Statistical Analysis of Neuronal Data (SAND4)
- Travel support will be available.
- Several sessions will be devoted to presentations by young investigators.
- All participants are encouraged to present posters.
- We expect selected papers to be published in a special issue of the Journal of Computational Neuroscience.
| Studies of the neural
basis of behavior typically use time-varying stimuli and produce time-varying
neuronal responses. Statistically, the setting involves both continuous
multiple time series and inhomogeneous point processes, sometimes dozens
or hundreds of them observed simultaneously. There are many challenging
analytical issues, including that of combining information obtained from
multiple modalities (EEG, fMRI, MEG, and extracellular recordings). This
workshop series aims to |
- define important
problems in neuronal data analysis and useful strategies for attacking
- evaluate analytical methods by their ability to yield insightful results in
- foster communication
between experimental neuroscientists and those trained in statistical
and computational methods;
- encourage young
researchers, including graduate students, to present their work;
- expose young researchers
to important challenges and opportunities in this interdisciplinary
domain, while providing a small meeting atmosphere to facilitate the
interaction of young researchers with senior colleagues.
- Dubois Bowman (Emory), fMRI imaging
- Ann Graybiel (MIT), multielectrode recording and behavior
- Matti Hamalainen (Mass. General Hospital), MEG imaging
- David Kleinfeld (UCSD), 2-photon imaging
- Sheila Nirenberg (Cornell) multielectrode recording
- Liam Paninski (Columbia), multielectrode recording
- Barry Richmond (NIH), Closing comments: Outlook for the future
- Charles Schroeder (Columbia) multielectrode recording
- Valerie Ventura (CMU), spike sorting
NIMH (Computational Neuroscience); NSF (Statistics; Computational Neuroscience);
Center for the Neural Basis of Cognition, Carnegie Mellon and University of
Pittsburgh; Department of Statistics, Carnegie Mellon; Machine Learning Department,
Carnegie Mellon; Department of Statistics, University of Pittsburgh.