Recent advances in electric technology make it possible to obtain recordings from multiple cortical neurons. This ability opens a number of possibilities for expanding the understanding of cortical function by allowing the aggregate behavior of neural populations to examine during complex tasks. This project is to develop the computational techniques necessary for efficiently processing data collected from multichannel electrode arrays and to use these techniques for studying the role primary motor cortex in the control animal movement. Systems identification techniques will be used to examine neural information processing. Algorithm development will focus on efficient linear nonparametric multiple-input, single-output techniques for quantifying the transfer of information between neural activity and physiological processes. The algorithm process will then be extended to incorporate nonlinear phenomena such as threshold nonlinearities inherent to neural integration. These techniques will be used to examine it sensory information linked volitional movements influences the plasticity of primary more cortex during performance of skilled motor tasks. If a link is demonstrated this may provide a path for enhancing acquisition of motor skills for a range of tasks.
