Dynamic and succinct statistical analysis of neuroscience data

Sanggyun Kim, Christopher J. Quinn, Negar Kiyavash, Todd P. Coleman

Research output: Contribution to journalArticlepeer-review


Modern neuroscientific recording technologies are increasingly generating rich, multimodal data that provide unique opportunities to investigate the intricacies of brain function. However, our ability to exploit the dynamic, interactive interplay among neural processes is limited by the lack of appropriate analysis methods. In this paper, some challenging issues in neuroscience data analysis are described, and some general-purpose approaches to address such challenges are proposed. Specifically, we discuss statistical methodologies with a theme of loss functions, and hierarchical Bayesian inference methodologies from the perspective of constructing optimal mappings. These approaches are demonstrated on both simulated and experimentally acquired neural data sets to assess causal influences and track time-varying interactions among neural processes on a fine time scale.

Original languageEnglish (US)
Article number6782438
Pages (from-to)683-696
Number of pages14
JournalProceedings of the IEEE
Issue number5
StatePublished - May 2014
Externally publishedYes


  • BRAIN initiative
  • Prediction with expert advice
  • directed information
  • human brain project
  • loss function
  • minimax regret
  • optimal transport theory
  • point processes

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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