Self-organization of Multiple Winner-take-all Neural Networks

Connection Science

Volumn 9, Issue 1, 1997, Pages 11-30

  Luttrell, Stephen P ^a  

^a DEFENCE SCIENCE AND TECHNOLOGY LABORATORY   (United Kingdom)

Author keywords

Factorial network; Firing neurons; Ocular dominance; Self organization; Winner take all

Indexed keywords

EID: 0031487888     PISSN: 09540091     EISSN: None     Source Type: Journal    
DOI: 10.1080/095400997116711     Document Type: Article

References (19)

1. Dayan, P., Hinton, G.E., Neal, R.M. & Zemel, R.S. (1995) The Helmholtz machine. Neural Computation, 7, 889-904.
2. Farvardin, N. (1990) A study of vector quantisation for noisy channels, IEEE Transactions in IT, 36, 799-809.
3. Goodhill, G. (1992) Correlations, competition and optimality: modelling the development of topography and ocular dominance, CSRP 226, University of Sussex.
4. Hinton, G.E. & Zemel, R.S. (1994) Autoencoders, minimum description length, and Helmholtz free energy. In J.D. Cowan, G. Tesauro & F. Alspector (Eds), Advances in Neural Information Processing Systems, 6. San Francisco: Morgan Kaufman.
5. Hinton, G.E., Dayan, P., Frey, B.J. & Neal, R.M. (1965) The 'wake-sleep' algorithm for unsupervised neural networks. Science, 268, 1158-1161.
6. Kohonen, T. (1984) Self-organisation and Associative Memory. Berlin: Springer-Verlag.
7. Kumazawa, H., Kasahara, M. & Namekawa, T. (1984) A construction of vector quantizers for noisy channels. Electronic Engineering Japan, 67B, 39-47.
8. Linde, Y., Buzo, A. & Gray, R.M. (1980) An algorithm for vector quantiser design. IEEE Transactions COM, 28, 84-95.
9. Luttrell, S.P. (1989a) Hierarchical self-organising networks. Proceedings of 1st IEE Conference on Artificial Neural Networks, pp. 2-6.
10. Luttrell, S.P. (1989b) Hierarchical vector quantisation. Proceedings of the IEE Part I, 136, 405-413.
11. Luttrell, S.P. (1990) A trainable texture anomaly detector using the adaptive cluster expansion (ACE) method. RSRE Memorandum, 4437.
12. Luttrell, S.P. (1991) A hierarchical network for clutter and texture modelling. Proceedings of SPIE Conference on Adaptive Signal Processing, 1565, 518-528.
13. Luttrell, S.P. (1994a) The cluster expansion: a hierarchical density model. Proceedings of 14th International MAXENT Workshop, pp. 269-278.
14. Luttrell, S.P. (1994b) A Bayesian analysis of self-organising maps. Neural Computation, 6, 767-794.
15. Luttrell, S.P. (1996a) Designing analysable networks. In E. Fiesler & R. Beale (Eds), Handbook of Neural Computation. Oxford: OUP.
16. Luttrell, S.P. (1996b) A theory of self-organising neural networks. In S.W. Ellacott, J.C. Mason & IJ. Anderson (Eds), Mathematics of Neural Networks: Models, Algorithms and Applications. Kluwer.
17. Rissanen, J. (1978) Modelling by shortest data description. Automatica, 14, 465-471.
18. Saund, E. (1995) A multiple cause mixture model for unsupervised learning. Neural Computation, 7, 51-71.
19. Zemel, R.S. & Hinton, G.E. (1994) Developing population codes by minimising description length. In J.D. Cowan, G. Tesauro, & F. Alspector (Eds), Advances in Neural Information Processing Systems 6. San Francisco: Morgan Kaufman.