How do recurrent neural networks share weights ? I have been reading it online but I cant figure out how it does this. Particularly because during backpropagation,the hidden cell at e.g. t=2 would receive the gradients coming from t=3. So both now the weights of both cells will be updated differently.
For example, after 1 iteration, will the weights w1,w2,w3,w4,w5 be similar ? Because w4 would have gradients coming from its output as well as the output from w5 whereas w5 would only have the gradient coming from its output.