  Recordings in the human medial temporal lobe have found many neurons that
respond to pictures (and related stimuli) of just one particular person out of
those presented. It has been proposed that these are concept cells, responding
to just a single concept. However, a direct experimental test of the concept
cell idea appears impossible, because it would need the measurement of the
response of each cell to enormous numbers of other stimuli. Here we propose a
new statistical method for analysis of the data, that gives a more powerful way
to analyze how close data are to the concept-cell idea. It exploits the large
number of sampled neurons, to give sensitivity to situations where the average
response sparsity is to much less than one response for the number of presented
stimuli. We show that a conventional model where a single sparsity is
postulated for all neurons gives an extremely poor fit to the data. In contrast
a model with two dramatically different populations give an excellent fit to
data from the hippocampus and entorhinal cortex. In the hippocampus, one
population has 7% of the cells with a 2.6% sparsity. But a much larger fraction
93% respond to only 0.1% of the stimuli. This results in an extreme bias in the
reported responsive of neurons compared with a typical neuron. Finally, we show
how to allow for the fact that some of reported identified units correspond to
multiple neurons, and find that our conclusions at the neural level are
quantitatively changed but strengthened, with an even stronger difference
between the two populations.
