Competitive Learning in Neural Networks

Question

I am playing with some neural network simulations. I'd like to get two neural networks sharing the input and output nodes (with other nodes being distinct and part of two different routes) to compete. Are there any examples/standard algorithms I should look at? Is this an appropriate question for this site?

Right now I'm using a threshold to distinguish between two routes, but I want to activate them simultaneously and let them decide ('this simulation isn't big enough for the two of us') by using time taken to traverse each route as the factor.

Update:

Thanks Gacek and Amro,

Gacek - I am not a machine learning student.../and this is my first experience with implementing neural networks...so what do you mean by 'quality coefficients'?

Amro - sorry...I shouldn't have ujsed 'competitive learning' in the question...will try to change that and maybe add some data. What I am trying to do is set up two networks which share inputs and produce the same output (not qualitatively)...they are literally connected to the same output neuron. Maybe you could look at it as a single network with two routes or pathways, and I am trying to make the thing make a choice based on the time it takes information to travel from stimulus node to response neuron along the two routes.

Answer 1

AFAIK, the word Competitive Learning refers to a specific type of networks where neurons compete to respond to an input, with the winning neuron's output being 1, and all others zeros.

From what I understood (without seeing any code), what you describe is rather like just training two ANN's of the same structure (but initialized differently) on the same training data, and eventually picking the best one (in terms of performance).