Do you have strange dreams? They could help your brain learn better

Recent research from the University of Bern published in the journal eLife suggests that strange dreams can help your brain learn more efficiently.

Strange dreams can help your brain learn better, say experts at the Human Brain Project

According to the National Sleep Foundation, we dream an average of four to six times a night. However, since we forget more than 95% of our dreams, you will only remember a few each month.

Although we dream all night, our most vivid and memorable dreams occur during rapid eye movement (REM) sleep, which begins about 90 minutes after you fall asleep. Unexpected life events, high levels of stress, and other changes can all have an effect on our dreams, making them weirder, more vivid, and memorable. The exact purpose of the dream is still a bit of a mystery to scientists, but recent research hopes to explain why people have strange dreams.

A new study from the University of Bern in Switzerland reveals that dreams, especially those that seem genuine but upon closer inspection are abnormal, help our brains learn and extract general ideas from past experiences. . The research, which was conducted as part of the Human Brain Project and published in eLifeproposes a new hypothesis on the meaning of dreams using methods inspired by machine learning and brain simulation.

The importance of sleep and dreams in learning and memory has long been recognized; the influence that a single sleepless night can have on our cognition is well documented. “What we lack is a theory that links this to the consolidation of experience, the generalization of concepts and creativity,” explains Nicolas Deperrois, lead author of the study.

During sleep, we generally experience two types of sleep phases, alternating one after the other: non-REM sleep, when the brain “replays” the sensory stimulus felt during wakefulness, and REM sleep, when puffs spontaneous bursts of intense brain activity produce vivid dreams.

The researchers used simulations of the cerebral cortex to model the impact of different sleep phases on learning. To introduce an unusual element to artificial dreams, they drew inspiration from a machine learning technique called Generative Adversarial Networks (GAN). In GANs, two neural networks compete to generate new data from the same data set, in this case, a series of simple images of objects and animals. This operation produces new artificial images that may appear superficially realistic to a human observer.

Cortical representation Dreaming

Learning cortical representation through disturbed and contradictory dreams. Credit: Deperrois et al. eLife 2022;11:e76384

The researchers then simulated the cortex during three distinct states: wakefulness, non-REM sleep and REM sleep. While awake, the model is exposed to images of boats, cars, dogs, and other objects. In non-REM sleep, the model replays sensory inputs with some occlusions. REM sleep creates new sensory inputs through GANs, generating twisted but realistic versions and combinations of boats, cars, dogs, etc. To test the performance of the model, a simple classifier evaluates how easily the identity of the object (boat, dog, car, etc.) can be read from the cortical representations.

“Non-REM and REM dreams become more realistic as our model learns,” says lead author and research team leader Jakob Jordan. “While non-REM dreams quite closely resemble waking experiences, REM dreams tend to combine these experiences in creative ways.” Interestingly, it was when the REM phase was removed in the model, or when these dreams were made less creative, that the[{” attribute=””>accuracy of the classifier decreased. When the NREM sleep phase was removed, these representations tended to be more sensitive to sensory perturbations (here, occlusions).

According to this study, wakefulness, non-REM, and REM sleep appear to have complementary functions for learning: experiencing the stimulus, solidifying that experience, and discovering semantic concepts. “We think these findings suggest a simple evolutionary role for dreams, without interpreting their exact meaning,” says Deperrois. “It shouldn’t be surprising that dreams are bizarre: this bizarreness serves a purpose. The next time you’re having crazy dreams, maybe don’t try to find a deeper meaning – your brain may be simply organizing your experiences.”

Reference: “Learning cortical representations through perturbed and adversarial dreaming” by Nicolas Deperrois, Mihai A Petrovici, Walter Senn and Jakob Jordan, 6 April 2022, eLife.
DOI: 10.7554/eLife.76384

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