Math that powers spam filters used to understand how brain learns to move our muscles

Jun 01, 2007

A team of biomedical engineers has developed a computer model that makes use of more or less predictable “guesstimates” of human muscle movements to explain how the brain draws on both what it recently learned and what it’s known for some time to anticipate what it needs to develop new motor skills.

The engineers, from Johns Hopkins, MIT and Northwestern, exploited the fact that all people show similar “probable” learning patterns and use them to develop and fine tune new movements, whether babies trying to walk or stroke patients re-connecting brain-body muscle links.

In their report this week in Nature Neuroscience, the team says their new tool could make it possible to predict the best ways to teach new movements and help design physical therapy regimens for the disabled or impaired.

Reza Shadmehr, Ph.D., professor of biomedical engineering at Hopkins, who with his colleagues built the new model, says the artificial brain in the computer, like its natural counterpart, is guided in part by a special kind of statistical “probability” theory called Bayesian math.

Unlike conventional statistical analysis, a Bayesian probability is a subjective “opinion,” that measures a “learner’s” individual degree of belief in a particular outcome when that outcome is uncertain. The idea as applied to the workings of a brain is that each brain uses what it already knows to “predict” or “believe” that something new will happen, then uses that information to help make it so.

“We used the idea that prior experience and belief affect the probability of future outcomes, such as taking an alternate route to work on Friday because you’ve experienced heavy traffic Tuesday, Wednesday and Thursday and believe strongly that Friday will be just as bad,” says Shadmehr. E-mail spam filters operate on a similar principle; they predict which key words are “probably” attached to mail you don’t want and “learning” as they go to fine tune what they exclude from your in-box.

The computer model, Shadmehr says, almost precisely duplicates the results of experiments that tested the ability of monkeys to visually track rapid flashes of light. Experiments using such rapid eye movements, or saccades, are a staple in studying how the brain controls movement.

Initially, the animal learner made large errors, but also stored the information about its mistakes in a memory bank so it could adapt and make more accurate predictions the next time around. Every time the learner repeated the task, it would sift through the prior knowledge in its memory banks and make a prediction on how to move, which in turn would also be memorized. While short term memory was periodically purged, repeated errors were transferred to a long term memory bank.

The computer learner was tasked with “looking” at a spot of light. Then all the lights were turned off. The spot of light was turned on again and the computer learner was again asked to look at that same spot. The learner’s speed and pattern in adapting its movements matched the experimental results of the monkeys almost perfectly. “We found that this Bayesian model can explain almost all of the phenomena we observe in regard to learning motor movements,” says Shadmehr.

Beyond possible use in helping stroke patients, the new tool might also be applied to better understand how we learn language, develop ideas and make memories. “How we learn to think operates under many of the same principles as how we learn to move,” Shadmehr says.


Source: Johns Hopkins Medical Institutions

Explore further: The new Candy Crush? Chinese language apps make learning a game

add to favorites email to friend print save as pdf

Related Stories

Stepping stones to NASA's human missions beyond

Jan 21, 2015

"That's one small step for (a) man; one giant leap for mankind." When Neil Armstrong took his first steps on the moon, many strides came before to achieve that moment in history. The same is true for a human ...

Electronics show a window into the 'Internet of Me'

Jan 05, 2015

New technology is getting more personal. So personal, it is moving to connect and analyze our movements, our health, our brains and our everyday devices. Welcome to the so-called Internet of Me.

Brain-training for baseball robot

Dec 24, 2014

The human brain continually monitors and influences all bodily movements, helping the body adapt to different circumstances in order to maintain fine motor control. The part of the brain responsible for fine ...

Recommended for you

New app first to use gesture for language learning

Jan 29, 2015

While you might think a person shaking her phone or tablet from side to side is having issues with the device, she might actually be playing a game that has her mimicking a steering wheel motion as part of ...

Linux distrib vendors make patches available for GHOST

Jan 29, 2015

Qualys said on Tuesday that there was a serious weakness in the Linux glibc library. During a code audit, Qualys researchers discovered a buffer overflow in the __nss_hostname_digits_dots() function of glibc. ...

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.