Brain Memory Dream Concept

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

Brain memory dream concept

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

According to experts from the Human Brain Project, strange dreams can help your brain learn better

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

Even though we dream all night, our most vivid and memorable dreams occur during rapid eye movement sleep (REM), 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 alien, more vivid and memorable. The exact purpose of dreaming is still a bit of a mystery to researchers, but recent research hopes to explain why people have strange dreams.

A new study from University of Bern in Switzerland reveals that dreams, especially those that seem real but on closer inspection are abnormal, help our brain to learn and extract general ideas from previous experiences. The research, which was carried out as part of the Human Brain Project and was published in eLifegives a new hypothesis about the meaning of dreams by using machine learning-inspired methods and brain simulation.

The importance of sleep and dreams for learning and memory has long been recognized; what impact a single sleepless night can have on our cognition is well documented. “What we lack is a theory that connects this with experience consolidation, concept generalization and creativity,” explains Nicolas Deperrois, the study’s lead author.

During sleep, we usually experience two types of sleep phases, alternating one after the other: non-REM sleep, when the brain “repairs” the sensory stimulus experienced when awake, and REM sleep when spontaneous outbursts of intense brain activity produce vivid dreams.

The researchers used simulations of the cerebral cortex to model how different sleep phases affect learning. To introduce an element of unusualness into artificial dreams, they took inspiration from a machine learning technique called Generative Adversarial Networks (GAN). In GANs, two neural networks compete with each other 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 can look superficially realistic to a human observer.

Cortical representation Dreaming

Cortical representation learning through disturbed and conflicting dreams. Credit: Deperrois et al. eLife 2022; 11: e76384

The researchers then simulated the cortex under three distinct conditions: wakefulness, non-REM sleep, and REM sleep. During wakefulness, the model is exposed to images of boats, cars, dogs and other objects. In non-REM sleep, the model plays sensory inputs with certain occlusions. REM sleep creates new sensory inputs through GAN, generates twisted but realistic versions and combinations of boats, cars, dogs, etc. To test the model’s performance, a simple classifier evaluates how easily the object’s identity (boat, dog, car, etc.) can be read from the cortical representations.

“Non-REM and REM dreams become more realistic as our model learns,” explains Jakob Jordan, senior author and leader of the research group. “While non-REM dreams resemble awake experiences quite closely, REM dreams tend to creatively combine these experiences.” Interestingly, it was when the REM sleep phase was suppressed in the model, or when these dreams were made less creative, that

accuracy
How close the measured value corresponds to the correct value.

“data-gt-translate-attributes =”[{” 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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