CONTENTS
PART Ⅰ The Classical Perspective
1. Why neural computing? A personal view / I. Aleksander = 1
2. A theory of neural networks / E. Caianiello = 8
3. Speech recognition based on topology-preserving neural maps / Teuvo Kohonen = 26
4. Neural map applications / G.Tattershall = 41
5. Backprogapation in non-feedforward networks / Luis B. Almeida = 74
6. A PDP Iearning approach to natural language understanding / N. E. Sharkey = 92
7. Learning capabilities of Boolean networks Stefano Patarnello and Paolo Carnevali = 117
PART Ⅱ The Logical Perspective
8. The logic of connectionist systems / I. Aleksander = 133
9. A probabilistic logic neuron network for associative learning / Wing-kay Kan ; Igor Aleksander = 156
10. Applications of N-tuple sampling and genetic algorithms to speech recognition / A. Badii, M. J. Binstead ; Antonia J. Jones ; T. J. Stonham ; Christine L. Valenzuela = 172
11. Dynamic behaviour of Boolean networks / D. Martland = 217
PART Ⅲ Analysis and Interpretation
12. Statistical mechanics and neural networks / C. Campell ; D. Sherrington ; K. Y. M. Wong = 239
13. Digital neural networks, matched filters and optical implementations / J. E. Midwinter ; D. R. Selviah = 258
14. Hetero-associative networks using link-enabling vs. link-disabling local modification rules / vernon G. Dobson; Alan Johnston ; Michael J. Wright = 279
15. Generation of movement trajectories in primates and robots / Rolf Eckmiller = 305
PART Ⅳ The PDP Perspective
16. A review of parallel distributed processing / I. Aleksander = 329
Bibliography = 381
Index = 394
