Forum for Science, Industry and Business

Sponsored by:     3M 
Search our Site:

 

Scientists Read Monkeys’ Inner Thoughts

23.07.2012
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 19th 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 ot 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.

Crucially, in one-third of the obstacle-avoidance trials, either no obstacle appeared or the obstacle didn’t block the monkey’s path. In either case, the monkey could move directly to the target once he got the “go” cue.

The population vector corresponding to target position showed up during the third hold of the novel task, but only if there was an obstacle. If an obstacle appeared and the monkey had to move its hand in a curved trajectory to reach the target, the population vector lengthened and pointed at the target. If no obstacle appeared and the monkey could move directly to the target, the population vector was insignificant.

In other words, the monkeys were encoding the position of the target only when it did not lie along a direct path from the starting position and they had to keep its position “in mind” as they initially moved in the “wrong” direction.

“It’s all,” Moran says, “in the design of the task.”

And then some magic happens

Pearce’s initial approach to analyzing the data from the experiment was the standard one of combining the data from the two monkeys to get a cleaner picture.

“It wasn’t working,” Pearce says, “and I was frustrated because I couldn’t figure out why the data looked so inconsistent. So I separated the data by monkey, and then I could see, wow, they’re very different. They’re approaching this task differently and that’s kind of cool.”

The difference between the monkey’s’ styles showed up during the second hold. At this point in the task, the target was visible, but the obstacle had not yet appeared.

The hyperactive monkey, called monkey H, couldn’t wait. His population vector during that hold showed that he was poised for a direct reach to the target. When the obstacle was then revealed, the population vector shortened and rotated to the direction he would need to move to avoid the obstacle.

The smooth operator, monkey G, in the meantime, idled through the second hold, waiting patiently for the obstacle to appear. Only when it was revealed did he begin to plan the direction he would move to avoid the obstacle.

Because he didn’t have to correct course, monkey G’s strategy was faster, so what advantage was it to monkey H to jump the gun? In the minority of trials where no obstacle appeared, monkey H approached the target more accurately than monkey G. Maybe monkey H is just cognitively adapted to a Whac-A-Mole world. And monkey G, when caught without a plan, was at a disadvantage.

Working with the monkeys, the scientists had been aware that they had very different personalities, but they had no idea this difference would show up in their neural recordings.

“That’s what makes this really interesting,” Moran says.

Diana Lutz | Newswise Science News
Further information:
http://www.wustl.edu

More articles from Life Sciences:

nachricht First use of vasoprotective antibody in cardiogenic shock
17.05.2019 | Deutsches Zentrum für Herz-Kreislauf-Forschung e.V.

nachricht A nerve cell serves as a “single” for studies
15.05.2019 | Rheinische Friedrich-Wilhelms-Universität Bonn

All articles from Life Sciences >>>

The most recent press releases about innovation >>>

Die letzten 5 Focus-News des innovations-reports im Überblick:

Im Focus: Self-repairing batteries

UTokyo engineers develop a way to create high-capacity long-life batteries

Engineers at the University of Tokyo continually pioneer new ways to improve battery technology. Professor Atsuo Yamada and his team recently developed a...

Im Focus: Quantum Cloud Computing with Self-Check

With a quantum coprocessor in the cloud, physicists from Innsbruck, Austria, open the door to the simulation of previously unsolvable problems in chemistry, materials research or high-energy physics. The research groups led by Rainer Blatt and Peter Zoller report in the journal Nature how they simulated particle physics phenomena on 20 quantum bits and how the quantum simulator self-verified the result for the first time.

Many scientists are currently working on investigating how quantum advantage can be exploited on hardware already available today. Three years ago, physicists...

Im Focus: Accelerating quantum technologies with materials processing at the atomic scale

'Quantum technologies' utilise the unique phenomena of quantum superposition and entanglement to encode and process information, with potentially profound benefits to a wide range of information technologies from communications to sensing and computing.

However a major challenge in developing these technologies is that the quantum phenomena are very fragile, and only a handful of physical systems have been...

Im Focus: A step towards probabilistic computing

Working group led by physicist Professor Ulrich Nowak at the University of Konstanz, in collaboration with a team of physicists from Johannes Gutenberg University Mainz, demonstrates how skyrmions can be used for the computer concepts of the future

When it comes to performing a calculation destined to arrive at an exact result, humans are hopelessly inferior to the computer. In other areas, humans are...

Im Focus: Recording embryonic development

Scientists develop a molecular recording tool that enables in vivo lineage tracing of embryonic cells

The beginning of new life starts with a fascinating process: A single cell gives rise to progenitor cells that eventually differentiate into the three germ...

All Focus news of the innovation-report >>>

Anzeige

Anzeige

VideoLinks
Industry & Economy
Event News

SEMANTiCS 2019 brings together industry leaders and data scientists in Karlsruhe

29.04.2019 | Event News

Revered mathematicians and computer scientists converge with 200 young researchers in Heidelberg!

17.04.2019 | Event News

First dust conference in the Central Asian part of the earth’s dust belt

15.04.2019 | Event News

 
Latest News

Discovering unusual structures from exception using big data and machine learning techniques

17.05.2019 | Materials Sciences

ALMA discovers aluminum around young star

17.05.2019 | Physics and Astronomy

A new iron-based superconductor stabilized by inter-block charger transfer

17.05.2019 | Materials Sciences

VideoLinks
Science & Research
Overview of more VideoLinks >>>