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Toward optimal target placement for neural prosthetic devices

Cunningham, John P. and Yu, Byron M. and Gilja, Vikash and Ryu, Stephen I. and Shenoy, Krishna V. (2008) Toward optimal target placement for neural prosthetic devices. Journal of Neurophysiology, 100 (6). pp. 3445-3457. ISSN 0022-3077. doi:10.1152/jn.90833.2008.

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Neural prosthetic systems have been designed to estimate continuous reach trajectories ( motor prostheses) and to predict discrete reach targets ( communication prostheses). In the latter case, reach targets are typically decoded from neural spiking activity during an instructed delay period before the reach begins. Such systems use targets placed in radially symmetric geometries independent of the tuning properties of the neurons available. Here we seek to automate the target placement process and increase decode accuracy in communication prostheses by selecting target locations based on the neural population at hand. Motor prostheses that incorporate intended target information could also benefit from this consideration. We present an optimal target placement algorithm that approximately maximizes decode accuracy with respect to target locations. In simulated neural spiking data fit from two monkeys, the optimal target placement algorithm yielded statistically significant improvements up to 8 and 9% for two and sixteen targets, respectively. For four and eight targets, gains were more modest, as the target layouts found by the algorithm closely resembled the canonical layouts. We trained a monkey in this paradigm and tested the algorithm with experimental neural data to confirm some of the results found in simulation. In all, the algorithm can serve not only to create new target layouts that outperform canonical layouts, but it can also confirm or help select among multiple canonical layouts. The optimal target placement algorithm developed here is the first algorithm of its kind, and it should both improve decode accuracy and help automate target placement for neural prostheses.

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Additional Information:© 2008 by the The American Physiological Society. Submitted 30 July 2008; accepted in final form 19 September 2008. We thank M. Sahani, G. Santhanam, and G. Gemelos for valuable technical discussions. We thank G. Santhanam and A. Afshar for the neural data used to fit the firing rate model for monkey H. We thank M. Risch for veterinary care, D. Haven for technical support, and S. Eisensee for administrative assistance. This work was supported by the Michael Flynn Stanford Graduate Fellowship to J. P. Cunningham, National Defense Science and Engineering Graduate fellowships to B. M. Yu and V. Gilja, Gatsby Charitable Foundation funding to B. M. Yu, National Science Foundation graduate research fellowships to B. M. Yu and V. Gilja, National Institute of Neurological Disorders and Stroke Grant CRCNS-R01, Christopher and Dana Reeve Foundation funding to S. I. Ryu and K. V. Shenoy, and the following grants to K. V. Shenoy: Burroughs Wellcome Fund Career Award in the Biomedical Sciences, Stanford Center for Integrated Systems, NSF Center for Neuromorphic Systems Engineering at Caltech, Office of Naval Research, Sloan Foundation, and Whitaker Foundation.
Funding AgencyGrant Number
Michael Flynn Stanford Graduate FellowshipUNSPECIFIED
National Defense Science and EngineeringUNSPECIFIED
Gatsby Charitable FoundationUNSPECIFIED
National Science FoundationUNSPECIFIED
National Institute of Neurological Disorders and StrokeCRCNS-R01
Christopher and Dana Reeve FoundationUNSPECIFIED
Burroughs Wellcome FundUNSPECIFIED
NSF Center for Neuromorphic Systems Engineering at CaltechUNSPECIFIED
Stanford Center for Integrated SystemsUNSPECIFIED
Office of Naval ResearchUNSPECIFIED
Sloan FoundationUNSPECIFIED
Whitaker FoundationUNSPECIFIED
Issue or Number:6
Record Number:CaltechAUTHORS:CUNjnp08
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:13328
Deposited By: Tony Diaz
Deposited On:17 Apr 2009 19:54
Last Modified:08 Nov 2021 22:36

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