In context of a neural network, what is the purpose of using non$-$linear activation functions like RelU or Sigmoid?

$1)$ To ensure the model outputs are bounded

$2)$ to avoid vanishing gradient issues

$3)$ introduce non$-$linearity into the model. allowing it to learn complex patterns

$4)$ To maintain linearity throughout the network layers