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Abstract Details

Activity Number: 192
Type: Contributed
Date/Time: Monday, July 30, 2012 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Consulting
Abstract - #306940
Title: Simulation and Model Calibration with Sensitivity Analysis for Threat Detection in Brain
Author(s): Sumona Mondal*+ and Keegan Lowenstein and Brian Leventhal and Kylie Drouin and Robert Dowman and Katie Fowler
Companies: Clarkson University and Clarkson University and Clarkson University and Clarkson University and Clarkson University and Clarkson University
Address: , Potsdam, NY, 13699,
Keywords: Neural Network ; Least-Square Minimization ; Analysis of Variance

In this study we use optimization techniques and sensitivity analyses to provide more rigorous, quantitative connectionist models of the functional interactions between the brain areas involved in detecting and orienting attention towards threat. A toolkit has been developed using flexible neural network modeling with automated parameter estimation. A sensitivity analysis is provided within the framework to identify significant model parameters and better understand model dependencies. These studies emphasize the importance of fitting the models to behavioral reaction time and brain activation data. They also show that the specific architecture of the model, and the numerical precision in the model parameters is important in determining an acceptable fit of experimental data, and that it is not the case that any model will work given the appropriate set of connection strength parameters.

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