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SUMMARY:Modeling the Evolution of Neurophysiological Signals - Fiecas\, M 
 (University of Warwick)
DTSTART:20140114T100000Z
DTEND:20140114T103000Z
UID:TALK49862@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:In recent years\, research into analyzing brain signals has dr
 amatically increased\, and the rich data sets being collected require more
  advanced statistical tools and developments in order to perform proper st
 atistical analyses. Consider an experiment where a stimulus is presented m
 any times\, and after each stimulus presentation\, time series data is col
 lected. The time series data exhibit nonstationary characteristics. Moreov
 er\, across stimuli presentation the time series are non-identical and the
 ir spectral properties may even change over the course of the experiment. 
 In this talk\, we will look at a novel approach for analyzing nonidentical
  nonstationary time series data. We consider two sources of nonstationarit
 y: 1) within each replicate and 2) across the replications\, so that the s
 pectral properties of the time series data are evolving over time within a
  replicate\, and are also evolving over the course of the experiment. We e
 xtend the locally stationary time series model t o account for replicated 
 data\, with potentially correlated replicates. We analyze a local field po
 tential data set to study how the spectral properties of the local field p
 otentials obtained from the nucleus accumbens and the hippocampus of a mon
 key evolve over the course of a learning association experiment.\n
LOCATION:Seminar Room 1\, Newton Institute
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