<p>Conventional digital computation methods have run into a seยญ rious speed bottleneck due to their serial nature. To overcome this problem, a new computation model, called Neural Networks, has been proposed, which is based on some aspects of neurobiology and adapted to integrated circuits. The incr
Dynamic Interactions in Neural Networks: Models and Data
โ Scribed by Michael A. Arbib (auth.), Michael A. Arbib, Shun-ichi Amari (eds.)
- Publisher
- Springer-Verlag New York
- Year
- 1989
- Tongue
- English
- Leaves
- 274
- Series
- Research Notes in Neural Computing 1
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Synopsis
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.
โฆ Table of Contents
Front Matter....Pages i-viii
Dynamic Interaction in Neural Networks: An Introductory Perspective....Pages 1-11
Front Matter....Pages 13-13
Dynamical Stability of Formation of Cortical Maps....Pages 15-34
Visual Plasticity in the Auditory Pathway: Visual Inputs Induced into Auditory Thalamus and Cortex Illustrate Principles of Adaptive Organization in Sensory Systems....Pages 35-51
The Hippocampus and the Control of Information Storage in the Brain....Pages 53-72
A Memory with Cognitive Ability....Pages 73-86
Feature Handling in Learning Algorithms....Pages 87-105
Self-Organizing Neural Network with the Mechanism of Feedback Information Processing....Pages 107-119
Front Matter....Pages 121-121
Interacting Subsystems for Depth Perception and Detour Behavior....Pages 123-151
Role of Basal Ganglia in Initiation of Voluntary Movements....Pages 153-167
Neural Mechanisms of Attention in Extrastriate Cortex of Monkeys....Pages 169-182
Neuronal Representation of Pictorial Working Memory in the Primate Temporal Cortex....Pages 183-191
Front Matter....Pages 193-193
Hierarchical Learning of Voluntary Movement by Cerebellum and Sensory Association Cortex....Pages 195-214
A Model for Oblique Saccade Generation and Adaptation....Pages 215-226
Cerebellar Mechanisms in the Adaptation of Vestibuloocular Reflex....Pages 227-237
A Kalman Filter Theory of the Cerebellum....Pages 239-259
Conditioning and the Cerebellum....Pages 261-277
Back Matter....Pages 279-280
โฆ Subjects
Artificial Intelligence (incl. Robotics); Computer Systems Organization and Communication Networks
๐ SIMILAR VOLUMES
Neural Network Modeling and Identification of Dynamical Systems presents a new approach on how to obtain the adaptive neural network models for complex systems that are typically found in real-world applications. The book introduces the theoretical knowledge available for the modeled system into the
Research in neural networks has escalated dramatically in the last decade, acquiring along the way terms and concepts, such as learning, memory, perception, recognition, which are the basis of neuropsychology. Nevertheless, for many, neural modelling remains controversial in its purported ability to