I once had the same feeling, but it was dispelled by acknowledging that NN neurons are not even approximations of how neurons work. At most, NNs are inspired by the topology of a subset of neurons, and that's where the similarity between NNs and biological neurons stop. It's like the connection between objects in real life and objects in programming. They're both useful abstractions that are inspired by things in the real world, but the similarities to things in the real world stop there.
Neurons have a lot going on, they send and receive signals through a multitude of mediums, not just neural impulses, and they're capable of plasticity when it comes to the connections they make between other neurons. Neurons also don't have simplistic activation functions, they're capable of doing a lot more with the information they receive and send. Also, gradient descent and back propogation don't take place in any part of the brain.
Through that lens, I see NNs as if they're like really complex and impressive Markov chain generators. They can produce results that look intelligent, but it's just statistical correlations, and not at all how the brain works.
Neurons have a lot going on, they send and receive signals through a multitude of mediums, not just neural impulses, and they're capable of plasticity when it comes to the connections they make between other neurons. Neurons also don't have simplistic activation functions, they're capable of doing a lot more with the information they receive and send. Also, gradient descent and back propogation don't take place in any part of the brain.
Through that lens, I see NNs as if they're like really complex and impressive Markov chain generators. They can produce results that look intelligent, but it's just statistical correlations, and not at all how the brain works.