Aimlessly wandering around a city or exploring the new mall may seem unproductive, but new research from HHMI’s Janelia Research Campus 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 of scientists from the Pachitariu and Stringer labs discovered that learning may occur even when there are no specific tasks or goals involved.
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.
“Even when you are zoning out or just walking around or you don’t think you are doing anything special or hard, your brain is probably still working hard to help you memorize where you are, organizing the world around you, so that when you’re not zoning out anymore—when you actually need to do something and pay attention—you’re ready to do your best,” says Janelia Group Leader Marius Pachitariu.
Observing unsupervised learning
The team, led by postdoc 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.
“As we thought more and more about it, we eventually ended up on the question of whether the task itself was even necessary,” Pachitariu says. “It’s entirely possible that a lot of the plasticity happens just basically with the animal’s own exploration of the environment.”
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 Ph.D., 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 new 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.
“It’s a door to studying these unsupervised learning algorithms in the brain, and if that’s the main way by which the brain learns, as opposed to a more instructed, goal-directed way, then we need to study that part as well,” Pachitariu says.
The researchers say these insights were enabled both by Janelia’s support teams, which helped the researchers design and run the experiments, and by the mesoscope, an instrument that enabled the team to record up to 90,000 neurons simultaneously, enhancing their ability to make new discoveries.
“Allowing a single lab to run projects at this scale is what is uniquely possible here and that gives us the flexibility to pursue different questions without necessarily having a concrete plan,” Pachitariu says.
More information:
Lin Zhong et al, Unsupervised pretraining in biological neural networks, Nature (2025). DOI: 10.1038/s41586-025-09180-y
Citation:
Zoning out could be beneficial—and may actually help us learn faster (2025, June 23)
retrieved 23 June 2025
from https://medicalxpress.com/news/2025-06-zoning-beneficial-faster.html
This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no
part may be reproduced without the written permission. The content is provided for information purposes only.
Aimlessly wandering around a city or exploring the new mall may seem unproductive, but new research from HHMI’s Janelia Research Campus 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 of scientists from the Pachitariu and Stringer labs discovered that learning may occur even when there are no specific tasks or goals involved.
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.
“Even when you are zoning out or just walking around or you don’t think you are doing anything special or hard, your brain is probably still working hard to help you memorize where you are, organizing the world around you, so that when you’re not zoning out anymore—when you actually need to do something and pay attention—you’re ready to do your best,” says Janelia Group Leader Marius Pachitariu.
Observing unsupervised learning
The team, led by postdoc 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.
“As we thought more and more about it, we eventually ended up on the question of whether the task itself was even necessary,” Pachitariu says. “It’s entirely possible that a lot of the plasticity happens just basically with the animal’s own exploration of the environment.”
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 Ph.D., 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 new 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.
“It’s a door to studying these unsupervised learning algorithms in the brain, and if that’s the main way by which the brain learns, as opposed to a more instructed, goal-directed way, then we need to study that part as well,” Pachitariu says.
The researchers say these insights were enabled both by Janelia’s support teams, which helped the researchers design and run the experiments, and by the mesoscope, an instrument that enabled the team to record up to 90,000 neurons simultaneously, enhancing their ability to make new discoveries.
“Allowing a single lab to run projects at this scale is what is uniquely possible here and that gives us the flexibility to pursue different questions without necessarily having a concrete plan,” Pachitariu says.
More information:
Lin Zhong et al, Unsupervised pretraining in biological neural networks, Nature (2025). DOI: 10.1038/s41586-025-09180-y
Citation:
Zoning out could be beneficial—and may actually help us learn faster (2025, June 23)
retrieved 23 June 2025
from https://medicalxpress.com/news/2025-06-zoning-beneficial-faster.html
This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no
part may be reproduced without the written permission. The content is provided for information purposes only.
