PT  - JOURNAL ARTICLE
AU  - Nasr, Khaled
AU  - Viswanathan, Pooja
AU  - Nieder, Andreas
TI  - Number detectors spontaneously emerge in a deep neural network designed for visual object recognition
AID  - 10.1126/sciadv.aav7903
DP  - 2019 May 01
TA  - Science Advances
PG  - eaav7903
VI  - 5
IP  - 5
4099  - http://advances.sciencemag.org/content/5/5/eaav7903.short
4100  - http://advances.sciencemag.org/content/5/5/eaav7903.full
SO  - Sci Adv2019 May 01; 5
AB  - Humans and animals have a “number sense,” an innate capability to intuitively assess the number of visual items in a set, its numerosity. This capability implies that mechanisms to extract numerosity indwell the brain’s visual system, which is primarily concerned with visual object recognition. Here, we show that network units tuned to abstract numerosity, and therefore reminiscent of real number neurons, spontaneously emerge in a biologically inspired deep neural network that was merely trained on visual object recognition. These numerosity-tuned units underlay the network’s number discrimination performance that showed all the characteristics of human and animal number discriminations as predicted by the Weber-Fechner law. These findings explain the spontaneous emergence of the number sense based on mechanisms inherent to the visual system.