Modeling Neural Activity:
Statistics, Dynamical Systems, and Networks

June 26-28, 2013
Lihue, Hawaii

Refresh this page for the latest updates.

Computational neuroscience has grown, in distinct directions, from the success of biophysical models neural activity, the attractiveness of the brain-as-computer metaphor, and the increasing prominence of statistical and machine learning methods throughout science. This has helped create a rich set of ideas and tools associated with ``computation'' to studying the nervous system, but it has also led to a kind of balkanization of expertise. There is, especially, very little overlap between mathematical and statistical research in this area. Important breakthroughs in computational neuroscience could come from research strategies that are able to combine what are currently largely distinct approaches.

One purpose of this workshop is to explore potentially fruitful interactions of modeling ideas that come from mathematics, statistics, and biophysics. An additional purpose of the workshop is to bring together U.S. and Japanese researchers in this area. While computational neuroscience is represented strongly in both the U.S. and Japan there has been too little concrete communication and interaction between research groups across our two countries. Interaction across American and Japanese researchers should facilitate the advance of cross-disciplinary work.



Organized by:

Shun-ichi Amari , RIKEN

Rob Kass, Carnegie Mellon

Emery Brown, MIT and Harvard Medical School

Nancy Kopell, Boston University

Hiro Nakahara, RIKEN

Shigeru Shinomoto , Kyoto

Susanna Still , University of Hawaii