I'm not the original commenter, but I feel similarly. Perhaps offering some thoughts on sections 3.1 and 3.2 could foster some discussion. Essentially, the hyp

I'm not the original commenter, but I feel similarly. Perhaps offering some thoughts on sections 3.1 and 3.2 could foster some discussion. Essentially, the hypothesis is that dreams are some kind of regularization that the brain performs on learning to prevent overfitting. The authors propose a couple of different arguments for why this may be useful (from deep learning) and why dreams in particular is where this regularization might happen (from neuroscience). I don't think anyone would object that overfitting is bad and regularization is useful. I might even agree with the author that some kind of anti-Hebbian or noise corruption may be happening in the brain to help with this. However, the implied analogies in this paper between the tricks used in deep learning and dreams feels really "just so" to me. I think their main evidence is that: 1. Dreams facilitate learning 2. The dreams we experience generally reflect the repeated task we experienced during the day 3. We often hallucinate variations of environments, not the exact environment we experienced I think overall from this, I feel it is fair to say that the brain does something at night to help improve learning. It's not immediately clear that this is analogous to dropout, training augmentation, or generative models used in deep learning. It is even less clear whether dreams are truly needed to perform this. The authors don't really dissociate dreaming from sleeping in most of the literature that they cite. Remember that your experience and the weight changes in your brain may not be that highly correlated. Many neural changes may happen during a dream which are not correlated with your conscious perceptions/recollection of that dream.