Spontaneous representation of numerosity zero in a deep neural network for visual object recognition
Summary: Conceiving “nothing” as a numerical value zero is considered a sophisticated numerical capability that humans share with cognitively advanced animals. We demonstrate that representation of zero spontaneously emerges in a deep learning neural network without any number training. As a signatu...
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2021
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oai:doaj.org-article:bf057a05c0f849e09117dac907aca6942021-11-20T05:09:38ZSpontaneous representation of numerosity zero in a deep neural network for visual object recognition2589-004210.1016/j.isci.2021.103301https://doaj.org/article/bf057a05c0f849e09117dac907aca6942021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2589004221012700https://doaj.org/toc/2589-0042Summary: Conceiving “nothing” as a numerical value zero is considered a sophisticated numerical capability that humans share with cognitively advanced animals. We demonstrate that representation of zero spontaneously emerges in a deep learning neural network without any number training. As a signature of numerical quantity representation, and similar to real neurons from animals, numerosity zero network units show maximum activity to empty sets and a gradual decrease in activity with increasing countable numerosities. This indicates that the network spontaneously ordered numerosity zero as the smallest numerical value along the number line. Removal of empty-set network units caused specific deficits in the network's judgment of numerosity zero, thus reflecting these units' functional relevance. These findings suggest that processing visual information is sufficient for a visual number sense that includes zero to emerge and explains why cognitively advanced animals with whom we share a nonverbal number system exhibit rudiments of numerosity zero.Khaled NasrAndreas NiederElsevierarticleBiological sciencesNeuroscienceSensory neuroscienceMachine learningScienceQENiScience, Vol 24, Iss 11, Pp 103301- (2021) |
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Biological sciences Neuroscience Sensory neuroscience Machine learning Science Q Khaled Nasr Andreas Nieder Spontaneous representation of numerosity zero in a deep neural network for visual object recognition |
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Summary: Conceiving “nothing” as a numerical value zero is considered a sophisticated numerical capability that humans share with cognitively advanced animals. We demonstrate that representation of zero spontaneously emerges in a deep learning neural network without any number training. As a signature of numerical quantity representation, and similar to real neurons from animals, numerosity zero network units show maximum activity to empty sets and a gradual decrease in activity with increasing countable numerosities. This indicates that the network spontaneously ordered numerosity zero as the smallest numerical value along the number line. Removal of empty-set network units caused specific deficits in the network's judgment of numerosity zero, thus reflecting these units' functional relevance. These findings suggest that processing visual information is sufficient for a visual number sense that includes zero to emerge and explains why cognitively advanced animals with whom we share a nonverbal number system exhibit rudiments of numerosity zero. |
format |
article |
author |
Khaled Nasr Andreas Nieder |
author_facet |
Khaled Nasr Andreas Nieder |
author_sort |
Khaled Nasr |
title |
Spontaneous representation of numerosity zero in a deep neural network for visual object recognition |
title_short |
Spontaneous representation of numerosity zero in a deep neural network for visual object recognition |
title_full |
Spontaneous representation of numerosity zero in a deep neural network for visual object recognition |
title_fullStr |
Spontaneous representation of numerosity zero in a deep neural network for visual object recognition |
title_full_unstemmed |
Spontaneous representation of numerosity zero in a deep neural network for visual object recognition |
title_sort |
spontaneous representation of numerosity zero in a deep neural network for visual object recognition |
publisher |
Elsevier |
publishDate |
2021 |
url |
https://doaj.org/article/bf057a05c0f849e09117dac907aca694 |
work_keys_str_mv |
AT khalednasr spontaneousrepresentationofnumerosityzeroinadeepneuralnetworkforvisualobjectrecognition AT andreasnieder spontaneousrepresentationofnumerosityzeroinadeepneuralnetworkforvisualobjectrecognition |
_version_ |
1718419564311609344 |