To build a machine that can fly, we need to build a machine that can flap its wings.
To build a car that moves, we must build a machine that can lift its two feet in alternating motion.
To build a camera that sees, we need to build a lens that can flex itself to change focus.
Sure, the biology gives us some clues, but it may not be the most useful way to view what is going on.
To really understand how insects fly, it is helpful to build and analyze various machines that can flap its wings - since aerodynamics is complex and the equations we use to model airplanes don't really work on that scale.
To understand how exactly humans walk, it really helps to build a machine that can lift its two feet in alternating motion and analyze how all the minor forces interact to make it work. It's immensely useful when we're building e.g. powered ankle prosthetics, and bipedal movement has some advantages in terrain, so we also want machines to be able to do that (e.g. https://www.youtube.com/watch?v=rVlhMGQgDkY)
For understanding human eyes - experimenting with lense systems that change focus was how we got to "augmented vision" e.g. humans with spectacles.
The same goes for analyzing and understanding how human brains work. It's also valuable to think about minds in general, but for many purposes we care about a particular mind, and all the individuals I currently care about are homo sapiens, not machines; so we need to understand their brains.
Most of frontier neuromorphic research of today, neither focus on creating an "general artificial intelligence" by copying human brain nor think that neuromorphic computing is the most probable discipline to achieve it. Instead, optimizing hardware for neural networks is the focus. If we want to achieve high number of (>10^14) weights, low energy consumption and spatial shrinkage we would like to give up running 1000 GPU clusters which only a handful of companies have. Neuromorphic computing only suggests that we need a better hardware (which may or may not require working with spiking neural nets as a consequence) in order to make AI hardware scalable, nothing more.