You can make a simple theory of anything. An airplane as a spherical ball of varying friction with a steerable propulsion. Or a massive electro-mechanical system. There's a fidelity curve as you progress in complexity. Fidelity curve of a literal ball stabilizes quickly, while the fidelity curve of an airplane extends far.

The brain is massively complex, we still don't understand the ways that it computes and learns in detail except generalities such as using electrical and chemical signals, neurotransmissors, and some knowledge of architecture and region differentiation.

Those models all point to an extremely complex system with quite a number of different specialized parts, with neurons individually having quite amazing complexity (and so on for capsules, neuronal systems).

I think it's unlikely a "simple" system (if you could consider a vanilla CNN simple, for example) will solve human-like AGI efficiently (either data-efficiently or computationally efficiently). There are too many requirements.

Also, while simplicity is good... a researcher shouldn't be caught obsessing over it exclusively, as it is not the end goal, which personally would be the advancement of understanding and of our capabilities instead.