@article {10.3844/jcssp.2011.611.618, article_type = {journal}, title = {Weighted Kernel Density Estimation of the Prepulse Inhibition Test}, author = {Zhou, Hongbo and Cheng, Qiang and Yang, Hong-Ju and Xu, Haiyun}, volume = {7}, number = {5}, year = {2011}, month = {May}, pages = {611-618}, doi = {10.3844/jcssp.2011.611.618}, url = {https://thescipub.com/abstract/jcssp.2011.611.618}, abstract = {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.}, journal = {Journal of Computer Science}, publisher = {Science Publications} }