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 t
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Source:Johns Hopkins Medical Institutions
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