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Sequential Monte Carlo Method for Bayesian Multiple Testing of Pairwise Interactions among Large Number of Neurons
- Source :
- ICNC-FSKD
- Publication Year :
- 2018
- Publisher :
- IEEE, 2018.
-
Abstract
- The problem of multiple testing arises in many contexts, including testing for pairwise interaction among a large number of neurons. Recently a method was developed to control false positives when covariate information, such as distances between pairs of neurons, is available. This method, however, relies on computationally-intensive Markov Chain Monte Carlo (MCMC). Here we develop an alternative, based on Sequential Monte Carlo, which only requires one pass of the data. This scheme considers data items sequentially, with relevant probabilities being updated at each step. Simulation experiments demonstrate that the proposed algorithm delivers results as accurately as the previous MCMC method. We illustrate the method by using it to analyze neural recordings from extrastriate cortex in a macaque monkey.
- Subjects :
- 0301 basic medicine
Quantitative Biology::Neurons and Cognition
Computer science
Bayesian probability
Markov chain Monte Carlo
03 medical and health sciences
symbols.namesake
030104 developmental biology
Covariate
Multiple comparisons problem
symbols
Pairwise comparison
Particle filter
Algorithm
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)
- Accession number :
- edsair.doi...........c820e232cc50e1de5a73e29e6b3556b8