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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.

A consensus on how to characterise the anterograde and retrograde memory processes that are lost or spared after hippocampal damage has not been reached. In this thesis, I critically re-examine the empirical literature and the assumptions behind current theories. I formulate a coherent view of what makes a task hippocampally dependent at acquisition and how this relates to its long-term fate. Findings from a neural net simulation indicate the plausibility of my proposals. My proposals both extend and constrain current views on the role of the hippocampus in the rapid acquisition of information and in learning complex associations. In general, tasks are most likely to require the hippocampus for acquisition if they involve rapid, associative learning about unfamiliar, complex, low salience stimuli. However, none of these factors alone is sufficient to obligatorily implicate the hippocampus in acquisition. With the exception of associations with supra-modal information that are always dependent on the hippocampus, it is the combination of factors that is important. Detailed, complex information that is obligatorily hippocampally-dependent at acquisition remains so for its lifetime. However, all memories are semanticised as they age through the loss of detailed context-specific information and because generic cortically-represented information starts to dominate recall. Initially hippocampally dependent memories may appear to become independent of the hippocampus over time, but recall changes qualitatively. Multi-stage, lifelong post-acquisition memory processes produce semanticised re-representations of memories of differing specificity and complexity, that can serve different purposes. The model simulates hippocampal and cortical interactions in the acquisition and maintenance of episodic and semantic events, and behaves in accordance with my proposals. In particular, conceptualising episodic and semantic memory as representing points on a continuum of memory types appears viable. Support is also found for proposals on the relative importance of the hippocampus and cortex in the rapid acquisition of information and the acquisition of complex multi-model information; and the effect of existing knowledge on new learning. Furthermore, episodic and semantic events become differentially dependent on cortical and hippocampal components. Finally, as a memory ages, it is automatically semanticised and becomes cortically dependent.

My project has been involved in analysing a class of interneuron that expresses the calcium‐binding protein parvalbumin (PV). In my thesis, I will describe the application of a method that involves the local injection of Creactivated recombinant adeno-associated viruses (AAVs) into a transgenic mouse line that expresses Cre recombinase in PV positive cells. This will drive the expression of a transgene specifically in PV positive cells, at a specific brain region. In the first part of my project, I used this method to introduce the molecular trans-synaptic tracer proteins wheat germ agglutinin and tetanus toxin heavy chain specifically to PV positive neurons to visualize their postand pre‐synaptic connections, respectively. What I found is that while our technique of combining Cre-activated AAVs in transgenic mice has allowed specific labelling of neurons in a brain region and cell type specific manner, we could not definitively identify trans-synaptically traced neurons. In the second part of my project I have used these novel AAV‐based techniques in mice to introduce tetanus toxin light chain (TeLC) to PV neurons in the dentate gyrus. This has been previously used to functionally remove PV neurons from the CA1 of the hippocampus. This protein inhibits neurotransmitter release by cleaving the vesicle docking protein, VAMP2. The DG has been implicated in the separation of sensory inputs (pattern separation) which increases the resolution of the encoded memory and thereby assists in the accurate recall. The lateral inhibition of excitatory activity in the DG is believed to aid accurate encoding. Using our AAV method, I found that PV positive interneurons are required for spatial working and reference memory. Using a new behavioural assay that I developed, I could also show that these neurons are needed to enhance the resolution of spatial information. However, I also discovered that long term expression of TeLC could result in neuronal cell death. I have therefore demonstrated that local injection of Cre recombinase activated AAVs allows for a quick, versatile method of genetic manipulation, provided long term expression (greater than 2 months) is not required.