Brain uses both neural 'teacher' and 'tinkerer' networks in learning

Jun 04, 2007

While most people need peace and quiet to cram for a test, the brain itself may need noise to learn, a recent MIT study suggests. In experiments with monkeys, the researchers found that neural activities in the brain gradually change, even when nothing new is being learned. Challenging the monkeys to adjust their task triggered systematic changes in their neural activities on top of this background "noise."

The researchers said their findings suggest a new theory of how the brain learns.

"What surprised us most was that the neural representation of movement seems to change even when behavior doesn't seem to change at all," said Sebastian Seung, professor of physics and computational neuroscience and a Howard Hughes Medical Institute investigator. "This was a surprising degree of instability in the brain's representation of the world."

Seung and Institute Professor Emilio Bizzi led the study, which was published in the May 24 issue of the journal Neuron. Lead author on the study was Uri Rokni, a postdoctoral fellow in Seung's laboratory.

In earlier work, Bizzi and colleagues measured neural activities in the motor cortex while monkeys manipulated a handle to move a cursor to targets on a screen. In control experiments, the monkeys had to move the cursor to targets in the same way they had been trained. In learning experiments, the monkeys had to adapt their movements to compensate for novel forces applied to the handle.

The scientists found that even when the monkeys were performing the familiar control task, their neural activities gradually changed over the course of the session.

To explore the significance of these background changes, Rokni analyzed the data from the learning component of Bizzi's experiments. He found he could distinguish learning-related neural changes from the background changes that occurred during the control experiments. From this analysis, Rokni developed a working theory that combined the concepts of a redundant neural network and that of a "noisy" brain.

"A good analogy to redundant circuitry, which accomplishes the same behavior by different wiring configurations, would be a piece of text, in which you can say the same thing with different words," Rokni explained. "Our theory holds that the learning brain has the equivalent of a 'teacher' and a 'tinkerer'--a learning signal and noise in the learning process, respectively.

"In producing a specific piece of text, the tinkerer just randomly changes the words, while the teacher continually corrects the text to make it have the right meaning. The teacher only cares about the meaning and not the precise wording. When the teacher and tinkerer work together, the text keeps changing but the meaning remains the same. For example, the tinkerer may change the sentence 'John is married' to 'John is single,' and the teacher may correct it to 'John is not single.'

"In the same way, learning in the brain has two components--error-correction and noise--so that even though the neural representation keeps changing, the behavior remains fixed. We think the tinkerer, that is the noise, is not merely a nuisance to the teacher but is actually helping the teacher explore new possibilities it wouldn't have considered otherwise."

To test this idea, Rokni constructed a mathematical model of a redundant cortical network that controls movement and used it to simulate the learning experiment with the monkeys. In this model, learning of the connections between neurons was assumed to be a considerably noisy process. "When we ran the simulation long enough, the performance became good, but the neural representation kept changing, very similar to the experiments," Rokni said.

According to Rokni, the concepts of redundant networks and "noisy learning" have important implications for neurobiology. "I don't think this concept of redundancy--that the brain can say the same thing in different ways--has really been fully appreciated until now," he said.

"More practically, people who are constructing devices that translate brain signals to operate such external devices as neural prostheses will have to take such constantly changing neural representations into account," said Rokni.

Source: MIT

Explore further: Collagen fibres not only passively support bone, tendons and ligaments, but also actively contract

add to favorites email to friend print save as pdf

Related Stories

Monkeys can learn to see themselves in the mirror

Jan 08, 2015

Unlike humans and great apes, rhesus monkeys don't realize when they look in a mirror that it is their own face looking back at them. But, according to a report in the Cell Press journal Current Biology on Jan ...

Buzzed birds slur their songs, researchers find

Dec 30, 2014

You know how that guy at the karaoke bar singing Journey's "Don't Stop Believin' " sounds a little off after he's had a few drinks? The same goes for buzzed birds, according to a team led by researchers from ...

Drone in flight test learns on the fly with special chip

Nov 05, 2014

The great computer challenge for many scientists centers around how well a computer can learn, react and adapt from the environment. Tom Simonite of MIT Technology Review on Tuesday had a report about recent ...

Recommended for you

Cochlear implant users can hear, feel the beat in music

10 hours ago

People who use cochlear implants for profound hearing loss do respond to certain aspects of music, contrary to common beliefs and limited scientific research, says a research team headed by an investigator at Georgetown University ...

User comments : 0

Please sign in to add a comment. Registration is free, and takes less than a minute. Read more

Click here to reset your password.
Sign in to get notified via email when new comments are made.