Trends in neural computation

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.

Kar-Ann Toh and Quoc-Long Tran and Dipti SrinivasanJi Zhu and Hui ZouJigang Wang and Predrag Neskovic and Leon N. CooperTianping ChenTakashi Kuremoto, Tsuyoshi Eto and Kunikazu Kobayashi and Masanao ObayashiSimona Doboli and Ali A. MinaiQingXiang Wu and Martin McGinnity and Liam Maguire and Bredan Glackin and Ammar BelatrecheJoseph Herbert and JingTao YaoYong LiuPing Sun and Xin YaoChunkai Zhang and Hong HuBao-Liang Lu and Jing LiYun Yang and Ke ChenYanlai Li and David ZhangVan Liu and Bojan Cukic and Johann Schumann and Michael JiangElias Kyriakides and Marios PolycarpouErfu Yang and Dongbing Gu and Huosheng HuSoowhan Han and Imgeun LeeYuan Kang and Min-Hwei Chu and Min-Chou ChenFredrik Linaker and Masumi Ishikawa

Hyperbolic Function networks for Pattern Classification	p. 1
Variable Selection for the Linear Support Vector Machine	p. 35
Selecting Data for Fast Support Vector Machines Training	p. 61
Universal Approach to Study Delayed Dynamical Systems	p. 85
A Hippocampus-Neocortex Model for Chaotic Association	p. 111
Latent Attractors: A General Paradigm for Context-Dependent Neural Computation	p. 135
Learning Mechanisms in Networks of Spiking Neurons	p. 171
GTSOM: Game Theoretic Self-organizing Maps	p. 199
How to Generate Different Neural Networks	p. 225
A Gradient-Based Forward Greedy Algorithm for Space Gaussian Process Regression	p. 241
An Evolved Recurrent Neural Network and Its Application	p. 265
A Min-Max Modular Network with Gaussian-Zero-Crossing Function	p. 285
Combining Competitive Learning Networks of Various Representations for Sequential Data Clustering	p. 315
Modular Neural Networks and Their Applications in Biometrics	p. 337
Performance Analysis of Dynamic Cell Structures	p. 367
Short Term Electric Load Forecasting: A Tutorial	p. 391
Performance Improvement for Formation-Keeping Control Using a Neural Network HJI Approach	p. 419
A Robust Blind Neural Equalizer Based on Higher-Order Cumulants	p. 443
The Artificial Neural Network Applied to Servo Control System	p. 461
Robot Localization Using Vision	p. 483

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