Weighted Kernel Density Estimation of the Prepulse Inhibition Test
Hongbo Zhou, Qiang Cheng, Hong-Ju Yang and Haiyun Xu
DOI : 10.3844/jcssp.2011.611.618
Journal of Computer Science
Volume 7, Issue 5
Problem statement: The goal of this study was to devise a more reliable and sensitive method for analysis of experimental data of the Prepulse Inhibition (PPI), the reduction in startle reaction towards a startle-eliciting “pulse” stimulus when it is shortly preceded by a sub-threshold “prepulse” stimulus. Approach: Different from the conventional simple averaging-based method, we proposed a probabilistic approach to modeling the PPI data. With this probabilistic description, we reconstructed complete response signals from the PPI data and devised a nonparametric weighted Kernel Density Estimation (KDE) method to tackle two important issues in PPI data related density estimation: instability and limited number of samples. We designed two sets of animal experiments using different medicines and compared the KDE based method with the conventional simple-averaging based method. Results: Our results showed that the KDE method performed better than the conventional method and offered some advantages over the conventional method. Conclusion: The new method provided a more reliable and sensitive approach to the post-session analysis of PPI data.
© 2011 Hongbo Zhou, Qiang Cheng, Hong-Ju Yang and Haiyun Xu. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.