An olfactory cocktail party: figure-ground segregation of odorants in rodents

Abstract

In odorant-rich environments, animals must be able to detect specific odorants of interest against variable backgrounds. However, several studies have suggested that both humans and rodents are very poor at analyzing the components of odorant mixtures, leading to the idea that olfaction is a synthetic sense in which mixtures are perceived holistically. We have developed a behavioral task to directly measure the ability of mice to perceive mixture components and found that mice can be easily trained to detect target odorants embedded in unpredictable and variable mixtures. We imaged the responses of olfactory bulb glomeruli to the individual odors used in the task in mice expressing the Ca++ indicator GCaMP3 in olfactory receptor neurons. By relating behavioral performance to the glomerular response patterns, we found that the difficulty of segregating the target from the background was strongly dependent on the extent of overlap between the representations of the target and the background odors by olfactory receptors. Our study indicates that the olfactory system has powerful analytic abilities that are constrained by the limits of combinatorial neural representation of odorants at the level of the olfactory receptors.

Reading Out Olfactory Receptors: Feedforward Circuits Detect Odors in Mixtures without Demixing

“…This task resembles the cocktail party problem in audition where the signals of different sound sources also arrive as a linear mixture at the sensory organ. Similar to our remarkable ability of disentangling sound sources, mice have been shown to excel at the olfactory equivalent (Rokni et al, 2014). Yet, with increasing number of mixture components this task can get difficult due to the overlapping, combinatorial representation of odorants by receptor neurons (Duchamp-Viret et al, 1999, Koulakov et al, 2007, Rospars et al, 2008, Shen et al, 2013) and sparse, distributed representations in higher olfactory areas (Stettler and Axel, 2009, Wilson and Sullivan, 2011).…”

“…1A). After learning, mice achieved accuracies close to 100% for few distractors, and performance declined with increasing number of background odors (Rokni et al, 2014). This is a seemingly remarkable feat given that a) mice had to generalize from around 1000 training trials to mixtures that they had never smelled before (more than 60% are novel in test phase, ~50,000 possible mixtures), b) glomerular patterns are highly overlapping, c) mixture responses arise from nonlinear interactions of single odors, and d) odor responses are highly variable from trial-to-trial.…”

“…Furthermore, mixtures may be represented as nonlinear functions of their parts (Rospars et al, 2008; Shen et al, 2013). In a previous study, we found that mice can be trained to perform a target-odor detection task and can do so remarkably well even in the presence of a large number of background odors (Rokni et al, 2014). These behavioral results led to the question of how mice solve this task given the nonlinear, noisy and high dimensional input that the glomeruli provide.…”

“…In this study, we sought to find the simplest decoding mechanism that uses experimentally constrained neuronal input and match the ability of mice to detect target odorants (Rokni et al, 2014). We measured representations of single odorants and hundreds of mixtures by olfactory receptors to derive a quantitative model of mixture responses, which also included saturation and experimentally-measured trial-to-trial variability.…”

Configural processing of odor mixture: Does the learning of elements prevent the perception of configuration in the newborn rabbit?

“…Elemental perception consists of perceiving the odor quality of the mixture as similar to the odor quality of its elements [e.g., [1][2][3][4]. Conversely, perception is robust configural when a specific percept, the configuration, emerges from the mixture and carries an odor quality distinct from the quality of each odorant [e.g., [5][6][7][8][9].…”