Anyone who has looked at the jagged recording of the electrical activity of a single neuron in the brain must have wondered how any useful information could be extracted from such a frazzled signal.

But over the past 30 years, researchers have discovered that clear information can be obtained by decoding the activity of large populations of neurons.

Now, scientists at Washington University in St. Louis, who were decoding brain activity while monkeys reached around an obstacle to touch a target, have come up with two remarkable results.

Their first result was one they had designed their experiment to achieve: they demonstrated that multiple parameters can be embedded in the firing rate of a single neuron and that certain types of parameters are encoded only if they are needed to solve the task at hand.

Their second result, however, was a complete surprise. They discovered that the population vectors could reveal different planning strategies, allowing the scientists, in effect, to read the monkeys’ minds.

By chance, the two monkeys chosen for the study had completely different cognitive styles. One, the scientists said, was a hyperactive type, who kept jumping the gun, and the other was a smooth operator, who waited for the entire setup to be revealed before planning his next move. The difference is clearly visible in their decoded brain activity.

The study was published in the July 19 advance online edition of the journal Science.

All in the task

The standard task for studying voluntary motor control is the “center-out task,” in which a monkey or other subject must move its hand from a central location to targets placed on a circle surrounding the starting position.

To plan the movement, says Daniel Moran, PhD, associate professor of biomedical engineering in the School of Engineering & Applied Science and of neurobiology in the School of Medicine at Washington University in St. Louis, the monkey needs three pieces of information: current hand and target position and the velocity vector that the hand will follow.

In other words, the monkey needs to know where his hand is, what direction it is headed and where he eventually wants it to go.

A variation of the center-out task with multiple starting positions allows the neural coding for position to be separated from the neural coding for velocity.





By themselves, however, the straight-path, unimpeded reaches in this task don’t let the neural coding for velocity to be distinguished from the neural coding for target position, because these two parameters are always correlated. The initial velocity of the hand and the target are always in the same direction.

To solve this problem and isolate target position from

movement direction, doctoral student Thomas Pearce designed a novel

obstacle-avoidance task to be done in addition to the center-out task.