Goal-based prosthetic readout

In this week’s Science, Richard Andersen’s lab shows the first use of a region (relatively) distant from primary motor areas for brain readout in a reaching task. Using a very small number of neurons (8-16 cells), the investigators were able to achieve a 60-70% accuracy in predicting reach movements to a particular target (out of 8 total targets). Read on for the abstract or here for the full article.

Cognitive Control Signals for Neural Prosthetics
S. Musallam, B. D. Corneil,* B. Greger, H. Scherberger, R. A. Andersen

Recent development of neural prosthetics for assisting paralyzed patients has focused on decoding intended hand trajectories from motor cortical neurons and using this signal to control external devices. In this study, higher level signals related to the goals of movements were decoded from three monkeys and used to position cursors on a computer screen without the animals emitting any behavior. Their performance in this task improved over a period of weeks. Expected value signals related to fluid preference, the expected magnitude, or probability of reward were decoded simultaneously with the intended goal. For neural prosthetic applications, the goal signals can be used to operate computers, robots, and vehicles, whereas the expected value signals can be used to continuously monitor a paralyzed patient’s preferences and motivation.

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