This text reviews the major theories of the mechanistic organization of cognitive functions learning, perception, categorization, motor control and decision making. An excellent introduction to neural networks, it integrates insights from neurobiology, psychology, computer science, and mathematics, making it particularly suitable for those with backgrounds in any of these fields. The history of neural networks and some of their major organizing principles are outlined here, as is the development of cognitive functions from the simple to the complex. Computer simulation exercises are also included, some of which implement known models, while others ask students to design networks.
|Publisher:||Taylor & Francis|
|Edition description:||Older Edition|
|Product dimensions:||6.00(w) x 9.00(h) x (d)|
Table of Contents
Contents: Preface. Brain and Machine; The Same Principles? Historical Outline. Associative Learning and Synaptic Plasticity. Competition, Lateral Inhibition, and Short-term Memory. Conditioning, Attention, and Reinforcement. Coding and Categorization. Optimization, Control, Decision, and Knowledge Representation. A Few Recent Technical Advances. Appendices: Basic Facts of Neurobiology. Difference and Differential Equations in Neural Networks.