Researchers from the UB and the IBEC determine how mammalsʼ olfactory system works to develop electronic noses
Mammalsʼ sense of smell is an excellent chemical sensing system that far outshines any man-made reproduction, so researchers have long been trying to analyze and recreate the animal olfactory system to develop artificial noses. Now, in a study conducted by lecturers from the Department of Electronics Santiago Marco, Agustín Gutierrez-Galvez and Jordi Fonollosa, researchers from the Institute for Bioengineering of Catalonia (IBEC), attached to the HUBc, the health campus of the University of Barcelona, have shed new light on this highly efficient system that could allow better chemical sensing systems with important applications in such critical areas as health, security or the food industry.
Mammalsʼ sense of smell is an excellent chemical sensing system that far outshines any man-made reproduction, so researchers have long been trying to analyze and recreate the animal olfactory system to develop artificial noses. Now, in a study conducted by lecturers from the Department of Electronics Santiago Marco, Agustín Gutierrez-Galvez and Jordi Fonollosa, researchers from the Institute for Bioengineering of Catalonia (IBEC), attached to the HUBc, the health campus of the University of Barcelona, have shed new light on this highly efficient system that could allow better chemical sensing systems with important applications in such critical areas as health, security or the food industry.
In their paper published in PlosOne, the researchers have analyzed how chemical information is coded and processed to better understand mammalsʼ olfactory system. To do this, they looked at the performance of the early rat olfactory system in identifying the quality of the incoming stimuli -that is, its ability to detect and discriminate different odours- and made an analysis of their results by quantifying the number of smells that could be coded by a particular set of odour receptors (ORs) in the system.
“Thereʼs a complex arrangement of OR neurons in the system which are distributed over the nasal epithelium, and the number of types depends on the species; for example, there are 387 different kinds of ORs in humans and 1,035 in mice,” explains Santiago Marco, group leader of IBECʼs Artificial Olfaction group. “We looked at the role played by this diversity and these varying quantities in encoding chemical information”, concludes the researcher.
Previous work has shown that a particular olfactory system adapts to the statistical properties of the set of chemicals to which it is exposed. It has entailed an immense feat of systematic mapping, due to the large number of receptors found in rats and the huge amount of potential ligands. They also looked at the capacity to discriminate smells depending on distribution, and the correlation among receptors. In addition, instead of relying on simplified theoretical models, they used actual olfactory bulb data from an extensive database made available by the University of California Irvine.
“We found that optimal performance corresponds to a set of sensors with a receptive range of 50%, so the ORs are not particularly selective,” says Marco. According to this researcher, the biological system has a remarkably low correlation, or overlap, of sensors, and good coverage of the odour space. For sensors with low correlation, adding more to the set maximizes the coding capacity of the system.
From this, the researchers surmise that biology has evolved toward a combination of more selective sensors for critical odours and a collection of less selective sensors to cover larger areas of the odour space. An extreme case of this evolutionary drive is the presence of highly specific sensors for pheromone detection. “This better understanding of odour coding in olfaction may provide valuable insights for the design of general-purpose artificial olfaction systems; for example, it was previously thought that chemical sensors are not selective enough, but our study shows that selectivity may not be the most relevant parameter”, points out Marco.
Article: Jordi Fonollosa, Agustín Gutiérrez Gálvez and Santiago Marco. “Quality coding by neural populations in the early olfactory pathway: analysis using information theory and lessons for artificial olfactory system”. PLoS ONE, in press. Doi: 10.1371/journal.pone.0037809.g008