Featuring an international team of authors, Neural Network Perspectives on Cognition and Adaptive Robotics presents several approaches to the modeling of human cognition and language using neural computing techniques. It also describes how adaptive robotic systems can be produced using neural network architectures. Covering a wide range of mainstream area and trends, each chapter provides the latest information from a different perspective.
Table of Contents
Part 1 Representation: Challenges for neural computing. Representing structure and structured representations in connectionist networks. Chaos, dynamics and computational power in biologically plausible neural networks. Information-theoretic approaches to neural network learning. Part 2 Cognitive modelling: Exploring different approaches towards everyday commonsense reasoning. Natural language processing with subsymbolic neural networks. The relational mind. Neuroconsciousness: a fundamental postulate. Part 3 Adaptive robotics: The neural mind and the robot. Teaching a robot to see how it moves. Designing a nervous system for an adaptive mobile robot.