The brain naturally employs dimensionality reduction for memory. Sleep is one example. Another, simpler one is reading -- how far back can you remember word for word when you are reading something? Maybe a sentence at most? But you still remember enough to understand what you're reading, because of efficient dimensionality reduction.

Some neural networks mimic this, such as LSTMs. But it's a poor mimicry at best. The brain has a natural, built-in selection mechanism. It seems to "know" what to remember and what to forget. How could we implement something like this in a deep neural network?

(This is key step to giving computers "personality". Which emerges from a selective set of memories and trained behavior)