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Behavioral/Systems/Cognitive Decoding Trajectories from Posterior Parietal Cortex

Summary: Behavioral/Systems/Cognitive
Decoding Trajectories from Posterior Parietal Cortex
Grant H. Mulliken,1 Sam Musallam,2 and Richard A. Andersen1,2
1Computation and Neural Systems and 2Division of Biology, California Institute of Technology, Pasadena, California 91125
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
8 targets) after just 4­5 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: brain­machine interface; trajectory decoding; neural prosthetics; sensorimotor control; posterior parietal cortex;


Source: Andersen, Richard - Division of Biology, California Institute of Technology


Collections: Biology and Medicine