Funny you should compare understanding biological neural networks to a microprocessor. Check out this paper (https://journals.plos.org/ploscompbiol/article?id=10.1371/jo...), which systematically addresses how applying traditional neuroscience methods to analyzing a microprocessor would get you.
I've read that paper years ago. I'm afraid I find it unimpressive. It is not so hard, really, to fail, when you fail to try. I have a computer science degree; I've built CPUs (from all the sequential and combinational circuits on up); I also work in neuroscience, and do some of the kinds of analysis this paper tries to criticize.
For example, a very basic approach is design experimental tasks that have contrasting conditions, or events to be predicted. They don't do that. Even something as simple as pressing the controller left vs right vs not moving at all in donkeykong would have been more interesting.
In any case, brains and traditional computer circuits work on different principles as I noted in my OP. Furthermore, there is quite a lot more redundancy in brains--and partial-redundancy is a bit of how it works; for example, if you have a population of neurons with different tuning curves, then the output in response to a stimulus can be integrated, and a likelihood distribution obtained for the actual value of a stimulus; that's just fundamentally different than how, say, a 5-stage pipelined RISC cpu works.
