Trends in Neural Computation [Paperback]

Trends in Neural Computation includes twenty chapters contributed by leading experts or formed by extending well-selected papers presented in the 2005 International Conference on Natural Computation. The book reviews the latest progress in a range of different areas of neural computation, including theoretical neural computation, biologically plausible neural modeling, computational cognitive science, artificial neural networks  architectures and learning algorithms and their applications in real-world problems.

Nowadays neural computation has become an interdisciplinary field in its own right; researches have been conducted ranging from diverse disciplines, e.g. computational neuroscience and cognitive science, mathematics, physics, computer science, and other engineering disciplines. From different perspectives, neural computation provides an alternative methodology to understand brain functions and cognitive process and to solve challenging real-world problems effectively.

Trends in Neural Computation includes twenty chapters either contributed from leading experts or formed by extending well selected papers presented in the 2005 International Conference on Natural Computation. The edited book aims to reflect the latest progresses made in different areas of neural computation, including theoretical neural computation, biologically plausible neural modeling, computational cognitive science, artificial neural networks  architectures and learning algorithms and their applications in real-world problems.

Hyperbolic Function Networks for Pattern Classification.- Variable Selection for the Linear Support Vector Machine.- Selecting Data for Fast Support Vector Machines Training.- Universal Approach to Study Delayed Dynamical Systems.- A Hippocampus-Neocortex Model for Chaotic Association.- Latent Attractors: A General Paradigm for Context-Dependent Neural Computation.- Learning Mechanisms in Networks of Spiking NelC†