My point is that a system X perfectly emulates a system Y if all of its parts are identical, that is, if Y has all the basic atoms of X in the same arrangement.

Anything short of that has to be saying that X is "like" Y in some relevant way. And here this has nothing to do with parameter count, complexity, or anything of this kind.

Here "X" has to be behaviourally similar to "Y".

Animal intelligence is essentially entirely concerned with managing animal bodies and their relationship to the environment; and secondarily, the environment. To do this requires a body (etc.).

No mathematical model over a dataset can emulate animal intelligence, because a mathematical model has no behaviour. A mathematical model can describe an animal, but a description is not an emulation: the formula E=MC^2 isnt a nuclear explosion.

A digital silicon machine instantiates a mathematical formula with an osciliating electric field. There is nothing about this electrical osciliation which behaves anything like animal intelligence.

> A digital silicon machine instantiates a mathematical formula with an oscillating electric field

Exactly! That is exactly what is done by a neuron, the brain building block. So I hope you can see why I think an AI can be close enough to simulate the brain, as we still not know the exact border of intelligence (and thus we may be closer than we think).

That isn't "exactly what is done by a neurone". It has nothing to do with it.