The sparseness of stimulus encoding by single neurons and by populations of 
neurons in the inferior temporal cortex

Aggelopoulos, NC; Franco , L; Jerez, JM; Rolls, ET

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The sparseness of stimulus encoding by single neurons and by populations of neurons in the inferior temporal cortex

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Aggelopoulos, NC
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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(Abstract)

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Citation

Aggelopoulos, N., Franco, L., Jerez, J., & Rolls, E. (2008). The sparseness of stimulus encoding by single neurons and by populations of neurons in the inferior temporal cortex. Poster presented at AREADNE 2008: Research in Encoding and Decoding of Neural Ensembles, Santorini, Greece.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-C945-1

Abstract

low stimulus selectivity with a sparseness of 1.0 indicating a neuron that is non-selective to the set of stimuli. The sparseness of the encoding of stimuli by single neurons and by populations
of neurons is fundamental to understanding the efficiency and capacity of representations
in the brain.
The sparseness of the responses of single neurons in the primate inferior temporal visual cortex
(the single neuron sparseness as) was measured to a set of 20 visual stimuli including
objects and faces in macaques performing a visual fixation task. Neurons included for analysis
had significant firing rate increases from baseline in response to some of the stimuli. The firing
rate distribution of 36 of the neurons was exponential. Twenty-nine percent of the neurons
had too few low rates to be fitted by an exponential distribution, and were fitted by a gamma
distribution. The sparseness as of the representation of the set of 20 stimuli provided by each
of these neurons had an average across all neurons of 0.77, indicating a rather distributed
representation.
The sparseness of the representation of a given stimulus by the whole population of neurons
(the population sparseness ap) also had an average value of 0.77. Ergodicity is the ability to
predict the distribution of the responses of the system at any one time (the population level)
from the distribution of the responses of a component of the system across time. Considering
this in neuronal terms, for the average sparseness of a population of neurons over multiple
stimulus inputs to be ergodic, it must equal the average sparseness to the stimuli of the single
neurons within the population, provided that the responses of the neurons are uncorrelated
(Foldiak 2003). As there is little or no correlation in the response profiles of inferior temporal
cortex neurons (Rolls et al, 2004), the similarity of the average single neuron sparseness as
and population sparseness for any one stimulus taken at any one time ap shows that the neural
representation of visual stimuli such as objects and faces is essentially ergodic.