Abstract

Multiple-electrode neuronal recording has created tremendous new opportunities for gaining insight into the neural substrate of behavior, and it has made possible the construction of brain-controlled robotic devices. A crucial problem is to describe evolution of neuronal activity during learning, which is of interest not only from the point of view of basic science, but also because knowledge of the changes that occur while a subject learns a task is necessary for the construction of reliable neural prosthetic algorithms. This research will develop and adapt statistical methods for analysis of multiple-electrode data from a series of experiments aimed at understanding the evolution of cortical activity in several areas of the brain while a monkey learns hand movement tasks. The results will lead to improvements in brain-controlled robotic devices for neural prostheses and, thus, will likely benefit people paralyzed by head or spinal cord trauma, amputees, and those with severe deficits caused by diseases such as stroke, amyotrophic lateral sclerosis, cerebral palsy, or multiple sclerosis.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
5R01EB005847-03
Application #
7255732
Study Section
Special Emphasis Panel (ZRG1-IFCN-B (50))
Program Officer
Peng, Grace
Project Start
2005-09-01
Project End
2009-06-30
Budget Start
2007-07-01
Budget End
2008-06-30
Support Year
3
Fiscal Year
2007
Total Cost
$293,468
Indirect Cost

  Institution

Name
Carnegie-Mellon University
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
052184116
City
Pittsburgh
State
PA
Country
United States
Zip Code
15213

  Publications

Golub, Matthew D; Yu, Byron M; Schwartz, Andrew B et al. (2014) Motor cortical control of movement speed with implications for brain-machine interface control. J Neurophysiol 112:411-29
Chase, Steven M; Kass, Robert E; Schwartz, Andrew B (2012) Behavioral and neural correlates of visuomotor adaptation observed through a brain-computer interface in primary motor cortex. J Neurophysiol 108:624-44
Ventura, Valerie; Gerkin, Richard C (2012) Accurately estimating neuronal correlation requires a new spike-sorting paradigm. Proc Natl Acad Sci U S A 109:7230-5
Xu, Yang; Sudre, Gustavo P; Wang, Wei et al. (2011) Characterizing global statistical significance of spatiotemporal hot spots in magnetoencephalography/ electroencephalography source space via excursion algorithms. Stat Med 30:2854-66
Legenstein, Robert; Chase, Steven M; Schwartz, Andrew B et al. (2010) A reward-modulated hebbian learning rule can explain experimentally observed network reorganization in a brain control task. J Neurosci 30:8400-10
Shimokawa, Takeaki; Koyama, Shinsuke; Shinomoto, Shigeru (2010) A characterization of the time-rescaled gamma process as a model for spike trains. J Comput Neurosci 29:183-191
Paninski, Liam; Ahmadian, Yashar; Ferreira, Daniel Gil et al. (2010) A new look at state-space models for neural data. J Comput Neurosci 29:107-26
Koyama, Shinsuke; Pérez-Bolde, Lucia Castellanos; Shalizi, Cosma Rohilla et al. (2010) Approximate Methods for State-Space Models. J Am Stat Assoc 105:170-180
Wang, Wei; Sudre, Gustavo P; Xu, Yang et al. (2010) Decoding and cortical source localization for intended movement direction with MEG. J Neurophysiol 104:2451-61
Chase, Steven M; Schwartz, Andrew B; Kass, Robert E (2010) Latent inputs improve estimates of neural encoding in motor cortex. J Neurosci 30:13873-82

Showing the most recent 10 out of 19 publications