Im trying to make an Echo State Network (ESN) in Python and althought I already read many articles about it, still there are many points I dont understand.(I had trouble putting 'ESN' in context to see what it actually is, but looking at PyBrain's feature list and the description of aureservoir I could classify ESN like this: 'Machine Learning' > Networks > Recurrent Networks (RNN) > Reservoir Computing > Echo State Network (ESN).)I've attempted to write a very basic ESN in Python and I'm not sure if I got ESN right, thus I'm asking this question.What I did was to fed a teach signal to it through a feedback weight matrix. From this, the output weight matrix could be calculated.Once the output weights are ready, I try to run the echo state network again. But should I still provide signal through the feedback matrix?How do the network run after it was taught? Without the teacher, does it need the previous output instead, as if the previous output were the teacher signal?The network is similar to this image and first Wback is fed from the teacher signal and then an 'y' is created replaying the network's states with the teacher signal but through the now known Wout. But should I use this 'frozen' 'y' in the subsequent time steps, or do I send the actual output back to the units through Wback each time?
Once taught, should I feed back the network's actual output into itself in an Echo State Network?
machine learning