The idea of neural network in its current form came from someone in 1943 going through actual neuroscience papers and translating them to mathematical model by abstracting away the noise. While brilliant, Mccullough and Pitts work was done in pre-historic times if you consider the amount of information we learned about the brain and cognition since.

Saying "We don't know much about human brain" is just being lazy. We know too much about the brain! Brain research has collected so much empirical data and a good amount of good theories. For an example of a good theory at various levels of abstraction see Moser, O'Keefe and Nadel work on place cells and grid cells, Kandel on learning, Edmund Rolls on vision, Olshausen & Field on sparse coding, Kanerva' SDM, Plate' convolutions, Widdows on geometry and meaning, Wickelgren and J. R. Anderson on associative memory, Fukushima' neocognitron, Hofstadter on analogical reasoning, Quillian on semantic memory, Pribram holonomic theory, Valiant' neuroids, Pearl' causality. More of these "bridges" and a meta-bridge is needed if you're serious about AI.