How do convolutional layers in a CNN feed forward when there is multiple input feature maps?

I've been trying to recreate LeNet 1(LeNet 1 architecture is pictured in the top diagram) in python using NumPy. I am unsure of how the forward pass works when there is multiple Input feature maps in a convolutional layer. Referring to the bottom diagram of the expanded view of convolutional layer C3 are the outputs calculated as follows?:

O1 = Cross_correlation(I1 , F1)

O2 = Cross_correlation(I1 , F2)

O3 = Cross_correlation(I1 , F3)

O4 = Cross_correlation(I2 , F4)

O5 = Cross_correlation(I2 , F5)

O6 = Cross_correlation(I2 , F6)

O7 = Cross_correlation(I3 , F7)

O8 = Cross_correlation(I3 , F8)

O9 = Cross_correlation(I3 , F9)

O10 = Cross_correlation(I4 , F10)

O11 = Cross_correlation(I4 , F11)

O12 = Cross_correlation(I4 , F12)

Would appreciate an answer with the notation I have used above so it is easy to understand! Thanks!