Aimlessly wandering around a city or exploring the new mall may seem unproductive, but new research suggests it could play an important role in how our brains learn. By simultaneously recording the activity of tens of thousands of neurons, a team led by Dr. Lin Zhong discovered that learning may occur even when there are no specific tasks or goals involved. This intriguing finding was published in Nature.
The new research finds that as animals explore their environment, neurons in the visual cortex—the brain area responsible for processing visual information—encode visual features to build an internal model of the world. This information can speed up learning when a more concrete task arises.
Observing unsupervised learning
The team, led by Lin Zhong, designed experiments where mice ran in linear virtual reality corridors featuring various visual textures, akin to real-world environments. Some textures were linked to rewards, while others were not. After the mice learned the rules of an experiment, Zhong made subtle adjustments, altering the textures and the presence of rewards.
After weeks of running these experiments, the team observed changes in neural activity within the animals’ visual cortex. However, they struggled to explain the observed neural plasticity—the changes in connections between neurons that enable learning and memory. They ended up on the question of whether the task itself was even necessary, because it is possible that plasticity happens just basically with the animal’s own exploration of the environment, i.e. unsupervised learning.
When the researchers explicitly tested this concept of unsupervised learning, they discovered that certain areas of the visual cortex were encoding visual features even without the animal being trained on a task. When a task was introduced, other areas of the cortex responded.
Additionally, the researchers found that mice exploring the virtual corridor for several weeks learned how to associate textures with rewards much faster than mice trained only on the task.
“It means that you don’t always need a teacher to teach you: You can still learn about your environment unconsciously, and this kind of learning can prepare you for the future,” Zhong says. “I was very surprised. I have been doing behavioral experiments since my PhD, and I never expected that without training mice to do a task, you will find the same neuroplasticity.”
Understanding how brains learn
The new findings reveal distinct areas in the visual cortex are responsible for different types of learning: unstructured, exploration-based unsupervised learning and instructed, goal-oriented supervised learning. The new research suggests that when animals learn a task, the brain might simultaneously use both algorithms—an unsupervised component to extract features and a supervised component to assign meaning to those features.
These insights could enhance our understanding of how learning occurs in the brain. While previous research on the visual cortex focused mainly on supervised learning, the new work opens a new door to studying these unsupervised learning algorithms in the brain if that’s the main way by which the brain learns, as opposed to a more instructed, goal-directed way. It also opens avenues for exploration, including how these different types of learning interact and how the visual model of the environment is integrated with spatial models from other brain regions.
Link to original paper:Unsupervised pretraining in biological neural networks | Nature
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