Did dreams evolve to transcend overfitting?

A fascinatingnew paper proposes that dreams evolved to help the brain generalize, which improves its performance on day to day tasks. Incorporating a concept from deep learning,Erik Hoel (2021):“...outlines the idea that the brains of animals are constantly in danger ofoverfitting, which is the lack of generalizability that occurs in a deep neural network when its learning is based too much on one particular dataset, and that dreams help mitigate this ubiquitous issue. This is the overfitted brian[sic] hypothesis. ” The Overfitted Brain Hypothesis (OHB) proposes that the bizarre phenomenology of dreams is critical to their functional role. This view differs from most other neuroscientific theories, which treat dream content as epiphenomenal — a byproduct of brain activity involved in memory consolidation, replay, forgetting, synaptic pruning, etc.  In contrast, Hoel suggests that “it is the very strangeness of dreams in their divergence from waking experience that gives them their biological function.”The hallucinogenic, category-breaking, and fabulist quality of dreams means they are extremely different from the “training set” of the animal (i.e., their daily experiences).. . .To sum up: the OBH conceptualizes dreams as a form of purposefully corrupted input, likely derived from noise injected into the hierarchical structure of the brain, causing feedback to generate warped or “corrupted” sensory input. The overall evolved purpose of this sto...
Source: The Neurocritic - Category: Neuroscience Authors: Source Type: blogs