I was trying to apply oja’s rule with toy data, but I can’t seem to find any examples where the input to two different ensembles is data as opposed to a function.

The only examples I could find were these (which hopefully clarify what I mean): https://github.com/s72sue/std_neural_nets/blob/master/classification/3-class_classification.ipynb

However when I attempt to create a simplified version, I keep getting the following error:

def stim_ans(t):

----> 9 return [input_ex[t][0], input_ex[t][1], input_ex[t][2],input_ex[t][3]]

10

only integers, slices (`:`

), ellipsis (`...`

), numpy.newaxis (`None`

) and integer or boolean arrays are valid indices

As a future follow up, is it possible to have weights learn via backprop (DL) and local rules? Would I have to use the deepRL version?

Anyway, any help is appreciated,

Mic