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Mapping the brain and its complex networked structure has been one of the most researched topics in the last decade and continues to be the path towards understanding brain diseases. In this paper we present a new approach to estimating the connectivity between neurons in a network model. We use systems identification techniques for nonlinear dynamic models(More)
— We propose a new method for fitting model parameters to the neural spike train obtained from an experimental neuron. Due to the uncertainty associated with measuring the accurate voltage in a noisy environment, it is essential to develop methods that rely solely on the interspike intervals (ISI). Existing techniques do not provide a smooth and continuous(More)
The increasing need of knowledge in the treatment of brain diseases has driven a huge interest in understanding the phenomenon of neural spiking. Researchers have successfully been able to create mathematical models which, with specific parameters, are able to reproduce the experimental neuronal responses. The spiking activity is characterized using spike(More)
This thesis describes the research and development of a hardware implementation of the self organizing map (SOM) for a network intrusion detection system. As part of the thesis research, Kohonen's SOM algorithm was examined and different hardware implementations for the SOM were surveyed. This survey resulted in the design and implementation of a(More)
iii DEDICATION This is dedicated to my wife and constant supporter, Kathleen. Her patience and support during these years in making up all the hours lost to my studies was critical to my success. To my parents Donald and Joan, who gave me the character and education that has enabled me to get this far. To my children Dalton, Ryan, and Kaitlyn and their(More)
65 where the function E (tn, 0, $) is determined from a recurrence equation in [2]. Proo) Follows from an application of dynamic programming techniques. Examination of these two feedback control laws reveals that the first is the continuous-discrete version of the continuous results presented in [ 1 ] and [ l l ] originally derived using dynamic programming(More)
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