Hi I have some questions with modelling. I hope people with the knowledge can advise on this:
What I am doing: I am training STDP using parameters from Song and Abott (2000,2001) to recognise a particular class of iris dataset, in this case, the first class only.
Architecture: I am using a 4 Poisson input neurons connect to an output neuron, with rates proportional to the intensity of the 4-feature vector of iris data (Class 0) multiplied by 50
Stimulus : 900ms of exposure time, followed by 900ms of no-firing period to allow neuron to decay to resting value
What happened: Weights were randomly initialised multplied by gmax AND after running the simulation for 900ms * the no of samples, all the weights blow up to gmax.
What I expected: If you observe the input data for class 0, the first 2 features are largest, followed by the third and then fourth feature in the order of large to small. Hence the final weights should be in a similar proportion after training but all are at maximum?? What is wrong here
I am not sure what went wrong here, is it the gmax, weight initialisation, STDP parameters or what? I would really appreciate it if someone could advise me on the right values or tell me if the model is wrong? I am kind of stuck for quite a long time doing this (I have to do this…) Thank you!!!
Additional question: If I am training a network to recognise all 3 classes, can I use a 4 input x 6 output neurons for this task? If yes do I encode all the classes into 1 long list as a Poisson spike train and use the same poisson spike train in the 4 x 6 architecture? Pardon my English
This is the link below: