Connectionist Models of Memory and Language (PLE: Memory) (Paperback)

Preface.  Acknowledgements.  Contributors.  Section 1: Memory  1. David W. Glasspool Competitive Queuing and the Articulatory Loop  2. Dimitrios Bairaktaris Temporal Chunking and Synchronization Using a Modular Recurrent Network Architecture  3. Gordon D. A. Brown, Tim Preece, Charles Hulme Learning to Learn in a Connectionist Network: The Development of Associative Learning  4. Noel E. Sharkey and Amanda J.C. Sharkey Interference and Discrimination in Neural Net Memory  5. Jacob M.J. Murre Transferof Learning in Back-propagation and in Related Neural Network Models  6. Joseph P. Levy and Dimitrios Bairaktaris Interactions Between Short- and Long-term Weights: Applications for Cognitive Modelling  Section 2: Reading  7. Robert I. Damper Self-learning and Connectionist Approaches to Text?Phoneme Conversion  8. David C. Plaut, James L. McClelland Mark S. Seidenberg Reading Exception Words and Pseudowords: Are Two Routes Really Necessary?  9. John A. Bullinaria Neural Network Models of Reading: Solving the Alignment Problem without Wickelfeatures  Section 3: Computation and Statistics  10. Robert W. Kentridge Cortical Neurocomputation, Language and Cognition  11. Nick Chater Neural Networks: The New Statistical Models of Mind  12. Steve Finch, Nick Chater, Martin Redington Acquiring Syntactic Information from Distributional Statistics  Section 4: Speech and Audition  13. Leslie S. Smith Onset/Offset Filters for the Segmentation of Sound  14. Mukhlis Abu-Bakar and Nick Chater Time-warping Tasks and Recurrent Neural Networks  15. Paul Cairns, Richard Shillcock, Nick Chater, Joseph P. Levy Bottom-up Connectionist Modelling of Speech  16. Trevor A. Harley and Siobhan B.G. MacAndrew.  Index.