Abstract

Our goal is to use local field potentials (LFPs) to rigorously study changes in neuronal activity in the hippocampus and the nucleus accumbens 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 that 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 the evolving evolutionary spectral density matrix, we used a two-stage procedure. In the first stage, we computed the within-trial time-localized periodogram matrix. In the second stage, we developed a data-driven approach for combining information across trials from the local periodogram matrices. We assessed the performance of our proposed method using simulated data. Finally, we used the proposed model to study how the spectral properties of the hippocampus and the nucleus accumbens evolved over the course of an associative learning experiment. This is joint work with Hernando Ombao (Department of Statistics, UC Irvine).