Sure, but now you're talking about running a physical simulation of neurons. Real neurons aren't just electrical circuits. Not only do they evolve rapidly over time, they're powerfully influenced by their chemical environment, which is controlled by your body's other systems, and so on. These aren't just minor factors, they're central parts of how your brain works.
Yes, in principle, we can (and have, to some extent) run physical simulations of neurons down to the molecular resolution necessary to accomplish this. But the computational power required to do that is massively, like billions of times, more expensive than the "neural networks" we have today, which are really just us anthropomorphizing a bunch of matrix multiplication.
It's simply not feasible to do this at a scale large enough to be useful, even with all the computation on Earth.
Beyond the established symmetries of physics - Charge, Spin, Color, Time, etc. - is there any such thing as symmetry of Information?
Why can't people make ai's by making a neuron sim and then scaling it up with a supercomputer to the point where it has a humans number of neurons and then raise it like a human?
I know current learning models work a little like neurons but why not just make a sim that works exactly like how we understand neurons work