Abstract
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 language | English (US) |
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Article number | 6782438 |
Pages (from-to) | 683-696 |
Number of pages | 14 |
Journal | Proceedings of the IEEE |
Volume | 102 |
Issue number | 5 |
DOIs | |
State | Published - May 2014 |
Externally published | Yes |
Keywords
- BRAIN initiative
- Prediction with expert advice
- directed information
- human brain project
- loss function
- minimax regret
- optimal transport theory
- point processes
ASJC Scopus subject areas
- General Computer Science
- Electrical and Electronic Engineering