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• W2946501903 abstract "Neural networks are an important tool in the field of data science as well as in the study of the very structures they wereinspired from i.e. the human nervous system. In this dissertation, we studied the application of neural networksin data modeling as well as their role in studying the properties of various structures in the nervous system. This dissertation has two foci: one relates to developing methods that help improve gls{generalization} in data models and the otheris to study the possible effects of the structure on the function. As the first focus of this dissertation, we proposed a set of heuristics that improve the gls{generalization} capability of the neural network models in regression and classification problems.To do so, we explored applying apprioi information in the form of gls{regularization} of the behavior of the models. We used smoothness and self-consistency as the two regularized attributes that were enforced on the behavior of the neural networks in our model. We used our proposed heuristics to improve the performance neural network ensembles in regression problems (more specifically in quantitative structure–activity relationship (QSAR) modeling problems). We demonstrated that these heuristics result in significant improvements in the performance of the models we used.In addition, we developed an anomaly detection method to identify and exclude the outliers among unknown cases presented to the model. This was to ensure that the data model only made a prediction about the outcome of the unknown cases that were within its domain of applicability. This filtering resulted in further improvement of the performance of the modelin our experiments.Furthermore, and through some modifications, we extended the application of our proposed heuristics to classification problems.We evaluated the performance of the resulting classification models over several datasets and demonstrated that the gls{regularization}s we employed in our heuristics, had a positive effect on the performance of the data model across various classification problemsas well. In the second part of this dissertation, we focused on studying the relationship between the structure and the functionality in the nervoussystem. More specifically, whether or not the structure implies functionality. In studying these possible effects, we elected to study CA3b in Hippocampus. For this reason, we used current related literature to derive a physiologically plausible model of CA3b. To make our proposed model as close as possible to its counterpart in the nervous system, we used large scale neural simulations, in excess of 45,000 neurons, in our experiments. We used the collective firings of all the neurons in our proposed structure to produce a time series signal. We considered this time-series signal which is a way to demonstrate the overall output of the structure should it be monitored by an EEG probeas the output of the structure. In our simulations, the structure produced and…" @default.
• W2946501903 created "2019-05-29" @default.
• W2946501903 creator A5007821011 @default.
• W2946501903 date "2019-01-01" @default.
• W2946501903 modified "2023-09-26" @default.
• W2946501903 title "Studies on applications of neural networks in modeling sparse datasets and in the analysis of dynamics of CA3 in hippocampus" @default.
• W2946501903 hasPublicationYear "2019" @default.
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