Can a neural network work with negative and zero inputs? As the title suggests, I have several features which have values of either -1, 0 or 1. If I feed this data into a neural network where I use ReLu as the activation function for the hidden layers, would the negative and 0 values pose a problem to the NN?
I have heard about dead neurons where using ReLu which is a stepwise function, causes any inputs less than or equal to 0 the neuron to stop learning and become dead. So naturally if a NN with activation function ReLu is fed 0 or negative inputs, those neurons will become dead.
Now my data contains several features with 0 and negative values. What to do in such a case? Should I use LeakyReLu or some other variation of ReLu? Or should I transform my data such that only positive values remain?
EDIT 1: If the negative and 0 inputs do not cause dead neurons then what causes dead neurons? Also then why do we have activation functions like LeakyReLu, PReLu, ELU if ReLu alone can handle dead neurons?

ReLu neurons can indeed effectively ignore some input values. This is a good thing. The neurons are coupled to the inputs via trained weights, so some inputs are ignored by some neurons and other inputs are ignored by other neurons. It allows weights in the subsequent layers to focus on interesting subsets of your input space
