efficient ways the dynamics of brain cognitive networks.The intended audience of the book is researchers who wish to explore the validity of mathematical principles in the understanding of cognitive systems. All the actors in cognitive science: philosophers, engineers, neurobiologists, cognitive psychologists, computer scientists etc. are akin to discover along its pages new unforeseen connections through the development of concepts and formal theories described in the book. Practitioners of both pure and applied mathematics e.g., network theorists, will be delighted with the mapping of abstract mathematical concepts in the terra incognita of cognition.

This book details a model of consciousness supported by scientific experimental data from the human brain. It presents how the Corollary Discharge of Attention Movement (CODAM) neural network model allows for a scientific understanding of consciousness as well as provides a solution to the Mind-Body problem. The book provides readers with a general approach to consciousness that is powerful enough to lead to the inner self and its ramifications for the vast range of human experiences. It also offers an approach to the evolution of human consciousness and features chapters on mental disease (especially schizophrenia) and on meditative states (including drug-induced states of mind). Solving the Mind-Body Problem bridges the gap that exists between philosophers of mind and the neuroscience community, allowing the enormous weight of theorizing on the nature of mind to be brought to earth and put under the probing gaze of the scientific facts of life and mind.