One of the #brain’s mysteries is how exactly it reorganizes new #information as you learn new tasks. The standard to date was to test how neurons learned new behavior one #neuron at a time. Carnegie Mellon University and the University of Pittsburgh decided to try a different approach. They looked at the population of neurons to see how they worked together while #learning a new behavior. Studying the intracortical population activity in the primary motor cortex of rhesus macaques during short-term learning in a brain–computer interface (BCI) task, they were able to study the reorganization of population during learning. Their new research indicates that when the brain learns a new activity that it is less flexible than previously thought. The researchers were able to draw strong hypothesis about neural reorganization during learning by using BCI. Through the use of BCI the mapping between #neural activity and learning is completely known.
“In this experimental paradigm, we’re able to track all of the neurons that can lead to behavioral improvements and look at how they all change simultaneously,” says Steve Chase, an associate professor of biomedical engineering at Carnegie Mellon and the Center for the Neural Basis of #Cognition. “When we do that, what we see is a really constrained set of changes that happen, and it leads to this suboptimal improvement of performance. And so, that implies that there are limits that constrain how flexible your brain is, at least on these short time scales.”
It is often challenging to learn new tasks quickly that require a high level of proficiency. Neural plasticity is even more constrained than previously thought as results of this research indicate.
