A new study has examined the relationship between pupil dilation/constriction and memory processing during sleep. The study, published in the journal, is titled, “Sleep microstructure orchestrates memory replay”. Nature And a study conducted by scientists at Cornell University, Ithaca, claims that pupil size while sleeping can indicate the memories you are reliving in your dreams.
Using advanced eye-tracking technology combined with EEG (electroencephalogram), researchers monitored the sleep patterns of mice to record their brain activity. Specifically, rats were given new information such as navigating a maze during the day and allowed to sleep at night.
Upon analyzing the data, it was found that two subphases occurred during NREM (non-rapid eye movement) sleep. The pupils contracted in a phase that indicated new memories were being repeated, while the pupils dilated when the rats were processing or reliving past experiences in their dreams. Both phases were completed rapidly.
“It’s like new learning, old knowledge, new learning, old knowledge, and it fluctuates gradually throughout sleep,” Azahara Oliva, a neuroscientist in the Department of Neurobiology and Behavior, told ScienceAlert.
Read this also The Science Behind a Good Night’s Sleep: The Sleep-Wake Cycle Explained
Making new memories but not at the expense of others
The study helps find the answer to why the formation of new memories does not erase old memories; For example, learning to play an instrument without forgetting to drive a car.
“Our results suggest that the brain can multiplex different cognitive processes during sleep to facilitate continuous learning without disruption,” the researchers wrote.
“We are proposing that there is this intermediate time scale in the brain that separates new learning from old knowledge.”
One of the main insights of this research is the brain’s ability to differentiate two sub-stages of sleep which prevents “catastrophic” forgetting of memories at the expense of past memories.
“This discovery provides a potential solution to the long-standing problem in both biological and artificial neural networks of preventing destructive interference as well as enabling memory integration,” the researchers wrote.
The results of the study have encouraged the scientific community which hopes to see results on humans which may lead to better memory enhancing techniques and may also help in training artificial intelligence.
