For well over 200 years, scientists and doctors have been poking and prodding brains in every which way in an effort to understand how they work. The earliest pokes were quite crude, often involving permanent forms of brain damage. Though neural injury continues to be an active area of research within neuroscience, technology has given neuroscientists a number of tools for stimulating and observing the brain in very subtle ways. Nonetheless, the basic experimental paradigm remains the same; poke the brain and see what happens. For example, neuroscientists studying the visual or auditory system can easily generate any image or sound they can imagine to see how an organism or neuron will respond. Since neuroscientists can now easily design more pokes then they could every deliver, a fundamental question is ``What pokes should they actually use?' The complexity of the brain means that only a small number of the pokes scientists can deliver will produce any information about the brain. One of the fundamental challenges of experimental neuroscience is finding the right stimulus parameters to produce an informative response in the system being studied. This thesis addresses this problem by developing algorithms to sequentially optimize neurophysiology experiments. Every experiment we conduct contains information about how the brain works. Before conducting the next experiment we should use what we have already learned to decide which experiment we should perform next. In particular, we should design an experiment which will reveal the most information about the brain. At a high level, neuroscientists already perform this type of sequential, optimal experimental design; for example crude experiments which knockout entire regions of the brain have given rise to modern experimental techniques which probe the responses of individual neurons using finely tuned stimuli. The goal of this thesis is to develop automated and rigorous methods for optimizing neurophysiology experiments efficiently and at a much finer time scale. In particular, we present methods for near instantaneous optimization of the stimulus being used to drive a neuron.

For nearly two decades researchers have been interested in identifying what specifically is learned when individuals learn a sequence (e.g., sequence of stimuli, sequence of motor movements, etc.). Despite extensive research in the area, considerable controversy remains surrounding the locus of learning. There are three main theories concerning the nature of spatial sequence learning: sequence learning is purely perceptual, sequence learning includes a motor component and sequence learning is based on stimulus-response (S-R) rules. The present studies sought to disentangle these theories by demonstrating that sequence learning has both a perceptual and motor component and that altering S-R rules alone disrupts sequence learning. Experiment 1 results fully supported this S-R rule theory of sequence learning. Experiment 2 results provided only partial support for this theory, though the data were also inconsistent with both of the other accounts.

This research focuses to develop some new techniques on statistical learning including methodology, computation and application. We also developed statistical quantification in nanomaterials. For a large number of random variables with temporal or spatial structures, we proposed shrink estimates of covariance matrix to account their Markov structures. The proposed method exploits the sparsity in the inverse covariance matrix in a systematic fashion. To deal with high dimensional data, we proposed a robust kernel principal component analysis for dimension reduction, which can extract the nonlinear structure of high dimension data more robustly. To build a prediction model more efficiently, we developed an active learning via sequential design to actively select the data points into the training set. By combining the stochastic approximation and D-optimal designs, the proposed method can build model with minimal time and effort. We also proposed factor logit-models with a large number of categories for classification. We show that the convergence rate of the classifier functions estimated from the proposed factor model does not rely on the number of categories, but only on the number of factors. It therefore can achieve better classification accuracy. For the statistical nano-quantification, a statistical approach is presented to quantify the elastic deformation of nanomaterials. We proposed a new statistical modeling technique, called sequential profile adjustment by regression (SPAR), to account for and eliminate the various experimental errors and artifacts. SPAR can automatically detect and remove the systematic errors and therefore gives more precise estimation of the elastic modulus.

Thesis (M.I.T.)--The Evergreen State College, 2007. / Title from title screen viewed (4/10/2008). Includes bibliographical references (leaves 76-78).

Thesis (PhD)--Macquarie University, Division of Information and Communication Sciences, Computing Department, 2008. / Bibliography: p. 503-514.

Thesis (PhD) -- Australian Catholic University, 2007. / A thesis submitted in fulfilment of the requirements of the degree of Doctor of Philosophy. Bibliography p. 339 - 356. Also available in an electronic version via the internet.

Thesis (D. Ed. Min.)--Midwestern Baptist Theological Seminary, 2006. / Abstract. Includes bibliographical references (leaves 128-132).

Thesis (M.A.)--Wheaton College Graduate School, 2002. / Abstract. Includes bibliographical references (leaves 48-50).

Thesis (M. Ed.)--University of Hong Kong, 1996. / Includes bibliographical references (leaf 72-82).

Thesis (M. Ed.)--Memorial University of Newfoundland, 1997. / Bibliography: leaves 96-100.