Johan N. Lundström

This proposal is framed by a technological goal: We aim to digitize smell. Achieving this is currently prevented by gaps in basic science. We aim to fill these gaps, culminating in a proof of concept for our model. The primary gap we identify is lack of data on what humans typically smell. Phrased conceptually, in Aim 1 we ask what are the natural statistics of human olfactory perceptual space. We address this in a series of three experiments, highlighted by one where we equip participants with a wearable sampling apparatus we designed and built for this proposal. The apparatus measures sniffing behaviour to identify odor sampling, measures neural activity to verify olfactory perception, takes video of the visual scene, analyses total levels of volatile organic compounds in real time, and collects odor samples for detailed analysis off line. In other words, we generate an olfactory equivalent of Google Street View, with the addition of chemical, perceptual and neural data. Using this we will characterise the natural statistics of human olfactory perceptual space. Moreover, a major contribution of this proposal will be in posting this massive data as a publicly available recourse. Next, in Aim 2 we will use this data to digitize human olfactory perceptual space. We build on a model that allows us to recreate odors using a restricted set of odor primaries. We will test our model in two frameworks: One we call SmelloVision, where we develop the algorithmic framework to generate an odor to match any digital image, and one we call TelleSmell, where we develop a device to sense the environment, the algorithmic framework to transfer the data, and a device to generate the corresponding odor remotely. We provide pilot data for Aim 2 where we sensed an odor in Mainz (Germany), transmitted the data over IP to Rehovot (Israel), where we successfully recreated the smell. This was, as far as we know, the first transmission of odor over IP.

One of the primary functions of olfaction is to assess whether a smell is pleasant or unpleasant (i.e., odor valence). This enables us to make crucial decisions about approaching or avoiding the odor (e.g., avoiding rotten food). Due to limitations in non-invasive brain imaging techniques, next to nothing is known about how the core and initial olfactory system —from the peripheral to the primary olfactory cortex — simultaneously encode this fundamental odor precept in humans. In this project, we will use new techniques that enable us to investigate the core olfactory system simultaneously, from the olfactory epithelium (EPT) to the olfactory bulb (OB), and the olfactory cortex (piriform cortex, PC). Specifically, we will determine how each node codes odor valence and how this information is communicated between each node to create the final odor valence percept. Exploring the mechanisms of odor valence is necessary to understand the most fundamental principle of human olfaction. Today, we have far more knowledge about how perception works in vision and hearing. This project aims to establish a similar level of knowledge for olfaction.

Intensity coding is at the core of perception, allowing us to discriminate between the candle and the sun, the whisper and the shout. Odors also exist across a vast intensity spectrum, from the barely perceptible fragrance of a light rain to the overwhelming blast of rotten meat. Unlike the well-documented mechanisms in vision and audition, the encoding of odor intensity in the olfactory system remains largely unexplored due to technological limitations. Our project aims to elucidate the most basic principles underlying perceived odor intensity. We will achieve this by studying the bottom-up and top-down mechanisms that shape perceived odor intensity by investigating the encoding and communication of odor signals across the major nodes of the olfactory system: the olfactory epithelium, olfactory bulb, and piriform cortex. Utilizing novel techniques developed in our lab, which enables simultaneous recordings from these critical nodes, we will conduct a series of behavioral experiments. Our objectives are threefold: to determine how odor intensity is coded within each node, how these perceptions are communicated across the system, and how cognitive processes and contextual information influence perceived intensity. Our approach addresses the limitations of traditional human and animal studies and holds promise for advancing our fundamental understanding of olfaction. Critically, the findings can offer insights into the common principles shaping perception across all our senses.

This proposal is framed by a technological goal: We aim to digitize smell. Achieving this is currently prevented by gaps in basic science. We aim to fill these gaps, culminating in a proof of concept for our model. The primary gap we identify is lack of data on what humans typically smell. Phrased conceptually, in Aim 1 we ask what are the natural statistics of human olfactory perceptual space. We address this in a series of three experiments, highlighted by one where we equip participants with a wearable sampling apparatus we designed and built for this proposal. The apparatus measures sniffing behaviour to identify odor sampling, measures neural activity to verify olfactory perception, takes video of the visual scene, analyses total levels of volatile organic compounds in real time, and collects odor samples for detailed analysis off line. In other words, we generate an olfactory equivalent of Google Street View, with the addition of chemical, perceptual and neural data. Using this we will characterise the natural statistics of human olfactory perceptual space. Moreover, a major contribution of this proposal will be in posting this massive data as a publicly available recourse. Next, in Aim 2 we will use this data to digitize human olfactory perceptual space. We build on a model that allows us to recreate odors using a restricted set of odor primaries. We will test our model in two frameworks: One we call SmelloVision, where we develop the algorithmic framework to generate an odor to match any digital image, and one we call TelleSmell, where we develop a device to sense the environment, the algorithmic framework to transfer the data, and a device to generate the corresponding odor remotely. We provide pilot data for Aim 2 where we sensed an odor in Mainz (Germany), transmitted the data over IP to Rehovot (Israel), where we successfully recreated the smell. This was, as far as we know, the first transmission of odor over IP.

The aim is to identify smells from nature that reduce human stress and implement the findings in urban settings.There is a lack of in-situ perceptions of natural smells as stress reducers in cities, how natural smells contribute to stress reduction is vaguely studied, knowledge of smell and wellbeing is limited to a few plant species, and linkages to memory and smell from nature are limited. Further, present management, design and establishment of urban green areas biases towards visual features although humans perceive their surroundings with all senses, including smell.Our objectives are to conduct in-situ surveys of urban settings asking respondents about smell perception, provide psychophysiological experiments measuring stress reduction of natural smells through heart rate variability and skin conductance, to examine if natural smell increase memory, and if there are “thresholds” where nature smell compensate for negative visual features, negative sounds and non-natural smells.We will implement our findings together with stakeholders and a reference group by establishing plants in three parks, one focusing on smell and the others on multisensory. The ultimate goal is to reduce human stress syndromes, contribute to rehabilitation from anosmia (loss of smell) and create positively perceived urban environments.Moreover, this project will provide a basis for research connected to the multisensory integration (sight, sound and smell) of biodiversity and urban settings.