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This manuscript-based thesis explores the relationship between learning and development. The first manuscript reviews the important empirical regularities identified in human discrimination shift learning, including a qualitative age-related change in performance observed in childhood. Leading theoretical accounts of the empirical data are discussed, suggesting that none provides a comprehensive interpretation. The manuscript presents the novel, spontaneous overtraining interpretation. It hypothesizes that age-related changes in human shift learning stem from differences in amount of processing. Successful neural network simulations of the reversal and nonreversal shift tasks and of the optional shift task are reported as tests of the hypothesis. / The second manuscript reports simulations of additional discrimination shift tasks. These are the intradimensional and extradimensional shift tasks, in which novel stimuli are introduced in the relearning phase. Preschoolers and adults exhibit the same pattern of behavior in this variant of shift learning. Simulation results show that the spontaneous overtraining hypothesis captures this effect. / The third chapter reports an empirical validation of the shift learning model. If the shift learning performance of adults is a consequence of more extensive processing, it follows that adults in whom such processing is prevented should perform as preschoolers. Sixty adults took part in a shift learning experiment with a Brown-Peterson task as a cognitive load. Results mirror those observed with preschoolers. As a control, 40 adults performed the shift learning experiment without the cognitive load. These results replicate the typical adult performance. Overall, these experiments lend additional support to the model developed in Manuscript 1. / The final manuscript is a theoretical discussion of the relationship between learning and development. Two classes of neural networks are discussed, and their underlying assumptions about learning and development are highlighted. These are static architecture and generative architecture networks. It is argued that only generative algorithms, such as used in the shift learning simulations, qualify as developmental models. Both classes of networks are further contrasted with respect to innateness. The comparison suggests that only generative networks can acquire genuinely new representations. The manuscript proposes a novel formulation of Piaget's constructivism from the generative neural network perspective.

This thesis is concerned with the function and implementation of synchronization in networks of oscillators. Evidence for the existence of synchronization in cortex is reviewed and a suitable architecture for exhibiting synchronization is defined. A number of factors which affect the performance of synchronization in networks of laterally coupled oscillators are investigated. It is shown that altering the strength of the lateral connections between nodes and altering the connective scope of a network can be used to improve synchronization performance. It is also shown that complete connective scope is not required for global synchrony to occur. The effects of noise on synchronization performance are also investigated and it is shown that where an oscillator network is able to synchronize effectively, it will also be robust to a moderate level of noise in the lateral connections. Where a particular oscillator model shows poor synchronization performance, it is shown that noise in the lateral connections is capable of improving synchronization performance. A number of applications of synchronizing oscillator networks are investigated. The use of synchronized oscillations to encode global binding information is investigated and the relationship between the form of grouping obtained and connective scope is discussed. The potential for using learning in synchronizing oscillator networks is illustrated and an investigation is made into the possibility of maintaining multiple phases in a network of synchronizing oscillators. It is concluded from these investigations that it is difficult to maintain multiple phases in the network architecture used throughout this thesis and a modified architecture capable of producing the required behaviour is demonstrated.

No description available.

Thesis (M.A.) in Mathematics--University of Maine, 2004. / Includes vita. Includes bibliographical references (leaves 40-42).

Thesis (M.S.)--University of Nevada, Reno, 2006. / "December 2006." Includes bibliographical references (leaves 42-44). Online version available on the World Wide Web.

Thesis (Ph.D.)--McMaster University, 2005. / Supervisor: Suzanna Becker. Includes bibliographical references.

Thesis (Ph. D.)--University of California, San Diego, 1998. / Vita. Includes bibliographical references (leaves 92-96).

Thesis (Ph. D.)--University of California, San Diego and San Diego State University, 1999. / Vita. Includes bibliographical references (leaves 192-203).

Thesis (M.S.)--University of North Texas, Dec., 2009. / Title from title page display. Includes bibliographical references.