When you get better at a skill—recognizing a familiar face in a crowd, spotting a typo at a glance, or anticipating the next move in a game—sensory neurons in your brain become more coordinated, sharing information rather than acting more independently. That’s the conclusion of a new study by researchers at the University of Rochester and its Del Monte Institute for Neuroscience , published in Science , which challenges a long-held assumption in neuroscience that learning improves efficiency by minimizing repetition across neural signals.
Led by Shizhao Liu, a graduate student in the labs of Ralf Haefner and Adam Snyder , both faculty members in the Department of Brain and Cognitive Sciences , the study shows that learning instead increases shared activity among neurons. The findings could provide insights into learning disorders and inspire more flexible, human-like artificial intelligence tools.
“The dominant view in neuroscience has been that learning makes the brain more efficient by pushing neurons to act more independently, so information can be read out more cleanly,” Liu says. “Our results support a different idea, that sensory areas of the brain aren’t just passively encoding the world. They’re actively performing inference by combining what’s coming in with what the brain has learned to expect.”
For decades, researchers believed that learning streamlined how the brain processes information by reducing shared activity among neurons, allowing information to be read out more efficiently. The idea shaped how researchers thought about everything from perception to decision-making.
But the research from Liu, Haefner, Snyder, and their team suggests a different mechanism. Rather than becoming more independent, neurons become more coordinated as learning unfolds, increasing the amount of information they share, particularly when the brain is actively engaged in a task and making decisions.
This coordination reflects the brain’s growing reliance on internal expectations. As learning progresses, feedback from higher-level brain areas appears to shape how sensory neurons respond, allowing perception to incorporate both incoming information and what the brain has learned from past experiences.
The researchers tracked the activity of the same small networks of neurons in the visual cortex over several weeks as subjects learned to tell apart different visual patterns. The team measured whether neurons were increasingly acting on their own or sharing more information as learning progressed.
The researchers discovered that before learning, neurons mostly worked independently. But as subjects honed their visual skills, the neurons started to behave more like a well-trained sports team, communicating and working together in a coordinated way.
“It’s a bit like a group of people solving a problem,” Snyder says. “Instead of everyone working in isolation as efficiently as possible, learning makes them communicate more. That shared information makes each individual better informed and potentially makes the group more flexible and adaptive.”
Importantly, this coordinated effect only appeared when subjects were actively performing a task and making decisions based on what they saw. When they passively looked at the same images without needing to respond, the effect disappeared.
The neurons most important for the task showed the biggest boost in coordination, especially at the moments when decisions were made.
But these are flexible, not permanent, changes. The researchers believe these shifts are guided by feedback signals from higher-level brain areas, allowing neurons to adjust their behavior on the fly, depending on the task.
The results support a growing idea in neuroscience that the brain isn’t a simple conveyor belt that passes information forward. Instead, it constantly blends what we see with what we expect to see, creating a richer, more informed picture of the world. And that blending requires groups of neurons to act together, not separately.
Understanding how the brain coordinates neurons during learning could provide new insights into learning disorders and conditions that affect perception. It could also help scientists design artificial intelligence systems that generalize better by taking inspiration from the way the brain flexibly blends prior expectations with new sensory information.
“Most current artificial intelligence systems are built on discriminative architectures that map sensory inputs directly to outputs,” Haefner says. “Our new research suggests that incorporating generative feedback loops—in which internal models shape sensory representations—may lead to systems that learn faster from limited data, are more robust to uncertainty, and adapt more flexibly to changing tasks.”
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