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Type of Document	Dissertation
Author	Mulliken, Grant Haverstock
URN	etd-05282008-192406
Persistent URL	http://resolver.caltech.edu/CaltechETD:etd-05282008-192406
Title	Continuous sensorimotor control mechanisms in posterior parietal cortex : forward model encoding and trajectory decoding
Degree	PhD
Option	Computation and Neural Systems
Advisory Committee	Advisor Name Title Shinsuke Shimojo Committee Chair Christof Koch Committee Member Joel Burdick Committee Member Pietro Perona Committee Member Richard A. Andersen Committee Member
Keywords	sensorimotor control state estimation brain-machine interface forward model posterior parietal cortex neural prosthetics trajectory decoding neurophysiology
Date of Defense	2008-04-22
Availability	restricted
Abstract During goal-directed movements, primates are able to rapidly and accurately control a movement despite substantial delay times (more than 200 milliseconds) incurred in the sensorimotor control loop. To compensate for these large delays, it has been proposed that the brain uses an internal forward model of the arm to estimate current and upcoming states of a movement, which would be more useful for rapid online control. To study online control mechanisms in the posterior parietal cortex (PPC), we recorded from single neurons while monkeys performed a joystick task. Neurons encoded the static target direction and the dynamic heading direction of the cursor. The temporal encoding properties of many heading neurons reflected a forward estimate of the current state of the cursor that is neither directly available from passive sensory feedback nor compatible with outgoing motor commands, and is thus consistent with PPC serving as a forward model for online sensorimotor control. In addition, we found that the space-time tuning functions of these neurons mostly encode straight and approximately instantaneous trajectories. Recent advances in cortical prosthetics have focused on recording neural activity in motor cortices and decoding these signals to control the trajectory of a cursor on a computer screen. Building on our encoding results, we demonstrate that joystick-controlled trajectories can also be decoded from PPC ensembles, presumably extracting the dynamic state of the cursor from a forward model. Remarkably, we found that we could accurately reconstruct a monkey’s trajectories using only 5 simultaneously recorded PPC neurons. Furthermore, 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 thoughts. 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 2D space) as well as an increase in offline decoding performance of the PPC ensemble. This work marks an important step forward in the development of a neural prosthesis using signals from PPC.
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