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The Journal of Neuroscience, November 26, 2008, 28(48):12913-12926; doi:10.1523/JNEUROSCI.1463-08.2008

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Behavioral/Systems/Cognitive
Decoding Trajectories from Posterior Parietal Cortex Ensembles

Grant H. Mulliken,¹ Sam Musallam,² and Richard A. Andersen^1,2

¹Computation and Neural Systems and ²Division of Biology, California Institute of Technology, Pasadena, California 91125

Correspondence should be addressed to Grant H. Mulliken at his present address: McGovern Institute for Brain Research, Massachusetts Institute of Technology, Building 46, Room 6165, 77 Massachusetts Avenue, Cambridge, MA 02139. Email: grantm{at}mit.edu

High-level cognitive signals in the posterior parietal cortex (PPC) have previously been used to decode the intended endpoint of a reach, providing the first evidence that PPC can be used for direct control of a neural prosthesis (Musallam et al., 2004). Here we expand on this work by showing that PPC neural activity can be harnessed to estimate not only the endpoint but also to continuously control the trajectory of an end effector. Specifically, we trained two monkeys to use a joystick to guide a cursor on a computer screen to peripheral target locations while maintaining central ocular fixation. We found that we could accurately reconstruct the trajectory of the cursor using a relatively small ensemble of simultaneously recorded PPC neurons. Using a goal-based Kalman filter that incorporates target information into the state-space, we showed that the decoded estimate of cursor position could be significantly improved. Finally, we tested whether we could decode trajectories during closed-loop brain control sessions, in which the real-time position of the cursor was determined solely by a monkey's neural activity in PPC. The monkey learned to perform brain control trajectories at 80% success rate (for 8 targets) after just 4–5 sessions. This improvement in behavioral performance was accompanied by a corresponding enhancement in neural tuning properties (i.e., increased tuning depth and coverage of encoding parameter space) as well as an increase in off-line decoding performance of the PPC ensemble.

Key words: brain–machine interface; trajectory decoding; neural prosthetics; sensorimotor control; posterior parietal cortex; neurophysiology

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Received April 4, 2008; revised Sept. 13, 2008; accepted Oct. 21, 2008.

Correspondence should be addressed to Grant H. Mulliken at his present address: McGovern Institute for Brain Research, Massachusetts Institute of Technology, Building 46, Room 6165, 77 Massachusetts Avenue, Cambridge, MA 02139. Email: grantm{at}mit.edu

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