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Curious where this claim came from. Obviously a lack of sleep will degrade your day-to-day performance, but I almost never have dreams.
> Sleep loss, specifically dream loss, leads to an overfitted brain that can still memorize and learn but fails to generalize appropriately Curious where this claim came from. Obviously a lack of sleep will degrade your day-to-day performance, but I almost never have dreams.
> Sleep loss, specifically dream loss, leads to an overfitted brain that can still memorize and learn but fails to generalize appropriately Curious where this claim came from. Obviously a lack of sleep will degrade your day-to-day performance, but I almost never have dreams.
I wish I recalled (..or saved) the paper, but waking up releases a chemical in the brain that makes you actively and quickly forget what you just dreamed about.
> Notably, the techniques that researchers employ to rescue overfitted artificial neural networks generally involve sampling from an out-of-distribution or randomized dataset. The overfitted brain hypothesis is that the brains of organisms similarly face the challenge of fitting too well to their daily distribution of stimuli, causing overfitting and poor generalization. By hallucinating out-of-distribution sensory s…
> Notably, the techniques that researchers employ to rescue overfitted artificial neural networks generally involve sampling from an out-of-distribution or randomized dataset. The overfitted brain hypothesis is that the brains of organisms similarly face the challenge of fitting too well to their daily distribution of stimuli, causing overfitting and poor generalization. By hallucinating out-of-distribution sensory s…
And if the brain is overfitted, how can it generate out-of-distribution samples? Seems like wildly cyclical reasoning.
> Notably, the techniques that researchers employ to rescue overfitted artificial neural networks generally involve sampling from an out-of-distribution or randomized dataset. The overfitted brain hypothesis is that the brains of organisms similarly face the challenge of fitting too well to their daily distribution of stimuli, causing overfitting and poor generalization. By hallucinating out-of-distribution sensory s…
And if the brain is overfitted, how can it generate out-of-distribution samples? Seems like wildly cyclical reasoning.
(This is in fact how modern neural network training often works: start with a set of 50,000 images, run them through a bunch of permutations — translation, rotation, lighting changes, etc. — and produce a training set of 1 million images which yield better generalization when trained against.)
Or sleep is needed to clean the toxins in the brain and dreams are just a funny consequence of disconnecting you from the world
* Lucid dreaming. In particular does lucid dreaming prevent the generalization benefits?
* Sensory deprivation hallucinations. It would seem this is connected to dreams, but is it?
* Tetris effect. It seems that seeing a lot of a given pattern primes the brain the brain to look for it everywhere.