Hunting for the signal
The key findings emerged from understanding how individual neurons worked together as a population to drive the muscles.
As the monkey prepared for movement but held its arm still, many neurons in both of the motion-control regions registered big changes in activity.
But this preparatory activity did not drive the movement. Why?
The scientists realized that, during the preparatory stage, the brain carefully balanced the activity changes of all those individual neurons inside each region. While some neurons fired faster, others slowed down so that the entire population broadcast a constant message to the muscles.
But at the moment of action, the population readings changed in a measurable and consistent way.
By looking at the data, the scientists could correlate these changes at the population level to the flexing of the muscles. This change at the population level differentiated action from preparation.
The Stanford researchers put great effort into the mathematical analysis of their data. They had to be sure that each of the two populations of neurons exhibited the key muscle-controlling changes in activity when (and only when) the muscles flexed. This was the signal they had set out to find.
Kaufman said he was about one year into what turned out to be a three-year project when he realized there might be broader ramifications to this population-level and dimensionality identification idea.
He was presenting an early version of the brain-to-muscle results at a scientific conference when a question from one his peers caused him to think. He had found population-level signals between the brain regions and the muscles. Did the two brain regions, each partially in control of the action, couple and uncouple with each other in a similar way?
"I started the analysis
|Contact: Tom Abate|
Stanford School of Engineering