Inside the cerebellum, unique neurons predict the timing of future events

Humans and other animals constantly make predictions about future events based on previous experiences and their perceptions of the surrounding environment. This predictive process is described by a mathematical framework known as Bayesian inference.

In this mathematical framework, probabilities about future events are updated as new evidence becomes available. Some neuroscience theories suggest that the mammalian brain makes predictions similarly, by continuously estimating what will happen next based on new sensory information.

Researchers at Radboud University and the Erasmus University Medical Center in the Netherlands carried out a mouse study aimed at better delineating the neural circuits that represent these probability-based predictions.

Their findings, outlined in a paper published in Nature Neuroscience, suggest that the probability distributions of temporal events are learned by circuits in the cerebellum. They also show that statistical information about the expected timing of future events is encoded by large, unique neurons in the cerebellum, called Purkinje cells.

“This work was inspired by decades of research in human behavior about how humans rely on previous experiences to counter increased uncertainty in the observed world,” Devika Narain, senior author of the paper, told Medical Xpress. “The question was where and how such previous experience is stored in the brain. This is what led us to perform this investigation.”

The role of Purkinje cells in encoding prior knowledge

Narain and her colleagues carried out experiments involving adult mice, which were trained to expect a specific event (i.e., a puff of air on one of their eyes) at specific times after seeing a flash of light.

They specifically looked at how expectations about future air puffs were represented in the cerebellum, a structure at the back of the brain that plays a role in coordination, motor learning, balance and posture.

“If I present you with a flash of light and puff you in the eye after a fixed time after this light, your eye will learn to close to anticipate the right time of the air puff to protect itself,” explained Narain.

“It even blinks at the right time when no air puff is presented at all. We make a probabilistic version of this task and find that depending on the probabilities of when the air puff occurs, the eye closes in a complex way to match those probabilities.”

While the mice were completing the eyeblink conditioning task, Narain and her colleagues also recorded the activity of a type of cell in the cerebellum, called Purkinje cells. Interestingly, they found that these cells changed their activity patterns over time, as the mice learned new timing statistics (i.e., how long after the bright light the eye puff took place).

In addition, when they silenced the activity of these cells, the researchers observed that mice no longer blinked in expectation of future air puffs.

“When the activity of Purkinje cells is disrupted, the predictive eyeblinks disappear,” said Narain. “This suggests the involvement of Purkinje cells in generating this predictive behavior based on prior knowledge of the stimulus statistics.”

Uncovering the neural underpinnings of statistical reasoning

The results of this recent study suggest that Purkinje cells contribute to the encoding and storage of statistical predictions about future events. In addition, they uncover a specific signal that appears to be associated with the prediction of event or stimulus timing.

“We found that when the stimulus distribution was very uncertain (probabilities were very spread out and hard to predict), the brain generated an internal signal to predict the first possible interval the air puff could arrive,” said Narain.

“In this way, we think it created an internal prediction to counteract the highly unpredictable external conditions. One implication of this finding is that the brain takes the probability of events into account even for very simple motor behaviors and generates internal signals to compensate for an unpredictable world.”

The findings gathered by Narain and her colleagues could soon inspire more research aimed at further examining the prediction-related neural processes and circuits they identified. Eventually, their work could help to improve existing models of how the brain supports statistical reasoning and future event prediction.

“We are now interested in discovering how this internal compensation mechanism works and how the brain learns to counteract unpredictability in the external world,” added Narain.

Who’s behind this story?

Ingrid Fadelli

Freelance journalist with BSc Psychology and MA International Journalism. Covers AI, robotics, neuroscience, and astrophysics since 2018.

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Lisa Lock

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