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?

Dead neurons refer to the fact that the network comes to a state where the back-propagation step (which is used to update the internal network's weights and biases) isn't effective enough, as the changes in values are zero or close to zero. One way to get a dead neuron situation is to initialize your network with weight and bias values of zero, with ReLU activations: this will produce an input value to the activation function of zero, which has a derivative of zero in that point. When updating the weights, the gradient will be zero so no changes are made.
The same issue can arise when a neuron received negative values to its ReLU activation function: since for x<=0 f(x)=0, the output will always be zero, with again zero gradient.
To remedy this, we have activation functions like LeakyReLU, which has nonzero activation for negative values, and thus nonzero gradient. A zero-initialized network will still have the same issue, as LeakyReLUs still have outputs of zero for any input, but manage to solve the problems with negative values.
