5: Henning’s odor prism Triangular prism proposed by Henning as an olfactory model. The primary odors are located at the corners. Other odors can be mixtures of the primaries and thus have coordinates inside the prism or on its surface. 

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... and are never replaced again. Each ORN expresses only one type of ORP on its surface [37]. The different types of ORN are segregated into 4 main zones. Within the zones, the ORN types are randomly distributed [9]. In situ hybridization experiments by Axel et al. [4] visualized the path- ways of ORNs carrying the same ORP. The expression of a special ORP gene caused a blue coloring of the ORN cell at the same time. Olfactory receptor neurons are bipolar neurons. Their axons end in the mucous membrane as well as in the olfactory bulb, an appendix of the brain. The olfactory bulb is divided into two interconnected wings. See Figure 2.4 for a schematic view of the bulb. There are certain spatial regions, so called glomeruli, where the ends of several ORNs gather. While ORNs are randomly distributed within the Olfactory Epithelium, all ORNs of the same type converge to receptor-specific glomeruli in the olfactory bulb. The glomeruli are able to stimulate the neuron of the next level (so-called mitral cells) to fire signals into higher brain areas. However, the question arises how humans are able to distinguish more than 10,000 odorants with just 1,000 different receptor types. It has been shown that mammals express each of the 1,000 coding receptor genes in approximately 5 6 of all ORNs [4]. Thus probably each neuron expresses only a specific gene. Furthermore polymerase chain reaction (PCR) experiments indicate that only identical receptor genes are activated in ORNs of the same type. These two discoveries by Malnic et al. [37] lead to the assumption that each ORN seems to carry one and only one characteristic ORP. So the sense of smell seems to be coded by a pattern system using an alphabet of about 1000 glomeruli. It should be mentioned that a single odorant can activate several different types of ORN and thus creates a specific pattern, but the same, single ORNs can respond to different odorants [9]. This kind of coding enables the sense of smell to detect more odorants than there are ORPs, because odorants can be identified by a pattern of activated, ORP-specific glomeruli. Even if extensive parts of the Olfactory Epithelium become damaged, the remaining neurons will still be able to activate their corresponding glomeruli. Similarly it is possible to amplify even smallest amounts of inhaled molecules at the glomerulus level. This means that the sense of smell is as sensitive as it is robust. Signals from the olfactory bulb are transmitted both into the neocortex, in which conscious processes take place, and into the limbic system, which initiates emotions. This might be one reason why smells not only supply actual information, but also lead to emo- tional and rather subconscious reactions [4]. It might be assumed that higher cortical areas easily decode incoming activation patterns from the glomeruli to decide which neurons have fired. However, the mechanism within the glomeruli is not clear [9]. It is neither known how many different types of ORN lead into the same glomerulus and what the ORP-specific coding looks like exactly, nor is it known how glomeruli project the processed input into higher cortical areas. Not only external sensory input (evoked by odorants) reaches the bulb, there are neurons connecting the bulb with higher levels of the brain. It is unknown what the interaction between higher cortical signals and the sensory input looks like, neither how the input is influenced by cortical areas nor how the incoming signals influence the cortical perception of the smell [1]. In fact, smells can be a strong reminder of childhood memories, evoke emotions (positive as well as negative) and help us avoid spoiled food. Most people even connect olfactory perception with pictures or situations, therefore all judgements of a smell might be influenced by subjective factors like personal experience and cultural background. The sense of smell seems to be based on a highly time dependent complex feedback system. From antique times, philosophers have searched for a physical continuum to measure and label sensations of smell. Aristotle (384 BC - 322 BC) tried to describe and classify olfactory sensation using the same scheme he used for taste, except for an olfactory quality he called fetid . But Aristotle felt taste was to put in order much better than smell seems to be [10], [36]. Later, in the 7'@9 and 7'@9 century, scientists tried to group odors into different classes, just as animal and plant species are classified. Linnaeus (1752) grouped odors into seven classes: aromatic, fragrant, ambrosial, alliaceous, hircine, repulsive and nauseous . A re- fined version of this classification by Zwaardemaker (1895) remained accepted until well into the 32 @9 century. These early models were based on personal experience rather than on experimental data [10]. Henning [21] tried to define primary odors experimentally. He proposed a prism with six corners, labeled as putrid, fragrant, spicy, resinous and ethereal (see Figure 2.5). So each odor would occupy a certain position in the prism, corresponding to its resemblance to the primary odors. For example the odor thyme would probably be located between fragrant and spicy . However, experimental subjects produced great variations in where on the prism different odors are placed, so Henning’s theory eventually fell out of favor [36]. In 1968 Woskow [56] applied an early multidimensional scaling (MDS) method to psychophysical data, assuming that his data were metric. He directly derived similarities from a matrix of CD3 odorants. The method yielded a three-dimensional space, but this surprisingly small dimension could be caused by his small set of odorants. Schiffman [46] reanalyzed Woskows data using a nonmetric MDS, since there is no a priori reason to assume that the data are metric. She found that no single physicochemical parameter could be used individually to predict odor quality. In Addition to these physicochemical maps, several empirical approaches have been widely used by the perfume industry. In all cases, two- or three-dimensional spaces are proposed. However, the scientific basis leading to these representations remains unclear. It might be supposed that in most cases these models are empirical categorizations rather than scientifically validated olfactory maps. But even today scientists must choose their stimuli based largely on their personal experience. There is no guarantee that the chosen stimuli are able to span the “olfactory perception space” appropriately. For these purposes, an adequate model is needed that would for example allow one to determine whether or not an odor C is between two other odors A and B . In the last decades the understanding of the first level signal processing in the nose made such a rapid progress, that it looked like neurophysiological and molecular biological results will lead to a complete understanding of the sense of smell. But still, there are a lot of things we still do not know. Unfortunately, almost all existing approaches focused on the understanding of relationships between odorant characteristics and odor quality. In 2000 Christine Chee-Ruiter then came up with a completely new idea. She proposed a method to extract information about odors from a huge psychophysical database about odor quality of almost 900 chemicals. So for the first time a model could be derived that tries to express the sense of smell at the perceptual level, not at the sensory level. Chee-Ruiter [12] has proposed an odor map constructed using a directed graph of odors, where each odor A is connected to its nearest neighbor B with respect to the following similarity measure: E I is said to be an approximation to the cross-entropy information measure. A small part of this graph can be seen in Figure 2.6, in the Appendix, Figure B.1, the complete graph is shown. The construction of a graph like this allowed Chee-Ruiter to visualize first-level structures in odor quality space. Furthermore, some contiguous regions are indicated on the map, suggesting that there is a relationship between atomic elements and odor quality. This hypothesis will be discussed in Chapter 6 in comparison to the results of our approach. In any case, one problem of interpreting odor space as a graph is the subjective spatial orientation of the resulting map. That is, structural decisions in laying out the graph may be based on subjective expectations. We can illustrate this using Figure 2.6. The odors cognac, melon and rum are located in the top-center region. Assuming one might decide cognac and rum should be closer together, without melon between them, melon could be moved close to fruity , without changing the graph as a whole. Now it should be clear that a graph has too many degrees of freedom to serve as a reliable map. In this chapter, we want to discuss how to extract odor perception information from experimental data. The topic of this chapter is thus twofold. First, we have to talk about psychophysical experiments; then, we will address the comparison of experimental results. Modern psychophysics is devoted to quantifying the relationship between a given stimulus and the triggered sensation, usually for the purpose of describing the processes underlying perception [36]. These relationships are documented using so-called observation vectors (or feature vectors ). Think of an experiment testing the odor quality values of odorants. Let B be one of the stimuli, say Y -Toluenethiol. This odorant is often used to give canned soups the typical aroma of meat. Even in low concentrations, it smells very intense and unpleasant, with a slightly sulfurous nuance. The subjects now have to smell this substance among other substances several times and have to judge the odor quality. This is done by fill- ing out a data sheet for each stimulus. The sheet consists of a set of odor descriptors, e.g. fruity ; the descriptors matching the subject’s perception are marked. The classical ...