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I'm trying to program a Hebbian learning rule for a simple fully connected deep neural network (DNN), which is structured as:

$$z_i = W_iy_{i-1}+b_i$$

and

$$y_i = g(z_i)$$

Based on Hebbian learning rule, weight update is proportional to the product of pre and post-synaptic activations.

Question:

Does this mean $W_i$ is updated based on $y_{i-1}$ and $y_i$ or $y_{i-1}$ and $z_i$?

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