I think that's moot: based on the universal approximation theorem [1], a big enough model is indistinguishable from the human brain, regardless of whether the mechanism of action is fundamentally the same or not. I believe this applies to anything that can somehow be modeled with a continuous function - whether that's possible for the human brain is an open question, though we only need a certain fidelity to be useful.
The more useful question is: can the token prediction model scale to the level of a human intelligence within a reasonable power budget compared to a brain? It's comparing apples to oranges right now but the human brain consumes under 20 watts, a tiny fraction of the TDP of a single A100 GP, and the state of the art isn't even close in performance. We've got a long way to go before we can conclusively answer these questions.
[1] https://en.wikipedia.org/wiki/Universal_approximation_theore...