Modeling Neural Activity: Statistics, Dynamical Systems and Networks

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The workshop "Modeling Neural Activity: Statisics, Dynamical Systems, and Networks (MONA)" will be held June 26-28, 2013, in Lihue, Hawaii. 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. A workshop "Modeling Neural activity: Statisics, Dynamical Systems, and Networks (MONA)" will be held June 26-28, 2013, in Lihue, Hawaii, with the purpose of exploring 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.Many disorders, such as ADHD, autism, and schizophrenia, as well as stroke and various neurodegenerative diseases, are thought to involve dysfunction of neural networks. Because computational neuroscience aims to supply principles for understanding the activity of individual and collective neural firing patterns, its successes can help in formulating mechanistic descriptions of pathophysiology. The workshop ``Modeling Neural Activity: Statisics, Dynamical Systems, and Networks (MONA)" is at the interface between mathematics, statistics, and neural network analysis, and has as its goal to generate new and productive lines of research.