| Description |
1 online resource (xvii, 222 pages) : illustrations |
| Series |
Springer series in cognitive and neural systems ; v. 3 |
|
Springer series in cognitive and neural systems ; v. 3.
|
| Contents |
The Relevance of the Time Domain to Neural Network Models; Foreword; References; Acknowledgements; Contents; Contributors; Chapter 1: Introduction; 1.1 Theoretical Background; 1.2 Engineering Development and Applications; 1.3 Biological Experimentation; References; Chapter 2: Adaptation and Contraction Theory for the Synchronization of Complex Neural Networks; 2.1 Introduction; 2.2 Preliminaries; 2.3 Adaptive Synchronization of Complex Networks; 2.3.1 Evolving the Network Structure; 2.4 Inducing Synchronization via Design; 2.4.1 An Algorithm for Proving Contraction; 2.4.2 Remarks |
|
2.5 Adaptive Synchronization of FitzHugh-Nagumo Neurons2.5.1 Properties of the Node Dynamics; 2.5.2 Numerical Validation; 2.5.2.1 Evolving the Network Topology; 2.6 Using Contraction; 2.6.1 Node Dynamics; 2.6.2 Synchronization Results; 2.6.2.1 Diffusive Coupling; 2.6.2.2 Local Field Potentials; 2.6.2.3 Excitatory-Only Coupling; 2.7 Materials & Methods; 2.8 Conclusions; References; Chapter 3: Temporal Coding Is Not Only About Cooperation-It Is Also About Competition; 3.1 Introduction; 3.2 Basic Notations; 3.3 Patterned Coherence Models (PCMs); 3.4 Earlier Approaches Based on Phase Models |
|
3.5 Why Temporal Coding? The Argument for Patterned Coherence Models3.6 Oscillatory Neural Network Model with Synchronization and Acceleration; 3.6.1 Simple Ansatz for Phase and Amplitude Dynamics; 3.6.2 Synchronization and Acceleration; 3.6.3 Competition for Coherence; 3.7 Examples; 3.7.1 Network Architecture and Parameter Choices; 3.7.2 Examples 1 and 2: Effect of Including Acceleration; 3.7.3 Example 3: Relevance of Classical Activity; 3.7.4 Example 4: Relevance of Pattern Weights; 3.7.5 Example 5: Assemblies Through Binding of Patterns; 3.8 Outlook |
|
3.8.1 Learning and Inclusion of Inhibitory Couplings3.8.2 Hierarchical Processing; 3.8.3 Mathematical Aspects; 3.8.4 Biological Relevance and Time Delays; 3.9 Summary; References; Chapter 4: Using Non-oscillatory Dynamics to Disambiguate Pattern Mixtures; 4.1 Introduction; 4.1.1 Evidence for Top-Down Feedback Regulation in the Brain; 4.1.2 Dif?culties with Large Scale Top-Down Regulatory Feedback; 4.2 Regulatory Feedback Networks; Modularity and Scalability; 4.2.1 Description of Function; 4.2.2 General Limits, Stability, Steady State, & Simulations; 4.2.3 Simple Two Node Demonstration |
|
4.3 The Binding Problem Demonstration4.4 Superposition Catastrophe Demonstration; 4.4.1 Results; 4.5 Discussion; 4.5.1 Plasticity Due to Dynamics in Regulatory Feedback Networks; 4.5.1.1 Plasticity and Neuroscience Implications; 4.5.1.2 Non-binary Connection Weights; 4.5.2 Measuring Dif?culty Processing; 4.5.2.1 Notes on Cycles; 4.5.3 Combined Oscillatory and Non-oscillatory Dynamics; 4.5.4 Summary; References; Chapter 5: Functional Constraints on Network Topology via Generalized Sparse Representations; 5.1 Introduction; 5.2 Background; 5.3 Network Dynamics and Learning |
| Summary |
A significant amount of effort in neural modeling is directed towards understanding the representation of external objects in the brain. There is also a rapidly growing interest in modeling the intrinsically-generated activity in the brain, as represented by the default mode network hypothesis, and the emergent behavior that gives rise to critical phenomena such as neural avalanches. Time plays a critical role in these intended modeling domains, from the exquisite discriminations in the mammalian auditory system to the precise timing involved in high-end activities such as competitive sports or professional music performance. The growth in experimental high-throughput neuroscience techniques has allowed the multi-scale acquisition of neural signals, from individual electrode recordings to whole-brain functional magnetic resonance imaging activity, including the ability to manipulate neural signals with optogenetic approaches. This has created a deluge of experimental data, spanning multiple spatial and temporal scales, and posing the enormous challenge of its interpretation in terms of a predictive theory of brain function. In addition, there has been a massive growth in availability of computational power through parallel computing. The Relevance of the Time Domain to Neural Network Models aims to develop a unified view of how the time domain can be effectively employed in neural network models. The book proposes that conceptual models of neural interaction are required in order to understand the data being collected. Simultaneously, these proposed models can be used to form hypotheses of neural interaction and system behavior that can be neuroscientifically tested. The book concentrates on a crucial aspect of brain modeling: the nature and functional relevance of temporal interactions in neural systems. This book will appeal to a wide audience consisting of computer scientists and electrical engineers interested in brain-like computational mechanisms, computer architects exploring the development of high-performance computing systems to support these computations, neuroscientists probing the neural code and signaling mechanisms, mathematicians and physicists interested in modeling complex biological phenomena, and graduate students in all these disciplines who are searching for challenging research questions |
| Bibliography |
Includes bibliographical references and index |
| Notes |
English |
| Subject |
Neural networks (Computer science)
|
|
Brain.
|
|
Time-domain analysis.
|
|
Neural networks (Neurobiology)
|
|
Computer systems.
|
|
Neural Networks, Computer
|
|
Computer Systems
|
|
Time Factors
|
|
Brain
|
|
brains.
|
|
SOCIAL SCIENCE -- Anthropology -- Physical.
|
|
Computer systems
|
|
Brain
|
|
Neural networks (Computer science)
|
|
Neural networks (Neurobiology)
|
|
Time-domain analysis
|
| Form |
Electronic book
|
| Author |
Ravishankar Rao, A.
|
|
Cecchi, Guillermo A.
|
| ISBN |
9781461407249 |
|
1461407249 |
|
