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Menini A, editor. The Neurobiology of Olfaction. Boca Raton (FL): CRC Press/Taylor & Francis; 2010.

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The Neurobiology of Olfaction.

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Chapter 12Active Sensing in Olfaction



A fundamental feature of sensory systems is that the animal can actively control the interaction between a stimulus and the sensory neurons detecting it. This active control is important because it allows an animal to sample regions of interest in space, to regulate stimulus intensity in order to maintain optimal receptor function, to extract features of interest from a complex stimulus, and to protect sensory neurons from damage due to excess exposure to strong or (in the case of chemoreception) toxic stimuli. Active sensation is especially prominent in olfaction; in vertebrates, for example, odorants cannot be detected without the movement of air or water into the nasal cavity, and vertebrates and invertebrates alike have impressively complex behavioral repertoires built around the process of sampling odorants. This chapter will focus on the importance of active sensing to olfactory system function. A key point is that active sensing is important not only in shaping how sensory neurons respond to a stimulus, but also in determining how incoming sensory information is processed at higher levels, modulated by behavioral state, and, ultimately, perceived by the animal. For example, active odorant sampling constrains the temporal structure of sensory input to the nervous system, a feature that probably has important consequences for how the postsynaptic networks that process olfactory information are designed and function. At the same time, sampling behavior is tightly linked to behavioral state, so that “top-down,” state-dependent modulation of sensory processing probably goes hand-in-hand with “bottom-up” changes in the nature of sensory input. Finally, in order to correctly process incoming information, sensory processing must be coordinated with the motor systems involved in stimulus sampling. This chapter will review how active sensing shapes olfactory system function at each of these levels. Because of the wide-ranging nature of the subject, treatment of individual topics is not exhaustive; the reader is referred to a number of excellent, more focused reviews at the end of the chapter. In addition, relevant chapters in this volume are cited where possible to minimize overlap of content.


Most terrestrial vertebrates sample odorants by drawing air into the nasal cavity and over the olfactory epithelium (OE). Odorants are usually sampled intermittently, either during the course of resting respiration or by the voluntary inhalation of air in the context of odor-guided behavior; the latter phenomenon is typically termed sniffing. Analogs of sniffing occur across the animal kingdom, with groups as diverse as crustaceans (Snow 1973; Koehl et al. 2001), fish (Nevitt 1991), semiaquatic mammals (Catania 2006), and insects (Suzuki 1975; Lent 2004) showing active, intermittent odorant sampling; in each case, sampling involves movement of the air or fluid containing the stimulus by the animal, or movement of the olfactory organ itself (for a review, see Dethier [1987]). For example, lobsters “flick” their olfactory organs (antennules) when sampling odorant-laden water (Schmitt and Ache 1979; Koehl et al. 2001), while the air-breathing shrew samples odorants underwater with an “inverted” sniff, in which air is partially exhaled onto a substrate and then reinhaled (Catania 2006). A common feature of all of these behaviors is that they are actively controlled by the animal and modulated depending on the properties of the stimulus itself (e.g., odorant concentration or hedonic value) (Youngentob et al. 1987; Bensafi et al. 2003; Johnson et al. 2003), the particular sensorimotor task being performed (e.g., detection vs scent-tracking) (Thesen et al. 1993), and behavioral context (e.g., exploration vs reward-based conditioning) (Clarke 1971; Lent and Kwon 2004; Kepecs et al. 2007). The persistence of this behavior in different species and ecological settings as well as its strong modulation during odor-guided behaviors (Figure 12.1), suggests that active, intermittent sampling of odorant is fundamentally important to olfaction (Dethier 1987).

FIGURE 12.1. Odorant sampling behavior in different animal species.


Odorant sampling behavior in different animal species. (A): Sniffing behavior in a freely moving mouse. Shown are raw recordings of intranasal pressure measured from the dorsal recess (top trace), a raster of individual sniff onsets (middle), and a moving (more...)

Odorant sampling behavior (i.e., sniffing) has been most comprehensively studied in mammals—particularly in rodents and humans. In rodents in particular, sniffing is precisely coordinated with other motor systems and is highly dynamic, with many parameters of a sniff varying on a cycle-by-cycle basis (Figure 12.1A) (Welker 1964; Macrides et al. 1982; Youngentob et al. 1987). The parameter of sniffing that has received the most attention and which changes most clearly in rodents is frequency: respiratory frequency increases from “resting” rates (near 2 Hz in larger rodents such as rats and hamsters; 3–5 Hz in mice) to rates ranging from 6 to 12 Hz when investigating novel odor sources or sampling odorants during operant tasks (Welker 1964; Macrides et al. 1982; Youngentob et al. 1987; Uchida and Mainen 2003; Verhagen et al. 2007; Kepecs et al. 2007; Wesson et al. 2008). Rats also alter other parameters of sniffing during odor-guided behavior, including amplitude, inhalation-exhalation waveform, and duration (Youngentob et al. 1987; Youngentob 2005). In humans, inhalation amplitude, duration, and number of sniffs are modulated during odorant sampling (Laing 1982, 1985; Sobel et al. 2000a). Changes in these parameters alter the instantaneous rate and total volume of airflow over the OE during a sniff (Youngentob et al. 1987; Sobel et al. 2000a).

While complex and dynamic, sniffing behavior is precisely controlled by the animal and can be surprisingly stereotyped (Figure 12.2A and B). For example, when sampling odorant from a delivery port in an operant two-odor discrimination task, rats show a brief bout of 6–10 Hz sniffing that is precisely timed to just precede odorant delivery, and a slightly higher-frequency sniff bout (10–12 Hz) just prior to receiving a water reward; each of these bouts is repeated with a temporal jitter of only a few hundred milliseconds across hundreds of trials (Kepecs et al. 2007; Wesson et al. 2009). Humans also show stereotyped and task-dependent sniffing patterns (Laing 1982), and are also capable of rapidly modulating sniffing in response to sensory input (Johnson et al. 2003).

FIGURE 12.2. Sniffing behavior in different olfactory and nonolfactory contexts.


Sniffing behavior in different olfactory and nonolfactory contexts. (A): Histogram of sniffing relative to time of odorant presentation in a head-fixed rat performing a lick/no-lick two-choice odor discrimination. This rat displays a brief bout of high-frequency (more...)

Thus, the pattern of sniffing expressed during a particular behavior can be thought of as a strategy for odorant sampling; these strategies are task and context specific and can be expected to vary between species as well as across individuals within a species. For example, rats increase sniff frequency and amplitude as odorant concentration approaches threshold values when performing an odor-detection task (Youngentob et al. 1987), but not when performing an odor-discrimination task (Wesson et al. 2009). Similarly, mice show a stereotyped bout of high-frequency sniffing when performing a two-odor discrimination task involving a nose poke into a sampling port, but not when performing the same discrimination task involving sampling odorant in cups of sand (Wesson et al. 2008b). Both rodents and humans show individual differences in sniffing behavior when sampling odorants (Wesson et al. 2009; Laing 1983). One might expect that odorant sampling strategies are optimized to the particular context (and individual) in which they are expressed. Indeed, measurement of sniff parameters in humans performing odor threshold and intensity tasks indicates that those expressed naturally by each subject are near-optimal for performance in the task; increasing the number of sniffs or varying sniff interval or magnitude leads to no improvement in performance over that during natural sniffing (Laing 1983, 1985). A striking example of context-specific sampling strategies in a more ethologically natural setting is seen in bird-hunting dogs: when tracking the scent of prey on the ground, dogs sniff at up to 4–6 Hz, but when tracking the same scent in the air, the animal will raise its head and run forward, forcing a continuous stream of air into the nose for up to 40 s (Thesen et al. 1993; Steen et al. 1996). The presumed advantage of this latter strategy is to enable a continuous sampling of odorant while the dog is moving at high speed and to decouple sampling from respiration during a time of heavy load on the respiratory system. Similarly, rodents exhibit prolonged bouts of sniffing at 4–8 Hz when sampling a novel odorant (Figure 12.2C), but show only brief or no increases in sniff frequency when sampling a familiar odorant for the purposes of odor discrimination (Welker 1964; Macrides 1975; Kepecs et al. 2007; Verhagen et al. 2007; Wesson et al. 2009). Brief sniffing (in fact, only a single sniff) appears to provide sufficient contact with the stimulus to enable odor identification (Goldberg and Moulton 1987; Uchida and Mainen 2003; Wesson et al. 2008a), while prolonged high-frequency sniffing is probably useful in gathering additional information about the location, spatial distribution, or dynamics of an odorant that is novel (Verhagen et al. 2007).

Finally, in analyzing sampling behavior and its role in olfaction, it is important to remember that, in the awake animal, sniffing (or its analog) is typically expressed as part of a larger behavioral repertoire that may include head movements, whisking (in rodents), licking, and locomotion (Welker 1964; Komisaruk 1970; Bramble and Carrier 1983). The tight coordination of sniffing with other behaviors can confound the interpretation of the role that sampling behavior plays in the process of olfaction. For example, sniff frequency may increase in animals that are actively engaged with their environment due simply to increased demand on the respiratory system. In addition, it is difficult to isolate sniffing behavior from the expression of other behaviors associated with active sensory sampling. For example, mice and rats increase sniff frequency in response to unexpected stimuli of any modality (Welker 1964; Macrides 1975; Harrison 1979), and increase sniff frequency when approaching and inserting their nose into a port—even when performing nonolfactory tasks (Figure 12.2D) (Wesson et al. 2008b, 2009). Mice and rats also increase respiratory frequency prior to receiving a reward and when otherwise engaged in motivated behavior (Figure 12.2E) (Clarke 1971; Clarke and Trowill 1971; Kepecs et al. 2007; Wesson et al. 2008b). Thus, in practice it is difficult to find criteria that define “sniffing” as a behavior solely associated with odorant sampling and distinct from respiration. As we will see later in this chapter, such distinctions are also difficult when considering the role of sampling behavior in shaping the neural processing of olfactory information: the same changes in behavioral state associated with sniffing can modulate olfactory processing through neural mechanisms, even at the lowest synaptic levels of the olfactory pathway.


Odorant sampling behavior plays a fundamental role in the neural coding and processing of odor information because it controls the access of odorant molecules to the sensory neurons themselves. In terrestrial vertebrates, for example, inhalation of air is required for olfactory receptor neurons (ORNs) to detect an odorant. More importantly, sampling behavior can directly shape receptor activation in two ways. First, intermittent odorant sampling imposes a strong temporal structure on the dynamics of ORN activation. Second, changes in sampling behavior can rapidly modulate the strength and, potentially, the patterns of ORN activation by changing the nature of airflow through the nasal cavity. Each of these effects is important for encoding odor information and for processing olfactory sensory input downstream.

12.3.1. Sampling Behavior Shapes the Temporal Structure of Receptor Neuron Activation

The temporal dynamics of ORN activation depend strongly on sampling behavior. In rodents, ORNs are not activated when odorant is simply blown at the nose; the animal must inhale for odorant to reach the (Wesson et al. 2008a). Once inhalation begins, ORN activation occurs relatively quickly: calcium imaging from the presynaptic terminals of ORNs reveals that odorant-evoked action potentials first reach the olfactory bulb (OB) 80–160 ms after the start of inhalation (Figure 12.3A and B) (Wesson et al. 2008a; Carey et al. 2009). This time is surprisingly short given published estimates of 150–600 ms for transduction times in ORNs in vitro (Firestein et al. 1990; Ma et al. 1999). Activation timing relative to inhalation is also precise, with response onset latencies varying by only approximately 50 ms from sniff to sniff during low-frequency sniffing (Carey and Wachowiak, pers. comm.). Inhalation-driven responses are transient: both presynaptic calcium imaging and electroolfactogram recordings from awake, freely breathing rats suggest that each inhalation of odorant evokes a burst of ORN input to an OB glomerulus, lasting 100–200 ms (Chaput and Chalansonnet 1997; Verhagen et al. 2007; Carey et al. 2009). Whether the transient nature of the ORN response is due to rapid clearance of odorant from the receptor site or rapid adaptation of ORNs (Reisert and Matthews 2001) is unclear.

FIGURE 12.3. Temporal dynamics of inhalation-evoked activation of olfactory receptor neurons.


Temporal dynamics of inhalation-evoked activation of olfactory receptor neurons. (A): Intranasal pressure (“sniff,” top) and receptor neuron response traces (“Glom 1,” “Glom 2”) imaged from two glomeruli (more...)

The initial response to odorant involves a progressive recruitment of activation of the population of ORNs that converge onto a single glomerulus over a time-window of at least 80–100 ms, rather than a synchronous activation (Figure 12.3B) (Carey et al. 2009). This value is similar to the rise-time of odorant-evoked excitatory postsynaptic potentials (EPSPs) in mitral/tufted (M/T) cells of anesthetized, freely breathing rats (Cang and Isaacson 2003; Margrie and Schaefer 2003), consistent with the idea that M/T cells integrate ORN inputs over the time-window of a single sniff. Surprisingly, behavioral measurements of odor perception times in awake rats, performed simultaneous with imaging of ORN inputs to the OB, indicate that at least some forms of odor identification occur before this initial response onset phase is even finished (Wesson et al. 2008a); studies of olfactory reaction times in rodents and rabbits are consistent with this conclusion (Karpov 1980; Uchida and Mainen 2003; Abraham et al. 2004; Rinberg et al. 2006b). Thus, the initial onset phase of the inhalation-evoked burst of ORN activity is likely to be particularly important for olfactory processing. How the relatively slow, asynchronous recruitment of ORN inputs to a glomerulus shapes this processing and contributes to odor coding has yet to be explored, either in experimental preparations or via modeling of neural processing in the OB.

A number of studies have suggested that ORNs may be activated by respiration alone, independent of odorant stimulation. This issue remains controversial, but is important in that inputs driven by inhalation alone would provide direct signals to the OB about the timing of sampling behavior. Many studies have reported respiratory patterning of postsynaptic activity in the OB in the absence of odorant (Adrian 1942; Macrides and Chorover 1972; Chaput et al. 1992; Rinberg et al. 2006a); others have found no such patterning or have observed patterned responses in some OB neurons but not others (Walsh 1956; Sobel and Tank 1993). A recent study of mouse ORNs recorded in vitro found that up to 60% of all ORNs responded to pressure pulses that were estimated to approximate pressure transients generated during sniffing, that these responses were absent in ORNs from mice missing components of the second messenger pathway that mediates odorant responses, and that respiration-linked field potentials in the OB were disrupted in these mice (Grosmaitre et al. 2007). A different study using presynaptic calcium imaging from ORNs in awake rats found that inhalation alone evoked detectable ORN input to a similar fraction (at least 50%) of glomeruli in the dorsal OB; the magnitude of inputs to most of these glomeruli was small, although inhalation evoked large-magnitude inputs to a few glomeruli (Carey et al. 2009). More work is needed to resolve whether the in vivo results reflect a mechanosensitive capability in some ORNs or, instead, reflect responses to odorants emitted by the animal or other environmental changes related to respiration (e.g., temperature or carbon dioxide level).

In addition to being shaped by the respiratory cycle, the temporal dynamics of ORN activation are intrinsically variable: temporal response parameters, such as latency, rise-time, and burst duration, vary for ORN inputs to different glomeruli, for the same ORN inputs activated by different odorants, and, to some degree, by concentration (Figure 12.3C). Diverse response dynamics are seen both in anesthetized and awake mice and rats, with the particular temporal pattern of ORN activation occurring reliably across multiple respiratory cycles and consistently for homologous glomeruli in different animals (Spors et al. 2006; Carey et al. 2009). Thus, these dynamics do not appear to be an artifact of the calcium-imaging method used to detect them. Instead, intrinsic temporal response patterns probably reflect odorant-specific differences in the kinetics of odorant access to the ORNs as well as ligand/receptor interactions. The significance of this temporal diversity is that patterns of sensory input to OB glomeruli evolve over time in an odorant-specific manner, and so may play a role in coding odor information. In awake rats, the time-window over which patterns of glomerular input evolve (the time from the earliest activated inputs to the peak of the latest activated inputs) is approximately 250 ms (Carey et al. 2009). This window roughly matches the amount by which discrimination time increases when mice and rats are asked to perform more difficult odor discriminations (Figure 12.3D) (Abraham et al. 2004; Rinberg et al. 2006b). Thus, the intrinsic variability in the dynamics of inhalation-evoked ORN inputs to the OB may set an upper limit on the time-window for integration of odor information in the behaving animal.

Increasing the frequency of respiration and odorant sampling—e.g., during exploration of a novel environment or active odor investigation—dramatically alters the temporal structure of ORN activity patterns. The main effect of high-frequency sniffing is to reduce the degree to which ORN activation is linked to the respiratory rhythm. As respiration frequency increases (from 1 to 2 Hz at rest to above 4 Hz during active sniffing in rats), the temporal coherence between the respiratory cycle and ORN activation dynamics diminishes; for many ORN populations (as defined by their convergence onto different OB glomeruli), ORN responses become tonic, with no clear modulation by the respiratory cycle (Verhagen et al. 2007; Carey et al. 2009). This effect is at least partly a result of reduced modulation of odorant levels in the OE: simulations of bulk airflow through the rat nasal cavity and odorant sorption into the OE indicate that odorant is not fully cleared during high-frequency sniffing (Zhao and Jiang 2008). Measurements of pressure transients resulting from active sampling of odorant also suggest that high-frequency sniff bouts involve a net influx of air into the nose (Youngentob et al. 1987). Thus, during high-frequency sniffing, exposure of ORNs to odorant changes from being transient to being continuous (though still modulated in absolute level), resulting in a tendency of ORNs to respond more tonically. This qualitative change in the temporal structure of ORN activation probably has significant consequences for postsynaptic processing of odor information; these are discussed in more detail in Section 12.4.

12.3.2. Sampling Behavior can Shape Patterns of Receptor Neuron Activation

Odorant sampling behavior also has the potential to modulate the strength and relative pattern of activation of ORNs. This modulation can be mediated by changes in the total volume (i.e., mass) of inhaled odorant, changes in the flow rate, and changes in sniff frequency, all of which affect the dynamics of odorant exchange in the nasal cavity. An animal may actively modulate each of these parameters, and adjustment of each parameter may optimize ORN responses for different odor-guided tasks.

Modulation of sniff volume has long been hypothesized to play a role in maintaining odor quality perception across different intensities. Animals encounter odorants over a wide range of concentrations, and must maintain at least some degree of constancy in quality perception across this range. While human psychophysical studies suggest that odor quality perception can vary with large changes in odorant concentration, this perception is relatively invariant over one to two orders of magnitude (Laing et al. 2003). However, many studies characterizing odor representations in the OB of anesthetized rodents have found that the pattern of activated ORNs or their corresponding glomeruli can change dramatically over a concentration range of 1 log unit or less (Rubin and Katz 1999; Meister and Bonhoeffer 2001; Wachowiak and Cohen 2001; Bozza et al. 2004). This result is expected, given that increasing odorant concentration activates increasing numbers of ORN types (Malnic et al. 1999). If the combination of activated ORNs or OB glomeruli encodes odor quality, then how is quality perception maintained across concentration? One possibility is that adjustments in sampling behavior may compensate for intensity changes by sampling more or less odorant per inhalation. This process would be analogous to the pupillary reflex in the eye.

The strongest evidence for such an effect comes from work in humans. Subjects performing odorant intensity estimates will suppress the strength of a single sniff at high-odorant intensities, resulting in a reduction in the total volume of inhaled odorant (Figure 12.4A) (Laing 1982; Warren et al. 1992; Johnson et al. 2003). This modulation of sniffing is surprisingly fast—as fast as 160 ms—leading to the hypothesis that subcortical pathways may mediate this response (Johnson et al. 2003; Mainland and Sobel 2006). One caveat in interpreting these data is that some odorants used in these studies can activate nasal trigeminal afferents at high concentrations, triggering a reflexive change in sniffing as a result of nasal irritation (Warren et al. 1994; Benacka and Tomori 1995); nonetheless, modulation of sniffing by high concentrations of “pure” olfactory stimuli can occur in as little as 260 ms (Johnson et al. 2003). Similar to this, our laboratory has observed that rats suppress inhalation amplitude when exposed to moderately high concentrations of certain odorants (Figure 12.4B) (Wesson and Wachowiak, pers. obs.). In at least some behavioral paradigms, animals will attempt to sample more odorant as concentration decreases. For example, rats performing an odor-detection task show higher-amplitude and higher-frequency sniffs as concentration nears their perceptual threshold (Figure 12.4C) (Youngentob et al. 1987). Similarly, human subjects performing a detection task and forced to sniff through only one nostril will increase sniff duration when sniffing through the low flow-rate nostril as compared to the high-flow-rate nostril (nasal patency is asymmetric in macrosmatic mammals, and alternates from side to side every few hours [Principato and Ozenberger 1970; Bojsen-Moller and Fahrenkrug 1971]), a behavior consistent with compensatory sniffing at near-threshold intensities (Sobel et al. 2000a). In summary, there is considerable evidence that animals actively adjust sniff parameters as a function of odorant intensity to facilitate concentration-invariant odor perception. However, it is important to recognize that, to date, no study has actually examined how—or even whether—such changes in sniff volume impact the neural representation of odorants in the periphery or in the central nervous system (CNS) in a manner consistent with this idea.

FIGURE 12.4. Modulation of sniffing as a function of odorant intensity.


Modulation of sniffing as a function of odorant intensity. (A): Decrease in sniff magnitude (peak inhalation and exhalation flow rate) as odorant intensity increases from low to high (progressing left to right) in humans performing an odor intensity judgment. (more...)

Another longstanding hypothesis is that modulating sniffing behavior can cause changes in flow rate that shape ORN response patterns by altering how odorant distributes across the OE (Adrian 1950; Mozell 1964). This idea—which we will call the sorption hypothesis—arises from the fact that odorant molecules must pass from an airborne vapor phase to an aqueous phase in the OE in order to contact ORNs. The nasal cavity of most vertebrates—and mammals in particular—is anatomically complex and forms a narrow airspace lined with epithelium onto which odorant molecules absorb as they flow through the cavity (Keyhani et al. 1995; Craven et al. 2007; Yang et al. 2007). This arrangement causes a “chromatographic effect,” in which odorants are preferentially absorbed in different locations depending on their solubilities in the mucus and their flow rate (Mozell and Jagodowicz 1973; Yang et al. 2007). The topography of odorant receptor expression across the OE correlates with the areas of maximal sorption for the receptors’ respective ligands, suggesting that receptors are optimally localized to take advantage of the chromatographic effect (Scott et al. 2000; Schoenfeld and Cleland 2006). Because the strength, duration, and frequency of respiration can change dramatically during odor-guided behavior and because these parameters affect the rate and total volume of airflow into and out of the nasal cavity, sampling behavior has the potential to alter odorant sorption and, as a consequence, patterns of ORN activation (Mozell et al. 1987; Youngentob et al. 1987). This phenomenon is described in more detail elsewhere in this volume (Chapter 13) and in several excellent reviews (Schoenfeld and Cleland 2005, 2006; Scott, 2006).

Indeed, many studies have confirmed that flow rate impacts the spatial distribution of odorant sorption across the OE, and that this, in turn, shapes both spatial and temporal patterns of ORN activity (Kent et al. 1996; Scott 2006). Physiological studies and detailed modeling of airflow and sorption in the nasal cavity have generated specific predictions about how flow rate should shape activity in the intact animal (Mozell et al. 1987; Hahn et al. 1994; Zhao et al. 2006; Yang et al. 2007). The most directly testable is the following: at low flow rates, strongly sorbed odorants will be largely removed from the airstream as they pass through the initial parts of the epithelium, resulting in fewer odorant molecules available to activate more posterior odorant receptors. At higher flow rates, more molecules of strongly sorbed odorant reach the posterior epithelium and so evoke responses that increase with increasing flow rate. In contrast, weakly sorbed odorants absorb slowly onto the epithelium and so tend to remain in the airstream. For these compounds, a bolus of odorant passing through the nasal cavity would deposit fewer odorant molecules onto the epithelium at high-flow rates than at low, since the bolus would pass through the nasal cavity with insufficient time for complete sorption to occur. Thus, responses to a strongly sorbed odorant should increase as flow rate increases, while responses to a weakly sorbed odorant should decrease (Hahn et al. 1994). Such effects have, in fact, been measured at the level of the OE, by adjusting nasal flow while measuring electroolfactogram responses or imaging membrane potential across the epithelial surface (Kent et al. 1996; Scott-Johnson et al. 2000; Scott et al. 2006; Mozell et al. 1991). One intriguing piece of behavioral evidence supporting the idea that sorption effects can shape odor quality perception comes from a study in humans, in which subjects judged the relative magnitude of each component of a binary mixture while sniffing through only one nostril. When sniffing through the lower flow-rate nostril, subjects judged the weakly sorbed odorant to be more intense, and judged the strongly sorbed odorant to be more intense when sniffing through the higher flow-rate nostril (Sobel et al. 1999), consistent with the sorption hypothesis. Together, these studies confirm that the parameters of respiration have the potential to alter primary odor representations, and raise the possibility that animals might alter sampling behavior in a way that generates an optimal odor representation (and perception) for a particular olfactory task (Mainland and Sobel 2006; Schoenfeld and Cleland 2006).

These studies have some important limitations, however. First, both the odorant uptake modeling and physiological studies have, for the most part, used steady-state flow rates, not the transient changes in airflow that occur during natural respiration and active sniffing. Whether the sorption effects seen with steady-state flows manifest differently during natural respiration has not, to our knowledge, been tested either computationally or physiologically. Second, actual flow rates in the nasal cavity are difficult to measure directly: total inspiratory and expiratory flow has been measured reliably in behaving rats and mice (Youngentob et al. 1987; Youngentob 2005), but the proportion of that flow passing over the olfactory region and its subcompartments has only been estimated by modeling. It also remains unclear whether the different sampling strategies expressed in behaving animals alter airflow sufficiently to alter ORN responses according to predictions. Finally, it remains unclear what impact these changes at the level of the OE will have on odor representations at the level of the OB. Thus, while chromatographic effects have the potential to shape patterns of ORN activity as a function of sampling behavior, the sorption hypothesis has yet to be tested under conditions of natural odorant sampling and at the level of neural patterns of activity in the CNS.

A third way in which sampling behavior can alter ORN response patterns—including at the level of the OB—is through changes in sniff frequency. As already described, animals strongly modulate sniff frequency and typically engage in bouts of high-frequency sniffing when investigating novel stimuli or exploring an environment (Welker 1964; Macrides 1975), and these increases shape the temporal structure of ORN responses (Carey et al. 2009). Sniff frequency also shapes the magnitude of ORN responses, although in unexpected ways. An intuitive prediction is that increases in sniff frequency lead to increased ORN responses—and perhaps recruitment of activation of new ORN populations—due to an increased influx of odorant per unit time. This prediction has been tested using presynaptic calcium imaging from ORN terminals in the dorsal OB of awake rats, which sampled the same odorant during low frequency (1–2 Hz) respiration or during high frequency (4–8 Hz) sniffing (Verhagen et al. 2007). Surprisingly, sampling an odorant at high frequency only weakly enhanced the initial response to the odorant, and did not recruit activation of new ORN populations. More importantly, sustained high-frequency sniffing of odorant had the opposite effect, with a strong attenuation in the magnitude of ORN responses (Figure 12.5A). This frequency-dependent attenuation is rapidly reversible, with ORN response magnitudes recovering within 1 s after sniff frequency returns to low levels. The effect is also not driven by changes in behavioral state, as it can be replicated during artificial “playback” of high-frequency sniffing patterns in anesthetized rats (Verhagen et al. 2007). A likely cellular mechanism mediating the frequency-dependent attenuation of ORN inputs to the OB is simple adaptation of ORN spiking. Rat ORNs in vivo respond to step odorant pulses with brief (<100 ms) action potential bursts (Duchamp-Viret et al. 2000), and receptor currents in isolated mouse ORNs show ~80% adaptation within 2 s of continuous odorant exposure (Reisert and Matthews 2001). At low respiration rates, ORNs can recover from adaptation in the interval between successive inhalations, but higher sniff frequencies allow less time for recovery between cycles and probably also include a tonic component in which odorant is continuously present in the nasal cavity (Youngentob et al. 1987; Uchida and Mainen 2003; Zhao and Jiang 2008).

FIGURE 12.5. Adaptive filtering of sensory inputs controlled by sniffing.


Adaptive filtering of sensory inputs controlled by sniffing. (A): Rapid attenuation of receptor neuron activation during sustained high-frequency sniffing of an odorant. Top traces show sniffing in a head-fixed rat, lower traces show receptor neuron responses, (more...)

What is the functional significance of this phenomenon? Importantly, frequency-dependent attenuation of ORN responses is specific to those glomeruli receiving odorant-evoked input, leaving other glomeruli free to respond to other odorants encountered during a sniff bout. As a result, this attenuation constitutes an “adaptive filter” of sensory input to the OB, in which ORNs activated by odorants present at the beginning of exploratory sniffing (i.e., “background” odorants) are selectively suppressed in the representation of subsequent sampled odorants (Figure 12.5B) (Verhagen et al. 2007). In contrast, during low-frequency sampling, odorants encountered against a background are encoded as the sum of the background and “foreground” response maps. This filtering can enhance the contrast between odorants having overlapping molecular features (or mixtures with shared components). A second important function of frequency-dependent attenuation is to increase the salience of temporally dynamic or spatially localized odorants relative to broadly distributed background odorants. These properties seem optimally suited for scanning the environment for changes in odor composition or concentration, and may explain why high-frequency sniffing is induced by any novel stimulus or during general exploratory behavior (Welker 1964; Vanderwolf and Szechtman 1987). Humans also modulate sniff duration during active odor sampling (Laing 1982, 1983; Sobel et al. 2000a); in this case, prolonged odorant inhalation may also attenuate receptor inputs via adaptation, enhancing the ability to detect changing olfactory stimuli in a single long sniff.

A key feature of all of the above phenomena is their direct dependence on odorant sampling parameters. Thus, most animals have a surprising degree of control over the way in which a complex and dynamic odor landscape is represented at the level of sensory input to the CNS. These low-order effects are likely to be magnified by processing in higher-order networks, and further modulated by top-down processes driven by changes in behavioral state, as discussed in more detail below.


It seems obvious to expect that the processing of olfactory information in the CNS will depend on the temporal structure and magnitude of inputs from ORNs, and so will be strongly shaped by sampling behavior. Indeed, temporally patterned activity relative to respiration is a key feature of most models of information coding and processing in the mammalian OB. For example, many experimental and computational studies have suggested that olfactory information is encoded in the timing of mitral cell spiking relative to the respiratory cycle or to the OB theta rhythm (Macrides and Chorover 1972; Chaput 1986; Hopfield 1995; Margrie and Schaefer 2003; Buonviso et al. 2006; Schaefer and Margrie 2007); details of these studies are described elsewhere in this volume (Chapter 13). However, most previous studies have explored odor coding in the OB and beyond during sampling regimes that are both low frequency (1–3 Hz) and highly regular. Neither of these features apply to active odor sampling (see Figure 12.1). Thus, how the OB network processes odor information during odor-guided behaviors, and, specifically, how changes in sampling behavior associated with particular sampling strategies shape synaptic processing, remains unclear.

12.4.1. Effects of Sampling Behavior on Olfactory Bulb (OB) Processing

Active sampling and olfactory processing have been examined mostly in the context of sniff frequency, and explored most heavily in the OB. A great deal of evidence suggests that during low-frequency respiration (1–2 Hz), the OB network enhances the temporal patterning that is present at the level of ORN inputs and increases the temporal precision and synchrony of firing of the principal output neurons of the OB. For example, in OB slice preparations, delivering patterned olfactory nerve stimulation at frequencies that roughly mimic resting respiration alters responses of postsynaptic neurons: external tufted (ET) cells become entrained to this input and synchronized with each other, M/T cell responses are amplified and synchronized, and gamma-frequency oscillations in M/T cell membrane potential emerge (Schoppa and Westbrook 2001; Hayar et al. 2004; Schoppa 2006). Modeling studies support these data: for example, a compartmental model of mitral cell firing properties predicts that ORN inputs that arrive at the OB in bursts (as they do during resting respiration) will cause an increase in the temporal patterning and spike timing precision of mitral cells (David et al. 2007). These phenomena arise from multiple mechanisms in the glomerular and subglomerular layers, the details of which are described elsewhere (see Chapter 13).

Very few studies have investigated how sniffing in the high-frequency (4–12 Hz) range alters postsynaptic response properties. Recordings from M/T cells in awake, freely moving rats show that M/T firing largely decouples from respiration at sniff frequencies above 4 Hz and adopts a more tonic pattern (Figure 12.6A) (Bhalla and Bower 1997; Kay and Laurent 1999). Decoupling was originally interpreted as reflecting state-dependent modulation of M/T cell responses by centrifugal inputs. However, this effect could simply be due to the reduction in temporal patterning of ORN inputs that occurs during high-frequency sniffing (Verhagen et al. 2007; Carey et al. 2009). A more recent study (Bathellier et al. 2008) artificially controlled sampling frequency in anesthetized mice, and reported that temporal patterning was maintained at high frequencies, but that response magnitudes were attenuated. This attenuation is consistent with the fact that ORN inputs become attenuated during high-frequency sniffing in awake rats (Verhagen et al. 2007).

FIGURE 12.6. Importance of sniffing in shaping response patterns in the olfactory bulb and the piriform cortex.


Importance of sniffing in shaping response patterns in the olfactory bulb and the piriform cortex. (A): In an awake, freely moving rat, a mitral/tufted cell shows firing rate increases locked to sniffing at low frequencies, but loses temporal patterning (more...)

Since the functional properties of the postsynaptic OB network are dynamic, it is also likely that changes in the frequency and temporal structure of ORN input lead to qualitative changes in the way that this network processes olfactory information during active sampling. For example, the synapse between ORNs and second-order neurons is subject to strong activity-dependent depression due to feedback presynaptic inhibition and vesicle depletion, and postsynaptic α-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate (AMPA) receptors show rapid and strong desensitization (Murphy et al. 2004; McGann et al. 2005; Wachowiak et al. 2005). Thus, tonic or high-frequency ORN inputs would suppress the overall strength of afferent synaptic drive onto M/T cells. Second, recurrent inhibition between mitral and granule cells enhances the temporal precision of M/T cell firing when excitatory inputs to M/T cells themselves are temporally patterned (Balu et al. 2004; Schoppa 2006), but modeling and experimental studies suggest that this precision is reduced or lost when inputs are temporally dispersed or occur tonically (Balu et al. 2004; David et al. 2007). Recurrent inhibition between mitral and granule cells may also be stronger at higher sniff frequencies (Young and Wilson 1999). These findings predict a reduction in M/T cell temporal patterning during high-frequency sniffing. A third prediction is that increasing sniff frequency leads to an increase in the strength of inhibition both within and between glomeruli due to an enhanced synchrony and strength of firing of cells. ET cells drive both feed-forward and feedback inhibition within the glomerular layer and become more synchronous and increasingly entrained to rhythmic ORN inputs as sniff frequency increases (Hayar et al. 2004). Thus, at high-sniff frequencies, ET cell-driven inhibition is predicted to generate an increasingly sharp time-window over which M/T cells may integrate ORN inputs (Wachowiak and Shipley 2006). As this temporal window narrows, only those M/T cells innervating glomeruli receiving the fastest-onset input may be activated. Increased ET cell activation at higher sniff frequencies may also increase the strength of interglomerular inhibition, because ET cells can also drive inhibition in neighboring glomeruli via short-axon interneurons (Aungst et al. 2003). The net result of these effects would be to sharpen overall M/T cell response patterns.

Thus, the changes in sniff frequency associated with active sensing have the potential to qualitatively alter how olfactory information is represented at the level of output from the OB. However, exactly how sniffing behavior changes odor representations at this level remains largely hypothetical. These predictions need to be tested using a combination of in vivo recordings (ideally, in awake animals), OB slice recordings using naturalistic input patterns mimicking different sampling behaviors, and modeling studies.

12.4.2. Active Sampling Effects Beyond the Olfactory Bulb (OB)

Even fewer data exist on the effect of sampling behavior on odor representations beyond the OB. As with the OB, most studies characterizing response properties of neurons in higher olfactory centers (primarily in the piriform cortex [PC]) have been performed in anesthetized animals breathing at low and regular rates. Nonetheless, these studies support the idea that temporally dynamic ORN inputs are integrated on a cycle-by-cycle basis during sniffing. For example, odorant-specific patterns of activation across small populations of neurons in the PC develop over the first 100–200 ms after inhalation (Nemitz and Goldberg 1983; Rennaker et al. 2007), a time-course similar to that of ORN input patterns (Figure 12.6B). PC neurons also show distinct temporal patterning relative to the respiratory cycle (Wilson 1998; Litaudon et al. 2003; Rennaker et al. 2007).

One effect of sampling behavior that may be important at the level of the cortex is laterality in odor sampling. Olfactory inputs remain unilateral at the level of the OB, but can cross sides at the level of the anterior olfactory nucleus via the anterior commissure. Interestingly, neurons in the PC show different degrees of laterality, with most neurons driven by ipsilateral inputs, but some driven by bilateral or strictly contralateral inputs (Wilson 1997). Thus, neurons in the cortex may be involved in comparing the strength of odorant input through the two nares and also in integrating inputs across the two nostrils. Both of these computations may be useful in tracking odors or possibly detecting gradients of odor intensity: for example, humans can follow an odor trail laid on a solid substrate more successfully when using two nostrils rather than one (Porter et al. 2007), and rats can be trained to detect differences in odor intensity and timing of arrival of odorant across the two nostrils (Rajan et al. 2006).

The PC also shows relatively rapid habituation to prolonged odorant stimulation: in the rat, PC pyramidal cells habituate within 30–40 s (Wilson 2000) (see also Chapter 14), and in humans, fMRI signals reflecting neural activity in the PC show habituation with a nearly identical time course (Sobel et al. 2000b). Habituation in the PC is odorant-specific, and so may facilitate the separation of background and foreground odorants (Kadohisa and Wilson 2006; Linster et al. 2007). This phenomenon is similar to the adaptive filtering that occurs at the level of sensory input to the OB during high-frequency sniffing (Verhagen et al. 2007), although in the cortex this adaptation occurs passively and during baseline respiration. It will be interesting to explore how cortical habituation functions during active sniffing and as sampling changes from moment to moment in the behaving animal.


While sampling behavior can shape ORN and postsynaptic responses via the “bottom-up” mechanisms described above, olfactory information processing is also subject to top-down modulation. In humans, attention to an olfactory task modulates activity in primary olfactory cortical areas (Zelano et al. 2005). In the OB, response properties of M/T cells in rats and mice performing odor-guided tasks can change rapidly depending on stimulus context (Karpov 1980; Kay and Laurent 1999), stimulus valence (Doucette and Restrepo 2008), whether an odorant is being actively or passively sampled (Fuentes et al. 2008), and other behavioral states related to active sensation, such as attention, arousal, and motivation (Karpov 1980; Tsuno et al. 2008). Since sniffing is tightly linked to such behavioral states, active bottom-up and top-down mechanisms are likely to be closely coordinated. Other forms of modulation less clearly associated with active sensing—such as during the sleep/wake cycle and after associative conditioning—are discussed elsewhere in the book (Chapters 14 and 15).

Active, top-down modulation of olfactory processing is likely to be mediated by multiple centrifugal systems, and probably occurs at all levels of the central olfactory pathway. Three neurotransmitter systems that have been strongly implicated in top-down modulation related to active sensing are acetylcholine, serotonin, and norepinephrine. Centrifugal cholinergic neurons originate from the horizontal limb of the diagonal band of Broca (HDB) and project to both the OB and the PC (Macrides et al. 1981; Luskin and Price 1982; Carson 1984). In other sensory modalities, the cholinergic system plays a role in attentional modulation of sensory processing, where it is generally thought to enhance processing by amplifying the signal-to-noise ratio of attended sensory responses relative to ongoing background activity (Sarter et al. 2005; Hasselmo and Giocomo 2006). Cholinergic modulation has been strongly implicated in shaping odor coding and perception in a manner consistent with this idea. For example, systemic administration of the cholinest-erase inhibitor, physostigmine, enhances rats’ ability to perform difficult odor discrimination tasks (Doty et al. 1999; Linster 2002; Mandairon 2006), and selective lesion of cholinergic neurons in the HDB increases rats’ generalization between similar odorants (Linster et al. 2001). At least some of these modulatory effects occur as early as the OB; local application of cholinesterase inhibitors sharpens odorant specificity of M/T cells and increases spontaneous discrimination of similar odorants, while nicotinic or muscarinic antagonists in the OB decrease behavioral discrimination (Mandairon et al. 2006; Chaudhury et al. 2009). These results predict that enhanced activation of the HDB inputs to the OB during active odor sampling or during perceptual learning increase the ability of the early olfactory system to form distinct representations for similar odors (Fletcher and Wilson 2002; Linster and Cleland 2002). At the circuit level, these effects are probably mediated—at least in part—by modulation of inhibition between mitral and granule cells (Elaagouby et al. 1991; Tsuno et al. 2008), although the strong cholinergic innervation of the glomerular layer suggests that these inputs also modulate signal transfer from sensory inputs to the mitral cell primary dendrite.

Serotonergic afferents to the OB originate in the dorsal and median raphe and most heavily innervate the glomerular layer, with less robust inputs to subglomerular layers (McLean and Shipley 1987; Gomez et al. 2005). Depletion of these afferents results in deficits in olfactory learning (McLean et al. 1993), although this effect may result from interactions with the noradrenergic inputs to the OB (Price et al. 1998), whose role in associative conditioning to odors is described elsewhere in this book (Chapter 14). The circuit mechanisms underlying serotonergic modulation remain unclear, although recent evidence suggests that a major effect of serotonin is to increase the excitability of GABAergic periglomerular interneurons (Hardy et al. 2005; Petzold et al. 2009). One effect of such modulation in vivo is to suppress odorant-evoked transmitter release from ORNs via GABAb-mediated presynaptic inhibition (Aroniadou-Anderjaska et al. 1999; McGann et al. 2005; Petzold et al. 2009). Thus, in addition to other potential roles, serotonergic afferents from the raphe can regulate the gain of sensory input to OB glomeruli. Interestingly, serotonergic neurons in the raphe have an important link to active sensing in the somatosensory system: neurons in the caudal raphe target premotor neurons in the facial nucleus and activate a central pattern generator that drives whisking during somatosensory exploration (Hattox et al. 2003; Cramer et al. 2007). Whisking and sniffing are each rhythmic, dynamically controlled behaviors that are typically expressed together as part of the same behavioral sequence and reflect a state of active investigation of the environment (Welker 1964; Komisaruk 1970). One intriguing possibility, then, is that activation of serotonergic neurons in the raphe drives active whisking and simultaneously modulates the processing of olfactory inputs to the OB.

The OB also receives strong innervation from noradrenergic inputs originating in the locus coeruleus, with fibers primarily targeting subglomerular layers (Shipley et al. 1985; McLean et al. 1989). This system—like the serotonergic and cholinergic systems—has been implicated in modulation of sensory processing as a function of arousal or attention. In the awake animal, locus coeruleus neurons are strongly activated by novel stimuli, and are thought to be important in driving exploratory behavior and in optimizing the coding and processing of sensory information (Aston-Jones and Bloom 1981; Sara et al. 1994, 1995; Hurley et al. 2004; Aston-Jones and Cohen 2005). Noradrenergic modulation in the OB affects odor-discrimination behaviors in rats in a manner similar to that of cholinergic modulation, enhancing the discrimination between similar odorants (Doucette et al. 2007; Mandairon et al. 2008). The projection patterns of noradrenergic inputs suggest that they modulate inhibition between mitral and granule cells, although the circuit mechanisms by which this modulation occurs and the contribution by different adrenergic receptor subtypes appears complex (Mandairon et al. 2008). Noradrenergic inputs from locus coeruleus also probably modulate the responses of neurons in the PC (Bouret and Sara 2002).

Finally, there are strong centrifugal projections from the PC and other higher-order olfactory centers—including the anterior olfactory nucleus, the entorhinal cortex, the amygdala, and the ventral hippocampus (Shipley and Adamek 1984; van Groen and Wyss 1990; McLean and Shipley 1992). Centrifugal afferents from the PC and anterior olfactory nucleus are presumed to be glutamatergic; those from the PC target the granule cell layer (Shipley and Adamek 1984), while those from the anterior olfactory nucleus target multiple OB layers (Brunjes et al. 2005). The role of any of these inputs in olfactory processing remains unclear, although the feedback from the PC to the OB has been hypothesized to mediate rapid, online modulation of OB output during odor-guided behavior. One interesting model predicts that odorants are identified with increasing precision with each successive cycle of feedback between the OB and the cortex, with each cycle driven by a sniff (Ambros-Ingerson et al. 1990). Centrifugal cortical inputs may also be important in shaping the temporal response properties of M/T cells during sniffing, either by providing a signal phase-locked to the sniff cycle that affects M/T cell spike timing (Margrie and Schaefer 2003; Kay 2005), or by triggering a switch from phasic to tonic firing modes when the animal switches from low- to high-frequency sniffing (Bhalla and Bower 1997; Kay and Sherman 2006).


A hallmark of active sensing in other sensory systems is their close association with the motor pathways controlling stimulus sampling. For example, in the visual and auditory systems, a saccade to actively sample a region of visual space increases the responsiveness of tectal and cortical neurons with receptive fields in the same region (Goldberg and Wurtz 1972; Winkowski and Knudsen 2006); this spatial attentional modulation is controlled by neurons in the gaze control centers of the brain, which send reafferent signals to sensory areas (Moore et al. 2003; Winkowski and Knudsen 2006). Likewise, in the rodent somatosensory system, responses of neurons in the barrel cortex (the primary cortical area for sensory input from the whiskers) are rapidly suppressed during active whisking (Ferezou et al. 2006; Hentschke et al. 2006), and monosynaptic connections exist between the primary sensory and motor cortices corresponding to the same whisker, providing a substrate for tightly controlled sensorimotor integration (Ferezou et al. 2007).

These examples from other sensory systems lead to the prediction that signals reflecting the motor drive to sniffing might shape sensory processing at early stages of the olfactory pathway. The strongest evidence in support of this idea comes from the fact that neurons in the OB show modulation in firing rate in phase with respiration, even in the absence of odorant (Adrian 1942; Walsh 1956; Macrides and Chorover 1972; Chaput and Holley 1979). Interpretation of these data is confounded by the likelihood that ORN inputs themselves are activated by respiration, as described above. However, several studies have found that respiration alone—when decoupled from nasal airflow in tracheotomized animals—can shape temporal response patterns in OB mitral cells, although inhalation-driven inputs appear to be a stronger determinant of response timing (Ravel et al. 1987; Ravel and Pager 1990; Sobel and Tank 1993). This issue remains controversial; one possibility is that centrifugal inputs reflecting the sniff cycle are activated only during active sniffing and not during passive respiration.

Nonetheless, temporal coupling between the dynamics of neural activity in the olfactory pathway and rhythmic odor sampling is one of the most robust features of the olfactory system (Adrian 1942; Macrides and Chorover 1972; Macrides 1975). This coupling probably plays an important role in mediating odor-guided behavior. For example, sniffing transiently synchronizes with the theta rhythm in the hippocampus during investigative sniffing (Macrides et al. 1982), and the magnitude of this coupling is correlated with performance on a two-odor discrimination task (Figure 12.7A) (Kay 2005). One explanation for this relationship is that sniff timing is adjusted to synchronize with hippocampal theta during active odor sensing (Macrides 1975; Macrides et al. 1982), rather than the theta rhythm being driven by sniff-related reafferent signals (Kay 2005).

FIGURE 12.7. Sensorimotor integration underlying sniffing behavior.


Sensorimotor integration underlying sniffing behavior. (A): Coherence between field potential recordings from the olfactory bulb and dorsal hippocampus during performance of a two-odor discrimination task. Lighter colors indicate higher coherence. There (more...)

It is also clear that the olfactory sensory inputs can strongly influence the motor systems controlling sniffing. First, as described above, olfactory stimuli can modulate sniffing behavior extremely rapidly—within approximately 200 ms after beginning an inhalation (Figure 12.7B) and in as little as 50–100 ms after sensory input arrives at the OB (Johnson et al. 2003; Wesson et al. 2008a). In fact, the spontaneous modulation of sniffing behavior in response to a novel odorant is faster than the conditioned response to a rewarded odorant (Wesson et al. 2008a). Analysis of the timing of individual sniffs relative to odorant presentation in rats performing odor-guided tasks indicates that animals can (and do) modulate their sniffing behavior on a cycle-by-cycle basis (Kepecs et al. 2007; Wesson et al. 2008a, 2009); this is an impressive feat, as cycle-by-cycle control of sniffing in the frequency range of 4–10 Hz suggests a sensorimotor control loop requiring well under 200 ms.

The neural pathway underlying this sensorimotor loop is unclear. In humans, suggests the speed of this response that it may be mediated by a subcortical pathway, at least in humans (Johnson et al. 2003). An important component of this pathway may be the cerebellum, which is activated during sniffing, may receive olfactory input from the PC, and is involved in optimizing motor output for sensory acquisition in other modalities (Sobel et al. 1998; Johnson et al. 2003; Mainland and Sobel 2006). The hippocampus has also been proposed to play a role in controlling sniffing behavior in response to olfactory inputs (Vanderwolf, 1992, 2001); this hypothesis arises from findings that gamma-frequency (30–80 Hz) activity in the dentate gyrus occurs during active sniffing, but not in response to other sensory inputs (Vanderwolf 2001), and that theta-frequency activity (2–10 Hz) synchronizes with sniffing during active odor sampling (Macrides 1975; Macrides et al. 1982). The pathway from the hippocampus to the motor centers controlling sniffing has not been elucidated. Finally, it is still possible that cortical centers play an important role in olfactory sensorimotor integration. Interestingly, electrical stimulation of the insular cortex and infralimbic cortex in anesthetized rats alters respiration; stimulation of the infralimbic cortex, in particular, elicits increases in respiration frequency that are remarkably similar to exploratory sniffing (Figure 12.7B) (Aleksandrov et al. 2007). This cortical pathway may be relatively short: there are direct connections between the OB and the anterior insular cortex and, possibly, the infralimbic cortex (Shipley and Adamek 1984); both cortices, in turn, send projections to the parabra-chial nucleus, which participates in respiratory rhythm generation (Moga et al. 1990).

Finally, there is evidence from work in humans that information about the motor control of sniffing can significantly influence odor perception. First, in human subjects in which odorant is “presented” via the bloodstream by intravenous injection, sniffing appears to “gate” perception of an odor (Mainland and Sobel 2006). Second, the amount of effort expended in a sniff affects perceived odor intensity. Increases or decreases in flow rate caused by manipulating airflow resistance during constant sniffing lead to changes in perceived intensity in the direction predicted by the effect of flowrate on ORN responses (Hahn et al. 1994); however, the same changes in flow rate caused by voluntary changes in sniff magnitude (i.e., inhalation pressure) generally do not lead to perceived intensity changes (Teghtsoonian et al. 1978; Teghtsoonian and Teghtsoonian 1984; Youngentob et al. 1986; Hornung et al. 1997). These results suggest that motor information about sniffing is rapidly integrated with incoming sensory information, and that the motor component is an essential part of the construction of an odor percept (Mainland and Sobel 2006).


As in other sensory systems, the sampling of olfactory stimuli is tightly controlled by the animal, with important consequences for information coding, processing, and perception. Indeed, considering olfaction as a system in which stimulus sampling, behavioral state, motor system function, and information processing strategies are closely coordinated is fundamental to understanding olfaction in the behaving animal. This chapter touched on how active sensing is important and integrated at each of these levels. For a more detailed review of the relationship between odor sampling and nervous system function at a particular level, the reader is referred to several excellent reviews (Schoenfeld and Cleland 2005, 2006; Buonviso et al. 2006; Mainland and Sobel 2006; Scott 2006; Wachowiak and Shipley 2006).


I would like to thank the past and present members of the Wachowiak laboratory, in particular D. Wesson, J. Verhagen, and R. Carey, for contributing to the viewpoints expressed here and for performing the critical experiments described from our laboratory. I would also like to thank H. Eichenbaum, M. Shipley, D. Katz, A. Fontanini, A. Yamaguchi, and K. Zhao for valuable discussions on topics presented here. The laboratory has been supported by grants from the National Institutes of Health (NIDCD) and from Boston University.


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