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This book is a collection of articles by leading researchers working at the cutting edge of neuro-computational modelling of neurological and psychiatric disorders. Each article contains model validation techniques used in the context of the specific problem being studied. Validation is essential for neuro-inspired computational models to become useful tools in the understanding and treatment of disease conditions. Currently, the immense diversity in neuro-computational modelling approaches for investigating brain diseases has created the need for a structured and coordinated approach to benchmark and standardise validation methods and techniques in this field  of research. This book serves as a step towards a systematic approach to validation of neuro-computational models used for studying brain diseases, and should be useful for all neuro-computational modellers.

Computational neuroscience. --- Neuropsychiatry. --- Mental illness --- Physiological aspects. --- Psychiatry, Physiological --- Medicine. --- Neurosciences. --- Neurology. --- Neural networks (Computer science). --- Biomedicine. --- Mathematical Models of Cognitive Processes and Neural Networks. --- Psychological manifestations of general diseases --- Psychophysiology --- Behavioral neurology --- Biological psychiatry --- Neurology --- Computational neurosciences --- Computational biology --- Neurosciences --- Medicine --- Nervous system --- Neuropsychiatry --- Neural sciences --- Neurological sciences --- Neuroscience --- Medical sciences --- Diseases --- Neurology . --- Neural networks (Computer science) . --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Artificial intelligence --- Natural computation --- Soft computing

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This book presents the most recent mathematical approaches to the growing research area of networks, oscillations, and collective motions in the context of biological systems. Bringing together the results of multiple studies of different biological systems, this book sheds light on the relations among these research themes. Included in this book are the following topics: feedback systems with time delay and threshold of sensing (dead zone), robustness of biological networks from the point of view of dynamical systems, the hardware-oriented neuron modeling approach, a universal mechanism governing the entrainment limit under weak forcing, the robustness mechanism of open complex systems, situation-dependent switching of the cues primarily relied on by foraging ants, and group chase and escape. Research on different biological systems is presented together, not separated by specializations or by model systems. Therefore, the book provides diverse perspectives at the forefront of current mathematical research on biological systems, especially focused on networks, oscillations, and collective motions. This work is aimed at advanced undergraduate, graduate, and postdoctoral students, as well as scientists and engineers. It will also be of great use for professionals in industries and service sectors owing to the applicability of topics such as networks and synchronizations.

Life Sciences. --- Computer Appl. in Life Sciences. --- Systems Biology. --- Mathematical Models of Cognitive Processes and Neural Networks. --- Computational Biology/Bioinformatics. --- Life sciences. --- Bioinformatics. --- Biological models. --- Biology --- Sciences de la vie --- Bio-informatique --- Modèles biologiques --- Biologie --- Data processing. --- Informatique --- Biology_xData processing. --- Biomathematics --- Health & Biological Sciences --- Biology - General --- Mathematical models --- Biomathematics. --- Mathematical models. --- Mathematics --- Systems biology. --- Computational biology. --- Neural networks (Computer science). --- Biological models --- Bio-informatics --- Biological informatics --- Information science --- Computational biology --- Systems biology --- Models, Biological --- Data processing --- Bioinformatics . --- Computational biology . --- Neural networks (Computer science) . --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Artificial intelligence --- Natural computation --- Soft computing --- Bioinformatics --- Biological systems --- Molecular biology

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This volume provides an overview of applications of conceptual spaces theory, beginning with an introduction to the modeling tool that unifies the chapters. The first section explores issues of linguistic semantics, including speakers’ negotiation of meaning. Further sections address computational and ontological aspects of constructing conceptual spaces, while the final section looks at philosophical applications. Domains include artificial intelligence and robotics, epistemology and philosophy of science, lexical semantics and pragmatics, agent-based simulation, perspectivism, framing, contrast, sensory modalities, and music, among others. This collection provides evidence of the wide application range of this theory of knowledge representation. The papers in this volume derive from international experts across different fields including philosophy, cognitive science, linguistics, robotics, computer science and geography. Each contributor has successfully applied conceptual spaces theory as a modeling tool in their respective areas of expertise. Graduates as well as researchers in the areas of epistemology, linguistics, geometric knowledge representation, and the mathematical modeling of cognitive processes should find this book of particular interest.

Philosophy. --- Epistemology. --- Mathematical Models of Cognitive Processes and Neural Networks. --- Philosophy (General). --- Genetic epistemology. --- Epistémologie génétique --- Philosophy --- Philosophy & Religion --- Speculative Philosophy --- Conceptual structures (Information theory) --- Computational Linguistics. --- Artificial intelligence. --- Cognitive science. --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Automatic language processing --- Language and languages --- Language data processing --- Linguistics --- Natural language processing (Linguistics) --- Data processing --- Neural networks (Computer science). --- Science --- Philosophy of mind --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Applied linguistics --- Cross-language information retrieval --- Mathematical linguistics --- Multilingual computing --- Knowledge representation (Information theory) --- Developmental psychology --- Knowledge, Theory of --- Neural networks (Computer science) . --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Artificial intelligence --- Natural computation --- Soft computing --- Epistemology --- Theory of knowledge --- Psychology

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This book introduces a variety of neural network methods for solving differential equations arising in science and engineering. The emphasis is placed on a deep understanding of the neural network techniques, which has been presented in a mostly heuristic and intuitive manner. This approach will enable the reader to understand the working, efficiency and shortcomings of each neural network technique for solving differential equations. The objective of this book is to provide the reader with a sound understanding of the foundations of neural networks, and a comprehensive introduction to neural network methods for solving differential equations together with recent developments in the techniques and their applications. The book comprises four major sections. Section I consists of a brief overview of differential equations and the relevant physical problems arising in science and engineering. Section II illustrates the history of neural networks starting from their beginnings in the 1940s through to the renewed interest of the 1980s. A general introduction to neural networks and learning technologies is presented in Section III. This section also includes the description of the multilayer perceptron and its learning methods. In Section IV, the different neural network methods for solving differential equations are introduced, including discussion of the most recent developments in the field. Advanced students and researchers in mathematics, computer science and various disciplines in science and engineering will find this book a valuable reference source.

Mathematics. --- Mathematical Models of Cognitive Processes and Neural Networks. --- Ordinary Differential Equations. --- Numerical and Computational Physics. --- Appl.Mathematics/Computational Methods of Engineering. --- Computational Mathematics and Numerical Analysis. --- Differential Equations. --- Computer science --- Engineering mathematics. --- Mathématiques --- Informatique --- Mathématiques de l'ingénieur --- Differential equations -- Data processing. --- Neural networks (Computer science). --- Engineering & Applied Sciences --- Computer Science --- Neural networks (Computer science) --- Differential equations --- Data processing. --- 517.91 Differential equations --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Differential equations. --- Computer mathematics. --- Physics. --- Applied mathematics. --- Artificial intelligence --- Natural computation --- Soft computing --- Numerical and Computational Physics, Simulation. --- Mathematical and Computational Engineering. --- Computer mathematics --- Discrete mathematics --- Electronic data processing --- Engineering --- Engineering analysis --- Mathematical analysis --- Mathematics --- Neural networks (Computer science) . --- Natural philosophy --- Philosophy, Natural --- Physical sciences --- Dynamics

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This monograph examines in detail models of neural systems described by delay-differential equations. Each element of the medium (neuron) is an oscillator that generates, in standalone mode, short impulses also known as spikes. The book discusses models of synaptic interaction between neurons, which lead to complex oscillatory modes in the system. In addition, it presents a solution to the problem of choosing the parameters of interaction in order to obtain attractors with predetermined structure. These attractors are represented as images encoded in the form of autowaves (wave memory). The target audience primarily comprises researchers and experts in the field, but it will also be beneficial for graduate students.

Computer Science --- Engineering & Applied Sciences --- Neural networks (Neurobiology) --- Neurons --- Neuroinformatics --- Mathematical models. --- Physiology. --- Biological neural networks --- Nets, Neural (Neurobiology) --- Networks, Neural (Neurobiology) --- Neural nets (Neurobiology) --- Mathematics. --- Neurosciences. --- System theory. --- Neural networks (Computer science). --- Statistical physics. --- Complexity, Computational. --- Mathematical Models of Cognitive Processes and Neural Networks. --- Nonlinear Dynamics. --- Complex Systems. --- Complexity. --- Neurosciences --- Medical informatics --- Cell physiology --- Neurophysiology --- Cognitive neuroscience --- Neurobiology --- Neural circuitry --- Data processing --- Engineering. --- Applications of Nonlinear Dynamics and Chaos Theory. --- Neural sciences --- Neurological sciences --- Neuroscience --- Medical sciences --- Nervous system --- Construction --- Industrial arts --- Technology --- Neural networks (Computer science) . --- Computational complexity. --- Complexity, Computational --- Electronic data processing --- Machine theory --- Systems, Theory of --- Systems science --- Science --- Physics --- Mathematical statistics --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Artificial intelligence --- Natural computation --- Soft computing --- Philosophy --- Statistical methods