Weighted Kernel Density Estimation of the prepulse inhibition test

Hongbo Zhou, Qiang Cheng, Hong Ju Yang, Haiyun Xu

Research output: Contribution to journalArticlepeer-review


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 simpleaveraging 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.

Original languageEnglish
Pages (from-to)611-618
Number of pages8
JournalJournal of Computer Science
Issue number5
StatePublished - 2011


  • Clozapine (CLZ)
  • Cuprizone (CPZ)
  • Dopamine hyperactivity
  • Kernel density estimation
  • Non-parametric
  • Prepulse inhibitation test
  • Quetiapine (QTP)
  • Random variables
  • Startle response

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Artificial Intelligence


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