  The nonlinear response of an optical microresonator is used in a time
multiplexed reservoir computing neural network. Within a virtual node approach
combined with an offline training through ridge regression, we solved linear
and nonlinear logic operations. We analyzed the nonlinearity of the
microresonator as a memory between bits and/or as a neural activation function.
This is made possible by controlling both the distance between bits subject to
the logical operation and the number of bits supplied to the ridge regression.
We show that the optical microresonator exhibits up to two bits of memory in
linear tasks and that it allows solving nonlinear tasks providing both memory
and nonlinearity. Finally, we demonstrate that the virtual node approach always
requires a comparison of the reservoir's performance with the results obtained
by applying the same training process on the input signal.
