It's not dumbing down. It's extracting the crux of the matter that the complexity of arguments is trying to hide, perhaps unintentionally. Either the brain implements a function that can be approximated by a neural network thanks to universal approximation theorem, or the function cannot be approximated (you need arguments for why it is the case), or magic.
This is technically true but kind of misses the point in my opinion. A neural network can approximate any function in theory but that doesn't mean it has to do so in a reasonable amount of time and with a reasonable amount of resources. For example, take the function that gives you the prime factors of an integer. It is theoretically possible for a neural network to approximate this for an arbitrarily large fixed window but is provably infeasible to compute on current hardware. In theory, a quantum computer could compute this much faster.
This is not to say that the human brain leverages quantum effects. It's just a well known example where the hardware and a specific algorithm can be shown to matter.
I also think it's strange to describe the brain as implementing a function. Functions don't exist. We made them up to help us think about building useful circuits (among other things). In this scenario, we would be implementing functions to help us simulate what is going on in brains.
It's not dumbing down. It's extracting the crux of the matter that the complexity of arguments is trying to hide, perhaps unintentionally. Either the brain implements a function that can be approximated by a neural network thanks to universal approximation theorem, or the function cannot be approximated (you need arguments for why it is the case), or magic.