Two-population model for medial temporal lobe neurons: The vast majority are almost silent

Recordings in the human medial temporal lobe have found many neurons that respond to pictures (and related stimuli) of just one particular person 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. Central to the model is the neuronal sparsity, defined as the total fraction of stimuli that elicit an above-threshold response in the neuron. The model exploits the large number of sampled neurons to give sensitivity to situations where the average response sparsity is 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 gives 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 can result in an extreme bias in the responsiveness of reported neurons compared with a typical neuron. Finally, we show how to allow for the fact that some 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.

Figure 1

Comparison of data from the hippocampus with fits for the number of neurons $n_k$ that respond to $k$ stimuli. The red circles are the experimental values and blue dots connected by lines are the model predictions for the expectation values of $n_k$. The blue error bars are the models' predictions for the one-standard-deviation variation of experimental results on repetition of the experiment. The top plots (a) are for fits with the one-population model. The middle plots (b) are for the intermediate model with one active and one silent population. The bottom fits (c) are for the model with two active populations. The right-hand plots differ from the left-hand plots only by having a larger vertical scale to show more clearly the $k\ge 1$ bins.

Figure 2

The same as Fig. 1, but for the entorhinal cortex.

Figure 3

The same as Fig. 1 but for the amygdala.

Figure 4

The same as Fig. 1 but for the parahippocampal cortex.

Figure 5

Plots of neuron responses predicted by the three-parameter maximum-likelihood fits to data in four regions of the MTL. The plots are of number of neurons as a function of number of responses. The shaded bars represent neurons in the almost-silent population while the open bars correspond to the distributed population. The red circles indicate the experimental values.

Figure 6

Plots of MLE fits to the hippocampus with the multiunit model.