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SUMMARY:&quot\;Modelling the evolution of brain signals&quot\; - Dr Mark F
 iecas\, University of Warwick
DTSTART:20160524T133000Z
DTEND:20160524T143000Z
UID:TALK65255@talks.cam.ac.uk
CONTACT:Alison Quenault
DESCRIPTION:Our goal is to use local field potentials (LFPs) to rigorously
  study changes in neuronal activity in the hippocampus and the nucleus acc
 umbens over the course of an associative learning experiment. We show that
  the spectral properties of the LFPs changed during the experiment. While 
 many statistical models take into account nonstationarity within a single 
 trial of the experiment\, the evolution of brain dynamics across trials is
  often ignored. In this talk\, we will discuss a novel time series model t
 hat captures both sources of nonstationarity. Under the proposed model we 
 rigorously define the spectral density matrix so that it evolves over time
  within a trial and also the across trials of an experiment. To estimate t
 he evolving evolutionary spectral density matrix\, we used a two-stage pro
 cedure. In the first stage\, we computed the within-trial time-localized p
 eriodogram matrix. In the second stage\, we developed a data-driven approa
 ch for combining information across trials from the local periodogram matr
 ices. We assessed the performance of our proposed method using simulated d
 ata. Finally\, we used the proposed model to study how the spectral proper
 ties of the hippocampus and the nucleus accumbens evolved over the course 
 of an associative learning experiment. This is joint work with Hernando Om
 bao (Department of Statistics\, UC Irvine).
LOCATION:Large  Seminar Room\, 1st Floor\, Institute of Public Health\, Un
 iversity Forvie Site\, Robinson Way\, Cambridge
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