What is the relation between the number of neurons of an ANN and the number of hidden layers?

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If we have N neurons in each of M hidden layers, then we have NxM neurons. But how does the mapping change? Will the neural network's output for the same training and testing data be the same as that of an ANN of a single hidden layer with NxM neurons? If no, what might be the reason?