I know of almost no human-like brain inspiration in current AI models. Current ANNs are similar to biological brains, but only in the sense that they can be understood as networks of threshold units. One can also understand most modern ANN models purely in terms of linear algebra. They are super-abstract, super-generic models of computation, and biological brains happen to fit this category (at a very high level of simplification and abstraction).

Then, gradient descent is a centralized and supervised learning algorithm that has nothing to do with all the decentralized and emergent processes that take place in biological brains. For example, consider all the complex interactions of the various neurotransmitters and neuroreceptors.

Biological brains (let alone human) are actual physical realizations of networks with very complex and specific topologies. New connections grow and are formed in a very real sense, and this process interacts with the environment in a variety of ways, plus it is spatially embedded.