Discovering the Neural Code: Data Mining Algorithms For Network Reconstruction

Start Date: 11/01/2006
End Date: 12/01/2008

With the advent of implantable multi-electrode arrays, computational neuroscience is poised for tremendous growth in the next decade, especially for developing biosensors, creating brain-computer interfaces, and the potential to decipher the neural code for key cellular functions. A key computational task is to unravel the network structure underlying a collection of measurements through the use of data mining techniques that
reconstruct order and dependencies. The goal of this project is to bring together Virginia Tech expertise in data  mining, computational science, and problem solving environments to develop advanced algorithms and software for network neuroscience. It will conduct research in advanced data mining algorithms for sequential and time-stamped data, with special emphasis on analysis of multi-neuronal data streams; create a suite of software incorporating these advanced algorithms and make them available to the larger neuroscience community; and
provide critical leadership in defining and creating the scientific discipline of "network neuroscience."

Grant Institution: General Motors Corporation

Amount: $220,262

People associated with this grant:

Naren Ramakrishnan