Conference on Modeling Neural Activity: Statistics, Dynamical Systems, and Networks

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This award supports participation in the conference on Modeling Neural Activity: Statistics, Dynamical Systems, and Networks (MONA2) held June 22-24, 2016 in Lihue, Hawaii. 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. This conference will bring together statisticians and computational neuroscientists from the US and Japan in order to enhance collaborations between scientists in the two countries. NSF funding will help support the involvement of the US researchers. Computational neuroscience has grown, in distinct directions, from the success of biophysical models of 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 study 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. This award seeks to encourage the creation and enhancement of collaborations between scientists from the US and Japan. More information can be found on the conference web site http://www.stat.cmu.edu/mona2.