“The activity of a neural population can be represented in a high-dimensional space (termed the neural space), wherein each dimension corresponds to the activity of one neuron. These characteristic activity patterns [of the recorded neural population in a brain at rest] comprise a low-dimensional subspace (termed the intrinsic manifold) within the neural space. The intrinsic manifold presumably reflects constraints imposed by the underlying neural circuitry. Here we show that the animals could readily learn to proficiently control the cursor using neural activity patterns that were within the intrinsic manifold. However, animals were less able to learn to proficiently control the cursor using activity patterns that were outside of the intrinsic manifold. These results suggest that the existing structure of a network can shape learning. On a timescale of hours, it seems to be difficult to learn to generate neural activity patterns that are not consistent with the existing network structure. These findings offer a network-level explanation for the observation that we are more readily able to learn new skills when they are related to the skills that we already possess”
So cool, so interesting. I would love to know at what ages and mental states and time of day, etc… it is easier to learn outside of the ‘intrinsic manifold’; or, when the intrinsic manifold becomes static. I’m sure there are sort of identifiable ‘learning windows’ when the intrinsic manifold shifts a lot.
If I am ever at the University of Pittsburgh, I will be interrogating these researchers…
“More broadly, our results help to provide a neural explanation for the balance we possess between adaptability and persistence in our actions and thoughts” mmmmm. Sometimes I think that I am secretly a systems neuroscientist.
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