If you''ve ever hit a patch of ice on the road that sent your car swerving left while you resolutelyand futilelysteered right to get back in your lane, you''ve experienced what neuroscientists call a visuomotor rotation task. On a dry road, your response would have been appropriate. But under icy conditions, the same sensory cue produces a decidedly negative result: a car fishtailing out of control. While you''re figuring out what movements will straighten out the car, the
neurons in your primary
motor cortexthe region of the
brain responsible for movementare taking notes. Chances are, your next icy encounter was less dramatic. But how does your brain learn to produce a different movement in response to the same
visual cue?
Neuroscientists investigate such questions by recording and analyzing the electrical
activity of neurons during
learning and performance of new sensory-motor transformations. Such studies, for example, show that populations of neurons in different brain areas map sensory cues and desired arm motion by creating an internal representation of the corresponding sensory and motor coordinates in a way that allows flexible responses to changing conditions. In previous studies, Rony Paz and Eilon Vaadia, of The Hebrew University in Israel, found that neurons in the primary motor cortex that fire before monkeys move their arm in a particular
direction have higher firing rates after the monkey learns to dissociate the arm direction from the cursor direction (an indicator of visual feedback). Interestingly, changes in activity preferentially occurred in a subset of neurons that were already tuned (that is, maximally activated during movement) to the direction experienced while learning.
While many studies indicate that learning new tasks can generate specific changes in brain activity, it had not been clear how or if such changes improve the internal representation inside the brain. Specifically, is the neuronal code any better after learning? Now Paz and Vaadia show that while these neurons are firing at higher rates they are also transmitting more information about specific task parameters.
Paz and Vaadia trained two rhesus monkeys to learn various visual-motor taskswhich involved operating a joystick to move a cursor on a screenand then changed the relationship between the visual feedback (the cursor) and hand movement. Using information-theory analysiswhich measures the amount of information that single neurons can tell about the movementthey were able to correlate neuron activity with direction of movement and, conversely, distinguish differences between directions based on neuron activity. Their analysis revealed that the neurons transmit more information about the direction of movement after the monkeys learn a task. To figure out what aspect of neuron activity conveys this improved information, Paz and Vaadia examined two features of neuron signalingresponse variability and directional sensitivitywhich they reasoned might plausibly accomplish this. Increased information content after learning a task, they found, corresponded to sensitivity to a single direction, and neurons attuned to that direction contributed to the increase.
These findings suggest that subsets of directionally sensitive neurons increase their firing rates to more finely tune their sensitivity to that direction. By successfully reconstructing the movement direction from the neuron signals captured after learning a task, Paz and Vaadia also demonstrate that the observed learning improvement can be extracted to predict behavior. The authors argue that their results suggest a close association between properties of neuronssuch as directional tuning of cellsand learning a skill that is focused on the same parameterin this case, direction. Together with results from visual and auditory areas, they propose that similar mechanisms may control the interplay between neurons and learning throughout the central nervo
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