How data representation affects neural networks?

Suppose A's possible values are ON or OFF.

Suppose I represent it as: if A ON then feature f=1 else f=0

Or, suppose I represent it with 2 features, where:

-if A is ON then f1=1 and f2=0

-if A is OFF then f1=0 and f2=1

How this kind of representation affects neural networks?

I always see big red flags when someone asks a very fundamental data science question with the words neural network included. Neural networks are very powerful and receive a great deal of attention in the media and on Kaggle, but they take more data to train, are difficult to configure, and require much more computing power. If you are just starting out, I suggest getting a foundation in linear regression, logistic regression, clustering, SVMs, decision trees, random forests, and naive Bayes before delving into artificial neural networks. Just some food for thought.