Activity of brainstem cholinergic neurons during 22 kHz ultrasonic vocalization in rats /

Adult rats emit 22 kHz ultrasonic alann calls in aversive situations. This type of call IS a component of defensive behaviour and it functions predominantly to warn conspecifics about predators. Production of these calls is dependent on the central cholinergic system. The laterodorsal tegmental nucleus (LDT) and pedunculopontine tegmental nucleus (PPT) contain largely cholinergic neurons, which create a continuous column in the brainstem. The LDT projects to structures in the forebrain, and it has been implicated in the initiation of 22 kHz alarm calls. It was hypothesized that release of acetylcholine from the ascending LDT terminals in mesencephalic and diencephalic areas initiates 22 kHz alarm vocalization. Therefore, the tegmental cholinergic neurons should be more active during emission of alarm calls. The aim of this study was to demonstrate increased activity of LDT cholinergic neurons during emission of 22 kHz calls induced by air puff stimuli. Immunohistochemical staining of the enzyme choline acetyltransferase identified cell bodies of cholinergic neurons, and c-Fos immunolabeling identified active cells. Double labeled cells were regarded as active cholinergic cells. There were significantly more (pnaIve non-airpuffed) animals. Although the numbers were low, there were also significantly more (p

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.

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.