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The overfitted brain: Dreams evolved to assist generalization

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Re: The overfitted brain: Dreams evolved to assist generalization

#4
> 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 stimulation every night, the brain is able to rescue the generalizability of its perceptual and cognitive abilities and increase task performance.

Bit of jump there from randomized/out of sample data to dreams are for generalization.

Re: The overfitted brain: Dreams evolved to assist generalization

#5
Seems like it isn't the randomization (high entropy) but rather the excessively low entropy. Sleeping brains are colder and more predictable (due to massive synchronization). REM sleep might be described as when our cortex turns on and tries sensemaking all the intrinsic oscillations.

Re: The overfitted brain: Dreams evolved to assist generalization

#8
post #2

I have too many dreams. It actually sucks since you don't get great sleep.

I have a sleeping disorder and if I wake up and remember having a dream I almost always also feel well rested.

I remember 1-3 dreams every night. All my dreams are vivid dreams, where I know they are dreams while they are happening and I even have some degree of control over the content.

Re: The overfitted brain: Dreams evolved to assist generalization

#9
It strikes me as rather unintuitive that the brain should be generating its own “out-of-distribution” data. It’s training itself on itself? Compare with an adversarial network, which can be composed of two entirely separate entities, whereas the biological brain is know to have a lot of “bleed-through”, e.g. our memories influence our perception and vice versa.
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