No equivalent of error backpropagation has ever been found in real neurons, and it&#x27;s biologically implausible.   So ANNs are almost certainly using a different learning mechanism from the one used in the brain.   Even single neurons are quite complex and very little of this complexity is present in neural networks.<p>The visual system (retina, lateral geniculate nucleus, visual cortex) was fairly well understood well before ANNs were developed.   A few uncontroversial ideas (e.g. that cells take their inputs from neighbouring cells in the previous layer) were adopted for use in ANNs.<p>I was around at the time of, and affected by, the AI winter.   There was certainly no consensus among those working in AI that they had got as far as they could.   Work stopped when funding was cut, often for political reasons.<p>The most mature area at the time, apparently ripe for commercialization, was expert systems.   However, it was very hard to commercialize them: customers couldn&#x27;t think of any suitable applications, and when they could, they couldn&#x27;t spare the time of their experts.<p>Finally, the main reason for the AI winter was probably that AI was unable to live up to the grossly inflated expectations, simply because the expectations were grossly inflated.   This seems to be happening again, with neural networks.