A new study investigates the relationship between pontine dilation/contraction and memory processing during sleep. The study, published in the journal, is titled, “Sleep microstructure organizes memory replay.” The nature And conducted by scientists at Cornell University, Ithaca, it claims that pupil size during sleep can indicate the memories you’re reliving in your dreams.

Using advanced eye-tracking technology combined with an EEG (electroencephalogram), the researchers monitored the sleep patterns of the mice to record their brain activity. Specifically, rats were given new information such as going on a maze during the day and allowed to sleep at night.

On analyzing the data, it was found that two sub-stages occurred during NREM (non-rapid eye movement) sleep. Pupils contract in a phase, indicating that new memories are being replayed, while pupils dilate when rats are processing or reliving past experiences in their dreams. are Both stages happened one after the other.

“It’s like new learning, old knowledge, new learning, old knowledge, and it slowly fluctuates during sleep,” Azhara Oliva, a neuroscientist in the Department of Neurobiology and Behavior, told Science Alert.

Read this also The Science Behind a Good Night’s Sleep: Explaining Sleep-Wake Cycles

Making new memories but not at the expense of others.

The study helps find answers to why the creation of new memories doesn’t erase old memories. For example, learning to play an instrument without forgetting to ride in a car.

“Our findings suggest that the brain can multiplex different cognitive processes during sleep to facilitate continuous learning without interruption,” the researchers wrote.

“We are suggesting that it is this intermediate timescale in the brain that separates new learning from old knowledge.”

A key insight from this research is the brain’s ability to separate two sub-phases of sleep that prevent “catastrophic” forgetting of memories at the expense of previous memories.

“This finding provides a potential solution to a long-standing problem in both biological and artificial neural networks to prevent destructive interference while also enabling memory integration,” the researchers write.

The study’s findings have excited the scientific community, which hopes to see results in humans that could lead to better memory-enhancing techniques and help train artificial intelligence.

(Tags translation)Cornell University



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