Search tips
Search criteria 


Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Curr Opin Neurobiol. Author manuscript; available in PMC 2010 August 1.
Published in final edited form as:
PMCID: PMC2761641

Function follows form: ecological constraints on odor codes and olfactory percepts


Sensory system function has evolved to meet the biological needs of organisms, but it is less often regarded that sensory system form has by necessity evolved to contend with the stimulus. For an olfactory system extracting meaningful information from natural scents, the ecological milieu presents unique problems. Recent studies provide new insights into the perceptual and neural mechanisms underlying how odorant elements are assembled into odor wholes, how odor percepts are reconstructed from degraded inputs, and how learning and experience sculpt olfactory categorical perception. These data show that spatial ensemble activity patterns in piriform cortex are closely linked to the perceptual meaning and identity of odor objects, substantiating theoretical models that emphasize the importance of distributed templates for the perception, discrimination, and recall of olfactory quality.


It is estimated that the chemical senses – smell and taste – emerged about one billion years ago (give or take a few hundred million). For a bacterium tumbling through the Pre-Cambrian stew of chemicals, the sense of smell represented a keen, if rudimentary, biological adaptation, sufficing for the chemical detection of sugars, amino acids, and other small molecules. This limited olfactory response repertoire enabled unicellular organisms to meet their immediate needs for survival: locating food, finding home, identifying mates, evading predators, and dodging their own metabolic waste.

In more complex organisms with antennae, snouts, and noses, olfactory systems continue to meet the same biological needs, albeit with greater sophistication. It goes without saying that insects, rodents, and canines possess an unusual delicacy of smell, but even the human sense of smell astonishes: humans can tell apart two odorants differing by only one carbon atom [1] and can detect certain odorants with an acuity better than that of rats [2]. According to Titchener, the Botocudos of Brazil and the indigenous people of the Malay Peninsula could hunt and track their game by scent [3], and recent behavioral data suggest this aptitude is extant in 18–26 year-old California residents [4]. And to the great Renaissance scholar Erasmus, the functional and anatomical capacity of the human nose could be truly far-reaching (pun intended) (Box 1).

Box 1

Many functions have been attributed to the human sense of smell. The behavioral relevance of human olfaction did not escape the notice of Erasmus (1466/1469 – 1536), humanist and scholar who detailed a myriad of remarkable features concerning the nose. The following excerpt is from the essay, “Of Benefice Hunters”, in The Colloquies of Desiderius Erasmus: Concerning Men, Manners and Things, by Desiderius Erasmus Roterodamus, 1516, translated from the Latin by N Bailey, London, 1725. At the bottom are some of the original doodles that Erasmus included to decorate this passage.

Characters: Pamphagus, having just returned home after decades abroad in the Antipodes; and his old companion Cocles, who in greeting Pamphagus hastens to remark upon his friend’s telltale facial feature still recognizable even after all those years:

COCLES (CO): …Well then, do you admire that I know you that have so remarkable a Nose

PAMPHAGUS (PA): I am not at all sorry for this Nose

CO: No, nor have you any Occasion to be sorry for having a Thing that is fit for so many Uses

PA: For what Uses ?

CO: First of all, it will serve instead of an Extinguisher, to put out Candles

PA: Go on

CO: Again, if you want to draw any Thing out of a deep Pit, it will serve instead of an Elephant's Trunk

PA: O wonderful

CO: If your Hands be employ'd, it will serve instead of a Pin

PA: Is it good for any Thing else ?

CO: If you have no Bellows, it will serve to blow the Fire

PA: This is very pretty; have you any more of it ?

CO: If the Light offends you when you are writing, it will serve for an Umbrella

PA: Ha, ha, ha ! Have you any Thing more to say ?

CO: In a Sea-fight it will serve for a Grappling-hook

PA: What will it serve for in a Land-fight ?

CO: Instead of a Shield

PA: And what else ?

CO: It will serve for a Wedge to cleave Wood withal

PA: Well said

CO: If you act the Part of a Herald, it will be for a Trumpet; if you sound an Alarm, a Horn; if you dig, a Spade; if you reap, a Sickle; if you go to Sea, an Anchor; in the Kitchen it will serve for a Flesh-hook; and in Fishing a Fish-hook

PA: I am a happy Fellow indeed, I did not know I carry'd about me a Piece of Household Stuff that would serve for so many Uses

An external file that holds a picture, illustration, etc.
Object name is nihms133801f5.jpg

If biological necessity defines the function of an olfactory system, then odor “ecology” – i.e., the natural state of odors encountered in the environment – defines its form. In other words, the ecological milieu of the olfactory landscape sets important constraints on what an olfactory system can or cannot do, and delimits its anatomical and functional organization. The general argument that the sensory stimulus itself shapes biological design has been elegantly set forth by David Dusenbery in his book, Sensory Ecology [5]. However, such ideas are sometimes marginalized from the neuroscientific discourse of olfaction, which tends to emphasize physiology over ethology, focusing on how the olfactory brain responds to odors, rather than why it responds to odors.

In the spirit of broadening this discourse, the current review considers recent neural and behavioral studies (from a variety of species) within a sensory ecological framework, illustrating how the olfactory system meets the challenges of extracting meaningful information from an odorous environment (Fig. 1).

Fig. 1
Contending with a complex odor landscape. (a) Real-world smells emitted from odorous objects (e.g., a hamburger) are usually composed of many different volatile molecular constituents. The brain can integrate these elemental stimuli (a1, left) into a ...

Odorant parts, perceptual wholes

The great majority of natural scents are composed of dozens, or even hundreds, of different molecular constituents. Pressure-cooked pork liver contains 179 compounds [6], and chocolate contains over 600 compounds [7], yet the olfactory system seamlessly knits these disparate parts into unified perceptual wholes. How this is accomplished remains largely unknown.

There is a long research tradition of using binary odorant mixtures [8,9] to approximate the ecological experience of smelling real-world aromas. A flurry of recent studies demonstrates that both peripheral and central factors, including odorant-receptor interactions [10], odorant structural similarity [11], component concentration [12,13], sensory enrichment and experience [14,15], and task strategy [16], determine whether an odor mixture is perceived as the mere sum of its parts (elemental processing) or as different from its parts (configural processing). That experiential and cognitive factors can alter the perceptual relationship between odorant parts and odor wholes [1416] implies that the brain has simultaneous access to elemental and configural representations, enhancing the range of adaptive behavior.

Intriguing work from the Kay lab [17*,18] indicates that the degree of spatial correspondence between odorants in the rodent olfactory bulb (OB) (as assessed using 2-deoxyglucose [2-DG] mapping) does not predict whether a given odorant mixture will smell similar or different to its components. These data challenge the idea that bulbar activation maps provide a linear read-out of odor quality perception [19], suggesting instead that higher-order areas may play a greater role in the odor mixture processing. This latter possibility would accord with recent human imaging studies revealing that odor mixtures evoke selective responses in higher-order regions such as orbitofrontal cortex (OFC) [20,21].

Innovative studies have begun examining the neural and perceptual processing of real-world odors directly. By coupling gas chromatography/mass spectrometry (GC/MS) technology with odor delivery and electrophysiology techniques, researchers can now measure neural activity in response to individual components of naturally occurring scents [22,23,24**]. This work suggests that the neural code for natural odors is characterized by distributed spatiotemporal responses in rodent OB (or the insect analogue in the antennal lobe), and that responses to the behaviorally relevant components of the mixture essentially sum to form the integrated neural code [23,24**]. For example, GC/MS analysis of a floral bouquet innately attractive to Manduca sexta moths identified nine (out of more than 60) volatile constituents eliciting robust multi-unit ensemble activity in the moth antennal lobe, and a synthetic combination of these nine constituents induced foraging behavior, faithfully mimicking the action of the natural bouquet [24**].

Inconstant stimuli, invariant percepts

Naturally encountered odors are highly subject to environmental vagaries. Changes in the speed and direction of the wind may influence the strength (intensity) of an airborne scent, and temperature and humidity fluctuations in the weather may change the proportion of less-volatile odorants within a natural odor mixture. Dietary factors also play a role: odiferous furans, sesquiterpenes, and cyanates are more variable in the urine of wild African elephants who indulge in a more “cosmopolitan” diet (which includes a wider diversity of grasses, tree leaves, and woody vegetation), compared to their captive brethren [25]. Each of these ecological factors can degrade the signal fidelity of an odor long before it reaches the olfactory epithelium, but remarkably the olfactory system is capable of reconstructing odor meaning from odorant fragments. How does the olfactory brain ensure perceptual constancy in the wake of an ecologically fickle stimulus?

A number of recent studies has considered the neural mechanisms underlying the stability of odor perception. In a clever experiment by Barnes et al. [26**], rodents were unable to discriminate a 10-component mixture (“10c”) from a related mixture lacking one of the original components (10c-1), but could distinguish 10c from a mixture in which one component was replaced with a novel molecule (10cR1). In a separate group of rats, single-unit recordings from anterior piriform neurons mirrored these behavioral effects: virtual ensemble activity of the 10c mixture (compiled across all cells and animals) was modestly correlated to 10c-1, but poorly correlated to 10cR1 (Fig. 2). Together these findings suggest that ensemble patterns in anterior piriform cortex (APC) fill in missing information about a complex odor mixture when one component is absent (“pattern completion”), ensuring perceptual stability of the original stimulus. At the same time, anterior piriform ensembles generate separable patterns about an odor mixture when one component is replaced (“pattern separation”), optimizing perceptual discrimination for compounds that may contain new meaningful information.

Fig. 2
Pattern completion and separation in rodent anterior piriform cortex (APC). Virtual neuron ensembles of single-unit activity were compiled by incorporating all cells tested in either aPC or olfactory bulb. (a) In APC, ensemble responses evoked by a 10-component ...

Other studies have focused on how the olfactory system maintains perceptual invariance across a range of odor concentrations. Both rodent [27] and fly [28] models converge on the idea that with increasing stimulus concentration, there is increasing inhibitory tone upon olfactory projection neurons, thereby damping down the spread of activation to non-relevant regions that might otherwise distort neural representations of the stimulus. Computational simulations of rodent OB [27] show that global feed-forward inhibition by interglomerular networks effectively normalizes mitral cell activity patterns, suggesting a parsimonious mechanism by which an odor’s quality is intensity independent.

Odor categorization: lumping not splitting

Given the multitude of different volatile odorous molecules (call this n), along with the vast combinatorial opportunities for blending these together (where C represents the number of components embedded within an odor blend), the set of potential odors (Ο) naturally encountered in the environment is immense:


It has been estimated that humans have the capacity to distinguish upwards of 400,000 smells [19], but the true number is probably much greater still – using the above equation, all possible blend combinations of just a 20-odorant set would yield over one million unique mixtures. (Note however that the actual number of odors that exist or the actual number of odors that can be detected is both unknown and controversial.) Without a way of carving this multidimensional odor landscape into perceptually relevant categories, organisms would be confronted with a computationally oppressive problem, approaching each new smell with complete naiveté and forced to learn its meaning afresh. Thus the ability to group together odors sharing similar meaning is a critical first step in using prior stored information to predict the functional relevance of novel odors.

Recent behavioral and neural data from different species is beginning to shed new light on odor categorical perception. Honeybees generalize their responses across perceptually distinct binary mixtures of floral odorants after learning that each leads to the same rewarding outcome [29]. Rodents trained to dig for a reward in response to an odor cue generalize toward structurally similar odorants depending on the saliency and strength of associative learning [30]. Both of these studies underscore the importance of behavioral contingency for category formation.

Based on single-unit recordings, Yoshida and Mori propose that individual neurons in rodent APC encode categorical information about odorants commonly found in natural foods [31], and that many of these neurons respond to combinations of odorants belonging to different food categories (onion/garlic, potato/sweet pepper, leaf green/cucumber, mustard/radish, resin/citrus, floral, fruity/banana, and fishy/spoiled). However, because each food category was tested using a panel of structurally similar odorants (e.g., terpene hydrocarbons α-pinene, d-limonene, α-phellandrene, and myrcene comprised the resin/citrus category), it remains difficult to establish whether the anterior piriform profiles specifically reflect categorical coding of food percepts per se, or, in line with other recent data from this brain area [32,33], are more representative of odorant chemical identity.

Recent work by Howard et al. [34**] combined high-resolution olfactory functional magnetic resonance imaging (fMRI), sensory psychophysical assays, and multivariate analyses to measure odor-evoked piriform spatial activity patterns and odor quality perception within the same set of human subjects, who smelled three stimulus exemplars per each of three odor categories (minty, woody, citrus). Multi-voxel ensemble patterns in posterior piriform cortex (PPC) coincided with perceptual ratings of odor quality, such that odorants with more (or less) similar fMRI patterns were perceived as more (or less) alike. These effects were not observed in APC, amygdala, or OFC, demonstrating that ensemble coding of odor categorical perception is regionally specific for PPC (Fig. 3). It is reasonable to speculate that the degree of correlation between odor-evoked PPC spatial patterns and pre-existing odor “templates” [35] would provide a convenient metric by which the brain could infer similarities among odorants and classify odor objects into discrete, meaningful categories.

Fig. 3
Odor categorical perception in human posterior piriform cortex (PPC). Multi-voxel patterns of fMRI ensemble activity were obtained from nine perceptually distinct odorants (three exemplars per each of minty, woody, and citrus categories). (a) Ensemble ...

Odor individuation: splitting not lumping

In an odiferous environment, perceptual generalization becomes counterproductive if it obscures perceptual discrimination. This potential conflict is minimized for olfactory systems “hardwired” to detect certain odors with intrinsic behavioral value (typically pheromones), but poses a much greater issue for the perception of naturally encountered odors that only gain their value via learning and experience. For example, the smell of lion and the smell of housecat are arguably both members of the “feline/dander” odor category, but the ability to discriminate between these two smells maximizes an organism’s response sensitivity while minimizing reckless or impulsive behaviors.

Prolonged presentation of an odorant (~60 sec), even in the absence of explicit training, induces stimulus-specific perceptual and neural habituation, a non-associative learning mechanism that sharpens receptive fields in rodent APC, but not in mitral/tufted cells, for chemically similar odorants [36]. Recent investigations suggest that such a mechanism plays a key role in perceptual segmentation of a new odorant from a stable odor background [37,38]. Other studies have shown that adult rats can distinguish chemically similar odorants, as well as odorant components from mixtures, following a 20-day odor enrichment period, though surprisingly, these perceptual gains are not specific to the enriched odorants [15,39,40]. In contrast, a recent fMRI study of olfactory perceptual learning in adult humans arrived at a different conclusion [41]. Here, a single 3.5-minute exposure to one odorant enhanced perceptual differentiation for up to 24 hours, but only among perceptually related odorants. For example, a subject exposed to minty L-carvone gained expertise in distinguishing minty odorants, but not floral odorants. These changes were accompanied by experience-dependent plasticity in PPC and OFC, whereby the same invariant odor input was capable of evoking a different fMRI response depending on prior experience. What factors ultimately dictate the generalization of learning to odor stimuli outside of the specific training set remain to be determined.

Paradigms of associative conditioning have long yielded important insights into the ontogeny and refinement of olfactory discrimination [42]. Building on animal models of olfactory fear learning [4346], Li and colleagues implemented a similar fMRI paradigm in human subjects, testing the impact of aversive Pavlovian conditioning on the perceptual and neural discrimination of odor enantiomers (mirror-image molecules) that are initially indistinguishable [47*]. As a result of pairing one of these chiral odorants (the conditioned stimulus, or CS+) with mild footshock (the unconditioned stimulus, or US), perceptual discrimination between the CS+ and its unshocked chiral counterpart was significantly enhanced. In parallel, spatial ensemble patterns of fMRI activity for these two odorants significantly decorrelated in PPC, indicating greater pattern discriminability (Fig. 4). These findings indicate that aversive learning induces piriform plasticity with corresponding gains in perceptual odor discrimination. That completely indiscriminable smells can be transformed into discriminable percepts accentuates the potency of learning and experience to enhance human olfactory perception.

Fig. 4
Learning to discriminate indistinguishable odors in human PPC. In an fMRI version of olfactory fear learning, subjects smelled pairs of odor enantiomers that were perceptually indistinguishable. One of these enantiomers, designated the target CS+ (tgCS+), ...

Complementary research is providing new insights into the biochemical and physiological mechanisms supporting the type of learning-related piriform plasticity described above. In adult mice, mRNA expression of brain-derived neurotrophic factor (BDNF), a key molecule involved in learning and memory, is elevated in PPC and basolateral amygdala, but not in OB or APC, two hours following odor-shock conditioning [48]. Likewise, single-unit recording data from Schoenbaum and colleagues show that neurons in PPC are highly associative and more likely than APC neurons to reverse their responses when the odor cue-outcome contingency is switched [49], suggesting that posterior piriform areas encode odor meaning rather than odorant identity per se. In postnatal rat pups, odor aversion (but not odor preference) learning was consistently associated with activity in PPC, as assessed using 2-DG uptake, implying that the specific hedonic value or meaning of an odor is coded here [50]. Finally, evidence that descending monosynaptic connections from OFC to APC are strengthened after odor discrimination learning directly highlights the role of top-down feedback in modulating the response properties of piriform cortical neurons [51*].


The main goal of this review article has been to illustrate some, but certainly not all, of the ways that the attendant sensory ecology of odorous objects critically shapes olfactory perception and coding. That this review is divided into separate subsections serves a useful heuristic, but unavoidably errs on the side of “splitting” over “lumping.” In fact all of the ecological concepts discussed above share considerable overlap. For example, to ask whether a binary odor mixture (AB) is perceived as the sum of its parts (A+B) or as a unique object (C) reformulates the question of whether two related odors are treated as members of the same perceptual category, or as members of different categories. Similarly, the perceptual competition between pattern completion and pattern separation closely reflects the dynamics of odor mixture processing, that is, the circumstances determining whether a complex mixture and a variant mixture are treated the same or different.

A common theme emerging across these recent studies is the pivotal associative role of piriform cortex in bridging sensation and perception. This hypothesis nicely accords with computational studies, which have long proposed that a spatially distributed architecture in piriform cortex would serve as a robust neural template for odor coding, memory, and recall [52,53]. Up until recently the idea that ensemble activity patterns in olfactory cortical structures could resolve an ecologically degraded odor input was derived mainly from anatomical data and network simulations, in the absence of direct functional evidence. By directly linking brain states to perceptual states, new findings in both rodents and humans now show that pattern-based odor representations in piriform cortex are specifically involved in (a) binding together odorant parts into perceptual wholes, (b) reassembling olfactory meaning out of fragmented stimuli, and (c) defining perceptual boundaries of odor qualities and categories. Together these properties are essential for the perceptual reconstruction of odor objects destabilized by the ecological whims of a noisy environment. Future research that incorporates the lessons of odor ecology into more robustly natural models of olfactory coding and perception will undoubtedly bring new scientific depth to our understanding of smell.


The author would like to thank N Bowman, JD Howard, W Li, J Plailly, and KN Wu for their tireless commitment to the human sense of smell. Research in the author’s laboratory is supported by grants from the National Institutes of Health/National Institute on Deafness and Other Communication Disorders (5K08DC007653 and 1R01DC010014).


Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

References and recommended reading

1. Laska M, Teubner P. Olfactory discrimination ability of human subjects for ten pairs of enantiomers. Chem Senses. 1999;24:161–170. [PubMed]
2. Laska M, Seibt A. Olfactory sensitivity for aliphatic esters in squirrel monkeys and pigtail macaques. Behav Brain Res. 2002;134:165–174. [PubMed]
3. Titchener EB. A Beginner's Psychology. New York: The Macmillan Company; 1915.
4. Porter J, Craven B, Khan RM, Chang SJ, Kang I, Judkewicz B, Volpe J, Settles G, Sobel N. Mechanisms of scent-tracking in humans. Nat Neurosci. 2007;10:27–29. [PubMed]
5. Dusenbery DB. Sensory Ecology: How Organisms Acquire and Respond to Information. New York, NY: W. H. Freeman; 1992.
6. Mussinan CJ, Walradt JP. Volatile constituents of pressure cooked pork liver. J Agric Food Chem. 1974;22:827–831.
7. Counet C, Callemien D, Ouwerx C, Collin S. Use of gas chromatography-olfactometry to identify key odorant compounds in dark chocolate. Comparison of samples before and after conching. J Agric Food Chem. 2002;50:2385–2391. [PubMed]
8. Jones FN, Woskow MH. On the intensity of odor mixtures. Ann N Y Acad Sci. 1964;116:484–494. [PubMed]
9. Laing DG, Panhuber H, Willcox ME, Pittman EA. Quality and intensity of binary odor mixtures. Physiol Behav. 1984;33:309–319. [PubMed]
10. Rospars JP, Lansky P, Chaput M, Duchamp-Viret P. Competitive and noncompetitive odorant interactions in the early neural coding of odorant mixtures. J Neurosci. 2008;28:2659–2666. [PubMed]
11. Wise PM, Miyazawa T, Gallagher M, Preti G. Human odor detection of homologous carboxylic acids and their binary mixtures. Chem Senses. 2007;32:475–482. [PubMed]
12. Le Berre E, Beno N, Ishii A, Chabanet C, Etievant P, Thomas-Danguin T. Just noticeable differences in component concentrations modify the odor quality of a blending mixture. Chem Senses. 2008;33:389–395. [PubMed]
13. McNamara AM, Magidson PD, Linster C. Binary mixture perception is affected by concentration of odor components. Behav Neurosci. 2007;121:1132–1136. [PubMed]
14. Coureaud G, Thomas-Danguin T, Le Berre E, Schaal B. Perception of odor blending mixtures in the newborn rabbit. Physiol Behav. 2008;95:194–199. [PubMed]
15. Mandairon N, Stack C, Linster C. Olfactory enrichment improves the recognition of individual components in mixtures. Physiol Behav. 2006;89:379–384. [PubMed]
16. Le Berre E, Thomas-Danguin T, Beno N, Coureaud G, Etievant P, Prescott J. Perceptual processing strategy and exposure influence the perception of odor mixtures. Chem Senses. 2008;33:193–199. [PubMed]
*17. Frederick DE, Barlas L, Ievins A, Kay LM. A critical test of the overlap hypothesis for odor mixture perception. Behav Neurosci. 2009;123:430–437. [PubMed]Starting with the hypothesis that two odorants evoking similar (or different) patterns of 2-DG activity in olfactory bulb should evoke configural (or elemental) representations when mixed together, the authors show that the degree of pattern overlap does not predict whether rodents can discriminate a binary mixture from its components. That odor-mediated behavior does not follow directly from bulbar activity maps suggests that olfactory codes of odor quality may reside in higher-order regions beyond the bulb.
18. Grossman KJ, Mallik AK, Ross J, Kay LM, Issa NP. Glomerular activation patterns and the perception of odor mixtures. Eur J Neurosci. 2008;27:2676–2685. [PubMed]
19. Mori K, Takahashi YK, Igarashi KM, Yamaguchi M. Maps of odorant molecular features in the mammalian olfactory bulb. Physiol Rev. 2006;86:409–433. [PubMed]
20. Boyle JA, Djordjevic J, Olsson MJ, Lundstrom JN, Jones-Gotman M. The human brain distinguishes between single odorants and binary mixtures. Cereb Cortex. 2009;19:66–71. [PubMed]
21. Grabenhorst F, Rolls ET, Margot C, da Silva MA, Velazco MI. How pleasant and unpleasant stimuli combine in different brain regions: odor mixtures. J Neurosci. 2007;27:13532–13540. [PubMed]
22. Lin DY, Zhang SZ, Block E, Katz LC. Encoding social signals in the mouse main olfactory bulb. Nature. 2005;434:470–477. [PubMed]
23. Lin DY, Shea SD, Katz LC. Representation of natural stimuli in the rodent main olfactory bulb. Neuron. 2006;50:937–949. [PubMed]
**24. Riffell JA, Lei H, Christensen TA, Hildebrand JG. Characterization and coding of behaviorally significant odor mixtures. Curr Biol. 2009;19:335–340. [PubMed]This paper presents an ingenious combination of gas chromatography/mass spectrometry, olfactometry, neural recordings, and a wind tunnel to measure ensemble brain activity from moth antennal lobe in response to individual odorant components of a natural floral scent highly attractive to this species. A synthetic mix of nine odorant constituents evoking the most robust excitatory responses was found to fully recapitulate flight foraging behavior.
25. Rasmussen LE, Wittemyer G. Chemosignalling of musth by individual wild African elephants (Loxodonta africana): implications for conservation and management. Proc Biol Sci. 2002;269:853–860. [PMC free article] [PubMed]
**26. Barnes DC, Hofacer RD, Zaman AR, Rennaker RL, Wilson DA. Olfactory perceptual stability and discrimination. Nat Neurosci. 2008;11:1378–1380. [PubMed]To examine the neural and perceptual basis of odor pattern completion versus separation, the authors cleverly designed a set of odorant mixture morphs with one or more odorant components of a 10-component (stock) mixture either removed or replaced. The behavioral proficiency with which rats could discriminate the stock mixture from the other mixtures closely paralleled the magnitude of ensemble decorrelation in anterior piriform cortex, but not in olfactory bulb. This paper, along with Ref. [34**], provides new support for the idea that information about an odor's perceived quality is embedded in distributed piriform ensemble activity.
27. Cleland TA, Johnson BA, Leon M, Linster C. Relational representation in the olfactory system. Proc Natl Acad Sci U S A. 2007;104:1953–1958. [PubMed]
28. Asahina K, Louis M, Piccinotti S, Vosshall LB. A circuit supporting concentration-invariant odor perception in Drosophila. J Biol. 2009;8:9. [PMC free article] [PubMed]
29. Wright GA, Kottcamp SM, Thomson MG. Generalization mediates sensitivity to complex odor features in the honeybee. PLoS ONE. 2008;3:e1704. [PMC free article] [PubMed]
30. Cleland TA, Narla VA, Boudadi K. Multiple learning parameters differentially regulate olfactory generalization. Behav Neurosci. 2009;123:26–35. [PMC free article] [PubMed]
31. Yoshida I, Mori K. Odorant category profile selectivity of olfactory cortex neurons. J Neurosci. 2007;27:9105–9114. [PubMed]
32. Gottfried JA, Winston JS, Dolan RJ. Dissociable codes of odor quality and odorant structure in human piriform cortex. Neuron. 2006;49:467–479. [PubMed]
33. Kadohisa M, Wilson DA. Separate encoding of identity and similarity of complex familiar odors in piriform cortex. Proc Natl Acad Sci U S A. 2006;103:15206–15211. [PubMed]
**34. Howard JD, Plailly J, Grueschow M, Haynes JD, Gottfried JA. Odor quality coding and categorization in human posterior piriform cortex. Nat Neurosci. 2009;12:932–938. [PubMed]In this study, multivariate pattern-based fMRI techniques and cortical flattening algorithms were used to characterize ensemble representations of odor quality in human PPC. Perceptually distinct odor categories evoked spatially overlapping but unique fMRI activity patterns in PPC, and odorants exhibiting more spatial concordance in PPC were perceived as smelling more alike in odor quality. This paper, along with Ref. [47*], marks the first implementation of pattern-based techniques in the context of human olfactory imaging, holding the future promise of relevant cross-species comparisons of olfactory spatial coding.
35. Freeman WJ. EEG analysis gives model of neuronal template-matching mechanism for sensory search with olfactory bulb. Biol Cybern. 1979;35:221–234. [PubMed]
36. Wilson DA. Comparison of odor receptive field plasticity in the rat olfactory bulb and anterior piriform cortex. J Neurophysiol. 2000;84:3036–3042. [PubMed]
37. Kadohisa M, Wilson DA. Olfactory cortical adaptation facilitates detection of odors against background. J Neurophysiol. 2006;95:1888–1896. [PMC free article] [PubMed]
38. Linster C, Henry L, Kadohisa M, Wilson DA. Synaptic adaptation and odor-background segmentation. Neurobiol Learn Mem. 2007;87:352–360. [PubMed]
39. Mandairon N, Stack C, Kiselycznyk C, Linster C. Enrichment to odors improves olfactory discrimination in adult rats. Behav Neurosci. 2006;120:173–179. [PubMed]
40. Escanilla O, Mandairon N, Linster C. Odor-reward learning and enrichment have similar effects on odor perception. Physiol Behav. 2008;94:621–626. [PubMed]
41. Li W, Luxenberg E, Parrish T, Gottfried JA. Learning to smell the roses: experience-dependent neural plasticity in human piriform and orbitofrontal cortices. Neuron. 2006;52:1097–1108. [PMC free article] [PubMed]
42. Wilson DA, Sullivan RM. Neurobiology of associative learning in the neonate: early olfactory learning. Behav Neural Biol. 1994;61:1–18. [PubMed]
43. Freeman WJ, Schneider W. Changes in spatial patterns of rabbit olfactory EEG with conditioning to odors. Psychophysiology. 1982;19:44–56. [PubMed]
44. Sullivan RM, Wilson DA. Dissociation of behavioral and neural correlates of early associative learning. Dev Psychobiol. 1995;28:213–219. [PMC free article] [PubMed]
45. Fletcher ML, Wilson DA. Experience modifies olfactory acuity: acetylcholine-dependent learning decreases behavioral generalization between similar odorants. J Neurosci. 2002;22:RC201. [PMC free article] [PubMed]
46. Yu D, Ponomarev A, Davis RL. Altered representation of the spatial code for odors after olfactory classical conditioning; memory trace formation by synaptic recruitment. Neuron. 2004;13:437–449. [PubMed]
*47. Li W, Howard JD, Parrish TB, Gottfried JA. Aversive learning enhances perceptual and cortical discrimination of indiscriminable odor cues. Science. 2008;319:1842–1845. [PubMed]Using a novel olfactory fMRI paradigm of fear learning, the authors show that odor discrimination between perceptually indistinguishable odor enantiomers is enhanced after aversive conditioning pairing one of the two odorants with footshock. Multivariate imaging analysis revealed parallel enhancement of cortical discrimination, as indexed by ensemble pattern divergence of fMRI activity patterns in human PPC from pre- to post-learning.
48. Jones SV, Stanek-Rattiner L, Davis M, Ressler KJ. Differential regional expression of brain-derived neurotrophic factor following olfactory fear learning. Learn Mem. 2007;14:816–820. [PMC free article] [PubMed]
49. Calu DJ, Roesch MR, Stalnaker TA, Schoenbaum G. Associative encoding in posterior piriform cortex during odor discrimination and reversal learning. Cereb Cortex. 2007;17:1342–1349. [PMC free article] [PubMed]
50. Raineki C, Shionoya K, Sander K, Sullivan RM. Ontogeny of odor-LiCl vs. odor-shock learning: similar behaviors but divergent ages of functional amygdala emergence. Learn Mem. 2009;16:114–121. [PubMed]
*51. Cohen Y, Reuveni I, Barkai E, Maroun M. Olfactory learning-induced long-lasting enhancement of descending and ascending synaptic transmission to the piriform cortex. J Neurosci. 2008;28:6664–6669. [PubMed]In vivo recordings of evoked field potentials from anesthetized rats reveal that odor rule learning strengthens synaptic transmission in the descending pathway from OFC to APC, suggesting a mechanism for modifying afferent-induced piriform activity. This study addresses a large gap in the research literature concerning how centrifugal circuits modulate responses in piriform cortex, a topic in need of much further investigation.
52. Haberly LB, Bower JM. Olfactory cortex: model circuit for study of associative memory? Trends Neurosci. 1989;12:258–264. [PubMed]
53. Hasselmo ME, Wilson MA, Anderson BP, Bower JM. Associative memory function in piriform (olfactory) cortex: computational modeling and neuropharmacology. Cold Spring Harb Symp Quant Biol. 1990;55:599–610. [PubMed]