The apparatus for tactile stimulation is described and discussed in detail in Pilz et al. (2004); changes are described in the following. Tactile stimuli were airpuffs of 100 Pascal, measured at the center of the cage; the air pressure before the air valve solenoid was 2 bar = 29 psi. The airpuffs were delivered through a PVC tube centered on the side of the test cage, 7.5 cm from the center of the cage. The end of the round tube was compressed to an inner ellipsoid diameter of 1*.*2 × 0*.*25 cm. The tube was directed toward a cage with the same size as in experiment 1, with the single difference that it was elevated above the measuring platform by 3.5 cm by means of four stilts. The tube nozzle was at the same height, but directed to the middle of the cage; thus the air was directed slightly away from the measuring platform, which minimized the risk that tactile artifacts could be falsely measured as startle responses.

The airpuff characteristics were measured using a 1-inch (2.54-cm) microphone (Model 4145; Bruel and Kjaer, Naerum, Denmark). The airpuffs had a duration of 20 ms plus rise and decay times each lasting 8 ms and a pressure of 100 Pa. To reduce noise generated by the air valve solenoid, the air passed through a "silencer." The silencer was a glass cylinder (diameter 5.3 cm, height 8.2 cm, volume 168 cubic cm) sealed with a rubber stopper. The rubber stopper had two holes, one for air inlet and the other for air outlet. The cylinder contained sound absorbing rubber foam (thickness 4.3 cm, on the side opposite the rubber stopper). To mask the sound of the airpuff itself, which had mainly low frequency components (Pilz et al., 2004), we performed all testing of the tactile startle response with background noise containing frequencies between 250 Hz and 20 kHz, with maximum intensity at 2 kHz. The noise was produced by a digital signal processing controlled system (Elf-Board with Siggen software; Medav, Uttenreuth, Germany), amplified and emitted by a second loudspeaker (Craaft HT 1640; Solton Music, Pocking, Germany) inside the soundabsorbing chamber. At a background noise level of 93 dB SPL RMS, the acoustic artifact of the tactile stimuli was completely masked.

Acoustic (14 kHz, 105 dB SPL, see Experiment 1) and tactile stimuli for all experiments had an intertrial interval of 15 s.

#### *Procedure*

As in Experiment 1, mice were adapted for 5 min daily to the startle apparatus. During each daily session the background noise was constant: 45 dB for the acoustic startle measures and 93 dB for the tactile startle measures. One-hundred stimuli were then given.

The four groups were:

Group acoustic: 9 days acoustic stimulation, then 1 day tactile stimulation.

Group acoustic-control: 9 days 45 dB background noise without stimulation, then 1 day with tactile stimulation.

Group tactile: 9 days tactile stimuli, then 1 day with acoustic stimuli.

Group tactile-control: 9 days 93 dB background noise without stimulation, then 1 day with acoustic stimuli.

#### *Statistical analysis*

Statistics were the same as in Experiment 1, with the following exceptions: since Mauchly's test was not significant, the uncorrected *F*-tests of the repeated measures ANOVA are reported for LTH (day and gender were statistically tested by two-factor ANOVAs).

#### **RESULTS**

The mice displayed strong LTH to acoustic stimuli on 9 days (mean day 9 = 29.6% of day 1; **Figure 3A**). The startle response to tactile stimulus on test day 10 was even slightly higher in the acoustic (i.e., LTH) group, compared to the acoustic-control group. There was no statistical difference between pretreatment groups. A two-factor ANOVA revealed a trend for a gender effect [*F(*1*,* 18*)* = 3*.*81, *p* = 0*.*067], but no interaction [*F(*1*,* 18*)* = 1*.*53, *p* = 0*.*23]. Most importantly, there was no significant effect of prestimulation on the test day [*F(*1*,* 18*) <* 1]. Pairwise comparisons showed that tactile startle on day T10 (acoustic and acoustic control groups, **Figure 3A**) was not different from day T1 (tactile group, **Figure 3B**; uncorrected *t*-tests, *t* ≤ 1*.*1, *p* ≥ 0*.*27), but differed at least partly from day T9 [acoustic control: *t(*20*)* = 1*.*9, *p* = 0*.*073; acoustically stimulated group: *t(*22*)* = 2*.*8, *p* = 0*.*010].

There was only a weak LTH to tactile stimuli [mean day 9 = 83.8% of day 1; **Figure 3B**; *F(*8*,* 80*)* = 2*.*33, *p* = 0*.*026; gender: *F <* 1; interaction gender × day: *F(*8*,* 80*)* = 1*.*18, *p* = 0*.*32]. Mean responses on test day 10 to acoustic stimuli were even slightly higher in the tactile (LTH) group compared to the tactilecontrol group. The two-factor ANOVA yielded no effect of gender [gender, interaction gender × prestimulation: *F(*1*,*18*) <* 1*.*8, *p >* 0*.*20], and no effect of prestimulation [*F(*1*,* 18*)* = 2*.*32, *p* = 0*.*15]. Pairwise comparisons showed that acoustic startle on day A10

**FIGURE 3 | (A)** Mice were startled with tactile stimuli on day 10 (T10) after 9 days (A1–9) of either 100 acoustic startle stimuli daily (group "acoustic"), or 9 days without acoustic stimulation ("control"). **(B)** Mice were startled on day 10 (A10) with acoustic startle stimuli, after 9 days (T1–9) with either 100 daily tactile startle stimuli ("tactile") or without stimulation ("control"). The amplitude decrease during the first 9 days was significant in both stimulated groups (*p <* 0*.*05). There was no significant difference between the previously stimulated and the unstimulated groups on day 10 (*p >* 0*.*1. Bars: s.e.m.; *n* = 44).

(tactile and tactile control groups, **Figure 3B**) was not different from day A1 (acoustic group, **Figure 3A**; uncorrected *t*-tests, *t* = 1*.*59, *p* = 0*.*13), but differed from day A9 (*t* = 2*.*51, *p* = 0*.*020).

#### **DISCUSSION**

#### *No cross-habituation of LTH*

There was clearly no cross-habituation if LTH was elicited by one stimulus modality, and then startle was tested by another stimulus modality. For acoustic LTH, i.e., 9 days of daily acoustic stimulation, this was very convincing since there was a large LTH to this modality of about 70% response decrease. If cross-habituation had occurred, we would expect a lower startle response to tactile stimuli after the acoustic LTH. However, the tactile response was the same in mice with acoustic LTH as in mice without acoustic training.

Tactile LTH training of 9 days produced a response decrease of only 16%. It is therefore not particularly convincing that no cross-habituation occurred. Nevertheless, the mice tested after this procedure did not startle less when subjected to acoustic stimuli than mice without tactile training.

Our results partially confirmed those of Jordan and Leaton (1982). They found LTH to acoustic stimuli to be modality specific in rats. There daily acoustic stimuli elicited more LTH in controls than in rats with a lesion of the MRF. This difference between lesioned and control rats was not extended to tactile stimulation. Since the MRF-lesion could have changed LTH specifically to acoustic stimuli, our results extend their finding not only to mice, but also to unlesioned controls.

While the decrease to tactile stimuli here was only 16%, in Plappert and Pilz (2005) it was 34% under comparable parameters. Borszcz et al. (1989) showed long-term sensitization counteracting LTH. Stimulus intensity for tactile stimuli was slightly higher than in the study of Plappert and Pilz (2005). This may have produced higher sensitization, and therefore smaller LTH. Absolute amplitudes elicited by acoustic and tactile stimuli were roughly the same (if averaged over all days; **Figure 3**). Hence, if our tactile stimuli induced sensitization, this should be specific for this modality, since it cannot be due to higher startle responses elicited by this stimulus. (Indeed, on day 1 tactile startle was lower than acoustic startle). Because in rats LTH to tactile stimuli is also relatively small compared to LTH to tonal stimuli (Jordan and Leaton, 1982), this might be characteristic for this modality.

However, another type of sensitization could have influenced the results. During acoustic stimulation days, the background noise SPL was 45 dB, while on days with tactile stimulation it was 93 dB. The 93 dB background noise was necessary to completely mask the acoustic artifacts produced by the airpuffs used for tactile stimulation, and it might have sensitized the mice. For this reason each experimental group had a control group with precisely the same background noise experience on the same days. E.g. the group which was subjected to acoustic stimuli (and thus 45 dB background noise) on days 1–9 (**Figure 3A**) had a control group of mice subjected to the same noise on days 1–9. When on day 10 the two groups were subjected to tactile stimuli for the first time, they also heard the higher background noise SPL for the first time, which might have influenced the results. However, the tactile startle amplitudes on this day were not different from those of the mice with tactile stimulation on day 1 (**Figure 3B**, T1), but higher than that of the same mice on day 9 (**Figure 3**, T9). Furthermore, the results were identical for the other direction of cross-habituation. Thus, since the quantitative and statistical results are as expected if cross-habituation is absent, and since the amplitudes of tactile and acoustic startle from the beginning were different, and furthermore, since the LTH to acoustic and tactile startle were different, we find it unlikely that these exact results are due to background noise changes. We cannot rule out influences of the background noise differences on these results; however, we believe the results indicate absence of cross-modality in LTH.

Testing another change of stimulus characteristics, Jordan and Poore (1998) found no specificity for stimulus frequency. LTH remained constant if stimulus frequency was changed by one or two octaves. The lack of generalization was restricted to LTH of startle, but could be measured in other paradigms. We feel that Jordan and Poore correctly argue that response latency of startle is much shorter than in the other paradigms. Typically latency of startle in rodents can be as short as 7 ms (Plappert and Pilz, 2013), electromyographically even below 6 ms (Caeser et al., 1989). This is probably too short for frequency specific processing. Therefore, although stimulus generalization, one of the nine (Thompson and Spencer, 1966) respectively 10 (Rankin et al., 2009) key characteristics of habituation, is absent inside the auditory stimulus modality, it is demonstrable between stimulus modalities.

Although LTH was different on training days 1–9 to acoustic and tactile stimuli, in both cases the startle elicited afterwards by the different modality was never lower in the LTH-trained mice than in the controls. Thus, we conclude that LTH in mice is stimulus modality specific. Furthermore, because this confirms the conclusions found in literature on rats, we believe that this specificity applies to rodents in general.

#### *Assumed neuronal action of LTH on startle pathway*

As already pointed out above, the neuronal pathway of acoustic startle consists of primary and secondary auditory neurons projecting onto giant neurons in the PnC, which are the sensorimotor interface and project themselves onto motoneurons (Plappert and Pilz, 2013). Tactile input from the face (i.e., relayed by the sensory trigeminal nerve) also comes from secondary sensory neurons to the giant neurons (Li and Yeomans, 1999; Scott et al., 1999; Simons-Weidenmaier et al., 2006). Whether and how haptic input from the body is relayed to the giant neurons is unclear [discussed in Pilz et al. (2004)]. Current discussions assume that they are also connected to giant neurons (e.g. Simons-Weidenmaier et al., 2006).

STH of startle is modality specific (similar to LTH shown here). Startle decrease elicited by acoustic stimuli is not transferred to the tactile modality; neither is the startle decrease elicited by tactile stimuli transferred to the acoustic modality (Pilz et al., 2004; Vogel and Wagner, 2005; Ponce et al., 2011). Since the sensory inputs to the common PnC interface are distinct for auditory and haptic information, and since several publications propose that the pathway from PnC neurons onwards is identical for both sensory modalities (Li and Yeomans, 1999; Scott et al., 1999; Simons-Weidenmaier et al., 2006), STH must be situated in the respective sensory branches. Indeed, it has been shown that this process probably takes place in the synapse of secondary sensory neurons on giant PnC neurons (Simons-Weidenmaier et al., 2006).

Here we demonstrated that LTH is also stimulus modality specific. With the same logic as for STH, LTH also should act on the respective sensory branches of the startle pathway. We cannot exclude unknown additional motor branches for both sensory modalities studied, as found for vestibular startle (Li et al., 2001). However, since the current knowledge regarding startle circuitry does not (yet) include this possibility for acoustic and tactile startle, we are confident that LTH acts prior to the motor part of the pathway of this response system. In addition, after our findings in Experiment 1, this is a second indication for a sensory location of neuronal LTH action.

This neural implementation would restrict LTH generalization. As pointed out above, startle latency is probably too short for stimulus specificity inside one modality. However, by acting differentially on sensory branches, some specificity of LTH would be preserved, which would prevent unnecessary inhibition of modality-different stimulation.

# **GENERAL DISCUSSION**

#### **STARTLE LTH DEPENDS ON STIMULUS TYPE USED**

LTH differed for different types of stimuli. In both experiments there was a strong LTH to 14 kHz tonal startle stimuli. LTH was significant but much weaker regarding tactile stimuli and almost non-existent to noise stimuli in our pretest. As already discussed in Experiment 1, the tonal stimuli were exactly the same each time they were presented, and thus might be best suited to elicit LTH. Noise stimuli are not constant in course of amplitude or frequency, which might interfere with their ability to elicit LTH. Since mice move in the test cages, the airpuffs hit different parts of the body. Pilz et al. (2004) show that this movement did not influence STH in their study. Nevertheless, haptic stimulation of different parts of the skin may also prove to be an inconsistent stimulus with respect to LTH. However, Jordan and Poore (1998) found evidence that refutes this explanation, i.e., they found LTH to explicitly non-constant stimuli. In their experiments, LTH was independent of stimulus frequency during testing compared to training. Whatever the correct explanation, tonal stimuli, such as 14 kHz, which is in the best hearing range of mice (Ehret, 1983), seem to be most useful for LTH training.

#### **STARTLE LTH IS A NON-ASSOCIATIVE LEARNING PARADIGM**

The strongest suggestion that startle LTH is non-associative can be derived from experiment 1, showing that LTH is constant after context change, thereby confirming original literature (Marlin and Miller, 1981; Jordan et al., 2000). Since Jordan et al. (2000) also show that for other response systems LTH is associative, the non-associative nature of LTH shown here is restricted to the startle response system. If this process is non-associative, it must depend solely on stimulus characteristics. This obviously is the case, since LTH depends on the stimulus modality used (Experiment 2). If LTH was elicited by acoustic or tactile stimuli, and then stimulus modality was changed (to tactile or acoustic, respectively), there was no transfer of LTH from one to the other modality. Thus, taken together, all data suggest that startle LTH in mice is non-associative, as discussed previously for rats (Marlin and Miller, 1981; Leaton and Supple, 1986; Jordan and Poore, 1998; Jordan et al., 2000).

#### **PROPOSED NEURONAL ACTION OF LTH ON STARTLE PATHWAY**

Two outcomes suggest the same location of neural action of LTH on the startle pathway. In Experiment 1, LTH was shown to possibly have a strong diminishing effect not only on startle amplitude, but also on STH of startle (in the one strain exhibiting reliable STH); to date, STH has only been found in the sensory branches of the startle pathway (Simons-Weidenmaier et al., 2006). In the second experiment, LTH proved to be sensory modality specific. This also indicates that the modality specific neuronal input branches of the startle pathway are the location of LTH action.

#### **SUMMARY**

A context change did not disrupt startle LTH; neither was there a transfer of LTH from one stimulus modality to the other (tactile to acoustic or vice versa). So, similar to previous data from rats, our results indicate that LTH in mice is a non-associative stimulus modality specific learning paradigm. Results from both experiments together with data from the literature suggest that the neuronal action of LTH is a chronic inhibition aiming at the modality specific sensory input branches of the startle pathway. Moreover, we found the best LTH to tonal startle stimuli compared to noise pulses or tactile airpuffs.

# **AUTHORS CONTRIBUTIONS**

Peter K. D. Pilz planned the experiments, tested data statistically and wrote the manuscript. Stephan W. Arnold and Anja T. Rischawy conducted the experiments, including optimization of the contexts and tactile stimulation, and compiled part of the descriptive statistics as well. Claudia F. Plappert planned experiments and contributed to the discussion and the manuscript. There was no conflict of interests for any author.

#### **ACKNOWLEDGMENTS**

This work was supported by the Deutsche Forschungsgemeinschaft (Grant PI450/2-1 to Peter Pilz). We thank Donna Blagg for correcting our English.

# **REFERENCES**

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 23 August 2013; accepted: 16 December 2013; published online: 09 January 2014.*

*Citation: Pilz PKD, Arnold SW, Rischawy AT and Plappert CF (2014) Longtermhabituation of the startle response in mice is stimulus modality, but not context specific. Front. Integr. Neurosci. 7:103. doi: 10.3389/fnint.2013.00103*

*This article was submitted to the journal Frontiers in Integrative Neuroscience.*

*Copyright © 2014 Pilz, Arnold, Rischawy and Plappert. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Saliency mapping in the optic tectum and its relationship to habituation

# *Arkadeb Dutta andYoram Gutfreund\**

*Rappaport Family Institute for Research in the Medical Sciences, Ruth and Bruce Rappaport Faculty of Medicine, Technion – Israel Institute of Technology, Haifa, Israel*

#### *Edited by:*

*Susanne Schmid, University of Western Ontario, Canada*

#### *Reviewed by:*

*Elisabetta Ladavas, University of Bologna, Italy Manuel S. Malmierca, University of Salamanca, Spain Hermann Wagner, Rheinisch-Westfaelische Technische Hochschule Aachen University, Germany*

#### *\*Correspondence:*

*Yoram Gutfreund, Rappaport Family Institute for Research in the Medical Sciences, Ruth and Bruce Rappaport Faculty of Medicine, Technion – Israel Institute of Technology, Efron 1 Street, Haifa, Israel e-mail<EMAIL_ADDRESS>
Habituation of the orienting response has long served as a model system for studying fundamental psychological phenomena such as learning, attention, decisions, and surprise. In this article, we review an emerging hypothesis that the evolutionary role of the superior colliculus (SC) in mammals or its homolog in birds, the optic tectum (OT), is to select the most salient target and send this information to the appropriate brain regions to control the body and brain orienting responses. Recent studies have begun to reveal mechanisms of how saliency is computed in the OT/SC, demonstrating a striking similarity between mammals and birds. The saliency of a target can be determined by how different it is from the surrounding objects, by how different it is from its history (that is habituation) and by how relevant it is for the task at hand. Here, we will first review evidence, mostly from primates and barn owls, that all three types of saliency computations are linked in the OT/SC. We will then focus more on neural adaptation in the OT and its possible link to temporal saliency and habituation.

**Keywords: habituation, saliency map, optic tectum, superior colliculus, spatial attention, barn owl, orienting response**

# **INTRODUCTION: HABITUATION AND SALIENCY MAPPING**

An animal can respond to only one stimulus source at a time, even though its sensory systems are bombarded by information arriving from multiple sources. This unequal relationship between response and stimulus numbers explains the evolvement of brain mechanisms to identify and select the most appropriate stimulus for behavioral manifestation. The process, commonly known as selective attention, saliency mapping, or sensory gating (Itti and Koch, 2001; Krauzlis et al., 2013), is a fundamental characteristic of human and animal behavior. Failing to choose the appropriate stimulus severely disrupts normal behavior, as can be seen in the various attention deficits disorders (Gitelman, 2003; Booth et al., 2005).

How can an animal decide if a stimulus is behaviorally relevant? What are the sensory cues and to what extent are they general across species? A dog among cats, a brown object among orange objects, or a pure tone succeeding a long period of broadband noise are all conspicuous stimuli. Such stimuli are perceived as salient and consequently behaviorally privileged as they give rise to attentional capture (Posner, 1980; Tiitinen et al., 1994), enhanced autonomic responses (Weisbard and Graham, 1971; Bala and Takahashi, 2000; Zimmer, 2006), and enhanced neural responses (Naatanen, 1995; Gottlieb et al., 1998; Ulanovsky et al., 2003; Reches and Gutfreund, 2008). Thus, a general rule is that a stimulus that is out-of-ordinary (unexpected) is salient and has a higher probability to induce responses. In nature, an out-of-ordinary event may come as a warning to prey to evade danger or an opportunity for food in the case of predators. Therefore, rapid detection of such unexpected events is fundamental for survival. A stimulus can be out-of-ordinary in space, as in the first two examples above, or out-of-ordinary in time, as in the third example above. The process of detecting stimuli that are out-of-ordinary in space is called stimulus competition, spatial saliency mapping, or camouflage breaking (Itti and Koch, 2000; Knudsen, 2007). The process of detecting stimuli that are out-ofordinary in time is called temporal saliency mapping, deviance detection, or change detection (Nelken and Ulanovsky, 2007; Gutfreund, 2012).

Imagine a dog resting in the center of a noisy living room, seemingly ignoring the loud noise of kids, and music playing but immediately raising its head to the faint sound of the door knob turning. Note that the event of the door knob turning is not louder than the background noise. This example demonstrates a major aspect of saliency mapping; that the saliency of a stimulus is not determined by its physical strength but by its relationship with the environment, or, in other words, by its context. The door knob turning is a salient event mainly because it breaks the regularity of the background and is therefore unexpected.

In the case of temporal saliency, as in the door knob example, an initial phase of learning and memorizing the regularity of the background must take place so that an incoming stimulus can be categorized as either background or deviant. Responses to stimuli matching the background are suppressed while responses to deviants from the background are not. **Figure 1** illustrates a conceptual model for temporal saliency mapping. Interestingly, a similar concept was used to model habituation of the orienting reflex (Sokolov, 1963; Siddle, 1991). Thus, there seems to be a considerable overlap between habituation and saliency mapping.

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Habituation is considered the most basic form of learning, existing in all animals (Sokolov, 1963; Thompson and Spencer, 1966; Thompson et al., 1972; Barry, 2009; Thompson, 2009). Although habituation has been described and studied in detail for decades, most of the previous studies on habituation were separated from studies on saliency mapping and attention (Dukewich, 2009). In this review, we aim at emphasizing the close relationship prevailing between habituation and saliency mapping and the likelihood that there is a considerable overlap between the neural mechanisms of the two processes.

In addition to habituation, associative learning may also be involved in saliency mapping (Anderson, 2013). It is obvious that the successful selection of stimuli cannot be based on external factors alone. Selection of a stimulus must be guided by a combination of external factors, such as stimulus intensity, stimulus history, spatial context, cross-modal interactions, etc., and internal factors, such as cognitive biases, behavioral tasks, reward history, motivations, etc. (Fecteau and Munoz, 2006). For example, when searching for a friend in crowd, a memorized knowledge such as shirt color or hair type biases the perceived saliency of stimuli. Such information about the relevance of the stimulus for the task at hand is called "top-down" information as opposed to information about the sensory aspects of the stimulus, which is called "bottom-up" information. Somewhere in the brain, top-down information must be integrated with bottom-up information to determine the saliency of the event (Fecteau and Munoz, 2006). Where in the brain this integration happens and what are the mechanisms involved are open questions. It is possible that top-down information can modulate the internal representation of the background created by the bottom-up stream (illustrated in **Figure 1**). This hypothesis is attractive as it implies that cognitive factors may influence stimulus selection by recruiting the same brain structures that are involved in temporal saliency and habituation. We will hypothesize here and show evidence that brain areas or networks that select stimuli for behavior are likely to show neural correlates of habituation. The brain structure that we will focus on is the optic tectum (OT), also known as the superior colliculus (SC) in

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mammalian species. We will first review evidence supporting a role of the OT/SC in both spatial and temporal saliency mapping and then focus more on temporal saliency mapping and its relation to habituation (for more comprehensive reviews on spatial saliency mapping see Itti and Koch, 2001; Mysore and Knudsen, 2011b).

# **SALIENCY MAPPING IN THE OT/SC**

All vertebrates possess a specialized brain system responsible for orienting the body toward stimuli of interest. This system, known as the gaze control system (also referred to as the oculomotor system), involves a number of midbrain and forebrain areas (see scheme of basic avian circuitry in **Figure 2**). The OT/SC is a midbrain structure serving as a critical hub in the gaze control system (Boehnke and Munoz, 2008). This structure is arguably one of the most phylogenetically conservative structures in the brain (Gaither and Stein, 1979; Shimizu and Karten, 1993; Luksch, 2003) and is considered homolog in all vertebrate species (Butler and Hodos, 2005; Maximino, 2008).

Both mammalian SC and avian OT contain a mapped representation of space. This map is multimodal with neurons responding to auditory, visual, and somatosensory signals (Knudsen, 1982; King and Palmer, 1985; Stein and Meredith, 1993; Nodal et al., 2005). The sensory map of space is superimposed over a motor

**FIGURE 2 | Illustration showing a selected part of the barn owl's auditory pathway with the OT as a hub.** Gray boxes indicate structures suggested to be related to gaze and attention control. The red outline indicates structures where SSA was reported; the blue outline indicates areas where long-term adaptation was reported (habituation-like responses); the yellow outline indicates structures where multisensory (visual/auditory) neurons were reported. Double-headed arrows indicate reciprocal connections. Abbreviations of names of structures appear within each box. The abbreviations of names of the equivalent mammalian structures are in parentheses: E, entopallium; AGF, arcopallium gaze fields; FEF, frontal eye fields; nRt, nucleus rotundus; OTs, superficial layers of the OT (layers 1–10); OTi/s, intermediate and deep layers of the OT (layers 11–15); IC, inferior colliculus; Ipc, nucleus isthmi pars parvocellularis; Imc, nucleus isthmi pars magnocellularis; nOv, nucleus ovoidalis; MGN, medial geniculate body; A1, auditory cortex 1.

map controlling gaze shifts (McHaffie and Stein, 1982; Sparks, 1986; Herrero et al., 1998; King, 2004). Hence, the OT/SC is known primarily as a gaze control center serving to translate sensory signals to eye and head movements. However, a careful examination of the anatomy with a detailed electrophysiological characterization of the neural responses suggests that the OT/SC is more than a simple motor system. In addition to its control of pre-motor areas, information from the OT/SC is also transmitted to a wide range of areas in the cortex and basal ganglia via thalamic nuclei (Takada et al., 1985; Robinson and Petersen, 1992; Bischof and Watanabe, 1997; Reches and Gutfreund, 2009; Krauzlis et al., 2013), indicating involvement in non-motor functions as well. The emerging hypothesis is that the evolutionary role of the OT/SC is to sort stimuli based on saliency, select the most salient stimulus, and send this information to the appropriate brain regions to direct orienting movements, attention and autonomic responses (reviewed in Boehnke et al., 2011; Knudsen, 2011; Krauzlis et al., 2013). In the following sections, we will review some evidence supporting this hypothesis.

Many studies suggest that neurons in the OT/SC are not simply sensitive to the intensity of the stimulus inside their receptive field (RF) but rather to the saliency of the position represented by their RF at a given instant (Horwitz and Newsome, 1999; McPeek and Keller, 2002; Pluta et al., 2011). This suggests that OT/SC plays a role in target selection (a target is a stimulus worth responding to). For example, in a study to show the role of SC in target selection for saccade execution, Horwitz and Newsome (1999) trained monkeys to choose one of two visual targets based upon the perceived direction of moving dots in a random-dot motion display around the center of fixation. One of the targets was presented inside the RF of the recorded SC neuron and the other well outside the field. Neural recordings revealed that some neurons in the SC responded to the target in their RF only if the direction of the dots was pointing to the direction of their RF location, thus, selecting the target well before the execution of the saccades. Moreover, in well trained monkeys, some neurons responded even without visual stimuli inside their RF, provided that the motion of the dots at the center is contingent with their RF (Horwitz and Newsome, 2001). One interpretation of these results is that the neurons represent the saliency of the stimulus inside their RFs. The learned association between the motion of the dots at the center and the rewarded target enhances the saliency of the position of the RFs pointed out by the motion and, on the other hand, reduces the saliency of the positions not pointed out by the central display.

Further compelling evidence for the causal role of the SC in stimulus selection was provided by inactivation experiments. When a restricted part of the SC is inactivated, monkeys tend to miss the behaviorally relevant targets if they are positioned in the region represented by the inactivated area. Instead, the monkeys choose distracters in areas whose representation is unaffected by the inactivation (McPeek and Keller, 2004). Lovejoy and Krauzlis (2009) expanded this finding to show that focal inactivation of the SC also disrupts the monkey's ability to select stimuli covertly in the inactivated regions (Krauzlis et al., 2013). Interestingly, in both studies, when the stimulus in the inactivated region was presented alone, the monkeys were able to respond

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and covertly attend to the stimulus (McPeek and Keller, 2004; Krauzlis et al., 2013), thus, focal inactivation of the SC does not create a focal sensory neglect. Only in situations when multiple stimuli are presented are the inactivation effects apparent. Thus, SC inactivation specifically disrupts the ability to select the behaviorally relevant stimulus among other non-relevant stimuli.

Analogous results pointing out the importance of the OT/SC in stimulus selection have been reported in other species as well (Ingle, 1975; Woods and Frost, 1977; Marin et al., 2007; Lai et al., 2011; Pluta et al., 2011). Of particular interest here is a series of recent studies in barn owls addressing mechanisms of competitive stimulus selection in the OT (Mysore et al., 2010, 2011; Mysore and Knudsen, 2011b). A stimulus presented alone commonly induces responses in the OT/SC that are larger compared to when it is presented together with other stimuli. This phenomenon, which has been attributed to lateral inhibition, may be interpreted as promoting competition between stimuli (McPeek and Keller, 2002; Knudsen, 2011). Interestingly, Mysore et al. (2010, 2011) have shown some novel properties of this lateral competitive interaction. First, the strength of the suppression does not depend on the distance between the stimuli, which shows that it is a global phenomenon covering the whole visual field (Mysore et al., 2010). Second, many of the neurons are suppressed by competing stimuli only if the strength of the stimulus inside their RF is weaker from the other stimuli but are not suppressed if the RF contains the strongest stimulus in the scene (Mysore et al., 2011). Thus, it seems that the OT tends to code the strength of a stimulus relative to its competitors, a feature that may promote a winner-take-all computation (Mysore and Knudsen, 2011a). Moreover, the stimulus representation in the OT is modulated by top-down connections from forebrain areas (Winkowski and Knudsen, 2006, 2007), thus, possibly allowing for the selection of stimuli not only via their relative strengths but also incorporating cognitive factors such as learned associations, internal states, etc. The mechanism of lateral competitive interactions in the barn owl OT were shown to be mediated by a GABAergic midbrain nucleus (Imc, nucleus isthmi pars magnocellularis) that receives topographic connections from the OT directly and indirectly through a nearby cholinergic nucleus (Mysore and Knudsen, 2013). Thus, the computation of stimulus selection is, at least partly, achieved within the tectal circuitry.

The results described above, as well as numerous other studies, support the above-mentioned hypothesis about the role of the OT/SC as a center of stimulus selection, and shows that this role is preserved across species and across sensory modalities. Two questions, however, remain to be answered. The first question is whether all these elaborate mechanisms of stimulus selection in the OT/SC evolved just for the purpose of gaze and attention. An event that is perceived by an animal as salient typically induces a wide range of behavioral responses (Sokolov, 1963; Barry, 2009; Bradley, 2009). Although a shift in gaze is the most apparent response, a series of autonomic reflexes occur along with it that prepares the body for possible action (Sokolov, 1963; Dean et al., 1989). These include galvanic responses (Bradley, 2009), changes in heart rate (Bradley, 2009), changes in brain wave activity (Naatanen, 1995), and pupillary dilation (Oleson et al., 1972; Stelmack and Siddle, 1982; Bala and Takahashi, 2000). This wide repertoire of responses, which is preserved remarkably across species, has been coined by Ivan Pavlov as the "orienting response" (Sokolov, 1963). Orienting responses can include gaze shifts (overt orienting), but do not have to (covert orienting). In addition, orienting movements can be executed by locomotory muscles of limbs instead of eyes. Therefore, if the hypothesis regarding the role of OT/SC as a center of saliency mapping holds true, then it is predicted that manipulation of activity in the OT/SC will affect orienting responses in general beside eye movements. Evidence supporting this prediction can be found in the literature: microstimulation in the OT/SC can induce pupil dilation responses (PDRs; Netser et al., 2010; Wang et al., 2012); responses in the EMG activity of neck muscles independent of eye or head movements (Corneil et al., 2007); ocular accommodation (Sawa and Ohtsuka, 1994); freezing responses (Dean et al., 1989); increased heart rate (Keay et al., 1988); arousal in cortical EEG (Redgrave and Dean, 1985); and suppression of eye blink reflex (Basso, 1996; Gnadt et al., 1997). Thus, it is evident that apart from its clear role in controlling eye and head movements, the OT/SC is also widely involved in executing a variety of orienting behaviors.

The second question is whether the OT/SC is also involved in temporal saliency detection. As discussed in the introduction, a major part of saliency mapping is history-dependent, that is, a stimulus that is different from its past, is likely to be perceived as salient. However, all of the papers cited above as evidence for the involvement of the OT/SC in saliency mapping, emphasize situations of spatial saliency where the saliency of the target is determined by the difference from the surround. If the OT/SC is indeed a center of saliency mapping in the brain, then it is likely to be involved in the habituation process. Hence, we predict that manipulating tectal activity will disrupt habituation of orienting responses. An indication that indeed this is the case has been provided by Netser et al. (2010). The pupils of barn owls, no different from other species, dilate slightly in response to sudden sounds (**Figure 3A**; Bala and Takahashi, 2000; Spitzer et al., 2003). Netser et al. (2010) measured the pupil diameter of barn owls exposed to a long sequence of identical auditory stimuli. As a result of the long period of repetition, the PDRs became habituated (**Figure 3B**). However, it was shown that if a brief low-level electrical microstimulation is applied to the OT at the site in the map corresponding to the location of the stimulus, the habituated behavioral responses are re-induced (**Figure 3C**). This could not be attributed to general desensitization by the microstimulation as it was significantly less induced by stimulations at other locations in the map (**Figure 3D**). This indicates that the release from habituation was due to manipulations at the local tectal circuitry and therefore supports its involvement in habituation.

Given its suggested role in habituation of the orienting response, we expect to find neural correlates of habituation in the OT/SC. In other words, we expect that neural representation will be strongly modulated by the history of events in a way that suppresses the representation of background stimuli and relatively enhances the representation of odd stimuli. In the following sections, we will discuss the requirements for neural correlates

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**FIGURE 3 | Pupil dilation responses (PDRs) and microstimulation in the OT. (A)** The left image shows a zoomed image of a barn owl's right eye. The image is a video frame taken from an infra-red sensitive video sequence showing the infra-red light reflected from the retina. The arrow points to the edge of the pecten, a retinal landmark absorbing light. The image on the left shows a similar video frame after processing and fitting the pupil edge with an ellipse. The red circle designates the fitted pupil edge. The cross designates the center of the circle and the blue line shows the horizontal edge of the pecten. The diameter of the circle and the average horizontal position of the pecten edge were used to measure pupil dilations and eye movements. **(B)** The graph shows PDRs to three consecutive auditory stimuli given every 12 s. The solid line designates the response to the first stimulus, the dashed line the response to the second stimulus and the dotted line the response to the third stimulus. The base line of all response profiles was reduced to zero level. The horizontal bar indicates the duration of the acoustic stimulus and the vertical line the onset of stimulation. **(C)** Results of coupling acoustic and tectal electrical stimulation. The inset shows the time course of

the stimulation protocol. Auditory stimuli were repeated every 12 s (gray bars). Occasionally, with a probability of 20%, a brief low-level microstimulation was injected shortly before the auditory stimulus (blue bars). The gray curve shows the population average PDR to the repeated auditory stimulus. The red curve shows the population average PDR to the auditory stimuli that followed a microstimulation. Note the release from habituation. **(D)** The inset shows the time course of the experiment. The gray vertical bars indicate auditory stimuli and the blue vertical bars electrical stimulations. Auditory stimuli were repeated every 10 s alternating between two positions: one matching the electrical stimulation site in the tectal map and the other not matching the electrical stimulation site. The gray curves show the population PDRs to the two auditory stimuli. The blue curve shows the population PDR to the non-matched auditory stimuli that were coupled with the microstimulation, and the red curve the population PDR to the matched auditory stimuli coupled with the microstimulation. Microstimulation at the site corresponding with the acoustic stimulus induces a stronger release from habituation. Modified from Netser et al. (2010).

of habituation and then review the literature suggesting such correlates in the OT of the barn owl.

#### **NEURAL CORRELATES OF HABITUATION**

Neural correlates of a particular behavior can be recorded at different levels, ranging from a single neuron to scalp-recorded EEG and fMRI. For example, in scalp-recorded potentials, an auditory component was found that shares some similarities with the phenomenon of habituation. This component is known as mismatch negativity (MMN; Naatanen, 1995). MMN is measured by presenting a sequence of auditory stimuli in which rare sounds or deviants are embedded occasionally. This type of stimulation paradigm is called oddball stimulation (Naatanen, 1995; Ulanovsky et al., 2004; see **Figure 4B**). In such conditions, the evoked potential to the deviant is usually stronger than the evoked potential to the standard. This phenomenon is believed to reflect a habituation-like process whereby the standard stimulus is memorized, allowing the detection and enhancement of responses to deviation from this memory (Nelken, 2004). However, scalp-recorded potentials reflect a global, indirect signal and is therefore limited in its ability to reveal fine details of the neural circuits that compute and represent habituation. For this purpose, identifying neural correlates of habituation at the single-neuron level is preferable.

Habituation is defined as a decline in behavioral response to a sustained or repeatable stimulus that is not fatigue (Thompson, 2009). At the single-neuron level, a reminiscent decline in the neural response to sustained or repeatable stimulus, called adaptation, is a ubiquitous property of sensory neurons (Westerman and Smith, 1984; Gutfreund and Knudsen, 2006). However, it is important to emphasize that habituation is not a mere reduction in

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**FIGURE 4 | Stimulus protocols used to differentiate stimulus-specific adaptation (SSA) from non-specific adaptation. (A)** The priming protocol. In the priming protocol a tuning curve is generated by presenting sequences of stimuli varying randomly over a range of stimuli (f1–fn). This is repeated in two conditions: in one, the stimuli for generating the tuning curve are spaced with a fixed time interval of no stimulation (the control); in the other, in between stimuli, a standard stimulus (fs) is presented several times. In non-specific adaptation (left graph), the tuning curve in the primed condition (gray curve) is expected to be attenuated evenly relative to the control. In SSA, the adaptation is expected to occur stronger near the fs stimulus, resulting in a dip in the tuning curve. **(B)** The oddball protocol. In the oddball protocol, two different stimuli are chosen (f1 and f2) so that both are within the response range of the neuron, giving rise to about the same response. One stimulus is chosen as the standard, being presented repeatedly for a long period. Within this sequence, the second stimulus, called the deviant, is

behavioral response with time. As described above, habituation is a learning process in which a standard or rather unchanging background scene is learned in order to allow an animal to respond selectively to behaviorally relevant stimuli (**Figure 1**; Barry, 2009). Thus, not all types of neural adaptations can serve as neural correlates of habituation. Two major types of adaptation have been described in the literature. One is the non-specific adaptation that depends on the history of activation of the neuron more than on specific features of the stimulus (Calford and Semple, 1995; Brosch and Schreiner, 1997; McAlpine et al., 2000; Ingham and McAlpine, 2004; Furukawa et al., 2005; Wehr and Zador, 2005; Gutfreund and Knudsen, 2006). The other is stimulusspecific adaptation (SSA), an adaptation to a specific stimulus that does not generalize to other stimuli, regardless of how active the neuron was (Ulanovsky et al., 2003, 2004; Antunes et al., 2010). SSA is of particular interest here since, similar to habituation, it depends on the history of the environment rather than on the activity of the neuron. Indeed, the SSA phenomenon has been called single-neuron habituation (Nelken and Ulanovsky, 2007). To distinguish SSA from non-specific adaptation, a variety of stimulation paradigms have been used (**Figure 4**). In all of them a standard stimulus is presented several times in a row to induce adaptation. In SSA the responses to stimuli that are physically close to the standard are expected to be more reduced compared embedded with a relatively low probability. Later, a second block is presented in which the roles are changed: the stimulus that was standard is now deviant, and vice versa. At the end of the experiments, the average responses to the standards are compared with the corresponding responses to the deviants. In non-specific adaptation, we expect no significance differences (left histogram). In SSA, we expect the responses to the deviant to appear consistently above the responses to standards (right histogram). **(C)** The constant order protocol. Two stimuli are presented in a long sequence, starting with several repetitions of f1 then switching to several repetitions of f2, and so on, alternating between the two stimuli. In non-specific adaptation, we expect that the response to the first stimulus in each sub-sequence will be adapted and therefore not larger than the response to the same stimulus when it is last in the sequence. In SSA, we expect a stronger response every time the sequence is switched from one stimulus to the other (right histogram in **C**). Modified from Gutfreund (2012).

to stimuli that are different, whereas in non-specific adaptation the responses to all stimuli are expected to be reduced. In the priming protocol (**Figure 4A**), if SSA exists we expect a dip in the tuning curve near the standard stimulus, whereas if the adaptation generalizes (non-specific) we expect the whole tuning curve to attenuate. In the oddball paradigm (**Figure 4B**), the responses to standard (common) stimuli are compared with responses to the same stimuli when presented rarely (as deviants). SSA implies that the neuron's response to the common stimulus and not to the deviant stimulus is reduced, and, as a result, the response to the deviant stimulus is stronger compared to the same stimulus when presented commonly. In the constant order paradigm (**Figure 4C**), we look at the points of shift between one stimulus type to the other. If SSA exists we expect that the responses to the first stimuli after the shift will be less affected by previous stimuli (because previous stimuli are different), and therefore will be larger compared to subsequent stimuli where previous stimuli are the same. The stimulation protocols illustrated in **Figure 4** may provide an experimental framework for seeking neural correlates of habituation.

 SSA was studied in greater

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detail in the auditory system of cats, rats, and monkeys (Ulanovsky et al., 2003; Perez-Gonzalez et al., 2005; Fishman and Steinschneider, 2012). Neurons sensitive to deviations were found at different levels of the auditory pathway, namely, in the inferior colliculus (IC), the auditory thalamus, and the auditory cortex (Ulanovsky et al., 2004; Anderson et al., 2009; Malmierca et al., 2009; Antunes et al., 2010; Farley et al., 2010; Ayala and Malmierca, 2013; Ayala et al., 2013). SSA is measured in anesthetized as well as awake animals (Richardson et al., 2013) and is therefore pre-attentive. Detailed characterization of SSA in the auditory system revealed that it is highly sensitive to minute deviations from the standard frequency. In the auditory cortex of cats, neurons have been found to respond significantly stronger to stimuli that are deviant from the standard by a frequency difference as small as 0.1% (Ulanovsky et al., 2003). Moreover, it was shown that this adaptation has several time scales ranging from sub-seconds to 10s of seconds (Ulanovsky et al., 2004). This implies that the expected model of the background can be updated on a fast time scale to encompass rapid changes in the stimulus environment, but at the same time, a longer history of stimulation is allowed to affect subsequent responses.

In summary, recent studies on auditory SSA in the cortex, the thalamus and the IC suggest that at the single-neuron level, habituation-like responses are widespread. The origin and mechanisms of this phenomenon are yet to be discovered. And, not less important, the questions of if and how this phenomenon at the single-neuron level is related to habituation at the behavioral level must be answered. Three major problems discussed below hinder the attempts to relate SSA to habituation.

#### **THE MULTIPLE FEATURE PROBLEM**

Stimulus-specific adaptation in the auditory cortex and the IC has been studied mostly using pure tone stimuli where the deviants differed from the standards in terms of sound frequency. Other sensory features such as stimulus intensity, stimulus location, or stimulus length were either not studied or gave rise to poor SSA (Ulanovsky et al., 2003; Farley et al., 2010). An exception is a very recent study in which it was shown that SSA exists in the auditory cortex of rats between stimuli of similar frequencies but differ in their temporal noise structure (Nelken et al., 2013). In nature, a stimulus can differ from what has been in the past along multiple features, i.e., frequency, amplitude, duration, location, etc., or combinations of features. A neuron that is sensitive to changes in the frequency of the stimulus but not to changes in other features is a limited "change detector" that cannot explain the general sensitivity to deviant stimuli observed behaviorally. It is therefore necessary to identify types of SSA that encompass multiple sensory features. Moreover, the phenomenon of habituation is amodal, independent of sensory modality (Thompson and Spencer, 1966). Neural correlates of habituation should therefore not be limited to auditory neurons.

#### **THE MEMORY TRACE PROBLEM**

The memory trace of adaptation is the time it takes from the last stimulus until its effect on the response to the next stimulus wears out. In the laboratory, this is measured by presenting sequences

of stimuli with various inter-stimulus time intervals (ISIs). The minimal ISI in which no adaptation occurs is the duration of the memory trace. SSA in the auditory cortex, the thalamus or the IC has been reported at ISIs as long as 2 s (Ulanovsky et al., 2003; Ayala and Malmierca, 2013) or has only been studied at ISIs < 2 s (von der Behrens et al., 2009; Antunes et al., 2010). Therefore, we can conclude that information about the standard is stored in memory for about 2 s. This poses a major problem because many examples of behavioral habituation have been reported with ISIs of 10s of seconds to minutes, even for short duration stimuli (Thompson and Spencer, 1966; Weinberger et al., 1975; Valentinuzzi and Ferrari, 1997; Bala and Takahashi, 2000; Zimmer, 2006; Dong and Clayton, 2009; Glanzman, 2009). It is therefore necessary to identify a form of SSA that maintains a longer memory trace.

#### **CHANGE DETECTION VERSUS PROBABILITY DETECTION**

Most studies of SSA in the auditory pathways were conducted using standard oddball paradigms whereby a deviant frequency is embedded with a certain probability in the sequence of standard frequency (**Figure 4B**). In such a probabilistic stimulus, SSA implies that the response of the neuron is modulated by the probability of the deviant; the smaller the probability, the larger the response. But sensitivity to probability is not necessarily equal to habituation. Habituation requires learning an expectation rule set by the standard and pointing out any deviations from this rule, while sensitivity to probability simply requires counting the number of stimuli over a period of time and responding accordingly. Rare stimuli are not always salient. For example, a stimulus can appear in a sequence of multiple different stimuli, each being rarely presented, but none is salient compared to the others (Taaseh et al., 2011). The standard oddball paradigm cannot distinguish between the two possibilities. Despite recent attempts to resolve this issue for SSA in the auditory cortex, it still remains an open question (Farley et al., 2010; Taaseh et al., 2011).

In summary, we have provided a short review of the phenomenon of SSA in the auditory system and argued that SSA is the closest reported phenomenon at the single-neuron level to act as a neural correlate of habituation. However, by itself, the wellstudied type of SSA in the main auditory pathway of mammals is not sufficient to account for habituation. We therefore now return to the OT/SC. If, as suggested above, the OT/SC is involved in saliency mapping and habituation, we expect to find a new type of SSA in this structure that is sensitive to multiple stimulus features and modalities and has a longer memory trace.

#### **NEURAL ADAPTATION IN THE OT/SC**

### **NEURAL ADAPTATION IN THE OT/SC IS STIMULUS-SPECIFIC**

Neural adaptation to visual stimuli is robust in the OT, particularly in the deep layers (Woods and Frost, 1977; Boehnke et al., 2011). Habituation-like responses, i.e., SSA, were reported in the OT of pigeons. Some neurons were shown to lose responses completely to repeated visual stimuli, but changes in the type of stimuli resulted in a return of the response to the initial level (Woods and Frost, 1977). Adaptation to visual stimuli in the SC was also reported in the monkey (Fecteau and Munoz, 2005). Recently, adaptation to auditory stimuli was characterized systematically in the barn

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owl's OT (Reches and Gutfreund, 2009; Netser et al., 2011). Oddball and constant order protocols (see **Figures 4B,C**, respectively) were used. It was found that most neurons in the OT responded more strongly to a low probability stimulus than to a high probability stimulus (in the oddball protocol) or more strongly to the first stimulus, which is different from its past compared to the last stimulus (in the constant order protocol). For example, neurons in the OT of the owl are tuned to the interaural time difference (ITD) of the sound, which is the major localization cue for the horizontal position. By presenting sounds through ear phones, it is relatively easy to manipulate the ITD of the sound delivered to the animal (Moiseff and Konishi, 1981). **Figure 5A** shows the average results from constant order experiments in which the stimulus sequence alternated every 10 stimuli between two ITD values (see **Figure 4C**). It can be seen that every time a switch occurred between one ITD to the other, the response increased and adapted again until the next switch occurred. By comparing the response to a stimulus when it is first in its sequence with the response to the same stimulus when it is last in its sequence, it is possible to quantify the SSA. Remarkably, significant changes in neural responses between the first and last stimuli were observed even when the two stimuli differed by an ITD difference as small as 20 μs (Reches and Gutfreund, 2008). Thus, the neural response to an auditory stimulus depends not only on the value of the ITD of

**FIGURE 5 | (A)** The histogram shows the average response of a population of neurons in the OT to a long sequence of stimuli (200 stimuli). The duration of each stimulus was 200 ms and the ISI was 1 s. The sequence alternated between two ITD values; every 10 repetitions the ITD of the sound was switched to the other value (protocol is illustrated in the inset at the bottom of the figure). The graph on the right shows the post-stimulus time histogram (PSTH) of one stimulus when it was first in its sub-sequence (deviant, light red curve) compared to the PSTH for the same stimulus when it was last in its sub-sequence (standard, black curve). **(B)** Same as **A**, but in this case, the two stimuli were narrowband stimuli (width of 1 kHz) differing in their central frequency. **(C)** Same as **A** and **B**, but in this case, the two stimuli differed in intensity. One stimulus (dark blue bars) was softer compared to the other (light red bars). The average response to deviant stimulus was greater in all

cases. Modified with permission from Reches and Gutfreund (2008). **(D,E)** Two examples of tectal recordings showing bimodal enhancement. The raster plots show results of unit responses to a sequence of five repetitions of a stimulus with an ISI of 800 ms. The time course of stimulation is designated by the horizontal black bars. Stimuli were either five visual stimuli, five auditory stimuli, or five congruent bimodal stimuli (visual and auditory stimuli appearing together from the same location). The solid vertical line designates the onset of the first stimulus in the sequence. The dashed vertical lines designate the onset of subsequent stimuli. In both examples **(D,E)**, stronger responses to bimodal stimuli can be seen in the response to the first stimulus in the sequence compared to the unimodal responses. This bimodal enhancement is missing in the responses to the subsequent stimuli. Modified from Zahar et al. (2009).

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the stimulus but also on how this value differs from previous ITD values.

Sensitivity to changes in the input stream was not limited to changes in the ITD of the sound. Testing for changes in the frequency, intensity, or interaural level difference (ILD) of the sound all gave rise to the same basic result. The neurons readily responded more strongly to changes in the input stream along all stimulus dimensions (Reches and Gutfreund, 2008). In all cases (ITD, ILD, intensity, and frequency), SSA was evident and developed rapidly after one trial (**Figures 5A–C**). This similarity between the different features is especially striking, taking into account that the four features are represented and computed in markedly different ways but all still exhibit the same SSA. ITD and ILD, the two primary binaural localization cues, are processed in parallel in two separate and independent brainstem pathways (Takahashi et al., 1984; Takahashi and Konishi, 1988; Adolphs, 1993; Albeck and Konishi, 1995); frequency separation is maintained in the ascending auditory pathways from the cochlea up to the level of the lateral shell of the IC where information is combined across frequency-specific channels (Euston and Takahashi,2002). Soundlevel information is presumably manifested in the firing rates of neurons in the ascending pathways. Therefore, the fact that all four independent acoustic features showed a qualitatively similar pattern of adaptation suggests that SSA is an important property in the neural representation of the auditory scene in the OT. This property possibly underlies the owl's ability to attend and orient abruptly to novel events. For comparison, similar tests were performed in the IC, the main source of auditory information to the OT. Significant SSA in the IC was found only to the frequency of the sound but not to other stimulus features such as ITD, ILD, and intensity (Reches and Gutfreund, 2008). Thus, robust multifeature deviant detection develops in the OT and not earlier in the pathway.

The robustness of SSA in tectal neurons gives rise to an interesting ambiguity problem, i.e., the inability to discriminate between two conditions. For example, a soft sound usually produces weaker responses in the OT compared to a louder sound, however, the same soft sound can induce stronger neural responses than the loud sound when it is deviant in an environment characterized by loud sounds (Reches and Gutfreund, 2008). Thus, neural responses in the tectum seem unable to code the sensory identity of the incoming sound unambiguously. This, together with the finding that OT/SC neurons are mostly broadly tuned to sensory features such as frequency, amplitude modulations, orientation, direction, and modality (Mize and Murphy, 1976; Zahar et al., 2009) is consistent with the hypothesis that the OT represents the location of the stimulus and how salient it is. The exact identification of the stimulus is not a computational task of OT, presumably carried out in a different pathway.

#### **MULTISENSORY INTEGRATION IN THE OT/SC ENHANCES DEVIANCE DETECTION**

In the laboratory, saliency mapping is usually studied in unimodal settings, however, in nature it is primarily an amodal task. The saliency of an event is determined by a combination of modalities (Stein and Meredith, 1993; Pluta et al., 2011), therefore we expect multisensory integration to take place in the saliency mapping pathways. Indeed, multisensory integration is a hallmark of the OT/SC (Stein and Meredith, 1993). Visual, auditory, and somatosensory inputs converge onto the SC, resulting in multisensory neurons that integrate information between modalities (Meredith and Stein, 1986; Meredith et al., 1987). When a visual and auditory stimuli are presented from the same location and at the same time (congruent bimodal stimulation), many neurons enhance their responses dramatically compared with the responses to the visual or auditory stimulus alone. This phenomenon, known as multisensory enhancement, has been characterized in great detail in the SC of cats and monkeys (Wallace et al., 1996; Stanford et al., 2005). Recently, multisensory enhancement was studied in barn owls using paradigms that allow testing for adaptation as well. In a simple adaptation paradigm where the same stimulus was repeated several times (Zahar et al., 2009), it was found that multisensory enhancement of the first stimulus presented before adaptation was robust and comparable to what has been reported in mammals. However, subsequent stimuli presented after adaptation did not show clear multisensory enhancement (**Figures 5D,E**; Zahar et al., 2009). A similar result was shown using oddball stimuli; the multisensory enhancement was much stronger when the stimuli were deviant in the sequence compared to when the stimuli were common (Reches et al., 2010). Thus, multisensory enhancement in the OT is able to increase deviance detection. The mechanisms of this phenomenon are not clear. However, it supports the idea that multisensory integration is used by the OT to enhance saliency mapping (Pluta et al., 2011) by enhancing SSA in congruent bimodal settings (Reches et al., 2010).

#### **THE MEMORY TRACE OF ADAPTATION IN THE OT IS RELATIVELY LONG**

A common notion in the adaptation of neural responses, backed up by computational models of synaptic suppression (Tsodyks and Markram, 1997), is that the dynamics of neural adaptation complies with stimulus duration (Marom, 2009): a short duration stimulus is expected to induce short-lasting adaptation, and vice versa (Varela et al., 1997; Ulanovsky et al., 2004). This concept is in line with most studies of auditory SSA described above. In these studies, the ISIs varied between 300 ms and 2 s, stimulus durations were in the range of 100–500 ms, and the probability of the deviant was 10–15%. None of the papers cited above reported a memory trace longer than 2 s, which is within the time scale of the stimulus timing. However, as mentioned earlier, this relatively short memory trace constitutes a major problem for linking neural SSA with mechanisms of habituation. Behavioral habituation does not comply with the above-mentioned principle of comparable time scales. For example, in the barn owl, reflexive pupil dilation and eye movements to sequences of relatively short stimuli with ISIs of 10–13 s readily habituated and recovered when the stimulus was switched from one type to the another (Bala and Takahashi, 2000; Spitzer et al., 2003; Netser et al., 2011).

To close the time gap between behavioral habituation and SSA in the OT, it is important to examine the memory trace of the SSA. This was done in the OT of barn owls by presenting sequences of identical sounds and measuring unit responses as a function of the position of the stimulus in the sequence. Stimuli

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with ISIs of 10, 30, and 60 s were tested. Remarkably, at all ISIs tested, a single, short (300 ms), and weak (20 dB above the unit's threshold) stimulus was sufficient to induce a significant reduction in the neural response to the second stimulus in the sequence (Netser et al., 2011). Moreover, just like in habituation, the system not only memorized that there was a stimulus earlier but also what type of stimulus. For example, when presenting three short auditory stimuli with an ISI of 60 s, the first two being identical and the third different, the response to the second stimulus was reduced compared to the first, but the response to the odd third stimulus, 60 s later, was completely recovered (Netser et al., 2011). The finding that the memory trace of specific adaptation in the OT can reach time spans of over a minute suggests that this type of adaptation is a neural correlate of behavioral habituation, further supporting the link between tectal neural circuitry and habituation.

# **FINAL REMARKS**

Habituation is commonly known as a reduction in behavioral responses to repeated stimuli. However, the scope of habituation is broader. A seminal work by Sokolov (1963) conceptualizes habituation as a process of learning and memorizing the background for the selection of incoming stimuli that are odd from the background. In this review, we aimed at emphasizing this sometimes forgotten aspect of habituation: the ability to select incoming stimuli if they do not match the stored representation of the background. We reviewed here evidence that the SC in mammals and the OT in birds are involved in the stimulus selection process. In addition, we reviewed recent results suggesting that the activity of tectal neurons may be correlated with habituation of the orienting response. Thus, combined together, these results point to the tectal/colliculi circuitry as a promising model system for studying habituation mechanisms.

In recent years, the avian OT has emerged as a model for studying the neural mechanisms of saliency mapping in space (stimulus competition) and time (habituation; Marin et al.,2005,2007,2012; Wang et al., 2006; Reches and Gutfreund, 2008; Lai et al., 2011; Mysore and Knudsen, 2011b, 2013; Netser et al., 2011). The advantage is that the midbrain circuitry in birds is highly segregated and experimentally accessible (Knudsen, 2011). The overall findings are strikingly similar to findings in other species, including primates. This similarity stresses the importance of a comparative approach to gain an evolutionary perspective on basic elements of animal behavior such as habituation and attention. Here, we focused on recent studies in barn owls regarding SSA in the OT and its possible link to habituation. Yet, two important questions remain to be answered:

#### **WHAT ARE THE NEURAL MECHANISMS OF SSA IN THE OT?**

The neural mechanisms underlying SSA in the auditory pathways as well as in the OT are unknown. One common model to explain SSA is that different stimuli activate separate paths to the recorded neuron and that basic adaptation mechanisms (synaptic depression or intrinsic cellular mechanisms) act at levels where the activation is separated (Eytan et al., 2003). An intriguing observation in the barn owl's SSA is a complete lack of cross-stimulus adaptation at long ISIs, even though the frequency content of

the two stimuli overlapped substantially (Netser et al., 2011). It is therefore unlikely that this type of SSA is accounted for solely by basic response suppressions at lower, frequency-specific levels. To compute the deviancy of complex broadband sounds, a network is required that compares the neural responses to the current stimulus with previous responses based on an integration of information about frequency and amplitude modulations. Details of such a network are yet to be discovered.

#### **WHAT IS THE RELATIONSHIP BETWEEN SSA AND BEHAVIOR?**

The phenomenon of SSA has been studied mostly in anesthetized animals. It is yet to be shown what effects it has on behavior. As previously mentioned, not all types of SSA are linked necessarily to behavioral habituation and saliency mapping. Some may be related to scene analysis or optimal coding (reviewed in Winkler et al., 2009). One approach for studying the relationships between neural adaptation and behavior would be to record behavioral and neuronal responses simultaneously and examine the trial-by-trial correlations between SSA and behavioral habituation. Another approach would be to inactivate brain areas that contribute to SSA and examine the effects on behavioral habituation and on the animal's ability to respond to changes in the environment. Future experiments on these directions are likely to shed light on the neural basis of habituation.

#### **ACKNOWLEDGMENTS**

This work was supported by grants from the Israeli Science Foundation, a grant from the German-Israel Foundation (GIF), and a grant from the Binational Science Foundation (BSF).

#### **REFERENCES**

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 02 October 2013; paper pending published: 06 November 2013; accepted: 02 January 2014; published online: 16 January 2014.*

*Citation: Dutta A and Gutfreund Y (2014) Saliency mapping in the optic tectum and its relationship to habituation. Front. Integr. Neurosci. 8:1. doi: 10.3389/fnint.2014.00001 This article was submitted to the journal Frontiers in Integrative Neuroscience*

*Copyright © 2014 Dutta and Gutfreund. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

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# Adaptation in the auditory system: an overview

# *David Pérez-González1\* and Manuel S. Malmierca1,2 \**

*1 Auditory Neurophysiology Laboratory (Lab 1), Institute of Neuroscience of Castilla y León, University of Salamanca, Salamanca, Spain 2 Department of Cell Biology and Pathology, Faculty of Medicine, University of Salamanca, Salamanca, Spain*

#### *Edited by:*

*Susanne Schmid, University of Western Ontario, Canada*

*Reviewed by:*

*Pat Michie, University of Newcastle, Australia Bernhard H. Gaese, Goethe University Frankfurt, Germany*

#### *\*Correspondence:*

*David Pérez-González and Manuel S. Malmierca, Auditory Neurophysiology Laboratory (Lab 1), Institute of Neuroscience of Castilla y León, University of Salamanca, C/ Pintor Fernando Gallego 1, 37007 Salamanca, Spain e-mail<EMAIL_ADDRESS><EMAIL_ADDRESS>
The early stages of the auditory system need to preserve the timing information of sounds in order to extract the basic features of acoustic stimuli. At the same time, different processes of neuronal adaptation occur at several levels to further process the auditory information. For instance, auditory nerve fiber responses already experience adaptation of their firing rates, a type of response that can be found in many other auditory nuclei and may be useful for emphasizing the onset of the stimuli. However, it is at higher levels in the auditory hierarchy where more sophisticated types of neuronal processing take place. For example, stimulus-specific adaptation, where neurons show adaptation to frequent, repetitive stimuli, but maintain their responsiveness to stimuli with different physical characteristics, thus representing a distinct kind of processing that may play a role in change and deviance detection. In the auditory cortex, adaptation takes more elaborate forms, and contributes to the processing of complex sequences, auditory scene analysis and attention. Here we review the multiple types of adaptation that occur in the auditory system, which are part of the pool of resources that the neurons employ to process the auditory scene, and are critical to a proper understanding of the neuronal mechanisms that govern auditory perception.

**Keywords: auditory nerve, inferior colliculus, auditory cortex, stimulus-specific adaptation, mismatch negativity**

# **INTRODUCTION: THE AUDITORY SYSTEM NEEDS TO PRESERVE THE TIMING OF THE SIGNAL**

The challenging task that the auditory system faces is to process naturally occurring sounds, so that they can be identified, characterized, and localized, in order to be able to respond accordingly and in a timely manner. A complication lies in the nature of sound, which consists of rapid variations of the pressure in an elastic medium, usually air for most mammals. One of the basic features of the components of all sounds is their frequency (or how fast the sound waves change) and the auditory brain must be able to extract it very precisely. The range of frequencies that each animal is sensitive to varies greatly. Humans typically can hear sounds from 20 Hz to 20 kHz. Some animals have good low frequency hearing, similar to humans, like the guinea pig (Heffner et al., 1971), but other animals can hear much higher frequencies. For instance, mice can hear sounds over 80 kHz (Heffner and Masterton, 1980) and some bats up to 120 kHz (Koay et al., 1997). In order to process these very rapid variations of the signal, the auditory system requires fast and reliable responses from its elements. Timing information is also essential for the localization of sounds, since it requires a precise encoding of the time at which sounds arrive at each ear. The detection of the minimum change for sound localization in the horizontal plane in humans requires comparing the arrival time at both ears with a precision of a few microseconds (Hafter et al., 1979; Kollmeier et al., 2008).

The timing of action potentials, conveyed with the precision of microseconds, carries acoustic information in all higher vertebrates. For instance, responses of low-frequency auditory nerve fibers are locked to a particular phase of the stimulus waveform (Kiang et al., 1965; Johnson, 1980; Palmer and Russell, 1986), and thus carry a temporal code for sound frequency. The requirement of a precise and faithful transmission of timing information has given rise to the development of certain cellular specializations. The auditory nerve fibers that innervate the anterior ventral cochlear nucleus in mammals have large, specialized calyceal endings, also known as endbulbs, that surround the soma of the target neuron (for a review, see Ryugo and Parks, 2003). In other cells, the synchronization of their responses is enhanced thanks to the convergence of a few auditory nerve fibers through large endbulbs (Ryugo and Sento, 1991; Joris et al., 1994).

This faithful encoding of auditory information is maintained along the ascending auditory pathway up to the auditory cortex (AC), whose neurons are capable of maintaining millisecond precision in the encoding of auditory stimuli (Kayser et al., 2010). But, while the auditory system is so deeply dependent on timing, there are still many instances where adaptation processes take place. Adaptation, as we will consider in this paper, consists on a decrease of the response of a neuron or population or neurons during stimulation, and may manifest itself in several ways. For the sake of simplicity and descriptive purposes, here we differentiate adaptation from habituation, which is commonly used in reference to perceptual and behavioral phenomena, and is more closely related to learning processes. In this review, we will focus on the multiple forms that neuronal adaptation takes through the auditory system.

#### **ADAPTATION OF THE AUDITORY NERVE FIBERS**

Adaptation in the auditory system occurs as early as in the auditory nerve fibers. As has been classically described in other sensory neurons (Adrian, 1926; Adrian and Zotterman, 1926a,b), auditory nerve fibers (**Figure 1**) in all studied species show adaptation (e.g., Nomoto et al., 1964; Kiang et al., 1965; Feng et al., 1991). It takes the form of a higher instantaneous firing rate when a stimulus is switched on, slowing to a lower steady-state rate after a few tens of milliseconds (e.g., **Figure 1**; Sumner and Palmer, 2012). This particular type of adaptation is also known as spike-frequency adaptation, in which a neuron's response to a steady-state stimulus is not maintained at its initially high rate of spiking but instead declines over time to a lower, adapted rate (**Figure 1**). This is a common feature of many sensory neurons (Hille, 1992). This type of response is the origin of the classic "primary" response of auditory nerve fibers, a well-described example of adaptation in the peripheral auditory pathway (Westerman and Smith, 1984; Yates et al., 1985). It is interesting to note that the adaptation is stronger in high frequency fibers than in low frequency fibers (Sumner and Palmer, 2012), especially since low frequency fibers are the ones that show phase locking. This way, the timing information carried by phase locking fibers is preserved. One possible role for adaptation in the auditory system lies in determining the sensitivity of auditory neurons to the stimulus context. The rapid adaptation in auditory nerve fiber responses (Yates et al., 1985; Westerman and Smith, 1987), and the rapid recovery from adaptation (Yates et al., 1983), suggests that the time course of

adaptation in the peripheral nerve fibers might dominate the time course of adaptation in higher centers, unless it is somehow filtered out by neurons at subsequent stages. Indeed, adaptation in these early stages of the auditory pathway may have important implications in the processing of auditory cues at higher centers. In crickets, Givois and Pollack (2000) found that the receptors ipsilateral to the sound source became more adapted than the contralateral ones, since the sound arrives with higher intensity to the ipsilateral side. The different amounts of adaptation produced an imbalance in the interaural difference in response strength, increasing the difficulty of using the interaural level difference as a cue for sound localization. In that situation, they found that the neuronal response latency was more stable, and thus the interaural latency difference was a more reliable cue for sound source localization.

A phenomenon potentially related to adaptation in the auditory nerve is forward masking. It consists in the elevation in the threshold of a signal caused by the presence of a masker sound preceding it in time, and has been the subject of intense study over a number of decades (Harris and Dallos, 1979). Since the preceding, masking sounds caused an apparent reduction of the neuronal responses, adaptation in the auditory nerve has been proposed as a candidate for the neural site of forward masking (Smith, 1977, 1979). However, some studies suggest that forward

overplotted response (gray) from the number of fibers in parentheses, at different levels above their threshold, as indicated on the top of each panel. The mean values are plotted in black. Note how the initially high response rate decreases rapidly to a steady state, in an example of spike-frequency adaptation. Bin width is 0.5 ms. Reproduced from Sumner and Palmer (2012).

masking is better explained by temporal integration models, rather than adaptation (Oxenham, 2001), and so this issue is still a matter of debate.

# **ADAPTATION BECOMES MORE DIVERSE ALONG THE AUDITORY HIERARCHY**

Firing rate adaptation has been also found in other brainstem nuclei. For instance, Finlayson and Adam (1997) studied shortterm adaptation in the superior olivary complex, a group of auditory brainstem nuclei that are involved most notably in the extraction of binaural cues for sound source localization. They found that acoustic stimulation results in rapid and prolonged adaptation in monaurally driven excitation and inhibition of these neurons. For neurons where both the ipsilateral and contralateral inputs are equally affected by adaptation, the effect on localization accuracy is very small. On the other hand, they found that in some neurons the adaptation from ipsilateral and contralateral stimulation is unbalanced, which may affect the coding of localization cues. Finlayson and Adam (1997) conclude that this unbalanced adaptation should cause a poor localization performance by these neurons in noisy conditions.

As we examine higher auditory centers, we can find more complex types of adaptation. The inferior colliculus (IC), the mammalian midbrain auditory nucleus, has received quite considerable attention lately. The IC is a mandatory relay for almost all the ascending auditory information en route to the thalamus and cortex. It receives ascending inputs from most of the lower brainstem nuclei and descending inputs from the cortex (Malmierca and Hackett, 2010; Malmierca and Ryugo, 2011). Therefore, there is no doubt that the IC is strategically located and able to combine and process the information extracted by the previous auditory pathways, so it is not surprising to find more developed neuronal responses.

Processes of spike-frequency adaptation have been described in the IC (Ingham and McAlpine, 2004), in neurons sensitive to interaural phase disparities. In these neurons, it was possible to use a binaural stimulation paradigm that allowed separation of the adaptation of the binaural neurons from that happening at lower monaural levels, such as the auditory nerve fibers. This study revealed that these IC neurons had adaptation dynamics that were rather slow, compared with those calculated for the auditory nerve fibers (Yates et al., 1983, 1985; Westerman and Smith, 1987). The different time constants indicate that the adaptation found in the IC is different from that found in the auditory nerve, and moreover, it is not just inherited from the lower levels.

A different type of adaptation found in the IC is the adaptation of the population coding to stimulus statistics. Dean et al. (2005) studied this type of adaptation regarding the processing of sound level. Mammals can hear sounds extending over an immense range of sound levels with remarkable accuracy. How auditory neurons code sound level over such an extensive range is unclear, since firing rates of individual neurons increase with sound level over only a very limited portion of the full range of hearing. Using stimuli whose intensity changed in a probabilistic way, Dean et al. (2005) found that neural responses were rapidly adjusted by adaptation, in a manner that improved

the coding of the most probable sound levels by the neural population.

Neurons in the IC also show stimulus-specific adaptation (SSA, **Figure 2**). These neurons reduce their responses to a stimulus that is presented repeatedly, but when a novel sound is presented, the same neurons are able to overcome the adaptation and response quickly and vigorously (e.g., **Figures 2** and **3**; Pérez-González et al., 2005; Malmierca et al., 2009). An increase in response strength with the presentation of a stimulus change can be explained by a release from adaption, but particularly when measured in single neurons, it indicates that the underlying adaptation processes are stimulus- (or feature-) specific, thus enabling the system to differentiate stimuli not by their absolute dimensions but by their relative attributes across space and time (Moore, 2003). For these and other reasons, SSA has been proposed to play a role in the attention and the detection of auditory deviance, change and novel stimuli. While SSA was first described in the AC (Ulanovsky et al., 2003), the IC is the lowest nucleus where it is present (Lumani and Zhang, 2010; Zhao et al., 2011; Ayala and Malmierca, 2013; Ayala et al., 2013); it has also been found in the auditory thalamus (Anderson et al., 2009; Antunes et al., 2010; Bäuerle et al., 2011; Antunes and Malmierca, 2014; Duque et al., 2014). The different studies have noted that SSA is stronger in the non-lemniscal divisions of the subcortical auditory nuclei. For instance, it is more prominent in the rostral, dorsal and lateral subdivisions of the IC (Duque et al., 2012), and also in the medial division of the geniculate body (Antunes et al., 2010). On the other hand, in the lemniscal regions, like the central nucleus of the IC and the ventral nucleus of the geniculate body, fewer neurons show SSA, and it is weaker. It seems that SSA is generated *de novo* in each level, and it is not clear that SSA generated in one nucleus propagates to the other, either in a bottom-up or a top-bottom fashion (Antunes and Malmierca, 2011, 2014; Anderson and Malmierca, 2013).

While SSA in the IC was originally described from the neuronal responses using extracellular recordings in animals (**Figures 2** and **3**), including local field potentials (von der Behrens et al., 2009; Patel et al., 2012), a correlate has been found measuring ERP in the human auditory brainstem (**Figure 4**) using the frequencyfollowing response (Slabu et al., 2012), showing that the human IC is able to detect a novel acoustic event occurring among a series of repetitive ones. This is supported by the attenuated human brainstem response (see **Figures 4** and **5**) to a stimulus occurring with a low probability compared with that elicited by the same physical stimulus presented with much higher probability. Their findings suggest that the human auditory brainstem is able to encode acoustic regularities in a memory trace and to detect deviant events based on a comparison process between the current auditory input and the recent auditory history. These results are in agreement with previous studies using the frequency-following response, that showed that the human auditory brainstem encodes stimulus statistics over multiple time scales (Chandrasekaran et al., 2009; Skoe and Kraus, 2010a,b). Similar results have also been observed for cortical neurons (Ulanovsky et al., 2004) and human cortical-evoked potentials (Costa-Faidella et al., 2011). These and other studies have shown the presence of different types of adaptation and deviance-related activity over several time ranges of the

**FIGURE 2 | (A)** In the oddball paradigm, a low probability stimulus (f2, red, "deviant") is embedded in a train of high probability stimuli (f1, blue, "standard"). To compensate the responses to the different physical stimuli f1 and f2, a second sequence is presented where the probability of each stimulus is reversed. Examples of the responses of two neurons recorded using this paradigm is shown in **(B)**, an IC neuron showing SSA (adapting, left) and another not showing SSA (non-adapting, right). Here, f1 and f2 are pure tones of different frequencies, and the frequency difference is the same for both neurons. The top and middle panels in **(B)** show the dot raster in response to sequence 1 (top) and sequence 2 (middle), where the blue dots represent spikes in response to the standard and the red dots the response to the deviant. In the adapting neuron, the response to the standard stimulus decays after the first presentations, while the response to the deviant stimulus remains constant, as a typical example of stimulus-specific adaptation. The bottom panels show the PSTH for the responses to the standard and deviant stimuli, combining the spikes for both stimuli at the same probability. The value of the common SSA index (CSI) is shown for each neuron; CSI values close to one indicate strong SSA while values close to zero indicate weak SSA. Adapted from Duque et al. (2012).

auditory event-related potentials and strongly support the idea of a hierarchically organized system devoted to auditory deviance detection (Grimm et al., 2011; Grimm and Escera, 2012).

The mechanism for SSA is still under investigation, and several options have been put to the test. One of the possibilities under consideration is that SSA emerges from the intrinsic characteristics of the cell, such as the membrane properties. Duque et al. (2012) found that the strength of SSA in IC neurons is not constant within their receptive fields; instead it varies systematically in each neuron, being stronger in the high frequency region as well as near the threshold. If the origin of SSA were based in the intrinsic properties of the cell, its strength should be more uniform within the receptive field, so these results contradict this possibility. Another possible mechanism would be based on the effect of synaptic inhibition, but again, it seems to be unlikely. The pharmacological manipulation of GABAA receptors, in the IC (Pérez-González et al., 2012; Pérez-González and Malmierca, 2012) as well as in the auditory thalamus (Duque et al., 2014), has shown that, while not involved in the generation of SSA, the inhibitory inputs could modulate its strength, acting as a gain control mechanism, in some instances similar to the iceberg effect (**Figure 3**). Instead, a likely mechanism for SSA is one based on the differential adaptation of the inputs to the cell showing SSA (May and Tiitinen, 2010). Comparing multiple stimulus presentation paradigms, Taaseh et al. (2011) proposed that in the AC, SSA is mediated by "adaptation channels," that would span the receptive field of the neuron. In this model, SSA would emerge from the differential adaptation of the channels, as determined by the frequency of the stimuli and their separation. However, other complementary explanations may be needed to fully explain the formation of SSA.

#### **ADAPTATION IN THE AUDITORY CORTEX**

Because of its complex organization and connectivity, including the fact that it is receiving the information that has been extensively processed by all the previous nuclei in the pathway, it is not surprising to find the most numerous types of adaptation processes occur in the AC. Ter-Mikaelian et al. (2007) found that the responses to continuous stimuli adapted with faster kinetics in the primary AC than in the IC, indicating that different temporal filters operate in the different nuclei, which may influence the coding of information in each center. However, in some instances, adaptation processes are quite similar to the counterparts previously described in earlier nuclei. For instance, the SSA found in the AC is quite similar to that found in the IC (Malmierca et al., 2009; Duque et al., 2012) and auditory thalamus (Antunes et al., 2010; Duque et al., 2014), at least in its basic appearance [rat: (Lazar and Metherate, 2003; Szymanski et al., 2009; Farley et al., 2010; Taaseh et al., 2011); cat: (Ulanovsky et al., 2003, 2004)]. However, the range of parameters eliciting SSA seems to be unique for each center, probably reflecting the particular processing capabilities of the neurons. For instance, SSA is elicited by faster repetition rates in the IC (Malmierca et al., 2009) and the thalamus (Antunes et al., 2010) than in the cortex (e.g., Taaseh et al., 2011). On the other hand, the ability to produce SSA with slow repetition rates is not a characteristic exclusive to the cortex, since SSA has been demonstrated in the IC with similar or even longer interstimulus intervals (Zhao et al., 2011; Ayala and Malmierca, 2013). It is also likely that the cortical neurons are capable of processing more complex sequences than those in lower nuclei (e.g., **Figure 6**;Yaron et al., 2012), and indeed, some results suggest that the processing of sequences is hierarchically structured, with higher centers able to process more complex sequences (Althen et al., 2013; Escera and

whether the given stimulus was standard **(A)** or deviant **(B)**. In the baseline condition, the responses to the standard stimulus adapt rapidly after a few presentations **(A**, blue), while the responses to

Malmierca, 2014). However, related studies of subcortical structures are still scarce. Moreover, strong SSA has been reported in the primary AC, being the first lemniscal structure where it has been found, in contrast to the IC and thalamus, where SSA is more prominent in the non-lemniscal subdivisions. The different characteristics of SSA in cortical and subcortical nuclei invites caution when combining the studies performed in each of them. While they probably share some of the mechanisms proposed to create SSA, as explained earlier, each center may add particular conditions that may not extrapolate to the other. For instance, a study demonstrated an analog of SSA in cultured networks of cortical neurons. Eytan et al. (2003) used a paradigm of electrical stimulation similar to the oddball design, and found a depression in the responses to the standard and an increased response to the deviant. Furthermore, this selective enhancement of responses was abolished by blocking GABAergic inhibitory transmission using bicuculline. They proposed that the enhancement of the response to the deviant stimuli was caused by an adaptation of the inhibition, since both standard and deviant stimuli activated the inhibitory circuits. While this is a plausible explanation for cultured cortical neurons, it is unlikely to explain SSA in the IC *in vivo.* We have previously mentioned GABAA-mediated inhibition (**Figure 3**) does not have such effect in the IC (Pérez-González et al., 2012), and hence is another example of the differences in SSA between centers.

However, most examples of adaptation in the AC have been shown by the recordings of evoked potentials, since this part of the brain is very well suited for this technique. For the same reasons, the cortex is the center where most studies have been carried in humans. Using this technique, adaptation is expressed by reduced amplitude of the evoked response to repeated stimulation (Megela and Teyler, 1979). Adaptation in the cortex seems to be involved with the processes of deviance or change detection. These

processes have been studied through experiments that analyze a component of evoked potentials known as mismatch negativity (MMN, **Figure 7**). MMN is evoked by a passive oddball paradigm, where a deviant stimulus is embedded in a train of common, high probability stimuli. MMN is the comparison of the responses to the deviant and common stimuli, resulting in a wave that peaks 150–250 ms after the stimulus onset (Näätänen et al., 1978, 2007). In this context, adaptation would be involved in the reduction of the response to the repetitive, high probability

**FIGURE 4 | Diagrammatic representation of the auditory evoked potential components.** Average from several subjects. The stimuli were clicks presented monaurally, and the EEG was recorded from a electrode in the vertex position and a reference at the mastoid. The components from the first 10 ms (I–VI) correspond to the auditory brainstem responses (ABR), around 20–50 ms correspond to the middle latency responses, and 150–250 ms correspond to the activation of the frontal cortex. Note the logarithmic scales. Redrawn from Picton et al. (1974).

the auditory evoked potentials in response to paradigms of different complexity: the simple deviance detection of the frequency oddball paradigm (right) and a more complex "conjunction" paradigm (left), where the standard stimuli consisted of certain frequency-location combinations and the deviants broke that correspondence, combining one frequency with the opposite location. This figure shows the grand-average for 18 subjects, with the data

range (lower plots). In the middle latency range, there is a reduced response to the standards compared to the deviants, but only in the oddball condition. In contrast, in the long latency range this reduction of the standards occurs for both conditions. These findings suggest a hierarchy in the detection of deviance. Asterisks indicate significant differences. Reproduced from Althen et al. (2013).

stimuli. MMN has been proposed to reflect the comparison of the deviant stimulus with the neuronal trace of the previous stimuli, and it even could be considered some kind of "primitive intelligence" (Näätänen et al., 2001, 2007). One of the characteristics of this change detection system is that it is pre-attentive and automatic, not requiring conscious processing, as indicated by the fact that it persists during sleep and under anesthesia (King et al., 1995; Atienza et al., 2001, 2002). It has been proposed to rely upon a concatenated set of basic adaptation mechanisms and what Bregman referred to as a "bottom-up" or "primitive" grouping (Bregman, 1990; Fritz et al., 2007). The change detection system could be involved in the process of auditory attention (Fritz et al., 2007) or auditory stream segregation (Sussman et al., 2005).

The effects of adaptation in the AC are various. Condon and Weinberger (1991) showed that the repetitive presentation of a stimulus caused long-term frequency-specific changes in the receptive fields of cortical auditory neurons, indicating that adaptation produced a change in the processing of frequency information rather than a general reduction in responsivity. These plastic changes in the AC may be mediated by noradrenergic inputs. The locus coeruleus is a prominent source of noradrenaline which innervates widespread brain regions, including the tectum, the thalamus and the cortex (Sara, 2009). Edeline et al. (2011) used electrical stimulation of the locus coeruleus paired with auditory stimulation to produce plastic changes in the receptive fields of neurons in the AC and thalamus. In fact, it has been shown that the same neurons of the locus coeruleus experience adaptation to auditory stimuli, among others (Herve-Minvielle and Sara, 1995; Vankov et al., 1995). Cholinergic inputs are another

possible candidate for modulating adaptation in the cortex, since its role in processes of cortical plasticity has been shown previously (Metherate and Weinberger, 1989; Kilgard and Merzenich, 1998), but the extent of this possibility awaits future experiments. Acetylcholine is also a tentative modulator of adaptation phenomena in subcortical structures, since it has been shown that cholinergic nuclei in the tegmentum innervate the IC and the auditory thalamus (Motts and Schofield, 2011). Deouell et al. (2007) showed using fMRI that a region in the human medial planum temporale is sensitive to background auditory spatial changes, even when subjects are not engaged in a spatial localization task, and in fact attend the visual modality. During such times, this area responded to rare location shifts, and even more so when spatial variation increased, consistent with spatially selective adaptation.

### **RELEVANCE OF ADAPTATION IN THE AUDITORY SYSTEM**

One of the earliest roles assigned to cortical adaptation is the protection against cortical overstimulation (Megela and Teyler, 1979). This way, the reduction of neuronal activity during repetitive stimulation would have a protective effect, avoiding an overload of the processing systems. As we have mentioned previously, adaptation could also have a role in the detection of auditory change and novelty, as revealed by the experiments on SSA (e.g., Ulanovsky et al., 2003; Malmierca et al., 2009) and MMN (e.g., Escera et al., 1998; Näätänen et al., 2005), as well as auditory attention (Fritz et al., 2007).

Recently, adaptation has been proposed as a way of achieving an efficient coding of the incoming information (Wark et al., 2007). This would suggest that adaptation in stimulus encoding would

be sensitive to the variations in stimulus statistics. By adapting to the current distribution of the stimuli, their values and changes could be represented more precisely. This view is supported by findings like that neurons in the auditory midbrain adjust their responses to the statistics of sound level distributions (Dean et al., 2005), improving the accuracy of the neuronal population code and extending the range of sound levels that can be accurately encoded. This is probably a widespread function for adaptation, since even in grasshoppers it has been found that the recognition of temporal patterns is improved by neuronal adaptation (Ronacher and Hennig, 2004). A number of studies, most notably in the visual system, have suggested a role for adaptation of excitation in scaling neural output to take account of, for example, stimulus variance (Brenner et al., 2000; Fairhall et al., 2001). It is also well described that complex cells of the visual cortex adapt to the local contrast (Ohzawa et al., 1982; Laughlin, 1989; Carandini and Ferster, 1997), the effect being to position a neuron's dynamic range of discharge rates over the relevant range of contrasts.

But not all the response decrements are necessarily related to adaptation. Studying the decrement of the N1 auditory eventrelated potential (**Figure 4**) with stimulus repetition, Budd et al.

(1998) argue that this decrement is based on the separate refractory periods or recovery cycle processes of at least two neural generators contributing to activity in the N1 peak latency range, rather than on an adaptation process. An important feature of the N1 peak of the auditory event-related potential is its systematic reduction in amplitude when the eliciting stimulus is repeated. A major psychophysiological issue regarding the functional nature of N1 amplitude decrement has been the extent to which this response decrement reflects a psychologically relevant process or a more basic neurophysiological process. One method of distinguishing between the distinct processes of adaptation and refractoriness is that amplitude reductions caused by refractoriness should stabilize immediately after repetition of a stimulus while adaptation could entail a more progressive decline in responsiveness (Picton et al., 1976).

### **CONCLUSION**

Adaptation phenomena are widespread in the auditory system, different to habituation, and they appear in multiple forms. Spikefrequency adaptation is already present in the auditory nerve fibers, while nevertheless preserving the timing information. The

Reproduced from Näätänen et al. (2007).

responses of the auditory fibers, despite adaptation are able to carry enough timing information, like the onset and duration of sounds. It is noteworthy to note that phase-locking fibers, which would carry additional timing information, seem to experience weaker adaptation (Sumner and Palmer, 2012). This early balance of adaptation and timing information must be appropriate to allow the processing of basic acoustic features in the brainstem nuclei, such as sound location. It is interesting the fact that other, more elaborate types of adaptation appear in higher levels, maybe because the basic timing information is no longer required. These other types of adaptation could contribute to further processing of the information stream. For instance, in the midbrain the SSA contributes to change and deviance detection. At higher levels, adaptation allows neurons to process more intricate characteristics of the auditory environment, such as abstract relations, complex sequences and regularities, and eventually to contribute to processes like auditory attention and stream segregation.

#### **ACKNOWLEDGMENTS**

We would like to thank Dr. Alan Palmer for his critical and constructive comments on a previous version of the manuscript. Financial support was provided by the Spanish MINECO (BFU2009-07286) and (EUI2009-04083) in the frame of the ERA-NET NEURON to Manuel S. Malmierca, and by a postdoctoral fellowship from the Botín Foundation to David Pérez-González.

#### **REFERENCES**

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 18 October 2013; accepted: 05 February 2014; published online: 21 February 2014.*

*Citation: Pérez-González D and Malmierca MS (2014) Adaptation in the auditory system: an overview. Front. Integr. Neurosci. 8:19. doi: 10.3389/fnint.2014.00019*

*This article was submitted to the journal Frontiers in Integrative Neuroscience. Copyright © 2014 Pérez-González and Malmierca. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited andthatthe original publication inthis journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Stressors impair odor recognition memory via an olfactory bulb-dependent noradrenergic mechanism

# *Laura C. Manella , Samuel Alperin and Christiane Linster\**

*Department of Neurobiology and Behavior, Cornell University, Ithaca, NY, USA*

#### *Edited by:*

*Donald A. Wilson, New York University School of Medicine, USA*

#### *Reviewed by:*

*Susan J. Sara, Collège du France, France Max L. Fletcher, University of Tennessee Health Science Center, USA*

*Carolyn Harley, Memorial University of Newfoundland, Canada*

#### *\*Correspondence:*

*Christiane Linster, Department of Neurobiology and Behavior, Cornell University, W249 Mudd Hall, Ithaca, NY 14850, USA e-mail<EMAIL_ADDRESS>
Non-associative habituation and odor recognition tasks have been widely used to probe questions of social recognition, odor memory duration, and odor memory specificity. Among others, these paradigms have provided valuable insight into how neuromodulation, and specifically norepinephrine/noradrenaline (NE) influences odor memory. In general, NE levels are modulated by arousal, stress, and behavioral state, but there is sparse evidence of a direct relationship between NE and odor memory in adult rodents. The present study uses simple mild psychological stressors (bright light and sound) to modulate NE levels physiologically in order to probe stressors NE-dependent effect on odor recognition memory. In rats with bilateral bulbar cannulations, we show that these stressors modulate olfactory memory and that this effect is at least partially mediated by the olfactory bulb. Specifically, we show that the presence of stressors during the acquisition of odor memory suppresses memory for an odor when tested 30 min after familiarization to that odor. This suppression is blocked by infusing NE antagonists into the olfactory bulb prior to odor acquisition. Additionally, we find that infusion of bulbar NE is sufficient to suppress odor memory in a manner mimicking that of our stressors. These effects are unlikely to be solely mediated by locomotor/exploratory changes produced by stressors, although these stressors influence certain behaviors not directly related to odor investigation. This study provides important information about how behaviorally relevant changes in NE can influence top-down sensory processing and odor memory.

**Keywords: norepinephrine, noradrenaline, olfactory bulb, odor memory, stress, arousal, locus coeruleus**

# **INTRODUCTION**

Object recognition is a standard behavioral paradigm used to test non-associative recognition memory in animals. An object recognition task generally consists of two trials: a familiarization trial in which the animal has a chance to investigate an object, followed by a test trial in which the animal is presented with the familiar and a novel object. If the animal investigates the novel object more vigorously than the familiar object during the test trial, this is taken as an indication that the animal remembers the familiar object from the previous trial (Bevins and Besheer, 2006). The object recognition task can be used to test memory duration by varying the intertrial interval (ITI) between the familiarization trial and the test trial, as well as memory specificity by varying the similarity of familiar and novel objects. This task relies on the robust neophilic tendency characteristic of rodents and many other animals. This type of task has been used to test a diverse set of questions including, but not limited to the effects of stress (Beck and Luine, 1999; Bowman et al., 2003; Okuda et al., 2004; Scullion et al., 2009; Cazakoff et al., 2010), neuromodulation (Roozendaal et al., 2008; Jurado-Berbel et al., 2010), and aging and gene mutations (Bevins and Besheer, 2006) on memory.

Odor recognition tasks are similar to object recognition tasks in that they also rely on neophilia toward novel versus familiar odorants. Odor recognition and very similar habituation tasks have been successfully used to probe questions of social recognition, odor learning, memory duration, and contributions of various brain areas in these processes (Robert, 1993; Ferreira et al., 1999; Petrulis and Johnston, 1999; Johnston and Peng, 2000; McNamara et al., 2008; Wilson and Linster, 2008; Linster et al., 2009). Habituation has also been used recently using non-social odors to address questions of neuromodulation in general, and its specific effects in the olfactory bulb (OB) (Wilson and Sullivan, 1992; Wilson and Stevenson, 2003; Veyrac et al., 2007; Guerin et al., 2008; Mandairon et al., 2008; Mandairon and Linster, 2009; Escanilla et al., 2010; Freedman et al., 2013).

Non-associative odor learning has been shown to depend critically on a variety of neuromodulators. In particular, manipulation of norepinephrine/noradrenaline (NE) within the OB has pronounced effects on behavior. The OB is the recipient of rich centrifugal projections from noradrenergic neurons in the locus coeruleus (LC) (Aston-Jones et al., 2000; Sara, 2009). To date, most studies that manipulate NE within the OB show an effect on the novelty detection aspect of olfactory non-associative learning but not the memory formation *per se* (but see Guerin et al., 2008). Enhancement of NE in the OB increases spontaneous discrimination of similar odors (Escanilla et al., 2010), whereas blockade of noradrenergic α1 receptors in the OB impairs spontaneous discrimination of similar odors (Doucette et al., 2007; Mandairon et al., 2008). At the neural level, stimulation of the LC paired with odor presentations in anesthetized animals produced a marked reduction in mitral cell responses (the main output cells of the OB) to odor, and correlated with behavioral recognition memory to the paired odor later in the awake animal (Shea et al., 2008). Therefore, the LC stimulation-induced reduction of mitral cell activity is likely produced by a plasticity that is related to the long term odor recognition memory. A proposed explanation for behavioral recognition via anesthetized habituation in odor responsiveness of mitral cells is related to a study by Chaudhury et al. (2010). In this study, rate of habituation of mitral cell responsiveness to odors in anesthetized animals was comparable to behavioral habituation in awake animals when equating for the total amount of odor exposure and patterns of odor exposure between anesthetized and awake animals. Moreover, brain slice experiments show that NE affects excitability of both mitral and granule cells in the OB (Jiang et al., 1996; Nai et al., 2009; Smith et al., 2009). Computational models of NE modulation in the OB incorporating known physiological features can readily account for the influence of NE on processing of very low odor concentrations and odor discrimination (Escanilla et al., 2010; Linster et al., 2011; Devore and Linster, 2012).

While these studies have shed a great deal of light on the role of NE in OB odor processing, we still know relatively little about the relationship between endogenous patterns of NE release–such as those based on changes in vigilance (Rajkowski et al., 1994), arousal (Aston-Jones et al., 1996, 2000), wakefulness (Pavcovich and Ramirez, 1991; Rajkowski et al., 1994; Sands et al., 2000) or acute stress (Axelrod and Reisine, 1984; Aston-Jones et al., 1996, 2000; Valentino and Van Bockstaele, 2008)—and OB odor processing in adult rodents. However, the roles of stress, glucocorticoids, and NE have been well characterized in neonatal olfactory learning. Critical periods both overlap with and are dependent upon levels of corticosterone, stress, and specifically NE release into the OB (Moriceau and Sullivan, 2004; Moriceau et al., 2009, 2010). In adults, the application of an acute stressor that activates the LC (Valentino and Van Bockstaele, 2008), such as a bright light or sound, or a context that modifies the arousal of the animal can affect memory consolidation and recall, although the precise effects depend on the timing, context, intensity and duration of the stressor involved (reviewed in Joels et al., 2006; Sandi and Pinelo-Nava, 2007). However, these particular effects of acute stress have not been studied in the adult olfactory system.

In the present study, we examine whether natural changes in the NE levels in the OB affect short term odor recognition memory. We use a non-associative odor recognition paradigm to investigate how mild acute stressors—bright light or sound modulate odor memory. Our results demonstrate that the delivery of a mild acute stressor during the familiarization trial of an odor recognition task suppresses odor memory. We further show that this effect is at least partially mediated by OB NE: the effects of stressors can be blocked by bulbar NE antagonists and can be mimicked by infusion of NE into the OB.

# **MATERIALS AND METHODS**

#### **ANIMALS**

Adult male Long Evans Hooded rats, (Charles River Laboratories, Wilmington, MA, USA) initially weighing between 250 and 300 g were used. 12 rats were used for experiment 1 (odor memory duration). A second group of 12 rats was used for experiment 2 (modulation of odor memory). Rats were housed singly in standard laboratory cages (46 × 24 cm). Animals were housed on a reversed 12-h light cycle in constant temperature and food and water available *ad libitum*. All procedures were approved by the Cornell University Institutional Animal Care and Use Committee and were in accordance with NIH guidelines.

#### **CANNULATION SURGERY**

Rats underwent bilateral cannulation surgery under asceptic conditions. Anesthesia was induced using an intraperitoneal injection of ketamine-xylazine (50 and 5 mg/kg, respectively) and maintained using 1–3% isoflurane. At the start of the surgical procedure, rats received subcutaneous injections of prophylactic anitibiotics (Baytril, 5 mg/kg) and analgesics (meloxicam, 2 mg/kg and butorphanol, 2 mg/kg). Rats were then affixed to a stereotaxic instrument (David Kopf Instruments, Tujunga, CA) and the skull overlying the OB s was exposed. Guide cannulae (22 gauge; Plastics One, Roanoke, VA) were implanted bilaterally into the OB s at coordinates with respect to bregma (AP: +8 mm; ML: ±1.5 mm; DV: −4.5 mm), 1 mm dorsal to the target infusion site, and affixed to the skull using skull screws (Plastics One, Roanoke, VA) and dental cement. Each infusion cannula (28-gauge, Plastics One, Roanoke, VA) extended 1 mm from the end of the guide cannula directly into the center of the OB. Dummy cannulae were placed into the guide cannulae to keep the guide cannulae free of debris and prevent infection. Rats were allowed to recover for at least five days before acclimation to the experimental procedures commenced.

### **DRUGS**

All drugs were diluted in 0.9% sterile saline, which was also used as a vehicle control. To block NE signaling in the OB, we used a cocktail of NE antagonists consisting of the α1-receptor antagonist prazosin hydrochloride (1mM, Sigma, Natick, MA), the α2-receptor antagonist yohimbine hydrochloride (2 mM, MP Biomedicals, Solon, OH), and a non-selective β-receptor antagonist alprenolol hydrochloride (120 mM, Sigma, Natick, MA). NE was prepared at a variety of dosages (L-(−)-Norepinephrine (+)-bitartrate salt monohydrate; Sigma, Natick, MA). Drugs were prepared before beginning the experiment, separated into aliquots, and frozen at −20◦C for daily use. During experiments, 6μL of solution was infused into each OB simultaneously using a double-barreled Pump 11 Elite Nanomite Syringe Pump (Harvard Apparatus, Holliston, MA) at a rate of 2μL/min. Drugs were infused 20 min prior to the first trial for a given session. Past studies have shown this volume and rate to be adequate to spread throughout the OB but unlikely to spread beyond the OB (Mandairon et al., 2006).

### **STRESSORS**

We selected bright light and sound stimuli as mild stressors based on their history of being robust modulators of open field behavior (Archer, 1973; Roth and Katz, 1979; Katz et al., 1981) and startle response (Walker and Davis, 1997), as well as promoting phasic NE release from the LC (Koyama et al., 1994). Moreover, these are non-invasive, relatively non-traumatic stressors and pilot studies suggested that animals continue to engage in investigation of odors during familiarization trials regardless of whether a stressor was delivered. For bright light stimulation, a 40 watt desk lamp was placed at the end of the chamber facing inwards in the case of the odor recognition paradigm (**Figure 1**). For sound stress, we used a computer speaker playing music toward the testing chamber (Brick by Boring Brick, performed by Paramore) at a volume, on average, of approximately 70 dB, compared to background level of ambient noise of approximately 56 dB. Both the bright light and sound stressors were present for the familiarization trial (Trial1) for appropriate sessions.

#### **ODORS**

Monomolecular odorants were diluted to approximately 1 Pa vapor partial pressure to normalize the rate of particle dispersion based on concentration and volatility of odors (**Table 1**). For the odor recognition task, we used pairs of perceptually and chemically dissimilar odors. For a given experiment, a rat was presented with all odors sets randomly assigned across experimental conditions (**Table 2**).

#### **ODOR RECOGNITION TASK**

Rats were first acclimated to handling through at least three 10 min daily handling sessions before surgery. After surgery, ratswere

#### **Table 1 | Odor dilutions.**

**FIGURE 1 | Experimental paradigms.** The odor recognition task timeline and paradigm. Infusions were given 20 min prior to the familiarization trial (*Trial 1*) for all sessions (vehicle, NE antagonist cocktail, or a dosage of NE). For sessions where stressors were implemented, the stressor source (lamp for bright light or speaker for sound) was located adjacent to the testing chamber in the position shown in the figure during the entire odor exposure during *Trial 1*. For each trial, two inverted weigh dishes were placed on the top of the lid, in the far back corners. For *Trial 1*, as shown by an overhead view, the familiarization odor, "odor 1," is pipetted onto a one inch square kim wipe into each of two weigh dishes. Between Trials 1

and 2, a variable intertrial interval is implemented (30, 60, 120, 180 min, depending on the particular experiment). For the *Trial 2*, odor 1 is placed in one weigh dish and a novel odor, "odor 2," chemically and perceptually dissimilar from odor 1 is placed in a second weigh dish. The two dishes are placed in a randomized orientation. For each trial, investigation is defined as the rat's nose being within 1 cm of the weigh boat (white box in *Trial 1*, where each arrow is 1 cm). Memory is defined as significantly more investigation of odor 2 compared to odor 1. Additionally, note the quadrant markings at the base of cage in the photo of *Trial 1* that were used for transitions in video scoring.

acclimated to infusion procedures using 0.9% sterile saline infusions and to the testing chamber and room for at least 4 sessions on separate days.

To determine the effects of bright light stress and NE within the OB on odor memory, we used an odor recognition paradigm. This paradigm consists of two three-minute trials, separated by 30, 60, 120, or 180-min intertrial intervals (ITIs). At the start of each trial the rat was transferred from its home cage into the testing chamber: a clean, clear, plastic cage (46 × 24 cm) with a wire lid (**Figure 1**). Following a two-minute acclimation period, odor stimuli were introduced to the rear corners of the cage lid.  In *Trial 1* (familiarization trial), both weigh dishes contained the same odor (referred to as the familiar odor) while in *Trial 2* (test trial) one weigh dish was scented with familiar odor and the other was scented with a novel odor (**Table 2**). The orientation of the two odor stimuli during the test trial was randomized (**Figure 1**). The amount of time spent actively investigating each odor—defined as the nose within 1 cm of the weighing dish was determined. High resolution video was recorded during all test sessions and locomotor activity of the rat was determined off-line.

#### **EXPERIMENT 1: ODOR MEMORY DURATION**

To determine how long rats could remember a discrete odor stimulus for use in subsequent experiments, each rat ran through a variety of ITIs between familiarization and test trials. Each rat ran through a randomized block design, experiencing one ITI per day of each of the following ITIs in a randomized order: 30, 60, 120, or 180 min. Four odor sets made up of 4 perceptually and chemically disparate odors (**Table 2**) were used, randomized across sessions so that each odor set had equal representation across each ITI. To be able to compare with the results from experiment 2, rats were

infused with vehicle 0.9% sterile saline into their OBs bilaterally 20 min before the first trial (injection volume 6μl, rate of 2μl/min).

#### **EXPERIMENT 2: EFFECT OF STRESSORS AND BULBAR NE ON ODOR MEMORY**

Next, we determined if short-term odor recognition memory is affected by bright light or sound stressors, and NE blockers (Experiment 2a) or bulbar NE (Experiment 2b). To determine if stressors or bulbar NE infusions enhance odor memory duration, we tested ITIs of 60 and 180 min—ITIs in which our non-stressed rats could not remember, and subsequently an ITI of 30 min—an ITI in which non-stressed rats could remember (**Figure 3**). In order to minimize acclimation to stressors, we interleaved experimental sessions for the two experiments, randomizing all conditions across days (**Table 3**). Additionally, each session was spaced at least three days apart to additionally reduce acclimation to the stressors as well as to the experimental paradigm itself. For Experiment 2a and then Experiment 2b, odor sets from **Table 2** were used, and odor sets were randomized across conditions, similarly to in Experiment 1.

#### *Experiment 2a: does stress enhance odor memory (60 and 180 min ITIs)?*

To determine whether stressors have an effect on odor memory and whether this effect is dependent upon OB NE, we used a 2 × 3 × 2 design where infusions of drug (saline or NE antagonist cocktail) were paired with stressors (no stress, bright light, or sound) at two ITIs (60, 180 min). As a consequence, each rat experienced 20 sessions, each time with different combinations of stress, drug and ITI (**Table 3**).

#### *Experiment 2b: does bulbar NE enhance odor memory (60 and 180 min ITIs)?*

We also determined the effect of NE infusions on odor memory during the odor recognition task. We used a 5 × 2 design, whereby each rat received each drug (vehicle 0.9% saline, 1, 10, 100 or 1000μM NE) at different ITIs (60, 180 min), but in this case without the addition of stressors.

### *Experiments 2c and 2d: do stress or bulbar NE impair odor memory (30 min ITIs)?*

After collecting the data for 60 and 180 min ITIs, we determined whether stress/NE could suppress odor memory by testing an ITI of 30 min—an ITI for which our control rats could remember. The experimental design was identical to testing the longer ITIs above, with the exception that only a 30 min ITI was tested for all conditions. Thus, each rat experienced ten sessions in a randomized order (**Table 3**).

# **ANALYSIS**