Last I heard (and I believe this could be wrong) my professor said that we basically understand how a single neuron works. That like basically if we do X input we get Y output, up to some accuracy. He used this to discuss the idea behind neural networks -- that each neuron is simple enough to model, all we need to worry about is the weights and the dynamics of the network as a whole.
How much of a simplification is that? And how much does the accuracy of such a model matter, in the grand scheme of things?
How much of a simplification is that? And how much does the accuracy of such a model matter, in the grand scheme of things?