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

Andrew Magyar, John Collins

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Article number012712
JournalPhysical Review E - Statistical, Nonlinear, and Soft Matter Physics
Volume92
Issue number1
DOIs
StatePublished - Jul 15 2015

Fingerprint

Temporal Lobe
Population Model
neurons
lobes
Neuron
stimuli
Neurons
Sparsity
Population
hippocampus
Cell
Hippocampus
cells
Entorhinal Cortex
Statistical Data Interpretation
cortexes
Cortex
Model
Statistical method
Person

All Science Journal Classification (ASJC) codes

  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Condensed Matter Physics

Cite this

@article{9e1823defd8347c69141244f9b055eb1,
title = "Two-population model for medial temporal lobe neurons: The vast majority are almost silent",
abstract = "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.",
author = "Andrew Magyar and John Collins",
year = "2015",
month = "7",
day = "15",
doi = "10.1103/PhysRevE.92.012712",
language = "English (US)",
volume = "92",
journal = "Physical Review E",
issn = "2470-0045",
publisher = "American Physical Society",
number = "1",

}

Two-population model for medial temporal lobe neurons : The vast majority are almost silent. / Magyar, Andrew; Collins, John.

In: Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, Vol. 92, No. 1, 012712, 15.07.2015.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Two-population model for medial temporal lobe neurons

T2 - The vast majority are almost silent

AU - Magyar, Andrew

AU - Collins, John

PY - 2015/7/15

Y1 - 2015/7/15

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84938816429&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84938816429&partnerID=8YFLogxK

U2 - 10.1103/PhysRevE.92.012712

DO - 10.1103/PhysRevE.92.012712

M3 - Article

C2 - 26274208

AN - SCOPUS:84938816429

VL - 92

JO - Physical Review E

JF - Physical Review E

SN - 2470-0045

IS - 1

M1 - 012712

ER -