Leabra Params

This page contains various user's experiences for setting parameters in the Leabra algorithm.

See Modeling Principles for some more general tips on how to go about modeling.

Randy's General Tips for Basic Networks

In a basic 3 layer Leabra network, the key parameters that make the biggest difference are the inhibition parameters and Hebbian learning:

inhibition: Hidden layers should generally use either KWTA_AVG_INHIB or KWTA_KV2K with kwta_pt set to .6 in the former and .25 in the latter. Output layers often benefit somewhat by using KWTA_INHIB with kwta_pt set to .25, because they don't need the extra flexibility that these others provide. The main thing to manipulate is the kwta.pct parameter in the hidden layer(s), which generally can be between .10 and .25. With more systematic mappings where similarity-based generalization is beneficial, higher values are better. When the mappings are more random, sparser values are better, and sometimes even lower than .1 can work well (e.g., in the hippocampus model, for an extreme example).
Hebbian: try the lmix.hebb value in .01, .001, .0001, 0.0, kinds of values, and then hone in on more detailed values around the one that works the best. Also the savg_cor.cor value can be important -- it defaults to .4, but .8 and 1.0 can also work better in some circumstances.