# Updating connections weights in neural networks

I am learning about neural networks and have a couple of things I don't understand.

Firstly, in competitive learning I understand that only the neuron with the strongest output is reinforced. That is done in a manners imilar to:

Δwi*j = I(j)*h(i)


Where w* indicates the 'winning' neuron, j indicates the input we are considering, I(j) is the value of such input and h(i) is the sum of all weighted inputs. This is repeated for each connection leading to the winning neuron.

My question is... Why? Why not simply, for example, increase the connection by an arbitrary amount? Or by another function? I have done quite some research, but still can't make sense of this.

Thanks!

• I don't understand the formula. This site supports Latex, could you make it clearer? I don't know if you mean $\Delta w_i^* j$ or $\Delta w_{ij}^*$ or maybe $\Delta w i^* j$. Especially because in the next line you use $w^*$. And what's $i$? – Wandering Logic Mar 8 '15 at 21:13