Nathan Scott – PhD & Publications

Ph.D. Study

Evolving Spiking Neural Networks for Spatio- and Spectro-Temporal Data Analysis: Models, Implementations, Applications.

My Ph.D. looked at the applications of large-scale probabilistic computational spiking neural network modelling for spatio-temporal pattern recognition. I introduced design theory and high-performance computing implementations of these networks, and showed how they are a superior approach to addressing the problem of complex adaptive pattern recognition. Practical applications were explored, including in stroke rehabilitation and radioastronomy.

During this work, I was able to publish and share my work in a number of different forums, including as an invited lecturer at the IEEE CIS Summer School on Neuromorphic and Cyborg Intelligent Systems, and workshop leader at international conferences such as INNS Big Data and the Capo Caccia CSN Workshop. Articles directly related to my thesis have won several best paper awards, most notably being recognised as the best paper for 2016 in the Neural Networks Journal, and as best paper in the IEEE 8th International Conference on Intelligent Systems. I was also honoured to organise and chair multiple international A/A* rated conference special sessions, including at WCCI, IJCNN, and ICONIP conferences.

My thesis can be accessed here.

My Google Scholar Profile.

Please note that this publication list is not necessarily up to date.


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