What is the difference between n output layers and 1 output layer with n neurons? Suppose, I have 3 classes, 6 features. I need to generate 5 outputs.
What is the difference between 5 output layers with 1 neuron each, and 1 output layer with n neurons?

If they use the same activation function the outcome is the same. The only real difference is in optimization, it is easier to run a single layer with $n$ neurons since it all boils down to a single matrix multiplication (or multiplication-add in case of bias). For $n$ output layers you would need to perform $n$ matrix multiplications.
