Generating New Neural Patterns With Learning

Post by Anastasia Sares

What's the science?

Plasticity is a common buzzword in the neuroscientific community nowadays. It refers to the brain’s ability to re-organize itself in order to learn new skills or accommodate new information. But is it possible to induce plasticity in the brain and simulate real-world learning? This week in Proceedings of the National Academy of Sciences (PNAS), Oby and colleagues used a brain-computer interface to answer this question.

How did they do it?

The authors implanted a set of electrodes in the motor cortex of monkeys. The monkeys first observed passively as a random target appeared on the edge of the screen, and a cursor moved towards it. Based on the activity of the neurons recorded during this initial phase, the researchers created a rough mapping of which patterns of neurons were associated with different aspects of the movement. Then, they allowed the monkeys to take control of the cursor by feeding their neural activity directly into the computer. This is known as a brain-computer interface. Throughout, they gave the monkeys rewards when they successfully moved the cursor to the correct target and continued to refine the mapping. This “intuitive mapping,” corresponded to the monkey’s natural neural patterns for this task.

After the intuitive mapping was established, the researchers created new neural mappings that the monkeys would have to learn in order to perform the same task. There were two kinds of remapping. First, there was a simple transformation of the intuitive mapping, which would essentially keep the same neural patterns but re-assign the way they moved the cursor. Think of a video game controller that goes right when you press the “left” button. Second, they used a complex transformation, which forced the monkeys to produce completely new neural patterns, with different groups of neurons working in synchrony—a new kind of controller. The monkeys were then trained to complete the same task with these new mappings, either introducing them immediately or incrementally. Again, they were given rewards throughout, and their learning was tracked by measuring how many times they could move the cursor to the correct target in under 7.5 seconds.

What did they find?

The simple new mappings were easily learnable within a day and generally did not result in new patterns of neural activity. The complex mappings, on the other hand, were best learned over a number of days, with incremental training (gradually going from the intuitive mapping to the new mapping). The monkeys’ progress with the complex mappings over time resembled the way we learn other new, complex skills. The speed of their movements during the late stages of learning was faster than what would have been possible with the intuitive mapping, meaning that new neural activity patterns had been established in the monkey’s brains. Analyses of the neural activity for these complex mappings revealed changes in both the amount of firing for different neurons, as well as the correlation patterns between neurons.

What's the impact?

This work shows a causal link between the reorganization in a group of neurons and learning of a new skill. Though the physical connections between neurons were too small to be visible, the brains of these monkeys did develop different functional connections in order to improve on the task. Allowing neural activity to directly control the cursor eliminated many possible intermediary mechanisms. This also shows that fast, simple learning happens through a different mechanism than slow, complex learning.

Oby et al. New neural activity patterns emerge with long-term learning. PNAS (2019). Access the original scientific publication here.