I think everyone agrees that ANN are a useful abstraction of what happens in real neural networks. A "closer" abstraction in some sense, as you mention, is wha

I think everyone agrees that ANN are a useful abstraction of what happens in real neural networks. A "closer" abstraction in some sense, as you mention, is what is being called Spiking Neural Networks, so you may want to read up on that if you're interested. I don't think they are strictly more powerful in any way, for what it's worth, as they more or less just trade continuous-domain discrete-time computations for digital-domain continuous-time computations; although there's some interesting hardware work being done to implement them. That said, don't underestimate the complexity of what is happening in real neurons. It's true that an individual neuron is fairly simple (more or less! some may perform very complex encodings of sensory data!), but the complexity of the "computations" they perform goes up very quickly, and you have a _lot_ of them that are highly connected, so it may be that ANN are a good abstraction in the sense that they reduce the complexity of spike trains to a more abstract but more powerful representation. It's true though that they probably don't perform back-propagation, at least in the mathematical sense; I don't know the literature but I think an inhibitory mechanism is probably a better biological model for how "error detectors" can be used to suppress useless or noisy output.