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As computers begin to pervade aspects of our everyday lives, so the problem of communication from man-to-machine becomes increasingly evident. In recent years, there has been a concerted interest in speech recognition offering a user to communicate freely with a machine. However, this deceptively simple means for exchanging information is in fact extremely complex. A single utterance can contain a wealth of varied information concerning the speaker's gender, age, dialect and mood. Numerous subtle differences such as intonation, rhythm and stress further add to the complexity, increasing the variability between inter- and intra-speaker utterances. These differences pose an enormous problem, especially for a multi-user system since it is impractical to train for every variation of every utterance from every speaker. Consequently adaptation is of great importance, allowing a system with limited knowledge to dynamically adapt towards a new speakers characteristics. A new modified artificial neural network (ANN) was proposed incorporating One-Class-OneNetwork (OCON) subnet architectures connected via a common front-end adaptation layer. Using vowel phonemes from the TIMIT speech database, the adaptation was concentrated on neurons within the front-end layer, resulting in only information common to all classes, primarily speaker characteristics, being adapted. In addition, this prevented new utterances from interfering with phoneme unique information in the corresponding OCON subnets. Hence a more efficient adaptation procedure was created which, after adaptation towards a single class, also aided in the recognition of the remaining classes within the network. Compared with a conventional multi-layer perceptron network, results for inter- and intraspeaker adaptation showed an equally marked improvement for the recognition of adapted phonemes during both full neuron and front-layer neuron adaptation within the new modified architecture. When testing the effects of adaptation on the remaining unadapted vowel phonemes, the modified architecture (allowing only the neurons in the front-end layer to adapt) yielded better results than the modified architecture allowing full neuron adaptation. These results highlighted the storing of speaker information, common to all classes, in the front-end layer allowing efficient inter- and intra-speaker dynamic adaptation.

In this thesis, I describe the development of a novel method to selectively modulate neural activity cell-type selectively. Binding of Zolpidem, an allosteric modulator that enhances GABAa receptor function and the inverse agonist β-carboline, require a phenylalanine residue (F77) in the γ subunit.

This thesis presents some substantial theoretical analyses and optimal treatments of Kohonen's self-organising map (SOM) algorithm, and explores the practical application potential of the algorithm for vector quantisation, pattern classification, and image processing. It consists of two major parts. In the first part, the SOM algorithm is investigated and analysed from a statistical viewpoint. The proof of its universal convergence for any dimensionality is obtained using a novel and extended form of the Central Limit Theorem. Its feature space is shown to be an approximate multivariate Gaussian process, which will eventually converge and form a mapping, which minimises the mean-square distortion between the feature and input spaces. The diminishing effect of the initial states and implicit effects of the learning rate and neighbourhood function on its convergence and ordering are analysed and discussed. Distinct and meaningful definitions, and associated measures, of its ordering are presented in relation to map's fault-tolerance. The SOM algorithm is further enhanced by incorporating a proposed constraint, or Bayesian modification, in order to achieve optimal vector quantisation or pattern classification. The second part of this thesis addresses the task of unsupervised texture-image segmentation by means of SOM networks and model-based descriptions. A brief review of texture analysis in terms of definitions, perceptions, and approaches is given. Markov random field model-based approaches are discussed in detail. Arising from this a hierarchical self-organised segmentation structure, which consists of a local MRF parameter estimator, a SOM network, and a simple voting layer, is proposed and is shown, by theoretical analysis and practical experiment, to achieve a maximum likelihood or maximum a posteriori segmentation. A fast, simple, but efficient boundary relaxation algorithm is proposed as a post-processor to further refine the resulting segmentation. The class number validation problem in a fully unsupervised segmentation is approached by a classical, simple, and on-line minimum mean-square-error method. Experimental results indicate that this method is very efficient for texture segmentation problems. The thesis concludes with some suggestions for further work on SOM neural networks.

A number of researchers have investigated the application of neural networks to visual recognition, with much of the emphasis placed on exploiting the network's ability to generalise. However, despite the benefits of such an approach it is not at all obvious how networks can be developed which are capable of recognising objects subject to changes in rotation, translation and viewpoint. In this study, we suggest that a possible solution to this problem can be found by studying aspects of visual psychology and in particular, perceptual organisation. For example, it appears that grouping together lines based upon perceptually significant features can facilitate viewpoint independent recognition. The work presented here identifies simple grouping measures based on parallelism and connectivity and shows how it is possible to train multi-layer perceptrons (MLPs) to detect and determine the perceptual significance of any group presented. In this way, it is shown how MLPs which are trained via backpropagation to perform individual grouping tasks, can be brought together into a novel, large scale network capable of determining the perceptual significance of the whole input pattern. Finally the applicability of such significance values for recognition is investigated and results indicate that both the NILP and the Kohonen Feature Map can be trained to recognise simple shapes described in terms of perceptual significances. This study has also provided an opportunity to investigate aspects of the backpropagation algorithm, particularly the ability to generalise. In this study we report the results of various generalisation tests. In applying the backpropagation algorithm to certain problems, we found that there was a deficiency in performance with the standard learning algorithm. An improvement in performance could however, be obtained when suitable modifications were made to the algorithm. The modifications and consequent results are reported here.