If there are $D$ inputs from $x_1$ to $x_D$, how can I use a D-D-1 feed forward neural network to implement $\text{XOR}\left(x_1,x_2,\ldots,x_D\right)$ with signum as transformation function?
Here D-D-1 means $D$ inputs (not including $x_0$), one hidden layer with $D$ neurons (not including the constant neuron) and one output layer.