Two-population model for MTL neurons: The vast majority are almost silent

TL;DR

This study introduces a statistical model revealing two distinct neuron populations in the human medial temporal lobe, challenging the idea that most respond to a single concept, and showing a highly skewed response distribution.

Contribution

A new two-population model better explains neural response data, contrasting with traditional single-sparsity models, and accounts for potential multi-neuron units, providing deeper insight into concept cell responses.

Findings

Two distinct neuron populations with different sparsity levels.

Traditional models poorly fit the data, while the two-population model fits well.

Most neurons respond to a very small fraction of stimuli, indicating a skewed response distribution.

Abstract

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.