Behavioural and neural correlates of operant conditioning in Lymnaea stagnalis : role of previous experience during development /

The freshwater mollusc Lymnaea stagnalis was utilized in this study to further the understanding of how network properties change as a result of associative learning, and to determine whether or not this plasticity is dependent on previous experience during development. The respiratory and neural correlates of operant conditioning were first determined in normally reared Lymnaea. The same procedure was then applied to differentially reared Lymnaea, that is, animals that had never experienced aerial respiration during their development. The aim was to determine whether these animals would demonstrate the same responses to the training paradigm. In normally reared animals, a behavioural reduction in aerial respiration was accompanied by numerous changes within the neural network. Specifically, I provide evidence of changes at the level of the respiratory central pattern generator and the motor output. In the differentially reared animals, there was little behavioural data to suggest learning and memory. There were, however, significant differences in the network parameters, similar to those observed in normally reared animals. This demonstrated an effect of operant conditioning on differentially reared animals. In this thesis, I have identified additional correlates of operant conditioning in normally reared animals and provide evidence of associative learning in differentially reared animals. I conclude plasticity is not dependent on previous experience, but is rather ontogenetically programmed within the neural network.

Decoding algorithms using side-effect machines

Bioinformatics applies computers to problems in molecular biology. Previous research has not addressed edit metric decoders. Decoders for quaternary edit metric codes are finding use in bioinformatics problems with applications to DNA. By using side effect machines we hope to be able to provide efficient decoding algorithms for this open problem. Two ideas for decoding algorithms are presented and examined. Both decoders use Side Effect Machines(SEMs) which are generalizations of finite state automata. Single Classifier Machines(SCMs) use a single side effect machine to classify all words within a code. Locking Side Effect Machines(LSEMs) use multiple side effect machines to create a tree structure of subclassification. The goal is to examine these techniques and provide new decoders for existing codes. Presented are ideas for best practices for the creation of these two types of new edit metric decoders.

Hydrosilylation and hydroboration catalyzed by imido-hydride complexes of molybdenum (IV)

This thesis describes the synthesis, structural studies, stoichiometric and catalytic reactivity of novel Mo(IV) imido hydride complexes (Cp)(ArN)Mo(H)(PMe3) (1) and (Tp )(ArN)Mo(H)(PMe3) (2). Both 1 and 2 catalyze hydrosilylation of a variety of carbonyls. Detailed kinetic and DFT studies found that 1 reacts by an unexpected associative mechanism, which does not involve Si-H addition either to the imido group or the metal. Despite 1 being a d2 complex, its reaction with PhSiH3 proceeds via a a-bond metathesis mechanism giving the silyl derivative (Cp )(ArN)Mo(SiH2Ph)(PMe3). In the presence of BPh3 reaction of 1 with PhSiH3 results in formation of (Cp)(ArN)Mo(SiH2Ph)(H)2 and (Cp)(ArN)Mo(SiH2Ph)2(H), the first examples ofMo(VI) silyl hydrides. AI: 1 : 1 reaction between 2, PhSiD3 and carbonyl substrate established that hydrosilylation is not accompanied by deuterium incorporation into the hydride position of the catalyst, thus ruling out the conventional mechanism based on carbonyl insertion carbonyl. As 2 is nomeactive to both the silane and ketone, the only mechanistic alternative we are left with is that the metal center activates the carbonyl as a Lewis acid. The analogous nonhydride mechanism was observed for the catalysis by (ArN)Mo(H)(CI)(PMe3), (Ph3P)2(I)(O)Re(H)(OSiMe2Ph) and (PPh3CuH)6. Complex 2 also catalyzes hydroboration of carbonyls and nitriles. We report the first case of metal-catalyzed hydroboration of nitriles as well as hydroboration of carbonyls at very mild conditions. Conversion of carbonyl functions can be performed with high selectivities in the presence of nitrile groups. This thesis also reports the first case of the HlH exchange between H2 and Si-H of silanes mediated by Lewis acids such as Mo(IV) , Re(V) , Cu(I) , Zn(II) complexes, B(C6Fs)3 and BPh3.

Hyperspectral Mineral Identification using SVM and SOM

Remote sensing techniques involving hyperspectral imagery have applications in a number of sciences that study some aspects of the surface of the planet. The analysis of hyperspectral images is complex because of the large amount of information involved and the noise within that data. Investigating images with regard to identify minerals, rocks, vegetation and other materials is an application of hyperspectral remote sensing in the earth sciences. This thesis evaluates the performance of two classification and clustering techniques on hyperspectral images for mineral identification. Support Vector Machines (SVM) and Self-Organizing Maps (SOM) are applied as classification and clustering techniques, respectively. Principal Component Analysis (PCA) is used to prepare the data to be analyzed. The purpose of using PCA is to reduce the amount of data that needs to be processed by identifying the most important components within the data. A well-studied dataset from Cuprite, Nevada and a dataset of more complex data from Baffin Island were used to assess the performance of these techniques. The main goal of this research study is to evaluate the advantage of training a classifier based on a small amount of data compared to an unsupervised method. Determining the effect of feature extraction on the accuracy of the clustering and classification method is another goal of this research. This thesis concludes that using PCA increases the learning accuracy, and especially so in classification. SVM classifies Cuprite data with a high precision and the SOM challenges SVM on datasets with high level of noise (like Baffin Island).

Real-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TM

Genetic Programming (GP) is a widely used methodology for solving various computational problems. GP's problem solving ability is usually hindered by its long execution times. In this thesis, GP is applied toward real-time computer vision. In particular, object classification and tracking using a parallel GP system is discussed. First, a study of suitable GP languages for object classification is presented. Two main GP approaches for visual pattern classification, namely the block-classifiers and the pixel-classifiers, were studied. Results showed that the pixel-classifiers generally performed better. Using these results, a suitable language was selected for the real-time implementation. Synthetic video data was used in the experiments. The goal of the experiments was to evolve a unique classifier for each texture pattern that existed in the video. The experiments revealed that the system was capable of correctly tracking the textures in the video. The performance of the system was on-par with real-time requirements.