In a typical neural network you have a certain number of neurons in each layer, all neurons have incoming connections from each neuron in the previous layer (and visa-versa). This I have no problem figuring out how to propagate.
However when you have a network that doesn't have the neurons in layers (I'm looking into the NEAT algorithm); where neurons are just connected based on genetic algorithm (random creation - then testing of fitness) there isn't a simple way of figuring how to forward propagate, for example;
1 input neuron - connected to neurons 2 and 3 Neuron 2 is also connected to neuron 3 Neuron 3 is connected to the output.
Now in this case, if I just go through numerically then everything works out just fine. However with this subtle change;
1 input neuron - connected to neurons 2 and 3 Neuron **3** is also connected to neuron **2** Neuron **2** is connected to the output.
The going through; 1, 2, then 3 would mean neuron 2 doesn't include the input from neuron 3. I can see that there's a brute force way through this, but is there a better, more efficient way?