I know how to train a simple competitive network. Let's say I have three inputs $x_1, x_2, x_3$ and learning coefficient $\eta=0.5.$ Let's say I have two neurons $w_1, w_2$. For each input I will compute $\Vert x_i-w_j\Vert^2$ and the smallest distance will define the winner neuron. Then I will update the weight of the winner to $w_j = w_j +\eta(x-w_j)$ .
However, I am not sure how in this simple competitive network, we define the decision boundaries. For example in perceptron I know that i will draw the line $w_1+w_2-b=0$.
For example assume that I have after training:
x1=[1, 1] , class 0
x2= [-1, -1] class 1
x3 = [1, -1] class 0
w1 = [1.25, -0.25]
w2 = [-0.5, -2] .
What's the decision boundary?