Momentum is the ammount to reduce the learning parameters theta and phi after each iteration. Both will be their previous value times momentum. Too high momentum values will result in little global ordering of the map and more refining and too low values will do the oposit. Tolerance is the circumference around each neuron that will define the set of input-vectors that belongs to each neuron. Too high values will result in neurons with the same input vectors, and too low values will result in empty neurons.
Tolerance is used when the sweep-button is clicked.
It can be updated at any time. To large values will slow the computation speed drastically without revealing any new information. Phi tolerance is a limit for how long the algorithm will keep running. When the phi limit has been reached, the algorithm terminates and the phi limit will have to be decremented in order to continue running. Too low values will keep the algorithm running when the learning parameters are so low that no changes are made.