"Artificial neural networks use numerical weights and gradient-based optimization, but their learning mechanisms are not identical to those of biological nervous systems."
This neuroscience review examines backpropagation in artificial networks and the challenges of mapping that algorithm onto biological learning. It provides a foundation for distinguishing mathematical network weights and training procedures from the mechanisms of biological synapses while recognizing the productive scientific relationship between artificial neural networks and neuroscience.
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