CAUSAL INFERENCE WITH MULTIVARIATE NEUROPHYSIOLOGICAL DATA: SOME COMPUTATIONAL ISSUES
TITLE:
CAUSAL INFERENCE WITH MULTIVARIATE NEUROPHYSIOLOGICAL DATA: SOME COMPUTATIONAL ISSUES
DATE:
Friday, December 4th, 2009
TIME:
3:30 PM
LOCATION:
GMCS 214
SPEAKER:
Mark E. Pflieger, PhD, Source Signal Imaging, Inc.
ABSTRACT:
Biologically based computation in brains is highly parallel and integrative across multiple anatomical levels and time scales. Multi-channel neurophysiological modalities such as electroencephalography (EEG), magnetoencephalography (MEG), and electrocorticography (ECoG) provide clues about parallel processing in the brain at the systems level on time scales ranging from milliseconds to seconds and longer. How to interpret these clues is a difficult challenge√ɬ¢√¢¬Ç¬¨”one which requires the field of computational neuroscience to develop and integrate increasingly sophisticated tools for analyzing and modeling such data sets. In particular, methods are needed to identify and characterize cause-effect relationships which may signify modes of communication or indicate signaling events within brain-wide information processing networks. This talk will survey various approaches which have been designed to assess neurophysiological effective connectivity. Then we will focus on a particular inferential framework that is based on a quasi-causal information (QCI) measure. To conclude, we will consider some computational issues which so far have limited QCI analysis in practice.
HOST:
Jose Castillo
DOWNLOAD: