| Autor: |
Hasegawa, Ryohei P., Hasegawa, Yukako T., Segraves, Mark A. |
| Zdroj: |
Neural Information Processing (9783540691594); 2008, p997-1006, 10p |
| Abstrakt: |
The purpose of this study was to develop an algorithm capable of transforming neural activity to correctly report behavioral outcome during a cognitive task. We recorded from small groups of 2-5 neurons in the superior colliculus (SC) while monkeys performed a go/no-go task. Depending upon the color of a peripheral stimulus, the monkey was required to either make a saccade to the stimulus (go) or maintain fixation (no-go). In order to replicate the progress of the decision-making process and generate a virtual decision function (VDF), we performed a multiple regression analysis, with 1 msec resolution, on neuron activity during individual trials. Post hoc analyses by VDF predicted the monkey΄s choice with nearly 90% accuracy. These results suggest that monitoring of a limited number of SC neurons has sufficient capacity to predict go/no-go decisions on a trial-by-trial basis, and serves as an ideal candidate for a cognitive brain-machine interface (BMI). [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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