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Author  Title  Accn#  Year  Item Type  Claims 
11 
Mantica, Giorgio 
Emergent Complexity from Nonlinearity, in Physics, Engineering and the Life Sciences 
I09877 
2017 
eBook 

12 
Lehnert, Judith 
Controlling Synchronization Patterns in Complex Networks 
I09860 
2016 
eBook 

13 
Helias, Moritz 
Statistical Field Theory for Neural Networks 
I09583 
2020 
eBook 

14 
Hutt, Axel 
Synergetics 
I09580 
2020 
eBook 

15 
Czischek, Stefanie 
NeuralNetwork Simulation of Strongly Correlated Quantum Systems 
I09120 
2020 
eBook 

16 
Lubashevsky, Ihor 
Physics of the Human Mind 
I08732 
2017 
eBook 


11.


Title  Emergent Complexity from Nonlinearity, in Physics, Engineering and the Life Sciences : Proceedings of the XXIII International Conference on Nonlinear Dynamics of Electronic Systems, Como, Italy, 711 September 2015 
Author(s)  Mantica, Giorgio;Stoop, Ruedi;Stramaglia, Sebastiano 
Publication  Cham, Springer International Publishing, 2017. 
Description  XXV, 222 p. 113 illus., 86 illus. in color : online resource 
Abstract Note  This book collects contributions to the XXIII international conference ???Nonlinear dynamics of electronic systems???. Topics range from nonlinearity 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,Price  9783319478104 
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

Item Type  eBook 
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Call#  Status  Issued To  Return Due On  Physical Location 
I09877 


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12.


Title  Controlling Synchronization Patterns in Complex Networks 
Author(s)  Lehnert, Judith 
Publication  Cham, Springer International Publishing, 2016. 
Description  XV, 203 p : online resource 
Abstract Note  This 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 zerolag 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,Price  9783319251158 
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

Item Type  eBook 
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Call#  Status  Issued To  Return Due On  Physical Location 
I09860 


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13.


Title  Statistical Field Theory for Neural Networks 
Author(s)  Helias, Moritz;Dahmen, David 
Publication  Cham, Springer International Publishing, 2020. 
Description  XVII, 203 p. 127 illus., 5 illus. in color : online resource 
Abstract Note  This book presents a selfcontained 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 selfstudy 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,Price  9783030464448 
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

Item Type  eBook 
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Call#  Status  Issued To  Return Due On  Physical Location 
I09583 


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14.


Title  Synergetics 
Author(s)  Hutt, Axel;Haken, Hermann 
Publication  New York, NY, Springer US, 2020. 
Description  223 illus., 95 illus. in color. eReference : online resource 
Abstract Note  This 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 spatiotemporal 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 wellorganized 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 selforganization 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 wellorganized spatiotemporal 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,Price  9781071604212 
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

Item Type  eBook 
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Call#  Status  Issued To  Return Due On  Physical Location 
I09580 


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15.


Title  NeuralNetwork Simulation of Strongly Correlated Quantum Systems 
Author(s)  Czischek, Stefanie 
Publication  Cham, Springer International Publishing, 2020. 
Description  XV, 205 p. 51 illus., 48 illus. in color : online resource 
Abstract Note  Quantum 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 highperformance 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 nonlocal 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 manybody 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,Price  9783030527150 
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

Item Type  eBook 
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Call#  Status  Issued To  Return Due On  Physical Location 
I09120 


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16.
 
Title  Physics of the Human Mind 
Author(s)  Lubashevsky, Ihor 
Publication  Cham, Springer International Publishing, 2017. 
Description  XIV, 380 p. 83 illus., 41 illus. in color : online resource 
Abstract Note  This 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 mindbody 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,Price  9783319517063 
Keyword(s)  1. Cognitive psychology
2. Datadriven 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

Item Type  eBook 
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Accession#  
Call#  Status  Issued To  Return Due On  Physical Location 
I08732 


On Shelf 



 