Two arguments favour backpropagation as a modelling tool. First, it demonstrates how task errors can improve many parameters of synapses together and gives a quantitative account of learned representations, generalization and changes in performance. Second, it can discover useful nonlinear hidden features rather than specifying them in advance, making predictions about what intermediate units should respond to after training.
Two arguments oppose interpreting standard backpropagation literally as nervous-system learning. First, the hidden error requires an accurately weighted reverse signal, including the same effective forward weights; this weight-transport requirement is not supplied by the ordinary local Hebbian learning rule. Second, a supplied output target and differentiable layer-by-layer credit assignment idealize the feedback and temporal information available to biological neurons, which use spike trains, delays and local synaptic plasticity. Success at learning a task establishes computational usefulness, not that the nervous system implements the identical algorithm.
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