We have shown that a neural network specialized in familiarity discrimination can recognize as familiar many more stimuli than a network of similar size working as associative memory can recollect. It may be intuitively explained by comparing the two tasks: recall - for example, you see a person and you want to recall his/her name and the episode of the previous meeting with the person; and familiarity discrimination - you see a person and you want to determine whether or not you have seen him/her previously. In the first case, the network has to recall the whole representation of the name and the episode, which is encoded in the activity of a number of neurons - let us denote this number by N. By contrast, for familiarity discrimination, there is just a binary output: the stimulus is novel or familiar. The number of outputs in the case of familiarity discrimination is N times smaller (so, in this sense, familiarity discrimination is N times easier than recall). Therefore, intuitively, the capacity for familiarity discrimination is of order N times higher.
Using estimates of the size of the human perirhinal cortex (areas 35 and 36; Insausti et al., 1998) and assuming "idealised" noise-free neurons with uncorrelated activities, we estimated that according to the specialised familiarity discrimination models the human perirhinal cortex should be able to discriminate familiarity (with probability of error 10-6) for ~108 stimuli. This would mean that a person living for 100 years (~ 3x109 s) who was presented with a picture every 30 s could still recognise almost all these pictures as familiar. Thus a small network within perirhinal cortex can discriminate familiarity for many more stimuli than can be recalled by all remaining memory areas within the brain.