Taking effective results in machine learning, and somehow assuming that they apply to cognition - simply because neural nets were inspired by our limited knowledge of neural signaling and structure - is like trying to apply aircraft engineering to studying ornithology. For a better articulation of this point (from the reverse direction) check out the paper 'Could a Neuroscientist Understand a Microprocessor?' from 2017 - https://journals.plos.org/ploscompbiol/article?id=10.1371/jo...
Hard disagree ;-) You're talking about high level architecture of the brain. I don't think (not my area I may be wrong) we know how memories are encoded in a real brain. Is it weights or something else? If it's weights that's supporting my point (but we don't know what the weights represent in a brain, where in LLMs many weights are just token encodings). If brains store memories in something other than weights I'd really like to know as it's something I haven't read about yet.