Connectionist Models of Learning, Development and Evolution

SECTION I: Neural Basis of Cognition: 1. Applying Neuroanatomical Distinctions to Connectionist Cognitive Modelling; 2. Pseudopatterns and Dual-network Memory Models: Advantages and Shortcomings; 3. A Learning Algorithm for Synfire Chains; 4. Towards a Spatio-temporal Analysis Tool for fMRI Data: An Application to Depth-from-Motion Processing in Humans; 5. A Simple Model Exhibiting Scalar Timing; Modularity and Specialized Learning in the Organization of Behaviour; 6. Modelling Modulatory Aspects in Association Processes; 8. Recognition of Novelty Made Easy: Constraints of Channel Capacity on Generative Networks; 9. A Biologically Plausible Maturation of an ART Network.- SECTION II: Development and Category Learning: 10. Developing Knowledge about Living Things: A Connectionist Investigation; 11. Paying Attention to Relevant Dimensions: A Localist Approach; 12. Coordinating Multiple Sensory Modalities While Learning to Reach; 13. Modelling Cognitive Development with Constructivist Neural Networks; 14. Learning Action Affordances and Action Schemas; 15. A Three-layer Configural Cue Model of Category Learning Rtes; 16. A Revival of Turing's Forgotten Connectionist Ideas: Exploring Unorganized Machines; 17. Visual Crowding and Category-specific Deficits: a Neural Network Model.- SECTION III: Implicit Learning: 18. Implicit Learning of Regularities in Western Tonal Music by Self-organization; 19. Rules vs. Statistics in Implicit Learning of Biconditional Grammars; 20. Hidden Markov Model Interpretations of Neural Networks.- SECTION IV: Social Cognition: 21. A Connectionist Model of Person Perception and Stereotype Formation; 22. Learning about an Absent Cause: Discounting and Augmentation of Positively and Independently Related Causes; SECTION V: Evolution. 23. Exploring the Baldwin Effect in Evolving Adaptable Control Systems; 24. Borrowing Dynamics from Evolution: Association using Catalytic Network Models; 25. Evolving Modular Architectures for Neural Networks; 26. Evolution, Development and Learning - a Nested Hierarchy?- SECTION VI: Semantics: 27. Learning Lexical Properties from Word Usage Patterns: Which Context Words Should be Used?; 28. Associative Computation and Associative Prediction; 29. The Development of Small-world Semantic Networks; 30. What is the Dimensionality of Human Semantic Space?