Let's assume I have a simple feedforward neural network whose input contains binary 0/1 features and output is also binary two classes.
Is it better, worse or maybe totally indifferent, for every of such binary feature to be in just one column or maybe it would be better to split one feature into two columns in a way that the second column will have opposite value, like that:
feature_x (one column scenario)    feature_x (two columns scenario) [0, 1] [1, 0] [0, 1]
I know this might seem a bit weird and probably it is not necessary, but I have a feeling like there might be a difference for a network especially for its inner workings and how neurons in next layers see such data. Has anyone ever researched that aspect?