By allowing essentially unlimited combinations of internal representations in the network of the brain, one of them is always on hand for the given situation.

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By allowing essentially unlimited combinations of internal representations in the network of the brain, one of them is always on hand for the given situation.
This study showed that our seemingly miraculous pre-adaptation comes from connections between neurons that form recurrent loops where inputs can rebound and mix in the network, like waves in a pond, thus called "reservoir" computing. This mix of the inputs allows a potentially universal representation of combinations of the inputs that can then be used to learn the right behaviour for a new situation.
The authors demonstrated this by training a reservoir network to perform a novel problem-solving task. They then compared the activity of neurons in the model with activity of neurons in the prefrontal cortex of a primate that was trained to perform the same task.
Remarkably, there were striking similarities in the activation of neurons in both the reservoir model and the primate. The findings showed that by allowing essentially unlimited combinations of internal representations in the network of the brain, one of them is always on hand for the given situation.
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MEDINDIA




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