Motor, sensory, and cognitive learning require networks of neurons to generate new activity patterns. Because some behaviors are easier to learn than others1,2, we wondered if some neural activity patterns are easier to generate than others. We asked whether the existing network constrains the patterns that a subset of its neurons is capable of exhibiting, and if so, what principles define the constraint. We employed a closed-loop intracortical brain-computer interface (BCI) learning paradigm in which Rhesus monkeys controlled a computer cursor by modulating neural activity patterns in primary motor cortex. Using the BCI paradigm, we could specify and alter how neural activity mapped to cursor velocity. At the start of each session, we observed the characteristic activity patterns of the recorded neural population. These patterns comprise a low-dimensional space (termed the intrinsic manifold, or IM) within the high-dimensional neural firing rate space. They presumably reflect constraints imposed by the underlying neural circuitry. We found that the animals could readily learn to proficiently control the cursor using neural activity patterns that were within the IM. However, animals were less able to learn to proficiently control the cursor using activity patterns that were outside of the IM. This result suggests that the existing structure of a network can shape learning. On the timescale of hours, it appears 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 possess3,4.
Behavior is driven by coordinated activity across a population of neurons. Learning requires the brain to change the neural population activity produced to achieve a given behavioral goal. How does population activity reorganize during learning? We studied intracortical population activity in the primary motor cortex of rhesus macaques during short-term learning in a brain-computer interface (BCI) task. In a BCI, the mapping between neural activity and behavior is exactly known, enabling us to rigorously define hypotheses about neural reorganization during learning. We found that changes in population activity followed a suboptimal neural strategy of Reassociation: animals relied on a fixed repertoire of activity patterns and associated those patterns with different movements after learning. These results indicate that the activity patterns that a neural population can generate are even more constrained than previously thought and might explain why it is often difficult to quickly learn to a high level of proficiency.
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