We extend the in-situ Hebbian-linked SOMs network by Miikkulainen to come up with two unsupervised neural networks that learn the mapping between the individual modes of a multimodal dataset. The first network, the single-pass Hebbian linked SOMs network, extends the in-situ Hebbian-linked SOMs network by enabling the Hebbian link weights to be computed through one-shot learning. The second network, a modified counterpropagation network, extends the unsupervised learning of crossmodal mappings by making it possible for only one self-organising map to implement the crossmodal mapping. The two proposed networks each have a smaller computation time and achieve lower crossmodal mean squared errors than the in-situ Hebbian-linked SOMs network when assessed on two bimodal datasets, an audio-acoustic speech utterance dataset and a phonological-semantics child utterance dataset. Of the three network architectures, the modified counterpropagation network achieves the highest percentage of correct classifications comparable to that of the LVQ-2 algorithm by Kohonen and the neural network for category learning by de Sa and Ballard in classification tasks using the audio-acoustic speech utterance dataset.

and impaired readers as a test of their adequacy (see Coltheart, Rastle, Perry, Langdon, & Ziegler, 2001; Grainger & Jacobs, 1996; Plaut, McClelland, Seidenberg, & Patterson, 1996; Zorzi, Houghton, & Butterworth, 1998). Yet for all of the advancement that the past decade has seen, a complete theory of single-word processing remains somewhat distant, with numerous commitments regarding, for example, the processing of polysyllabic and polymorphemic words, still to be made. In this chapter, we focus specifically on some of the problems that words comprised of more than one morpheme present to modellers of single-word reading. At present, none of the aforementioned computational models (that have been evaluated extensively against benchmark findings of visual word recognition and reading aloud) deals effectively with such words. However, clear interest in extending our understanding of reading to polymorphemic words has been evident in recent years, with a surge of experimental work (s