I have seen two different representations of neural networks when it comes to bias. Consider a "simple" neural network, with just an input layer, a hidden layer and an output layer. To compute the value of a neuron in the hidden layer, the weights and neurons from the input layer are multiplied, shifted by a bias and then activated by the activation function. To compute the values in the output layer, you may choose not to have a bias and have an identity activation function on this layer, so that this last calculation is just "scaling".
Is it standard to have a "scaling" layer? You could say that there is a bias associated with each neuron, except those in the input layer correct (and those in the output layer when it is a scaling layer)? Although I suppose you could immediately shift any value you're given. Does the input layer have a bias?
I have seen bias represented as an extra unchanging neuron in each layer (except the last) having value 1, so that the weights associated with the connections from this neuron correspond to the biases of the neurons in the next layer. Is this the standard way of viewing bias? Or is there some other way to interpret what bias is that is more closely described by "a number that is added to the weighted sum before activation"?