Bio-inspired Modeling of Cognitive Tasks: Second International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2007, La Manga del Mar Menor, Spain, June 18-21, 2007, Proceedings, Part IJosé Mira, José R. Álvarez The first of a two-volume set, this book constitutes the refereed proceedings of the Second International Work-Conference on the Interplay between Natural and Artificial Computation, IWINAC 2007, held in La Manga del Mar Menor, Spain in June 2007. It includes all the contributions mainly related with theoretical, conceptual and methodological aspects linking AI and knowledge engineering with neurophysiology, clinics and cognition. |
Contents
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An InsectInspired Active Vision Approach forOrientation Estimation with Panoramic Images | 61 |
Natural Interaction with a Robotic Head | 71 |
A Network of Interneurons Coupled byElectrical Synapses Behaves as a CoincidenceDetector | 81 |
Efficient BP Algorithms for General FeedforwardNeural Networks | 327 |
Coefficient Structure of Kernel Perceptrons andSupport Vector Reduction | 337 |
The MaxRelevance and MinRedundancyGreedy Bayesian Network Learning Algorithm | 346 |
PotentialBenefits of ValenceControlled ActionSelection | 357 |
Detecting Anomalous Traffic Using StatisticalDiscriminator and Neural Decisional Motor | 367 |
A Learning Based WidrowHoff Delta Algorithmfor Noise Reduction in Biomedical Signals | 377 |
Hopfield Neural Network and BoltzmannMachine Applied to Hardware ResourceDistribution on Chips | 387 |
A New Rough Set Reduct Algorithm Based onParticle Swarm Optimization | 397 |
Computational Structure for GeneralizedVisual SpaceTime Chromatic Processing | 90 |
Physiological Laws of Sensory Visual System inRelation to Scaling Power Laws in BiologicalNeural Networks | 96 |
ANF Stochastic Low Rate Stimulation | 103 |
Functional Identification of Retinal GanglionCells Based on Neural Population Responses | 113 |
Towards a NeuralNetworks Based Therapy forLimbs Spasticity | 124 |
A Bioinspired Architecture for Cognitive Audio | 132 |
An Adaptable Multichannel Architecture forCortical Stimulation | 143 |
Spiking Neural P SystemsPower and Efficiency | 153 |
Solving Subset Sum in Linear Time by UsingTissue P Systems with Cell Division | 170 |
On a P ̆auns Conjecture in Membrane Systems | 180 |
A Parallel DNA Algorithm Using a MicrofluidicDevice to Build Scheduling Grids | 193 |
P System Models of Bistable Enzyme DrivenChemical Reaction Networks | 203 |
A Novel Improvement of Neural NetworkClassification Using Further Division ofPartition Space | 214 |
Morphisms of ANN and the Computation ofLeast Fixed Points of Semantic Operators | 224 |
Predicting Human Immunodeficiency VirusHIV Drug Resistance Using Recurrent NeuralNetworks | 234 |
Error Weighting in Artificial Neural NetworksLearning Interpreted as a Metaplasticity Model | 244 |
A First Approach to Birth Weight PredictionUsing RBFNNs | 253 |
Filtering Documents with a Hybrid NeuralNetwork Model | 261 |
A Single Layer Perceptron Approach toSelective Multitask Learning | 272 |
Multitask Neural Networksfor Dealing with Missing Inputs | 282 |
Theoretical Study on the Capacity of AssociativeMemory with Multiple Reference Points | 292 |
Classification and Diagnosis of Heart Soundsand Murmurs Using Artificial Neural Networks | 303 |
Requirements for Machine Lifelong Learning | 313 |
Multitask Learning with Data Editing | 320 |
Use of Kohonen Maps as FeatureSelector for Selective AttentionBrainComputer Interfaces | 407 |
Using a Realistic PredatorPrey Model | 416 |
Estimation of Dependency NetworksAlgorithm | 427 |
GrammarGuided Neural Architecture Evolution | 437 |
Evolutionary Combining of Basis FunctionNeural Networks for Classification | 447 |
Application toa Thermal Process | 457 |
Gaining Insights into Laser Pulse Shapingby Evolution Strategies | 467 |
Simulated Evolution of the Adaptability of theGenetic Code Using Genetic Algorithms | 478 |
GCS with RealValued Input | 488 |
A Study on Genetic Algorithms for the DARPProblem | 498 |
Optimization of the Compression Parameters ofa Phonocardiographic Telediagnosis SystemUsing Genetic Algorithms | 508 |
An Integrated Resolution of Joint Productionand Maintenance Scheduling Problem in HybridFlowshop | 518 |
Sensitivity Analysis for the Job Shop Problemwith Uncertain Durations and Flexible DueDates | 538 |
Comparative Study of Metaheuristics forSolving Flow Shop Scheduling Problem UnderFuzziness | 548 |
Fusion of Neural Gas | 558 |
Decision Making Graphical Tool forMultiobjective Optimization Problems | 568 |
Electromagnetic Interference Reduction inElectronic Systems Cabinets by Means ofGenetic Algorithms Design | 578 |
Evolutionary Tool for the Incremental Design ofControllers for Collective Behaviors | 587 |
A Possibilistic Approach for Mining UncertainTemporal Relations from Diagnostic EvolutionDatabases | 597 |
Temporal Abstraction of States Through FuzzyTemporal Constraint Networks | 607 |
An Interactive Genetic Software forAssisting to Music Composition Tasks | 617 |
Author Index | 627 |
Common terms and phrases
Abstract according active adaptive algorithm allows applied approach architecture Artificial associated behavior cell classification complexity computation connections considered consists corresponding dataset defined depends described detection effect efficient environment error evolution example experiments Figure final fitness function fuzzy genetic given graph implementation improve increases individual initial input introduced knowledge layer learning machine means measure membrane method module multiset natural neural network neurons objective observed obtained operation optimization output parameters patterns performance population position possible present probability problem production proposed quantum References representation represents respectively robot rules samples scheduling Science selection sequence shown shows signal similar simulation solution solve space Spain spikes step structure Table task techniques temporal theory unit University values vector weights
Popular passages
Page 215 - This value is called pbest. Another 'best ' value that is tracked by the global version of the particle swarm optimizer is the overall best value, and its location, obtained so far by any particle in the population. This location is called gbest.
Page 215 - best" value that is tracked by the particle swarm optimizer is the best value, obtained so far by any particle in the neighbors of the particle. This location is called Ibest.
Page 242 - J.: Diversity and complexity of HIV-1 drug resistance: a bioinformatics approach to predicting phenotype from genotype. Proc Natl Acad Sci USA 99 (2002) 8271-6 9.
Page 327 - Momentum allows a network to respond not only to the local gradient, but also to recent trends in the error surface.
Page 282 - Universidad Carlos III de Madrid, Avda de la Universidad 30, 28911 Leganes (Madrid) - SPAIN 2 IEMN-CNRS.
Page 215 - Each particle keeps track of its coordinates in the problem space, which are associated with the best solution (fitness) it has achieved so far (The fitness value is also stored). This value is called pbest. Another "best...