You're incorrect that neuroscientists and computational researchers don't collaborate. In fact, "computational neuroscience" departments have existed at major u

You're incorrect that neuroscientists and computational researchers don't collaborate. In fact, "computational neuroscience" departments have existed at major universities for a number of years. DeepMind was a spin-off of the Gatsby Computational Neuroscience Unit at UCL, and they continue to be influential in both neuroscience and (more famously) in artificial intelligence research. Here's an article from 2020 by authors at DeepMind and Geoff Hinton about potential biologically plausible mechanisms for backpropagation in the brain [0]. So, you can see that the fields actually influence each other in both directions. Computational researchers often propose ideas that neuroscientists then attempt to understand in biological systems. This same principle is true at all levels of computational and biological abstraction, from simulating individual neurons to machine learning and common sense. For the latter, research by Josh Tenenbaum might interest you. The reason this works is that there seem to be fundamental principles underlying information processing. Brains and CPU's are both systems that manipulate and store information, albeit in very different ways. Hell, even single cells and slime molds are capable of rudimentary decision making. So, the point is that we don't need to copy the brain. Instead, we just need to understand the principles of information well enough to build machines that can efficiently manipulate information and intelligence will arise out of that. Information theory and (by extension) statistics are the fields that deal most closely with this question, which is why they're used heavily by both neuroscientists and ML researchers. A rough analogy is that we don't build airplanes that flap their wings to fly. Instead, we understand aerodynamics well enough to generate lift and thrust through other mechanisms that evolution can't find. Like jet engines. Also, "slavishly" copying nature is insanely difficult. Biological neurons and brain tissue are extremely complex and poorly understood. Much of this complexity is likely incidental to information processing and would only hamper our efforts to build intelligent machines. Like, do we want our computer brain to get multiple sclerosis if the simulated neurons demyelinate? [0] https://www.nature.com/articles/s41583-020-0277-3 https://www.nature.com/articles/s41583-020-0277-3