title: Assessment of hippocampal and autonomic neural activity by point process models.
abstract: The development of statistical models that accurately describe the stochastic structure of neural oscillations is a fast growing area in quantitative research. In developing a novel statistical paradigm based on Bayes' theorem and the theory of point processes, we focused our recent research on two applications. The first studies how hippocampal neural activity represents and transmits information, whereas the second is aimed at characterizing activity of the central autonomic network as involved in cardiovascular control.
authors.affiliation: Neuroscience Statistics Research Laboratory, Dept of Anesthesia and Critical Care, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
authors.forename: Riccardo
authors.initials: R
authors.lastname: Barbieri
authors.forename: Zhe
authors.initials: Z
authors.lastname: Chen
authors.forename: Emery N
authors.initials: EN
authors.lastname: Brown
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000222:Adaptation, Physiological / Q000502:physiology; D000465:Algorithms; D000818:Animals; D001341:Autonomic Nervous System; D001499:Bayes Theorem; D001921:Brain / Q000473:pathology; D006339:Heart Rate; D006624:Hippocampus / Q000033:anatomy & histology / Q000473:pathology*; D006801:Humans; D008959:Models, Neurological; D009473:Neuronal Plasticity / Q000502:physiology; D018709:Statistics, Nonparametric