Abstract
This chapter locates Gaussian radial basis function (RBF) networks within the school of connectionist modelling that has successfully exploited the properties of multi layer perceptrons (MLPs). In particular, the highly regarded use of distributed representations in MLPs is established in RBF networks. RBF networks have been used to learn a categorization task, and the way in which the networks have learned the task is interpreted in terms of localist and distributed representations. The problem of catastrophic forgetting as a psychologically implausible feature of MLPs, and an undesirable property of neural network engineering systems, is also reconsidered.
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© 1998 Springer-Verlag London Limited
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Middleton, N. (1998). Distributed Representations in Radial Basis Function Networks. In: Bullinaria, J.A., Glasspool, D.W., Houghton, G. (eds) 4th Neural Computation and Psychology Workshop, London, 9–11 April 1997. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-1546-5_2
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DOI: https://doi.org/10.1007/978-1-4471-1546-5_2
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