Literature DB >> 19163509

Assessment of hippocampal and autonomic neural activity by point process models.

Riccardo Barbieri1, Zhe Chen, Emery N Brown.   

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.

Mesh:

Year:  2008        PMID: 19163509      PMCID: PMC2652877          DOI: 10.1109/IEMBS.2008.4650006

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  3 in total

1.  A point-process model of human heartbeat intervals: new definitions of heart rate and heart rate variability.

Authors:  Riccardo Barbieri; Eric C Matten; Abdulrasheed A Alabi; Emery N Brown
Journal:  Am J Physiol Heart Circ Physiol       Date:  2004-09-16       Impact factor: 4.733

2.  An analysis of hippocampal spatio-temporal representations using a Bayesian algorithm for neural spike train decoding.

Authors:  Riccardo Barbieri; Matthew A Wilson; Loren M Frank; Emery N Brown
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2005-06       Impact factor: 3.802

3.  Analysis of heartbeat dynamics by point process adaptive filtering.

Authors:  Riccardo Barbieri; Emery N Brown
Journal:  IEEE Trans Biomed Eng       Date:  2006-01       Impact factor: 4.538

  3 in total

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