text: Speaker: Ricardo Vigário (Aalto University School of Science and Technology, Finland)
  Date: Friday, July 2nd, 2010
  Title: "From elements to networks of neuronal activity – a machine learning approach"
  Neuroinformatics "combines neuroscience and the information sciences to develop and apply advanced tools for a major advancement in understanding the structure and function of the brain." After introducing the speaker's neuroinformatics research group, we will address issues related to the use and misuse of independent component analysis.
  Departing from the traditionally simple evoked response paradigm, into the more natural neurocinematics one, also the neuronal responses are expected to take on rather complex network configurations. We will review two approaches to identify such communication strategies. In a functional magnetic resonance imaging setup, the first one is a hierarchical method, and assumes the existence of basic focal activation areas, which are combined to account for the complex neuronal responses.
  Additional information is gathered directly from the stimuli. The second uses phase synchrony as the acting principle for the extraction of communication/control in high temporal resolution data, such as electro- and magnetoencephalograms.
  Bio: Ricardo Vigário, D.Sc., is a docent and senior researcher at the Aalto University School of Science and Technology, Finland, where he teaches and leads a research group in Neuroinformatics. He has a basic degree in Applied Physics and a M.Sc. in Biomedical Engineering from the Faculty of Sciences of the University of Lisbon and a D.Sc. (tech) in Computer Science from the Helsinki University of Technology (current Aalto University), from 1992, 1994 and 1999, respectively. He has held a Marie Curie post-doctoral position in GMD – FIRST, Germany; was a visiting lecturer in Graz, Austria and Zaragoza, Spain; and a visiting associate professor in Grenoble, France. He was a pioneer in the independent component analysis of electrophysiological data. His fields of interest include statistical machine learning; the analysis of neuronal responses to natural stimuli; and various communication strategies within the central