University of Tübingen researchers use an artificial neural network to model the visual system
Humans and animals have a “number sense,” an inborn ability to register the number of objects in a scene. The neural basis of this ability is believed to be what are called the number neurons, which respond to certain numbers and have been found in both human and animal brains.
Researchers have long wondered whether these number neurons are formed in the brain merely by the ability to see – and if so, how. Now, a team of researchers headed by Professor Andreas Nieder at the University of Tübingen’s Institute of Neurobiology has investigated the origins of number sense using an artificial neural network.
The results indicate that it is created spontaneously by the visual system, without any experience in counting. The study has just been published in the latest edition of Science Advances.
The researchers started out by training an artificial “deep learning” network to recognize pictured objects such as tennis balls, necklaces, spiders and dogs. “The network model is based on an architecture structured like the early developmental stage of the human visual cortex,” Andreas Nieder explains.
“It has been discovered there that nerve cells work together in different hierarchical levels to enable vision.” The artificial network learned to recognize objects on the basis of 1.2 million images which were classified into one thousand categories. After this training the network was able to classify thousands of new images with a high proportion of success.
Number sense arises from existing neural networks
The network is in two parts. One of these extracts the object’s characteristics from the images and transforms these into an abstract representation; the second part sorts the object into a category on the basis of probability. “We separated the two network parts and to the first part we presented not photos but simple dot patterns of one to 30 dots,” Nieder says.
In the following cycles of the experiment, the patterns were repeated with varying dot patterns and densities. Then the researchers analyzed whether the network’s artificial neurons reacted to the same number of points independently of other characteristics.
"Almost ten percent of the artificial neurons had each specialized in a certain number, although the network was never trained to differentiate between numbers. The network had spontaneously developed a sense of numbers," says Nieder.
Researchers had suspected that the ability to count developed from the visual system. The most fundamental task of vision is to recognize visible objects, Nieder says. The new study shows how number neurons can develop spontaneously from an artificial visual system that was only trained to recognize visible objects, he adds.
Furthermore, the artificial neurons’ responses resembled that of real number neurons in animals and humans. “Number sense does not seem to depend on a specific, specialized area of the brain, but rather on neural networks formed by vision. This now makes it possible to explain why even newborns or untrained, wild animals have a number sense," Nieder explains.
Professor Dr. Andreas Nieder
University of Tübingen
Institute of Neurobiology
Phone +49 7071 29-75347
Khaled Nasr, Pooja Viswanathan, Andreas Nieder: Number detectors spontaneously emerge in a deep neural network designed for visual object recognition. Science Advances 2019; 5:eaav7903, 8 May 2019
Dr. Karl Guido Rijkhoek | idw - Informationsdienst Wissenschaft
If Machines Could Smell ...
19.07.2019 | Fraunhofer-Institut für Produktionstechnik und Automatisierung IPA
Algae-killing viruses spur nutrient recycling in oceans
18.07.2019 | Rutgers University
Adjusting the thermal conductivity of materials is one of the challenges nanoscience is currently facing. Together with colleagues from the Netherlands and Spain, researchers from the University of Basel have shown that the atomic vibrations that determine heat generation in nanowires can be controlled through the arrangement of atoms alone. The scientists will publish the results shortly in the journal Nano Letters.
In the electronics and computer industry, components are becoming ever smaller and more powerful. However, there are problems with the heat generation. It is...
Scientists have visualised the electronic structure in a microelectronic device for the first time, opening up opportunities for finely-tuned high performance electronic devices.
Physicists from the University of Warwick and the University of Washington have developed a technique to measure the energy and momentum of electrons in...
Scientists at the University Würzburg and University Hospital of Würzburg found that megakaryocytes act as “bouncers” and thus modulate bone marrow niche properties and cell migration dynamics. The study was published in July in the Journal “Haematologica”.
Hematopoiesis is the process of forming blood cells, which occurs predominantly in the bone marrow. The bone marrow produces all types of blood cells: red...
For some phenomena in quantum many-body physics several competing theories exist. But which of them describes a quantum phenomenon best? A team of researchers from the Technical University of Munich (TUM) and Harvard University in the United States has now successfully deployed artificial neural networks for image analysis of quantum systems.
Is that a dog or a cat? Such a classification is a prime example of machine learning: artificial neural networks can be trained to analyze images by looking...
An international research group led by scientists from the University of Bayreuth has produced a previously unknown material: Rhenium nitride pernitride. Thanks to combining properties that were previously considered incompatible, it looks set to become highly attractive for technological applications. Indeed, it is a super-hard metallic conductor that can withstand extremely high pressures like a diamond. A process now developed in Bayreuth opens up the possibility of producing rhenium nitride pernitride and other technologically interesting materials in sufficiently large quantity for their properties characterisation. The new findings are presented in "Nature Communications".
The possibility of finding a compound that was metallically conductive, super-hard, and ultra-incompressible was long considered unlikely in science. It was...
24.06.2019 | Event News
29.04.2019 | Event News
17.04.2019 | Event News
19.07.2019 | Physics and Astronomy
19.07.2019 | Earth Sciences
19.07.2019 | Physics and Astronomy