Neural Network Dynamics


Book Description

Neural Network Dynamics is the latest volume in the Perspectives in Neural Computing series. It contains papers presented at the 1991 Workshop on Complex Dynamics in Neural Networks, held at IIASS in Vietri, Italy. The workshop encompassed a wide range of topics in which neural networks play a fundamental role, and aimed to bridge the gap between neural computation and computational neuroscience. The papers - which have been updated where necessary to include new results - are divided into four sections, covering the foundations of neural network dynamics, oscillatory neural networks, as well as scientific and biological applications of neural networks. Among the topics discussed are: A general analysis of neural network activity; Descriptions of various network architectures and nodes; Correlated neuronal firing; A theoretical framework for analyzing the behaviour of real and simulated neuronal networks; The structural properties of proteins; Nuclear phenomenology; Resonance searches in high energy physics; The investigation of information storage; Visual cortical architecture; Visual processing. Neural Network Dynamics is the first volume to cover neural networks and computational neuroscience in such detail. Although it is primarily aimed at researchers and postgraduate students in the above disciplines, it will also be of interest to researchers in electrical engineering, medicine, psychology and philosophy.







Dynamic Interactions in Neural Networks: Models and Data


Book Description

The study of neural networks is enjoying a great renaissance, both in computational neuroscience, the development of information processing models of living brains, and in neural computing, the use of neurally inspired concepts in the construction of "intelligent" machines. Thus the title of this volume has two interpretations: It presents models and data on the dynamic interactions occurring in the brain, and it exhibits the dynamic interactions between research in computational neuroscience and in neural computing, as scientists seek to find common principles to guide the understanding of the living brain and the design of artificial neural networks. This collection of contributions presents the current state of research, future trends and open problems in an exciting field of today's science.




Dynamic Interactions in Neural Networks: Models and Data


Book Description

This is an exciting time. The study of neural networks is enjoying a great renaissance, both in computational neuroscience - the development of information processing models of living brains - and in neural computing - the use of neurally inspired concepts in the construction of "intelligent" machines. Thus the title of this volume, Dynamic Interactions in Neural Networks: Models and Data can be given two interpretations. We present models and data on the dynamic interactions occurring in the brain, and we also exhibit the dynamic interactions between research in computational neuroscience and in neural computing, as scientists seek to find common principles that may guide us in the understanding of our own brains and in the design of artificial neural networks. In fact, the book title has yet a third interpretation. It is based on the U. S. -Japan Seminar on "Competition and Cooperation in Neural Nets" which we organized at the University of Southern California, Los Angeles, May 18-22, 1987, and is thus the record of interaction of scientists on both sides of the Pacific in advancing the frontiers of this dynamic, re-born field. The book focuses on three major aspects of neural network function: learning, perception, and action. More specifically, the chapters are grouped under three headings: "Development and Learning in Adaptive Networks," "Visual Function", and "Motor Control and the Cerebellum.




Neural Network Dynamics


Book Description




Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis


Book Description

The present book is devoted to problems of adaptation of artificial neural networks to robust fault diagnosis schemes. It presents neural networks-based modelling and estimation techniques used for designing robust fault diagnosis schemes for non-linear dynamic systems. A part of the book focuses on fundamental issues such as architectures of dynamic neural networks, methods for designing of neural networks and fault diagnosis schemes as well as the importance of robustness. The book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. The book is also devoted to advanced schemes of description of neural model uncertainty. In particular, the methods of computation of neural networks uncertainty with robust parameter estimation are presented. Moreover, a novel approach for system identification with the state-space GMDH neural network is delivered. All the concepts described in this book are illustrated by both simple academic illustrative examples and practical applications.




Introduction to Neural Dynamics and Signal Transmission Delay


Book Description

In the design of a neural network, either for biological modeling, cognitive simulation, numerical computation or engineering applications, it is important to investigate the network's computational performance which is usually described by the long-term behaviors, called dynamics, of the model equations. The purpose of this book is to give an introduction to the mathematical modeling and analysis of networks of neurons from the viewpoint of dynamical systems.




Hierarchy and dynamics in neural networks


Book Description

Hierarchy is a central feature in the organisation of complex biological systems and particularly the structure and function of neural networks. While other aspects of brain connectivity such as regionalisation, modularity or motif composition have been discussed elsewhere, no detailed analysis has been presented so far on the role of hierarchy and its connection to brain dynamics. Recent discussions among many of our colleagues have shown an increasing interest in hierarchy (of spatial, temporal and dynamic features), and this is an emerging key question in neuroscience as well as generally in the field of network science, due to its links with concepts of control, efficiency and development across scales (e.g. Hilgetag et al. Science, 1996; Ravasz et al. Science, 2002; Bassett et al. PNAS, 2006; Mueller-Linow et al. PLoS Comp. Biol., in press). The proposed Research Topic will address recent findings from a theoretical as well as experimental perspective including contributions under the following four headings: 1) Topology: Detecting and characterizing network hierarchy; 2) Experiments: Neural dynamics across hierarchical scales; 3) Dynamics: Activity spread, oscillations, and synchronization in hierarchical networks; 4) Dynamics: Stable functioning and information processing in hierarchical networks.