New Study Shows How Brain Balances Learning New Skills While Retaining Old Ones

A novel computation model that explains how the brain maintains the balance between plasticity and stability, and how it can learn very similar tasks without interference between them has been developed by scientists. To learn new motor skills, the brain must be plastic: able to rapidly change the strengths of connections between neurons, forming new patterns that accomplish a particular task. However, if the brain were too plastic, previously learned skills would be lost too easily.

The key, the neuroscientists at MIT said, is that neurons are constantly changing their connections with other neurons. However, not all of the changes are functionally relevant- they simply allow the brain to explore many possible ways to execute a certain skill, such as a new tennis stroke.

"Your brain is always trying to find the configurations that balance everything so you can do two tasks, or three tasks, or however many you're learning. There are many ways to solve a task, and you're exploring all the different ways," lead author Robert Ajemian said.

As the brain learns a new motor skill, neurons form circuits that can produce the desired output- a command that will activate the body's muscles to perform a task such as swinging a tennis racket. Perfection is usually not achieved on the first try, so feedback from each effort helps the brain to find better solutions.

This works well for learning one skill, but complications arise when the brain is trying to learn many different skills at once. Because the same distributed network controls related motor tasks, new modifications to existing patterns can interfere with previously learned skills.

That connectivity offers an advantage, however, because it allows the brain to test out so many possible solutions to achieve combinations of tasks. The constant changes in these connections, which the researchers call hyperplasticity, is balanced by another inherent trait of neurons- they have a very low signal to noise ratio, meaning that they receive about as much useless information as useful input from their neighbors.

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The MIT team said noise is a critical element of the brain's learning ability. They found that it allows the brain to explore many solutions, but it can only be utilized if the network is hyperplastic.

The study was published in the National Academy of Sciences.

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Source-ANI