A team of scientists led by Indian-origin electrical engineers has
shed new light on how the brain plans for and executes movements in
reaction to a "go" signal.
"This research holds great promise in many areas of neuroscience, in
particular human prostheses that can be controlled by the brain,” said
Krishna Shenoy, who led the study with Maneesh Sahani, both electrical
engineers at the Stanford School of Engineering.
The existing
hypothesis, known as “rise-to-threshold,” held that in anticipation of a
“go” cue, our brains begin to plan the motions necessary to
satisfactorily complete the movement by simply increasing the activity
of neurons.
Neurons begin to fire, but not enough to cause the movement to take place.
Upon
the “go” signal, the brain accelerates this neural firing until it
crosses a “threshold” initiating the motion. According to the theory,
the longer a preparatory period one has, the greater the neural activity
will be and, thus, the faster the reaction time.
But the Stanford
team was able to document a process based less on the amount of
activity and more on the trajectory of the neural activity through the
brain.
In graphs of neural activity prior to display of the
target, the study monkeys' neural activity appears somewhat scattered.
The moment a target is displayed, however, the neural activity
concentrates in an activity region that the researchers dubbed the
“optimal sub-space.”
“We can watch as the pattern of neural
activity gets focused in a specific region at the moment the target
appears, and then when the ‘go’ cue is given, the activity moves again,
ending with the successful touching of the target,” explained Shenoy.
The
key to reaction time, the researchers found, is the relationship
between where the neural activity is and its speed along the ideal
trajectory just prior to the go cue.
If the neural activity is closer to the final destination, then the reaction time will be shorter; if farther away, then longer.
From
this new understanding, the researchers were able to shape a deeper
understanding of the neural patterns and craft a model to predict
reaction time.
"Our model allows us to predict with four times
greater accuracy what the reaction time of any single arm motion is
going to be based on the neural activity observed prior to movement,”
added Sahani.
The findings were recently published in the journal Neuron.
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