# Equation to find the collaboration between neighbors in SOM in unsupervised learning

In Kohonen's SOM algorithm, the equation to find the collaboration is:

$$\mathit{Damp}(i,j) = \exp\left(-\frac{\mathit{LDist}(i,j)^2}{2\sigma^2}\right)$$

I know that LDist is the lattice distance and $$\sigma$$ is the standard deviation. I am just wondering why they are squared? Can anyone help me to visualize the equation or explain to me what is going on in the above equation?

• Are you familiar with the normal distribution? – Yuval Filmus Mar 25 '20 at 19:22
• Yes. I am familiar with normal distribution and I know that the above equation is related to that. I just want a clear explanation why the two terms (sigma and distance) are squared in the equation as I could not find one that explains this phenomenon clearly. – Tahseen Adit Mar 25 '20 at 20:23
• The distance function is to some extent an arbitrary choice; there are others you could use. The one you describe is known as Gaussian kernel, and is popular in machine learning. – Yuval Filmus Mar 25 '20 at 20:29