  When one is presented with an item or a face, one can sometimes have a sense
of recognition without being able to recall where or when one has encountered
it before. This sense of recognition is known as familiarity. Following
previous computational models of familiarity memory we investigate the
dynamical properties of familiarity discrimination, and contrast two different
familiarity discriminators: one based on the energy of the neural network, and
the other based on the time derivative of the energy. We show how the
familiarity signal decays after a stimulus is presented, and examine the
robustness of the familiarity discriminator in the presence of random
fluctuations in neural activity. For both discriminators we establish, via a
combined method of signal-to-noise ratio and mean field analysis, how the
maximum number of successfully discriminated stimuli depends on the noise
level.
