Optimal Target Placement for Neural Communication Prostheses

Neural prosthetic systems have been designed to estimate continuous reach trajectories as well as discrete reach targets. In the latter case, reach targets are typically decoded from neural activity during an instructed delay period, before the reach begins. We have recently characterized the decoding speed and accuracy achievable by such a system. The results were obtained using canonical target layouts, independent of the tuning properties of the neurons available. Here we seek to increase decode accuracy by judiciously selecting the locations of the reach targets based on the characteristics of the neural population at hand. We present an optimal target placement algorithm that approximately maximizes decode accuracy with respect to target locations. Using maximum likelihood decoding, the optimal target placement algorithm yielded up to 11 and 12% improvement for two and sixteen targets, respectively. For four and eight targets, gains were more modest (5 and 3%, respectively) as the target layouts found by the algorithm closely resembled the canonical layouts. Thus, the algorithm can serve not only to find target layouts that outperform canonical layouts, but it can also confirm or help select among multiple canonical layouts. These results indicate that the optimal target placement algorithm is a valuable tool for designing high-performance prosthetic systems