Researchers from Tel Aviv University have created a robot that can smell and identify odours using a biological sensor. The researchers connected the sensor to an electronic system. They used a machine learning algorithm to detect odours with a level of sensitivity that is 10,000 times higher than that of a commonly used electronic device. The sensor sends electrical signals as a response to the presence of a nearby odour, which the robot can detect and interpret. According to the University, the researchers say, "The sky's the limit," and believe this technology may also be used to identify explosives, drugs, diseases, and more.

Though scientists have long known that mice can pick out scents -- the smell of food, say, or the odor of a predator -- they have been at a loss to explain how they are able to perform that seemingly complex task so easily. But a new study, led by Venkatesh Murthy, professor of molecular and cellular biology, suggests that the means of processing smells may be far simpler than researchers realized. Using a machine-learning algorithm, Murthy and colleagues were able to "train" a computer to recognize the neural patterns associated with various scents, and to identify whether specific odors were present in a mix of smells. The study is described in a Sept. 1 paper in the journal Neuron. Along with Murthy, the paper was co-authored by Alexander Mathis, Dan Rokni, and Vikrant Kapoor, postdoctoral fellows working in Murthy's lab, and Professor Matthias Bethge from the Werner Reichardt Centre for Integrative Neuroscience & Institute of Theoretical Physics in Germany.

Summary The olfactory system, like other sensory systems, can detect specific stimuli of interest amidst complex, varying backgrounds. To gain insight into the neural mechanisms underlying this ability, we imaged responses of mouse olfactory bulb glomeruli to mixtures. We used this data to build a model of mixture responses that incorporated nonlinear interactions and trial-to-trial variability and explored potential decoding mechanisms that can mimic mouse performance when given glomerular responses as input. We find that a linear decoder with sparse weights could match mouse performance using just a small subset of the glomeruli ( 15). However, when such a decoder is trained only with single odors, it generalizes poorly to mixture stimuli due to nonlinear mixture responses.