Neural activity in posterior parietal cortex (PPC) can be harnessed to estimate not only the endpoint of a reach (Musallam, Corneil et al. 2004) but also to control the continuous trajectory of an end-effector (Mulliken, Musallam et al. 2008). Here we expand on this work by showing that trajectory information can be extracted robustly from PPC neurons in more realistic, less constrained tasks. Although it is thought that the visuo-motor areas in PPC rely on gaze-centered reference frames to encode movement-related parameters, for the first time we were able to show that hand movement can be decoded accurately without constraining gaze. Furthermore, to evaluate the potential of PPC signals for controlling prosthetic limbs under realistic conditions we increased the complexity of the task by studying point-to-point reaches in a 3D workspace instead of relying on the classic lower-dimensional 2D center-out task.;Specifically, we trained two monkeys to perform arm movements to guide a 3D cursor on a computer display to targets presented at random locations. We found that we could accurately reconstruct the trajectory of the cursor using a relatively small ensemble of simultaneously recorded PPC neurons. We also 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 the monkeys' neural activity in PPC. The monkeys learned to perform brain control trajectories at up to 100% success rate after just a few sessions. This improvement in behavioral performance was accompanied by an increase in off-line decoding performance of the PPC ensemble. In addition to these spatial learning effects we observed learning in the temporal domain. In a task where the animal's cursor was intentionally perturbed by superimposed artificial dynamics of a realistic prosthetic limb model, we found gradual adaptation of single-neuron activity, suggesting that PPC neurons are able to update their representation of limb movement when its dynamics change. Both spatial and temporal learning will be crucial for achieving satisfactory results when the central nervous system is forced to adapt to an assistdevice with initially unknown kinematic and dynamic characteristics.;Based on our findings we conclude that PPC is a strong candidate brain region for the extraction of signals to control neural prosthetic limbs. The decoding accuracies found are similar to results reported from the more commonly targeted motor areas, but unlike the motor areas PPC provides access to additional variables such as gaze and intended reach-targets.
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