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, 7-11 September 2015
 
 
Springer (Verlag)
  • erschienen am 7. März 2017
  • |
  • XXV, 222 Seiten
 
E-Book | PDF mit Wasserzeichen-DRM | Systemvoraussetzungen
978-3-319-47810-4 (ISBN)
 
This 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.
1st ed. 2017
  • Englisch
  • Cham
  • |
  • Schweiz
Springer International Publishing
  • 27
  • |
  • 86 farbige Abbildungen, 27 s/w Abbildungen
  • |
  • 27 schwarz-weiße und 86 farbige Abbildungen, Bibliographie
  • 11,61 MB
978-3-319-47810-4 (9783319478104)
10.1007/978-3-319-47810-4
weitere Ausgaben werden ermittelt
  • Intro
  • Preface
  • Acknowledgements
  • Contents
  • Contributors
  • Introduction
  • List of Talks from NDES2015
  • Part I Classical and Quantum Dynamics
  • Nonlinear Systems Characterization Using Phase Space Density
  • 1 Introduction
  • 2 Density in Phase Space
  • 2.1 Determining Partitions
  • 2.2 Information Criteria
  • 3 Identifying Sprott Attractors
  • 3.1 Comparing Densities
  • 3.2 Distinguishing Attractors
  • 3.3 Noise Considerations
  • 4 Conclusions
  • References
  • Complex Bifurcation of Arnol'd Tongues Generated in Three-Coupled Delayed Logistic Maps
  • 1 Summary
  • 2 Conclusion
  • References
  • Phase Response Properties of Rulkov Model Neurons
  • 1 Introduction
  • 2 Methods
  • 2.1 Neuron Models
  • 2.2 Phase Response Curves
  • 3 Results and Discussion
  • 3.1 Original Rulkov Map: Type-2 PRC
  • 3.2 The Hyperpolarizing Rulkov Model: Type-1 PRC
  • 3.3 Periodic Forcing of Rulkov Maps
  • 4 Conclusions
  • References
  • Treating Many-Body Quantum Systems by Means of Classical Mechanics
  • 1 Introduction
  • 2 Bose--Hubbard Model
  • 3 Classical Bose--Hubbard Model
  • 4 Bose--Hubbard Dimer
  • 5 Bose--Hubbard Trimer
  • 6 Many-Site Bose--Hubbard Model
  • 7 Statistical Analysis of the Energy Spectrum
  • 8 Conclusion
  • References
  • Mean-Field Transport of a Bose-Einstein Condensate
  • 1 Introduction
  • 2 Our Transport Setup
  • 3 Numerical Results
  • 3.1 Case (a): Directed Free Expansion
  • 3.2 Case (b): Expansion into a Wannier-Stark Lattice
  • 4 Conclusions and Perspectives
  • References
  • Part II Chaotic Oscillations
  • Continuous and Differentiable Approximation of a TaO Memristor Model for Robust Numerical Simulations
  • 1 Introduction
  • 2 Model
  • 3 Continuous and Differentiable DAE Set
  • 4 Conclusions
  • References
  • Ultrawideband Microwave 3--7GHz Chaotic Oscillator Implemented as SiGe Integrated Circuit
  • 1 Introduction
  • 2 Oscillator Model
  • 3 Simulation
  • 4 Experiment
  • 5 Conclusions
  • References
  • Cryptanalysis of a Random Number Generator Based on a Chaotic Oscillator
  • 1 Introduction
  • 2 Target System
  • 3 Attack System
  • 4 Numerical Results
  • 5 Conclusions
  • References
  • Part III Networks
  • Experiments on Clustering and Synchronous Patterns in a Configurable Network of Chaotic Oscillators
  • 1 Introduction
  • 2 The Experimental Setup
  • 2.1 Experimental Results
  • References
  • Complex Structures and Behavior from Elementary Adaptive Network Automata
  • 1 Introduction
  • 2 A Ring that Rules Them All
  • 3 Emerging Topologies
  • 4 Classes of Emergent Behavior
  • 5 Exemplary Discussion of a Class with Critical Behavior
  • 5.1 Rule 209
  • 5.2 Node Dynamics of Rule 209
  • 5.3 Two Critical Points in Dependence of pact
  • 5.4 Network Topology at the Critical Point pcrit+act
  • 5.5 Critical Behavior at pcrit+act as the Interplay Between Topology and Node Dynamics
  • 6 Conclusion
  • References
  • Hebbian Learning Clustering with Rulkov Neurons
  • 1 Introduction
  • 2 The Rulkov Neuron Hebbian Learning Clustering Algorithm (RHLC)
  • 3 Stability and Computational Complexity of the RHLC
  • 3.1 Stability of the RHLC Results
  • 3.2 Computational Complexity of the RHLC
  • 4 Application to Data
  • 5 Conclusions
  • References
  • Part IV Biological Dynamics
  • Network Physiology: From Neural Plasticity to Organ Network Interactions
  • 1 Introduction
  • 2 Time-Delay Stability Method: A New Approach to Physiologic Network Interactions
  • 3 Brain-Brain Networks: New Aspects of Neural Plasticity in Response to Change in Physiologic State
  • 3.1 Hierarchical Reorganization of Brain Wave Interactions
  • 3.2 Specific Roles of Different Brain Areas in Brain Wave Network Interactions
  • 3.3 Network Interactions of the Same Brain Rhythm Across Brain Areas
  • 4 Brain-Organ Networks: New Signatures of Neuroautonomic Control
  • 5 Organ-Organ Networks: New Maps of Dynamical Organ Interactions
  • 6 Summary
  • References
  • Temporal Excitation Patterns on the Cerebral Cortex as a Result of Migraine Modeling
  • 1 Introduction
  • 2 Modeling and Numerical Simulation of CSD on Real Geometries
  • 2.1 A Distributed Computational Model for CSD Propagation
  • 2.2 Numerical Approximation
  • 2.3 Computational Domain
  • 2.4 Simulation Protocol
  • 3 Temporal Activity
  • 4 Results
  • 5 Conclusions
  • References
  • Persistent Memories in Transient Networks
  • 1 Introduction
  • 2 Model
  • 3 Results
  • 4 Discussion
  • 5 Methods
  • References
  • A Multiplex Network Model to Characterize Brain Atrophy in Structural MRI
  • 1 Introduction
  • 2 Materials
  • 3 Multiplex Model
  • 4 Feature Selection and Classification
  • 5 Results
  • 5.1 Binary Classification
  • 5.2 Three Class Evaluation
  • 6 Conclusions and Discussion
  • References
  • Topological Complex Networks Properties for Gene Community Detection Strategy: DRD2 Case Study
  • 1 Introduction
  • 2 The DRD2 Community Detection
  • 3 Results
  • 3.1 Methodological Assessment
  • 3.2 Consensus Analysis
  • 3.3 A Membership Degree Measurement
  • 4 Conclusion
  • References
  • Power Laws in Neuronal Culture Activity from Limited Availability of a Shared Resource
  • 1 Introduction
  • 2 Power Law Statistics in Development Characterization
  • 3 Structure Within Bursts of Mature Cultures
  • 4 Exponential Resource Decay Model for Power Law Activity Statistics
  • 5 Conclusions and Outlook
  • 6 Methods
  • 6.1 Neuronal Culture Preparations
  • 6.2 Neural Recordings
  • 6.3 Fitting Using Discrete Maximum-Likelihood
  • References
  • Index
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