Is it still called linear separation with a layer of more than 1 neuron A single neuron will be able to do linear separation. For example, XOR simulator network:

x1 --- n1.1
   \  /    \
    \/      \
             n2.1 
    /\      /
   /  \    /
x2 --- n1.2 


Where x1, x2 are the 2 inputs, n1.1 and n1.2 are the 2 neurons in hidden layer, and n2.1 is the output neuron.

The output neuron n2.1 does a linear separation. How about the 2 neurons in hidden layer? 

Is it still called linear separation (at 2 nodes and join the 2 separation lines)? or polynomial separation of degree 2?

I'm confused about how it's called because there are curvy lines in this wiki article: https://en.wikipedia.org/wiki/Overfitting
What you have depicted is a nonlinear classificator. Although each stage does a linear separation, the sequential composition of linear separations is nonlinear. The nonlinearity of the neuron is key in this regard, as otherwise it would all be equivalent to a matrix multiplication, which is linear. You were right about the degree, although it's rarely called like that. People usually describe just the number of layers, and I guess the main reason is that it's not directly equivalent to such polynomials, as that depends on other factors (e.g. the activation function).