Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2002. / Includes bibliographical references (leaves 78-91). Also available in electronic version. Access restricted to campus users.

Thesis (Ph. D.)--Michigan State University. Electrical Engineering, 2008. / Title from PDF t.p. (Proquest, viewed on Aug. 17, 2009) Includes bibliographical references (p. 120-135). Also issued in print.

Thesis (M. S.)--Psychology, Georgia Institute of Technology, 2009. / Committee Chair: Dr. Paul Corballis, Ph.D.; Committee Member: Dr. Daniel Spieler, Ph.D.; Committee Member: Dr. Eric Schumacher, Ph.D.; Committee Member: Dr. Krish Sathian, M.D., Ph.D.; Committee Member: Dr. Randall Engle, Ph.D.

Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1993. / Vita. Abstract. Includes bibliographical references (leaves 134-135). Also available via the Internet.

Thesis (M.C.S.)--Carleton University, 1993. / Also available in electronic format on the Internet.

Thesis (doctoral)--Lund University, 1997. / Added t.p. with thesis statement inserted.

No description available.

Source: Dissertation Abstracts International, Volume: 70-03, Section: B, page: 1623. Adviser: Ross Shegog. Includes bibliographical references.

A wide range of conditions are characterised by focal neurological symptoms, yet the pathophysiolgy often remains poorly understood. This thesis has focussed on applying functional neuroimaging in clinical groups. Migraines with aura are amongst the most common conditions posing a significant burden to sufferers. Elevated connectivity was detected in the visual cortex of migraine with aura patients, potentially complementing one of the leading proposed mechanisms for attacks. Minor strokes patients are also affected by focal symptoms after events which in some cases can be prolonged. Altered connectivity was observed in a number of regions reflecting previous findings for acute stroke. A group of transient ischaemic attack patients were also analysed, revealing subtle differences necessitating further study. Lastly disorders of consciousness pose acute challenges for treatment and ongoing care. Task based imaging was applied to form a more accurate picture of residual cognition. Additionally the correlation between measures derived from resting state data and cerebral glucose consumption was explored.

Neural networks are used in various fields to make predictions about the future value of a time series, or about the class membership of a given object. For the network to be effective, it needs to be trained on a set of training data combined with the expected results. Two aspects to keep in mind when considering a neural network as a solution, are the required training time and the prediction accuracy. This research compares the classification accuracy of conjugate gradient descent neural networks and Bayesian learning neural networks. Conjugate gradient descent networks are known for their short training times, but are not very consistent and results are heavily dependant on initial training conditions. Bayesian networks are slower, but much more consistent. The two types of neural networks are compared, and some attempts are made to combine their strong points in order to achieve shorter training times while maintaining a high classification accuracy. Bayesian learning outperforms the gradient descent methods by almost 1%, while the hybrid method achieves results between those of Bayesian learning and gradient descent. The drawback of the hybrid method is that there is no speed improvement above that of Bayesian learning. / Dissertation (MEng (Electronics))--University of Pretoria, 2005. / Electrical, Electronic and Computer Engineering / unrestricted