Computational Intelligence and Bioinspired Systems

We present in this volume the collection of finally accepted papers of the eighth edition of the IWANN conference (International Work-Conference on Artificial Neural Networks). This biennial meeting focuses on the foundations, theory, models and applications of systems inspired by nature (neural networks, fuzzy logic and evolutionary systems). Since the first edition of IWANN in Granada (LNCS 540, 1991), the Artificial Neural Network (ANN) community, and the domain itself, have matured and evolved. Under the ANN banner we find a very heterogeneous scenario with a main interest and objective: to better understand nature and beings for the correct elaboration of theories, models and new algorithms. For scientists, engineers and professionals working in the area, this is a very good way to get solid and competitive applications. We are facing a real revolution with the emergence of embedded intelligence in many artificial systems (systems covering diverse fields: industry, domotics, leisure, healthcare, ). So we are convinced that an enormous amount of work must be, and should be, still done. Many pieces of the puzzle must be built and placed into their proper positions, offering us new and solid theories and models (necessary tools) for the application and praxis of these current paradigms. The above-mentioned concepts were the main reason for the subtitle of the IWANN 2005 edition: Computational Intelligence and Bioinspired Systems. The call for papers was launched several months ago, addressing the following topics: 1. Mathematical and theoretical methods in computational intelligence.

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Mathematical and Theoretical Methods.- Role of Function Complexity and Network Size in the Generalization Ability of Feedforward Networks.- Analysis of the Sanger Hebbian Neural Network.- Considering Multidimensional Information Through Vector Neural Networks.- Combining Ant Colony Optimization with Dynamic Programming for Solving the k-Cardinality Tree Problem.- Evolutionary Computation.- A Basic Approach to Reduce the Complexity of a Self-generated Fuzzy Rule-Table for Function Approximation by Use of Symbolic Interpolation.- Average Time Complexity of Estimation of Distribution Algorithms.- A Comparison of Evolutionary Approaches to the Shortest Common Supersequence Problem.- Simultaneous Evolution of Neural Network Topologies and Weights for Classification and Regression.- Applying Bio-inspired Techniques to the p-Median Problem.- Optimal Strategy for Resource Allocation of Two-Dimensional Potts Model Using Genetic Algorithm.- Memetic Algorithms to Product-Unit Neural Networks for Regression.- Lamarckian Clonal Selection Algorithm Based Function Optimization.- Neurocomputational Inspired Models.- Artificial Neural Networks Based on Brain Circuits Behaviour and Genetic Algorithms.- Modeling Synaptic Transmission and Quantifying Information Transfer in the Granular Layer of the Cerebellum.- The After-Hyperpolarization Amplitude and the Rise Time Constant of IPSC Affect the Synchronization Properties of Networks of Inhibitory Interneurons.- TiViPE Simulation of a Cortical Crossing Cell Model.- A Model of Spiking-Bursting Neuronal Behavior Using a Piecewise Linear Two-Dimensional Map.- Real-Time Spiking Neural Network: An Adaptive Cerebellar Model.- Modeling Neural Processes in Lindenmayer Systems.- Modeling Stimulus Equivalence with Multi Layered Neural Networks.- Instability of Attractors in Auto-associative Networks with Bio-inspired Fast Synaptic Noise.- Lookup Table Powered Neural Event-Driven Simulator.- Learning and Adaptation.- Joint Kernel Maps.- Statistical Ensemble Method (SEM): A New Meta-machine Learning Approach Based on Statistical Techniques.- Neural Network Modeling by Subsampling.- Balanced Boosting with Parallel Perceptrons.- A Reinforcement Learning Algorithm Using Temporal Difference Error in Ant Model.- Selection of Weights for Sequential Feed-Forward Neural Networks: An Experimental Study.- Exploiting Multitask Learning Schemes Using Private Subnetworks.- Co-evolutionary Learning in Liquid Architectures.- Extended Sparse Nonnegative Matrix Factorization.- Radial Basic Functions Structures.- Using a Mahalanobis-Like Distance to Train Radial Basis Neural Networks.- Robustness of Radial Basis Functions.- Improving Clustering Technique for Functional Approximation Problem Using Fuzzy Logic: ICFA Algorithm.- Input Variable Selection in Hierarchical RBF Networks.- Approximating I/O Data Using Radial Basis Functions: A New Clustering-Based Approach.- Application of ANOVA to a Cooperative-Coevolutionary Optimization of RBFNs.- Self-organizing Networks and Methods.- Characterizing Self-developing Biological Neural Networks: A First Step Towards Their Application to Computing Systems.- Cooperative Bees Swarm for Solving the Maximum Weighted Satisfiability Problem.- Deriving Cortical Maps and Elastic Nets from Topology-Preserving Maps.- Evolution of Cooperating ANNs Through Functional Phenotypic Affinity.- Robust Growing Hierarchical Self Organizing Map.- Support Vector Machines.- Web Usage Mining Using Support Vector Machine.- Multi-kernel Growing Support Vector Regressor.- Cellular Neural Networks.- Stability Results for Cellular Neural Networks with Time Delays.- Global Exponential Stability Analysis in Cellular Neural Networks with Time-Varying Coefficients and Delays.- Hybrid Systems.- Diversity and Multimodal Search with a Hybrid Two-Population GA: An Application to ANN Development.- Identification of Fuzzy Systems with the Aid of Genetic Fuzzy Granulation.- Clustering-Based TSK Neuro-fuzzy Model for Function Approximation with Interpretable Sub-models.- Genetically Optimized Hybrid Fuzzy Neural Networks with the Aid of TSK Fuzzy Inference Rules and Polynomial Neural Networks.- IG-Based Genetically Optimized Fuzzy Polynomial Neural Networks.- Hierarchical Neuro-fuzzy Models Based on Reinforcement Learning for Intelligent Agents.- Neuroengineering and Hardware Implementations.- Interfacing with Patterned in Vitro Neural Networks by Means of Hybrid Glass-Elastomer Neurovectors: Progress on Neuron Placement, Neurite Outgrowth and Biopotential Measurements.- Using Kolmogorov Inspired Gates for Low Power Nanoelectronics.- CMOL CrossNets as Pattern Classifiers.- Analog VLSI Implementation of Adaptive Synapses in Pulsed Neural Networks.- Smart Sensing with Adaptive Analog Circuits.- Spiking Neurons Computing Platform.- Inter-spike-intervals Analysis of Poisson Like Hardware Synthetic AER Generation.- Ultra Low-Power Neural Inspired Addition: When Serial Might Outperform Parallel Architectures.- An Asynchronous 4-to-4 AER Mapper.- Fast Optoelectronic Neural Network for Vision Applications.- A Computational Tool to Test Neuromorphic Encoding Schemes for Visual Neuroprostheses.- Test Infrastructure for Address-Event-Representation Communications.- Automatic Generation of Bio-inspired Retina-Like Processing Hardware.- Two Hardware Implementations of the Exhaustive Synthetic AER Generation Method.- On the Design of a Parallel Genetic Algorithm Based on a Modified Survival Method for Evolvable Hardware.- A Novel Approach for the Implementation of Large Scale Spiking Neural Networks on FPGA Hardware.- A Quaternary CLB Design Using Quantum Device Technology on Silicon for FPGA Neural Network Architectures.- A Dynamically-Reconfigurable FPGA Platform for Evolving Fuzzy Systems.- FPGA Implementation of Hopfield Networks for Systems Identification.- An FPGA-Based Adaptive Fuzzy Coprocessor.- Pattern Recognition.- Cascade Ensembles.- Ensembles of Multilayer Feedforward: Some New Results.- Layered Network Computations by Parallel Nonlinear Processing.- Fast Classification with Neural Networks via Confidence Rating.- Characterization and Synthesis of Objects Using Growing Neural Gas.- ARGEN + AREPO: Improving the Search Process with Artificial Genetic Engineering.- Perception and Robotics.- Modelling Perceptual Discrimination.- Memory Retrieval in a N…