How Our Brain Balances Old and New Skills - 0 views
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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
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if the brain were too plastic, previously learned skills would be lost too easily.
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A new computational model developed by MIT neuroscientists explains how the brain maintains the balance between plasticity and stability
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and how it can learn very similar tasks without interference between them.
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The key
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is that neurons are constantly changing their connections with other neurons
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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
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As the brain learns a new motor skill, neurons form circuits that can produce the desired output
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according to this theory
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As the brain explores different solutions, neurons can become specialized for specific tasks
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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
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Perfection is usually not achieved on the first try, so feedback from each effort helps the brain to find better solutions
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complications arise when the brain is trying to learn many different skills at once
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Because the same distributed network controls related motor tasks, new modifications to existing patterns can interfere with previously learned skills.
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particularly tricky when you're learning very similar things
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such as two different tennis strokes
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computer chip,
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instructions for each task would be stored in a different location on the chip.
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the brain is not organized like a computer chip. Instead, it is massively parallel and highly connected - each neuron connects to, on average, about 10,000 other neurons
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That connectivity offers an advantage, however, because it allows the brain to test out so many possible solutions to achieve combinations of tasks
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neurons
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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 constant changes in these connections,
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researchers call hyperplasticity
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balanced by another inherent trait of
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Most models of neural activity don't include noise, but the MIT team says noise is a critical element of the brain's learning ability
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This model helps to explain how the brain can learn new things without unlearning previously acquired skills
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the paper shows is that, counterintuitively, if you have neural networks and they have a high level of random noise, that actually helps instead of hindering the stability problem
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Without noise, the brain's hyperplasticity would overwrite existing memories too easily
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low plasticity would not allow any new skills to be learned, because the tiny changes in connectivity would be drowned out by all of the inherent noise
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The constantly changing connections explain why skills can be forgotten unless they are practiced often, especially if they overlap with other routinely performed tasks
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skills such as riding a bicycle, which is not very similar to other common skills, are retained more easily
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Once you've learned something, if it doesn't overlap or intersect with other skills, you will forget it but so slowly that it's essentially permanent
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researchers are now investigating whether this type of model could also explain how the brain forms memories of events, as well as motor skills