[gregatragenet3]

Probably related. Mammals dream, and we humans experience time in dreams much faster than the actual passage of time. Dreaming is likely the same mechanism as 'experience replay' used in AI reinforcement learning. We're just training our neutral networks using minibatches of our experiences from our waking hours.

[argonaut]

"Likely" is not the word I would go for. "according to my wild guess" is a more accurate term.

[matt4077]

I've heard it before and it's not their wild guess but a seriously discussed idea. It's just that it's hard to set up experiments to test these sort of ideas.

[oldmanjay]

It can be a seriously discussed wild guess. It's not like there's some dichotomy to be satisfied there.

[cdetrio]

I read somewhere a conjecture that sleep for human brains is analogous to regularization schemes (such as dropout) for neural networks. Dropout helps the net avoid overfitting; nets that are overfitted have a difficult time distinguishing noise from signal. Sleep for mammals might serve a similar purpose, to help the brain avoid overfitting to noise (imagine if you had difficulty distinguishing dreams from reality).

[MollyR]

Yea, I would suspect this is true as well. Upenn had some sleep studies that were looking this idea, I'n not sure how mature the research was/is though.