Summary of “Brain-Computer Interfaces Show That Neural Networks Learn by Recycling”

The hallmark of intelligence is the ability to learn.
The brain may be highly flexible and adaptive overall, but at least over short time frames, it learns by inefficiently recycling tricks from its neural repertoire rather than rewiring from scratch.
Now, while observing activity in the brain during learning, Yu and his colleagues have seen evidence of a similar lack of plasticity at the neural level.
In 2014, the researchers observed that test subjects could learn new tasks more easily if they involved patterns of neural activity within the intrinsic manifold rather than outside it.
Then the team switched the neural activity requirements for moving the cursor and waited to see what new patterns of neural activity, corresponding to new points in the intrinsic manifold, the animals would use to accomplish them.
Why would the brain use less than the best strategy for learning? The group’s findings suggest that, just as the neural architecture constrains activity to the intrinsic manifold, some further constraint limits how the neurons reorganize their activity during the experiments.
Chase likened the motor cortex to an old-fashioned telephone switchboard, with neural connections like cables linking inputs from other cortical areas to outputs in the brain’s cerebellum.
The researchers can’t yet rule out the possibility that reassociation is a fast interim way for the brain to learn new tasks; over a longer time period, realignment or rescaling might still show up.

The orginal article.