Program on Challenges in Computational Neuroscience (CCNS)

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Neuroscience is accumulating exponentially growing volumes of data on specific aspects of the healthy and diseased brain, in different species, and at different ages. Brain theory, modeling, and statistics will be essential to turn this data into better understanding of the brain.

The Challenges in Computational Neuroscience program will develop mathematical and statistical methods in neuroscience to meet this critical need. Key problems include understanding the mechanisms that bridge multiple spatial and temporal scales, linking the activity of individual components (e.g., molecular biology, genetics, and neuron networks) and their interactions to the overall complex dynamic behavior of the brain and nervous system.

The CCNS program will address the underlying methodological, theoretical, and computational challenges. Probability and statistics, dynamical systems, geometry, and computer science will be combined with respect to theory and in applications. Researchers in neuroscience, biomedical engineering, computer science, applied mathematics, and statistics are encouraged to apply to the program.

CCNS Working Groups

Clinical Brain Imaging
Working Group Leader: Ciprian Crainiceanu, Johns Hopkins University

Computational Approaches to Large-scale Inverse Problems with Applications to Neuroscience
Working Group Leader: Arvind Saibaba, North Carolina State University

Understanding Neuromechanical Processes in Locomotion with Physical Modeling and Network Analysis
Working Group Leaders: Laura Miller, UNC and Katie Newhall, UNC

Mathematical and Statistical Approaches to Modeling Brain Networks: circuits and systems
Working Group Leaders: Rob Kass, Carnegie Mellon University; Uri Eden, Boston U.; Mark Kramer, Boston U.

Theory of neural networks: structure and dynamics
Working Group Leaders: Carina Curto, PSU; Brent Doiron, U. of Pittsburgh; Chris Hillar, MSRI

Acquisition, Reconstruction, and Processing of MRI Data
Working Group Leader: Daniel Rowe, Marquette University

Imaging Genetics
Working Group Leader: Hongtu Zhu, UNC

Structural Connectivity
Working Group Leaders: David Dunson, Duke University; Hongtu Zhu, UNC

Functional Imaging Methods and Functional Connectivity
Working Group Leaders: Hernando Ombao, UCI; John Aston, University of Cambridge

Big Data Integration in Neuroimaging
Working Group Leaders: Martin Lindquist and Timothy Johnson

Analysis of Optical Imaging Data
Working Group Leader: Mark Reimers

All CNCS participants are asked to acknowledge support in any relevant publications and presentations, “This material was based upon work partially supported by the National Science Foundation under Grant DMS-1127914 to the Statistical and Applied Mathematical Sciences Institute.”

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Additional information on each group and can be found at Working Groups.

Questions: email CCNS@samsi.info