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1 Petrovici, Mihai Alexandru Form Versus Function: Theory and Models for Neuronal Substrates I10320 2016 eBook  
2 Treur, Jan Network-Oriented Modeling I10173 2016 eBook  
3 Mantica, Giorgio Emergent Complexity from Nonlinearity, in Physics, Engineering and the Life Sciences I09877 2017 eBook  
4 Lehnert, Judith Controlling Synchronization Patterns in Complex Networks I09860 2016 eBook  
5 Helias, Moritz Statistical Field Theory for Neural Networks I09583 2020 eBook  
6 Hutt, Axel Synergetics I09580 2020 eBook  
7 Czischek, Stefanie Neural-Network Simulation of Strongly Correlated Quantum Systems I09120 2020 eBook  
8 Lubashevsky, Ihor Physics of the Human Mind I08732 2017 eBook  
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TitleForm Versus Function: Theory and Models for Neuronal Substrates
Author(s)Petrovici, Mihai Alexandru
PublicationCham, 1. Imprint: Springer 2. Springer International Publishing, 2016.
DescriptionXXVI, 374 p. 150 illus., 101 illus. in color : online resource
Abstract NoteThis thesis addresses one of the most fundamental challenges for modern science: how can the brain as a network of neurons process information, how can it create and store internal models of our world, and how can it infer conclusions from ambiguous data? The author addresses these questions with the rigorous language of mathematics and theoretical physics, an approach that requires a high degree of abstraction to transfer results of wet lab biology to formal models. The thesis starts with an in-depth description of the state-of-the-art in theoretical neuroscience, which it subsequently uses as a basis to develop several new and original ideas. Throughout the text, the author connects the form and function of neuronal networks. This is done in order to achieve functional performance of biological brains by transferring their form to synthetic electronics substrates, an approach referred to as neuromorphic computing. The obvious aspect that this transfer can never be perfect but necessarily leads to performance differences is substantiated and explored in detail. The author also introduces a novel interpretation of the firing activity of neurons. He proposes a probabilistic interpretation of this activity and shows by means of formal derivations that stochastic neurons can sample from internally stored probability distributions. This is corroborated by the author???s recent findings, which confirm that biological features like the high conductance state of networks enable this mechanism. The author goes on to show that neural sampling can be implemented on synthetic neuromorphic circuits, paving the way for future applications in machine learning and cognitive computing, for example as energy-efficient implementations of deep learning networks. The thesis offers an essential resource for newcomers to the field and an inspiration for scientists working in theoretical neuroscience and the future of computing
ISBN,Price9783319395524
Keyword(s)1. COMPUTER SIMULATION 2. EBOOK 3. EBOOK - SPRINGER 4. Mathematical Models of Cognitive Processes and Neural Networks 5. Neural networks (Computer science)?? 6. NEUROBIOLOGY 7. Neurosciences 8. Numerical and Computational Physics, Simulation 9. PHYSICS 10. Simulation and Modeling
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TitleNetwork-Oriented Modeling : Addressing Complexity of Cognitive, Affective and Social Interactions
Author(s)Treur, Jan
PublicationCham, 1. Imprint: Springer 2. Springer International Publishing, 2016.
DescriptionXVI, 499 p. 134 illus., 52 illus. in color : online resource
Abstract NoteThis book presents a new approach that can be applied to complex, integrated individual and social human processes. It provides an alternative means of addressing complexity, better suited for its purpose than and effectively complementing traditional strategies involving isolation and separation assumptions. Network-oriented modeling allows high-level cognitive, affective and social models in the form of (cyclic) graphs to be constructed, which can be automatically transformed into executable simulation models. The modeling format used makes it easy to take into account theories and findings about complex cognitive and social processes, which often involve dynamics based on interrelating cycles. Accordingly, it makes it possible to address complex phenomena such as the integration of emotions within cognitive processes of all kinds, of internal simulations of the mental processes of others, and of social phenomena such as shared understandings and collective actions. A variety of sample models ??? including those for ownership of actions, fear and dreaming, the integration of emotions in joint decision-making based on empathic understanding, and evolving social networks ??? illustrate the potential of the approach. Dedicated software is available to support building models in a conceptual or graphical manner, transforming them into an executable format and performing simulation experiments. The majority of the material presented has been used and positively evaluated by undergraduate and graduate students and researchers in the cognitive, social and AI domains. Given its detailed coverage, the book is ideally suited as an introduction for graduate and undergraduate students in many different multidisciplinary fields involving cognitive, affective, social, biological, and neuroscience domains
ISBN,Price9783319452135
Keyword(s)1. APPLICATION SOFTWARE 2. Applications of Graph Theory and Complex Networks 3. COMPLEXITY 4. COMPUTATIONAL COMPLEXITY 5. Computer Appl. in Social and Behavioral Sciences 6. Data-driven Science, Modeling and Theory Building 7. EBOOK 8. EBOOK - SPRINGER 9. ECONOPHYSICS 10. Mathematical Models of Cognitive Processes and Neural Networks 11. Neural networks (Computer science)?? 12. PHYSICS 13. Sociophysics
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TitleEmergent Complexity from Nonlinearity, in Physics, Engineering and the Life Sciences : Proceedings of the XXIII International Conference on Nonlinear Dynamics of Electronic Systems, Como, Italy, 7-11 September 2015
Author(s)Mantica, Giorgio;Stoop, Ruedi;Stramaglia, Sebastiano
PublicationCham, 1. Imprint: Springer 2. Springer International Publishing, 2017.
DescriptionXXV, 222 p. 113 illus., 86 illus. in color : online resource
Abstract NoteThis book collects contributions to the XXIII international conference ???Nonlinear dynamics of electronic systems???. Topics range from non-linearity in electronic circuits to synchronisation effects in complex networks to biological systems, neural dynamics and the complex organisation of the brain. Resting on a solid mathematical basis, these investigations address highly interdisciplinary problems in physics, engineering, biology and biochemistry
ISBN,Price9783319478104
Keyword(s)1. BIOCHEMISTRY 2. Biochemistry, general 3. COMPLEX SYSTEMS 4. DYNAMICAL SYSTEMS 5. EBOOK 6. EBOOK - SPRINGER 7. ELECTRONICS 8. Electronics and Microelectronics, Instrumentation 9. Mathematical Models of Cognitive Processes and Neural Networks 10. MICROELECTRONICS 11. Neural networks (Computer science)?? 12. STATISTICAL PHYSICS 13. Statistical Physics and Dynamical Systems 14. Systems biology
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TitleControlling Synchronization Patterns in Complex Networks
Author(s)Lehnert, Judith
PublicationCham, 1. Imprint: Springer 2. Springer International Publishing, 2016.
DescriptionXV, 203 p : online resource
Abstract NoteThis research aims to achieve a fundamental understanding of synchronization and its interplay with the topology of complex networks. Synchronization is a ubiquitous phenomenon observed in different contexts in physics, chemistry, biology, medicine and engineering. Most prominently, synchronization takes place in the brain, where it is associated with several cognitive capacities but is - in abundance - a characteristic of neurological diseases. Besides zero-lag synchrony, group and cluster states are considered, enabling a description and study of complex synchronization patterns within the presented theory. Adaptive control methods are developed, which allow the control of synchronization in scenarios where parameters drift or are unknown. These methods are, therefore, of particular interest for experimental setups or technological applications. The theoretical framework is demonstrated on generic models, coupled chemical oscillators and several detailed examples of neural networks
ISBN,Price9783319251158
Keyword(s)1. Applications of Graph Theory and Complex Networks 2. DYNAMICAL SYSTEMS 3. DYNAMICS 4. EBOOK 5. EBOOK - SPRINGER 6. Mathematical Models of Cognitive Processes and Neural Networks 7. Neural networks (Computer science)?? 8. PHYSICAL CHEMISTRY 9. PHYSICS 10. SYSTEM THEORY 11. Systems Theory, Control 12. VIBRATION 13. Vibration, Dynamical Systems, Control
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I09860     On Shelf    

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TitleStatistical Field Theory for Neural Networks
Author(s)Helias, Moritz;Dahmen, David
PublicationCham, 1. Imprint: Springer 2. Springer International Publishing, 2020.
DescriptionXVII, 203 p. 127 illus., 5 illus. in color : online resource
Abstract NoteThis book presents a self-contained introduction to techniques from field theory applied to stochastic and collective dynamics in neuronal networks. These powerful analytical techniques, which are well established in other fields of physics, are the basis of current developments and offer solutions to pressing open problems in theoretical neuroscience and also machine learning. They enable a systematic and quantitative understanding of the dynamics in recurrent and stochastic neuronal networks. This book is intended for physicists, mathematicians, and computer scientists and it is designed for self-study by researchers who want to enter the field or as the main text for a one semester course at advanced undergraduate or graduate level. The theoretical concepts presented in this book are systematically developed from the very beginning, which only requires basic knowledge of analysis and linear algebra
ISBN,Price9783030464448
Keyword(s)1. EBOOK 2. EBOOK - SPRINGER 3. MACHINE LEARNING 4. Mathematical Models of Cognitive Processes and Neural Networks 5. MATHEMATICAL STATISTICS 6. Neural networks (Computer science)?? 7. Neurosciences 8. Probability and Statistics in Computer Science 9. STATISTICAL PHYSICS 10. Statistical Physics and Dynamical Systems
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TitleSynergetics
Author(s)Hutt, Axel;Haken, Hermann
PublicationNew York, NY, 1. Imprint: Springer 2. Springer US, 2020.
Description223 illus., 95 illus. in color. eReference : online resource
Abstract NoteThis volume of the ???Encyclopedia of Complexity and Systems Science, Second Edition??? (ECSS), introduces the fundamental physical and mathematical concepts underlying the theory of complex physical, chemical, and biological systems. Numerous applications illustrate how these concepts explain observed phenomena in our daily lives, which range from spatio-temporal patterns in fluids from atmospheric turbulence in hurricanes and tornadoes to feedback dynamics of laser intensity to structures in cities and rhythms in the brain. The spontaneous formation of well-organized structures out of microscopic system components and their interactions is one of the most fascinating and challenging phenomena for scientists to understand. Biological systems may also exhibit organized structures emanating from interactions of cells and their networks. For instance, underlying structures in the brain emerge as certain mental states, the ability to coordinate movement, or pathologies such as tremor or epileptic seizures. When we try to explain or understand these extremely complex biological phenomena, it is natural to ask whether analogous processes of self-organization may be found in much simpler systems of the inanimate world. In recent decades, it has become increasingly evident that there exist numerous examples in physical and chemical systems in which well-organized spatio-temporal structures arise out of disordered states. As in living organisms, the functioning of these systems can be maintained only by a flux of energy (and matter) through them. Synergetics combines elements from physics and mathematics to explain how a diversity of systems obey the same basic principles. All chapters in this volume have been thoroughly revised and updated from the first edition of ECSS. The second edition also includes new or expanded coverage of such topics as chaotic dynamics in laser systems and neurons, novel insights into the relation of classical chaos and quantum dynamics, and how noise in the brain tunes observed neural activity and controls animal and human behavior.
ISBN,Price9781071604212
Keyword(s)1. Applications of Nonlinear Dynamics and Chaos Theory 2. COMPLEX SYSTEMS 3. COMPLEXITY 4. COMPUTATIONAL COMPLEXITY 5. EBOOK 6. EBOOK - SPRINGER 7. Mathematical Models of Cognitive Processes and Neural Networks 8. Neural networks (Computer science)?? 9. STATISTICAL PHYSICS 10. Statistical Physics and Dynamical Systems 11. SYSTEM THEORY 12. Systems biology
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TitleNeural-Network Simulation of Strongly Correlated Quantum Systems
Author(s)Czischek, Stefanie
PublicationCham, 1. Imprint: Springer 2. Springer International Publishing, 2020.
DescriptionXV, 205 p. 51 illus., 48 illus. in color : online resource
Abstract NoteQuantum 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
ISBN,Price9783030527150
Keyword(s)1. CONDENSED MATTER 2. CONDENSED MATTER PHYSICS 3. EBOOK 4. EBOOK - SPRINGER 5. MACHINE LEARNING 6. Mathematical Models of Cognitive Processes and Neural Networks 7. Neural networks (Computer science)?? 8. QUANTUM PHYSICS
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TitlePhysics of the Human Mind
Author(s)Lubashevsky, Ihor
PublicationCham, 1. Imprint: Springer 2. Springer International Publishing, 2017.
DescriptionXIV, 380 p. 83 illus., 41 illus. in color : online resource
Abstract NoteThis book tackles the challenging question which mathematical formalisms and possibly new physical notions should be developed for quantitatively describing human cognition and behavior, in addition to the ones already developed in the physical and cognitive sciences. Indeed, physics is widely used in modeling social systems, where, in particular, new branches of science such as sociophysics and econophysics have arisen. However, many if not most characteristic features of humans like willingness, emotions, memory, future prediction, and moral norms, to name but a few, are not yet properly reflected in the paradigms of physical thought and theory. The choice of a relevant formalism for modeling mental phenomena requires the comprehension of the general philosophical questions related to the mind-body problem. Plausible answers to these questions are investigated and reviewed, notions and concepts to be used or to be taken into account are developed and some challenging questions are posed as open problems. This text addresses theoretical physicists and neuroscientists modeling any systems and processes where human factors play a crucial role, philosophers interested in applying philosophical concepts to the construction of mathematical models, and the mathematically oriented psychologists and sociologists, whose research is fundamentally related to modeling mental processes
ISBN,Price9783319517063
Keyword(s)1. Cognitive psychology 2. Data-driven Science, Modeling and Theory Building 3. EBOOK 4. EBOOK - SPRINGER 5. ECONOPHYSICS 6. Mathematical Methods in Physics 7. Mathematical Models of Cognitive Processes and Neural Networks 8. Neural networks (Computer science)?? 9. PHILOSOPHY OF MIND 10. PHYSICS 11. Sociophysics
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