Thesis (Ph.D.)--University of Nebraska-Lincoln, 2009. / Title from title screen (site viewed January 5, 2010). PDF text: xii, 124 p. : ill. ; 1 Mb. UMI publication number: AAT 3360161. Includes bibliographical references. Also available in microfilm and microfiche formats.

Thesis (Ph. D.)--University of California, San Diego, 2000. / Vita. Includes bibliographical references.

Thesis (M.A.)--Miami University, Dept. of Psychology, 2005. / Title from first page of PDF document. Document formatted into pages; contains [1], v, 48 p. : ill. Includes bibliographical references (p. 16-20).

Thesis (Ph. D.)--McMaster University (Canada), 2004. / Advisor: Shepard Siegel. Includes bibliographical references (leaves 64-71). Also available online.

Thesis (Ph. D.)--University of Texas at Austin, 2005. / Vita. Includes bibliographical references.

Recent improvements in high performance fluorescent sensors and scientific CMOS cameras enable optical imaging of neural networks at a much larger scale. Our lab has demonstrated the ability of wide-field calcium-imaging (using GCaMP6f) to capture the concurrent dynamic activity from hundreds to thousands of neurons over millimeters of brain tissue in behaving mice. The expansiveness of the neuronal network captured by the system requires innovation in data analysis methods. This thesis explores data analysis techniques to extract dynamics of hippocampal neural network containing a large number of individual neurons recorded using GCaMP6, while mice were learning a classical eye puff conditioning behavior. GCaMP6 fluorescence signals in each neuron is first considered one dimension, and each dataset thus contains hundreds to thousands dimensions. To understand the network structure, we first performed dimension reduction technique to examine the low-dimension evolution of the neural trajectory using Gaussian Process Factor Analysis, which smooths across dimensions, while extracting the low dimension representation. Because of the slow time course of GCaMP6 signals, the Factor Analysis was biased to the long lasting decay phase of the signal that does not represent neural activities. We found that it is critical to first estimate the spike train inference prior to application of dimension reduction, such as using the Fast Nonnegative Deconvolution method. While the low-dimension presentation described intriguing features in the neural trajectories that paralleled the learning behavior of the animal, to further quantify the network changes we directly examined the network in the high dimension space. We calculated the changes in the distance of the network trajectory over time in the high dimension space without any filtering, and compared across different phases of the behavioral states. We found that the speed of the trajectory in the high dimension space is significantly higher when animal learned the task, and the trajectory travelled much further away from baseline during the delay phase of the conditioning behavior. Together, these results demonstrate that dimension reduction analysis technique and the network trajectory within the non-reduced high dimension space can capture evolving features of neural networks recorded using calcium imaging. While this thesis concerns the hippocampal dynamics during learning, such data analysis techniques are expected to be broadly applicable to other behaviorally relevant networks.

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Thesis (Ph. D.)--State University of New York at Binghamton, Department of Psychology, 2006. / Includes bibliographical references.

The effect of post-training self-stimulation on associations formed during classical conditioning was studied. In the first three experiments, rats experienced tone-shock, tone-water and tone-light pairings, respectively. Control groups experienced the same situations except that the temporal relationship between the stimuli excluded the possibility of an association being formed. Following this training period, the experimental animals pressed a bar for electrical self-stimulation of the brain; control animals did not self-stimulate. Retention of the association between the training stimuli was evaluated by measuring conditioned suppression of drinking (experiments 1 and 3), or extinction of secondary reinforcement. The results showed that self-stimulation can retroactively and non-contingently improve memory for associations in various classical conditioning situations, independently of the presence of a natural reinforcer or of the responses they elicit. In another experiment, the effects of varying amounts of tone preexposure (latent inhibition) and of varying amounts of post-training self-stimulation on retention of a tone-shock association, measured by the amount of tone-induced suppression of drinking, were studied. Increasing amounts of tone pre-exposure produced decreased retention over all levels of self-stimulation, and increasing amounts of self-stimulation produced increased retention over all levels of pre-exposure. The interaction of these two factors suggests that they both acted on the strength of the tone-shock association: the pre-exposure acted proactively to weaken the association; the self-stimulation acted retroactively to strengthen it. The effect of delaying the post-training stimulation for various times after the tone-shock pairings was also investigated. Retention was improved by self-stimulation delayed for 15 or 30 minutes, but was not affected by self-stimulation delayed for an hour. These experiments suggest that direct activation