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Neural network approaches to time series prediction are briefly discussed, and the need to specify an appropriately sized input window identified. Relevant theoretical results from dynamic systems theory are briefly introduced, and heuristics for finding the correct embedding dimension, and hence window size, are discussed. The method is applied to two time series and the resulting generalisation performance of the trained feedforward neural network predictors is analysed. It is shown that the heuristics can provide useful information in defining the appropriate network architectur

Abstract. Most computational models of gender classification use global information (the full face image) giving equal weight to the whole face area irrespective of the importance of the internal features. Here we use a two-way representation of face images that includes both global and featural information. We use dimensionality reduction techniques and a support vector machine classifier and show that this method performs better than either global or feature based representations alone.

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This paper describes a series of modular neural network simulations of visual object processing. In a departure from much previous work in this domain, the model described here comprises both supervised and unsupervised modules and processes real pictorial representations of items from different object categories. The unsupervised module carries out bottom-up encoding of visual stimuli, thereby developing a "perceptual" representation of each presented picture. The supervised component then classifies each perceptual representation according to a target semantic category. Model performance was assessed (1) during learning, (2) under generalisation to novel instances, and (3) after lesion damage at different stages of processing. Strong category effects were observed throughout the different experiments, with living things and musical instruments eliciting greater recognition failures relative to other categories. This pattern derives from within-category similarity effects at the level of perceptual representation and our data support the view that visual crowding can be a potentially important factor in the emergence of some category-specific impairments. The data also accord with the cascade model of object recognition, since increased competition between perceptual representations resulted in categoryspecific impairments even when the locus of damage was within the semantic component of the model. Some strengths and limitations of this modelling approach are discussed and the results are evaluated against some other accounts of category-specific recognition failure.