Ehhhh, maybe early on, but AFAIK most recent work w.r.t neural networks, except when explicitly trying to mimic actual neurons, isn't really trying to approximate or simplify anything.

The basic mechanic of using wings to generate lift might have been inspired by birds in flight, but a 737 is not a simplified pigeon.

I agree. A 737 is a shadow of a cardboard cutout of a paper origami model of a simplified pigeon. We have barely scratched the surface of what biological systems can do, especially in the dynamic movement and shape changing aspects as well as the energy efficiency of computational and control systems that are involved in biological systems.

In every system we create, in which we mimic biological systems (whether this is communication, transport, robotic, computational, chemical, etc), the resultant systems in relative terms are extremely simplistic, clunky and inefficient. Our systems may be highly complex as we look at them. But in reality, they are very, very simple in comparison to the original system that we want to mimic. We, at this point, lack the finesse of designing anything comparable. We have a long , long way to go yet.

For early perceptrons, maybe; even that feels like a stretch. Every neural network model that has been created after that has nearly zero relation to any sort of biological, cognitive system.

The neocognitron and convolutional neural networks were inspired on experiments on cats performed by Hubel and Wiesel.

I think the designers of many of those models would disagree with you.

Okay, what models?