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This chapter provides an overview of current knowledge about hearing in ferrets and how this species is contributing to advances in understanding of many aspects of auditory function. In recent years, ferrets have become increasingly popular as an animal model for studying hearing for several reasons. First, as with their ability to see, the functional onset of hearing takes place in ferrets several weeks after birth, allowing postnatal access to an immature nervous system at a stage that can only be studied in utero in some other more precocial species. Second, the range of sound frequencies that are audible to ferrets entirely overlaps, and extends beyond, that of humans. By combining methods for selectively lesioning pathways with behavioral testing, studies in ferrets have provided the first functional evidence for the role that descending connections might play in hearing function.
<Verso Running Head>Section III / Research and Applications Hearing and
Auditory Function in Ferrets
<Recto Running Head>Research and ApplicationsChapter 29 / Hearing and
Auditory Function in Ferrets
Hearing and Auditory
Function in Ferrets 29
Wellcome Trust
Fernando R. Nodal and Andrew J. King
In recent years, ferrets have become increasingly popular as an animal model for
studying hearing, and are now used for this purpose in a number of laboratories
around the world. There are several reasons for this. First, as with their ability to
see, the functional onset of hearing takes place in ferrets several weeks after birth,
allowing postnatal access to an immature nervous system at a stage that can only
be studied in utero in some other more precocial species. Second, the range of
sound frequencies that are audible to ferrets (approximately 20 Hz–40 kKHz)
entirely overlaps, and extends beyond, that of humans (Fig. 29.1). In contrast to
many rodents, particularly mice, whose audible range is shifted to higher
frequencies, the sensitivity of ferrets to low- frequency sounds allows the study of
various aspects of hearing, including pitch perception, sound localization, and the
processing of communication calls, within a similar frequency range to that used
by humans. Finally, because of their inquisitive nature and intelligence, ferrets can
be readily trained to carry out a variety of auditory behavioral tasks, which has led
to the development of paradigms for measuring their ability to detect, discriminate,
and localize sounds. Moreover, for some aspects of hearing, their performance in
these tasks closely resembles that of human listeners, providing an impetus for
using ferrets to investigate the neural basis of auditory perception as well as the
effects of hearing loss and its restoration.
Behavioral Studies of Hearing in Ferrets
The first auditory behavioral study in ferrets used a positive conditioning paradigm
to measure the range and sensitivity of the animals to tones of different frequency
[1]. They were trained to initiate a trial by touching a spout positioned in front of a
central start platform and to approach a peripheral water spout located at either 30°
to the right when they heard a sound or to the left when they heard no sound.
Correct responses were reinforced by the delivery of a water reward. The same
principle was then adopted to measure <termDef>minimum audible
angle</termDef>s (<abbrev>MAA</abbrev>s), i.e.that is, the ability of ferrets to
discriminate between sounds presented from one of two speakers separated by
progressively smaller angles in the horizontal plane [2,3]. Subsequent studies
expanded this to a 12-speaker task, measuring both the accuracy of the approach to
target responses and of the initial head orienting response made following sound
presentation [4]. Ferrets are very easy to train in this type of task and it has even
proved possible, using a left-–right response paradigm, to measure MAAs in the
vertical plane [5,6].
The horizontal plane MAAs measured in ferrets are comparable to with those
reported for other carnivores [7]. Their spatial acuity varies with the location of
the stimulus from 10° to –15° when animals are tested with brief noise bursts
around the midline to 25–30° at more lateral locations [3,8]. This spatial
dependence in localization accuracy is also seen when ferrets are trained to
approach the source of 1 of 12 loudspeakers arranged at 30° intervals in the
horizontal plane [4]. The accuracy of approach-to-target responses declines as the
duration of the stimulus is reduced (Fig. 29.2A,B), particularly for lateral and
posterior target locations, although responses to sound, presented in the frontal
region of space and directly behind the animal, remain largely unchanged (Fig.
29.2C,E). Sound-evoked head movements made by the animal before it leaves the
central start platform have a latency of 200 milliseconds ms and vary
systematically in magnitude with the direction of the stimulus (Fig. 29.2D,F). In
contrast to the approach-to-target behavior, the accuracy of the head orienting
responses is less affected by changes in stimulus duration (Fig. 29.2B), suggesting
that these are triggered by the onset of the sound and that the improved
performance observed when animals are approaching longer duration sounds may,
at least, in part, be due to re-sampling of the stimulus after the initial head turn has
been made.
The ability of animals to localize sound depends on the geometry of the head and
external ears [9]. Because of the physical separation of the ears on either side of
the head, sounds originating from one side will arrive at the closer ear first.
Depending on their wavelength, they may also be attenuated at the far ear as a
result of the acoustic shadow cast by the head, while interactions with the folds of
the external ears can modify the spectral shape of the sound. Localization of a
sound source therefore relies on the sensitivity of the auditory system to a
combination of <termDef>interaural time difference</termDef>s
(<abbrev>ITD</abbrev>s), i<termDef>interaural level difference</termDef>s
(<abbrev>ILD</abbrev>s), and spectral cues. In order to understand the basis for
localization behavior in ferrets, closed-field techniques have been developed to
measure their sensitivity to binaural cues [10]. This involves attaching earphones
to lightweight titanium holders that are mounted on the animal’'s head, so that they
can be easily disconnected and consistently repositioned just in front of the ear
canals (Fig. 29.3A). Ferrets are extremely sensitive to both ITDs and ILDs (Fig.
29.3B,C), with thresholds that can largely account for their ability to localize
stimuli in the horizontal plane [11]. Indeed, their behavioral sensitivity to these
cues closely resembles that found in other species, including humans, confirming
the suitability of ferrets as a model for studying spatial hearing.
Ferrets are also used to investigate aspects of sound identification. Although little
is currently known about how they perceive their own vocalizations (Fig. 29.4),
their ability to discriminate the periodicity and timbre of artificial vowels that
cover the same frequency range has been measured. Vowel identification involves
picking out the formant peaks in the spectral envelope of the sound, and is
therefore a timbre discrimination task. Ferrets readily learn to distinguish different
vowels and, like humans, are able to generalize across a range of stimulus
periodicities and to maintain their behavior in the presence of background noise
The periodicity of a sound corresponds to its perceived pitch and conveys
information about speaker identity (males tend to have lower pitch voices than
females), age, and emotional state. Because pitch is a perceptual attribute and is
not related in a straightforward way to the physical properties of sound, it is hard
to demonstrate conclusively that other species experience pitch in the same way as
humans. Nevertheless, there is now plenty of evidence that non-human animals are
sensitive to the same periodicity cues as humans [13]. For example, ferrets are
sometimes able to spontaneously distinguish harmonic complex tones, which, in
humans, have a clear pitch associated with them, from inharmonic tones [14], and
can be trained to discriminate the relative pitch of pairs of tones that either rise orf
fall in frequency [15]. Walker et al. [16] showed that ferrets can be trained to
discriminate two consecutive artificial vowels by indicating whether the pitch of
the second sound was higher or lower than that of the first (Fig. 29.5). In this case,
however, their thresholds were much poorer than those found in humans and other
species. Moreover, the animals appeared to perform the two2-alternative forced
choice task by attending to the absolute pitch height of the second sound, rather
than the direction of the pitch change between the two vowels. This does not
mean, however, that ferrets are unsuitable species to use for studying pitch
perception, since much lower thresholds have been found when they perform a
go/no-go change detection task.
Finally, auditory temporal processing has been examined behaviorally in ferrets by
measuring their thresholds for detecting short gaps in a sound (Ref. 40Kelly et al.,
1996), while other studies have tested their hearing abilities in more complex
acoustic environments [17–20]. This work has shown that, like humans, ferrets use
binaural cues to help them pick out sounds against other sounds originating from
different directions in space [17], can suppress echoes when localizing sounds
[19], and appear to be able to perceptually organize sound sequences into auditory
streams [18].
Organization of the Auditory System
The anatomy of the ear and the organization of the central auditory pathway of the
ferret follow the same principles that have been described in other mammals.
Although relatively little research has been carried out on the middle ear or inner
ear in this species, other than in the context of studying the effects of upper
respiratory infections (e.g., Ref. [21]), a great deal is known about how its
immobile external ears modify incoming sounds to provide spectral cues for
source location [22–24]. All the auditory nuclei or relay stations in the brain,
which have been characterized in numerous mammalian species (for a review of
the neuronanatomy of the auditory system, see Ref. [25]), are well developed and
easily distinguishable in the ferrets (Fig. 29.6). Thus, after leaving the cochlea, the
hearing part of the inner ear, the auditory nerve fibers enter the <termDef>cochlear
nucleus</termDef> (<abbrev>CN</abbrev>) in the brainstem [26,27], which in
turn sends bilateral projections to the <termDef>superior olivary
complex</termDef> (<abbrev>SOC</abbrev>) [28]. This is where convergence of
information from both ears is registered for the first time. Both the CN and SOC
project to the <termDef>inferior colliculus</termDef> (<abbrev>IC</abbrev>) in
the midbrain for further signal processing [26,29]. The IC and other brainstem
areas provide the principal auditory input to the <termDef>superior
colliculus</termDef> (<abbrev>SC</abbrev>) [30,31], another midbrain nucleus.
The IC also projects to the medial geniculate body or auditory thalamus [32],
which constitutes the gateway to the auditory cortex located in the
<termDef>ectosylvian gyrus</termDef> (<abbrev>EG</abbrev>) (Fig. 29.6B,C).
Some species-specific differences have been noted in these anatomical studies,
such as the proportion of ipsi versus contralateral projections from the lateral
superior olive to the IC in ferrets and cats (Moore et al., 1988). Overall, however,
the connectivity between the different auditory regions of the ferret’'s brain is
broadly consistent withhich thoseat found in other species.
In addition to the afferent connections from cochlea to cortex, the auditory
pathway is characterized by extensive efferent or descending connections (Fig.
29.6C), the size of which often outweighs those carrying information in the
ascending auditory pathway. The functions of these descending pathways remain
unclear, but they are thought to modulate the processing of acoustical signals at
lower levels in the pathway [33]. As in other species, descending projections in
ferrets link the auditory cortex with the IC [34] and SC [35], and the SOC with the
cochlea [36]. As we shall discuss later in this chapter, by combining methods for
selectively lesioning these pathways with behavioral testing, studies in ferrets have
provided the first functional evidence for the role that descending connections
might play in hearing [36,37].
Physiological Studies of Auditory Processing in Ferrets
The functional organization of the central auditory pathways has been studied by
measuring the responses of neurons in each region to a variety of sounds
(reviewed in Refs. [9 and ,38]). The animal species most commonly used are
guinea pigs, gerbils, and cats, with macaque monkeys and marmosets being the
subject of many of the studies on auditory cortical processing. The use of ferrets in
this field dates back to the 1980s [1,39,40], and has focused mainly on the
midbrain and cortex. Indeed, the only investigation so far of the response
properties of neurons at more peripheral levels was carried out by Sumner and
Palmer [41], who described the sound frequency tuning, spontaneous firing rate,
relationship between firing rate and sound level, and phase locking of auditory
nerve fibers (Fig. 29.7). To a large extent, the coding of information about sound
frequency and level by auditory nerve fibers reflects the properties of the cochlear
inner hair cells to which they are connected. Perhaps surprisingly, Sumner and
Palmer [41] found that the auditory nerve in the ferret more closely resembles that
of the guinea pig or chinchilla than that of the cat.
Several studies have examined the response properties of neurons in the IC of the
ferret (e.g., Refs. [39, 42, and 43]), sometimes as part of a wider investigation of
how the representation of natural sounds changes at higher levels of the auditory
system (Schnupp and King, 1997) [44,45]. This species has also been used
extensively for investing the auditory response properties of neurons in the SC
[46]. Unlike other parts of the auditory pathway, the spatial receptive fields of SC
neurons are organized topographically to form a map of auditory space, which lies
in spatial register with the representations of other sensory modalities. This
arrangement allows the different sensory signals associated with a particular object
to be registered at the same location, and often by the same neurons, within the
SC, so that they can trigger orienting movements that help to redirect attention
toward the object [47]. Although studies of how multisensory inputs interact to
determine the responses of SC neurons have been carried out primarily in other
species [48], experiments in ferrets have helped to reveal how a map of auditory
space is constructed in the brain from the localization cues generated by the way
sounds interact with the head and external ears (King et al., 1994) [23,49].
In recent years, interest in the ferrets as a model for auditory cortical processing
has greatly increased. A common feature of all sensory systems is that they
comprise multiple cortical areas that can be defined both physiologically and
anatomically, and which are collectively involved in the processing of the world
around us. As in other mammalian species, a number of different auditory cortical
fields with distinct functional properties are found in the ferrets [50–52] (Fig.
29.6B). The middle portion of the EG comprises the primary auditory cortex (A1)
and the <termDef>anterior auditory field</termDef> (<abbrev>AAF</abbrev>):
the neurons found there have short latency responses that are most sensitive to
particular sound frequencies, which vary systemically in value with neuron
location within each cortical area to form “tonotopic” maps. There is little doubt
that an equivalent area to the region designated as A1 is found in many different
mammalian species, including humans, and the same is probably true of AAF.
Drawing homologies between the other fields described in the ferret auditory
cortex and those described in other species has, however, proved to be less
straightforward. Two more tonotopically organized, secondary cortical areas are
found on the posterior EG, and the neurons found there can be distinguished from
those in the primary areas by the temporal characteristics of their responses.
Finally, two further fields have been described in the anterior EG, where neurons
respond to sound but lack the tonotopic order that is characteristic of the rest of
auditory cortex.
Each of the auditory fields in the ferret cortex has also been shown to receive
visual inputs, which are likely to arise from different parts of the visual cortex
[53]. In fact, the region labeled in Fig. 29.6B as the <termDef>anterior ventral
field</termDef> (<abbrev>AVF</abbrev>) is probably more accurately
designated a higher- level multisensory area, since it contains a particularly high
incidence of visually responsive neurons and lies close to a somatosensory field
[54]. Other reports have also highlighted the multisensory nature of neurons in the
anterior EG [55,56]. Inputs from other sensory modalities to auditory cortex have
also been observed in several a growing number of other species too, including
primates [57]. Although their functions are not well understood, they appear to
enhance the capacity of cortical neurons to signal the location [58] or identify
identity [59,60] of of a sound source, and are therefore likely to contribute to the
well known effects of vision on auditory perception.
Recording studies in ferrets have provided novel insights into the way in which
various sound attributes, including periodicity, timbre, and spatial location are
represented in the cortex [61–63], and have also contributed to our understanding
of how the brain encodes natural sounds such as speech (Rabinowitz et al., 2013)
[64,65]. Using the same artificial vowel sounds used to measure pitch and timbre
perception in the behavioral studies described in an earlier section of this chapter,
Bizley et al. [62] found that sensitivity to both spatial and non-spatial sound
attributes is widely distributed across different cortical fields. Regional differences
do exist, however, with the firing patterns of neurons in certain areas being more
informative about the periodicity or timbre of the sound, whereas those in other
areas are more sensitive to its location in space. This is broadly consistent with
evidence in other species for distinct cortical processing streams for different
aspects of the auditory scene [66,67]. At the same time, the ferret recordings have
revealed that these stimulus attributes are not only coded by different spatial
patterns of activation but also by different temporal patterns within the same
population [63]. In other words, the firing rate of a neuron can be modulated by
the identity (i.e., spectral timbre) of an artificial vowel sound in one time window
and the periodicity of the vowel in a later time window. This is known as
“multiplexing” and allows the neurons to carry mutually invariant information
about these two perceptual features. In keeping with this neurophysiological
finding, ferrets were found to detect changes in vowel identity faster than they
detected changes in the periodicity of these sounds [63]. This cortical processing
strategy potentially allows for a more dynamic representation of perceptual
attributes or even the acquisition of representations of new attributes that become
relevant or are learned through behavioral training.
The primary reason for exploring the response properties of neurons in the brain is
to attempt to identify the neural basis for perception. One approach to this is to
make quantitative comparisons of the psychometric performance of an animal in a
given detection or discrimination task with “neurometric” measures of how well
single neurons or neural ensembles can detect a change in the stimulus. Using this
approach, Bizley et al. [68] found that relatively small networks of neurons
throughout the auditory cortex of anesthetized ferrets can account for the ability of
trained animals to detect the direction of a pitch change. This appears to be at odds
with studies in other species that have reported the existence of a localized pitch
center in the cortex, where neurons are tuned to different periodicities [69]. This
remains controversial, however, and establishing a more direct link requires
making simultaneous behavioral and electrophysiological recordings from the
same subject. This approach has mainly been restricted to larger animals,
particularly non-human primates, and has rarely been used in the auditory system.
Recently, however, it has been demonstrated in ferrets trained to perform a pitch
discrimination task that auditory cortical activity represents not only the
periodicity of the sound, but also the pitch that the animals appear to hear as
assessed by their behavioral responses [70].
In order to demonstrate that a given area of the brain is involved in a particular
aspect of perception, it is also necessary to show that silencing or manipulating
neural activity in that area alters the animal’'s behavior. A commonly used
approach is to lesion the brain region in question, which, in ferrets, has been used
to show that the SOC [71], IC [8], and auditory cortex [2,72] are both all necessary
for accurate sound localization. These results support the widely accepted idea that
although the different cues for sound localization are extracted and computed in
the brainstem, higher-level areas, and the auditory cortex, in particular, play a
critical role in determining the location of a sound source. A reversible
pharmacological inactivation method that utilizes subdural placement of a polymer
to release the GABAA agonist muscimol over a period of weeks has been
developed in ferrets [73], and used to confirm that both primary- and higher- level
auditory fields contribute to localization accuracy when animals make their
responses by approaching the source of the sound for a fluid reward [73,74]. The
accuracy of the initial head orienting response is preserved, however, following
aspiration lesions [72] or pharmacological inactivation [73], suggesting that the
neural circuitry involved in mediating these responses may be different.
Development and Plasticity of the Auditory System
Interest in the use of ferrets for studying the auditory system was driven initially
by their suitability for developmental studies. Compared withto most other
mammals, ferrets are particularly immature at birth and do not begin to hear until
near the end of the first postnatal month (Moore and Hine, 1992). Despite their
relative immaturity, young ferrets are robust and highly suitable for
electrophysiological recording experiments. Several studies have examined the
maturation of auditory circuits at different ages (Gabriele et al., 2000; Henkel
et al., 2007) [31], or have documented how the response properties of auditory
neurons change following hearing onset. For example, recordings from the SC
[75] and auditory cortex [76] have shown that the spatial tuning of the neurons is
much broader in young ferrets than it is in adults. It turns out this can be explained
by changes in the localization cue values that take place as the head and ears grow
(Schnupp et al., 2003). Thus, presenting infant ferrets with stimuli via earphones
that replicate how sounds are filtered by the head and ears of adult animals led to
an immediate sharpening of the spatial receptive fields (Fig. 29.8). At the same
time, there is considerable evidence that central auditory circuits undergo
experience-dependent changes during development and studies in ferrets have
provided valuable insights into this process.
One commonly used approach for investigating the plasticity of auditory circuits
in the brain is to change the balance of inputs from the two ears, either by ablating
the cochlea or by introducing a reversible conductive hearing loss or some other
manipulation on one side. Unilateral cochlear removal in ferrets results in an
increase in the number of neurons that project from the CN on the non-operated
side to the ipsilateral IC [77], and an increase in the proportion of IC neurons that
are excited by the intact ear [78]. This anatomical reorganization is also seen when
ferrets are raised with one ear occluded by an earplug [79].
Although comparable results have been obtained in other species, these studies
have been extended in ferrets to examine the functional consequences of hearing
loss at different ages. Plugging one ear, either in infancy or adulthood, results in an
impairment in binaural hearing when assessed by measuring the ability of ferrets
to detect a tone in the presence of noise originating from other directions, with
binaural unmasking gradually recovering following earplug removal (King et al.,
2000) [80] (Fig. 29.9). While these findings provide little indication of any
adaptation to the partial loss of hearing in one ear, a different result was obtained
when the ability of adult ferrets that had been raised with a unilateral earplug was
measured (King et al., 2000) [24]. Even though the earplug was still in place, these
animals were able to localize sounds accurately (Fig. 29.10A) and the auditory
spatial tuning of neurons in the SC adjusted so that a near-normal map of auditory
space emerged (Fig. 29.10B). This adaptive plasticity does not involve a retuning
of neurons to the altered binaural cues, but is instead brought about by developing
a greater dependence on the unchanged spectral localization cues provided by the
intact ear [24].
Plasticity of auditory spatial processing in the SC has also been demonstrated by
manipulating other sensory cues. As mentioned in an earlier section of this chapter,
a characteristic feature of the organization of the SC across different species is the
presence of topographically aligned sensory maps for each of the modalities
represented there. Bringing together spatial signals that are registered by different
sense organs is challenging, but studies in ferrets and barn owls have been at the
forefront in showing that vision, which generally provides more precise and
reliable spatial information, plays an instructive role in merging multisensory
spatial information during development through its influence on the maturation of
the auditory space map (reviewed in Ref. [81]) (Fig. 29.11). These studies suggest
that visual inputs to the SC might provide a template for guiding the development
and plasticity of the auditory responses (King et al., 1988) [30,82], so that
information from the different senses can be synthesized appropriately.
Dynamic Neural Coding and Plasticity in the Adult Brain
A growing number of studies have demonstrated that sensory systems can adjust
their sensitivity to compensate for the often considerable changes in input statistics
between different sensory scenes. Studies in ferrets have shown that the response
properties of neurons in the IC [42] and auditory cortex [83–85] are not fixed, but
can change rapidly over time. For example, cortical neurons rescale their gain to
compensate for the contrast between a sound and its background: when the
contrast is low, the gain of the neurons increases so that they became more
sensitive to level changes, but when contrast is high, they become less sensitive
[83,84]. The stimulus values to which auditory neurons are tuned can also change
in response to the recent history of stimulation [42,86].
Another remarkable example of the plasticity of the adult brain has been
demonstrated by studies in ferrets showing that the <termDef>spectrotemporal
receptive field</termDef>s (<abbrev>STRF</abbrev>s) of auditory cortical
neurons can change rapidly when animals are engaged in a behavioral task
[87,88]. In these experiments, ferrets were trained by aversive conditioning to
refrain from licking a spout when they detected a target tone or a change in the
frequency of the sound in order to avoid a mild electric shock. The selectivity of
the neurons was found to change rapidly, sharpening the representations of the
stimuli that were relevant to the specific demands of the task. These changes,
which were readily observed after just a few trials and sometimes persisted for
several hours [88], are thought to reflect the influence of attention on cortical
processing. However, the nature of the task is not the only factor that determines
how the STRFs change. Its consequences are also important, as experiments in
which ferrets were trained to make different behavioral responses in the same
sound discrimination task have revealed [89] (Fig. 29.12). The animals either had
to suppress their licking behaviourbehavior to avoid a mild electrical shock or
were rewarded for licking a spout when they correctly identified the target sound.
Changes in the STRFs were observed in both paradigms, but occurred in opposite
directions with responses to the target sound becoming enhanced in the avoidance
task, but suppressed when the animals were rewarded. Both changes promote
better discriminability, but it seems that the responses of cortical neurons to stimuli
associated with aversive outcomes are selectively amplified.
Plasticity of cortical responses is also associated with the perceptual improvements
associated with long-term training. The sensitivity of neurons to the trained
stimulus can be enhanced altered in various ways. While some studies have
focused on changes in the proportion of neurons that respond to behaviorally
relevant aspects of the sound (e.g., Refs. [90 and ,91]), recordings in ferrets have
shown that learning on a complex sound discrimination task is associated with
increases in the amount of information that cortical neurons transmit about these
stimuli in their temporal firing patterns [92]. Moreove,r, behavioral studies in adult
ferrets have demonstrated that the capacity of the auditory system to compensate
for abnormal spatial cues is not restricted to a sensitive period of development. As
in humans, pPlugging one ear changes the binaural cue values corresponding to
each direction in space and results in a profound deficit in sound localization,
which is characterized by an increase in the number and magnitude of the errors
made with a notable bias towards the side of non-occluded ear. If provided with
appropriate training, however, adult ferrets with wearing a unilateral earplug
rapidly recover their ability to localize sound, with the extent and rate of that
recovery being determined by the frequency with which they are trained [93] (Fig.
29.13A). This capacity to compensate for a conductive hearing loss in one ear has
considerable implications for rehabilitation strategies following the loss or
restoration of hearing, and led directly to studies that demonstrated an equivalent
capacity for learning-induced plasticity of spatial hearing in humans [94].
In keeping with the physiological evidence in other species that perceptual
learning is associated with changes in the response properties of neurons in early
sensory cortex, the ability of adult ferrets to adapt to the altered spatial cues
produced by plugging one ear is lost if the auditory cortex is silenced [72,74] (Fig.
29.13B) or if cholinergic inputs to the cortex from the basal forebrain are removed
[95]. The finding that adaptation depends on cholinergic neuromodulation implies
a possible role for attention in this process and is consistent with other work,
suggesting that acetylcholine plays a critical role in sensory processing under
challenging conditions. The experience-dependent recalibration of sound
localization in adult ferrets has also been used to demonstrate the behavioral
importance of descending projections in the auditory system. Thus, unilateral
ablation of the projection from A1 to the IC by chromophore-targeted laser
photolysis impaired the spatial plasticity that normally occurs after plugging one
ear (Fig. 29.13C), without affecting the ability of the animals to localize sound
under normal hearing conditions [37]. Behavioral adaptation by adult ferrets to a
unilateral earplug also depends on the integrity of the olivocochlear bundle, the
descending projection from the SOC, where sensitivity to binaural localization
cues is first derived, to the cochlea [36].
Cochlear Implant Model
With the advent of cochlear implants, it is possible to partially restore hearing in
the deaf by directly stimulating the inner ear. Although traditionally fitted in one
ear only, bilateral implantation is becoming more common, particularly in
children, in an attempt to improve sound localization and hearing in noisy
environments, which both rely heavily on having two functioning ears. However,
results from patients have been quite variable and it is likely that age at onset of
deafness, duration of implant use, and targeted auditory training can influence the
performance of cochlear implant users. There is, a therefore, a pressing need for
animal models that can be used to investigate both the behavioral and
physiological outcomes of cochlear implantation and the importance of experience
in restoring auditory function. Although a range of species have been used to study
the effects of intracochlear electrical stimulation, the size of the speech processors
that are currently available commercially restricts their use in behavioral studies to
larger mammals, such as primates or carnivores.
The development of a ferret model of cochlear implantation [96] (Fig. 29.14) has
been driven by the substantial amount of physiological and behavioral data
available on numerous aspects of hearing in this species, and, in particular, by the
evidence described in the previous sections that ferrets are particularly suitable
animals for investigating the development and plasticity of auditory function.
Protocols have been developed for deafening ferrets with aminoglycoside
administration, followed by implantation and chronic stimulation of intracochlear
electrical arrays in one or both ears. By connecting the electrodes to clinical
processors via modified stimulator-–receivers worn within a custom-made jacket
(Fig. 29.14), behaviorally -relevant ILDs and ITDs can be provided during chronic
stimulation, which are used by the animals to orient towards acoustic stimuli [96].
These experiments provide a unique opportunity for investigating the role of
experience and training in establishing and maintaining auditory spatial abilities,
and should also contribute to the improvement of neuroprosthetic therapies in
Concluding Remarks
In this chapter, we have attempted to provide an overview of our current
knowledge about hearing in ferrets and how this species is contributing to
advances in our understanding of many aspects of auditory function. Thirty years
ago, the viability of the ferret as a model of auditory processing was questioned
(D.R. Moore, personal communication), whereas today they are one of the most
commonly used species for studying the higher auditory system. It is likely that
the popularity of the ferret as a model for auditory research will continue to
increase thanks to the technical advances that allow neural activity to be recorded
in freely moving, behaving animals. The prospect of combining the wide
repertoire of behavioral tasks that can be performed by ferrets with chronic
recording of neural activity will undoubtedly further our understanding of complex
functions like such as speech recognition or scene analysis and is likely to be key
to revealing the nature of the changes that give rise to highly prevalent conditions
such as tinnitus. Moreover, the advent of new ways of controlling neural activity
by light through the use of optogenetics, which are now being used in a range of
species, including ferrets, is rapidly expanding the methods available for studying
neural circuits, and may pave way toward new treatment possibilities for a range
of neurological conditions.
The authors’' research is supported by a Wellcome Trust Principal Research
Fellowship (WT076508AIA) to AJK.
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Fig. 29.1.Hearing range and audiogram of the ferret. (A) The audible frequency
range of different mammals, including humans, ferrets, and some of the most
common species used for auditory research. Note the total overlap of the ferret’'s
hearing range with that of humans, which makes the ferrets a more suitable species
to study the processing of low-frequency vocalizations and sound localization cues
than rodents whose vocalizations sometimes lie beyond the range of human
hearing. (B) Typical audiogram of two ferrets, whose audible frequency range, as
indicated in panel A, extends from 20 Hz to 50 kHz with maximum sensitivity
around 7–11 kHz. <source>Reproduced with permission from Kelly et al.
Fig. 29.2.Sound localization behaviourbehavior in ferrets. (A) Overall percentage
across 12, equally spaced target locations of trials classified by their error size for
different durations of a broadband stimulus (from 2000 to 40 millisecondss). The
incidence of incorrect trials and the size of the errors increased as the stimulus
duration was reduced. (B) The behavioral responses were quantified by calculating
the <termDef>mutual information</termDef> (<abbrev>MI</abbrev>) between
target location and response location. The MI values (in bits) are shown for both
the initial head orienting and the subsequent approach-to-target responses at
different stimulus durations. The MI between target and response location
increased with stimulus duration for the approach-to-target task, indicating that the
animals became more accurate, but remained constant for the relationship between
final head bearing and target location. The bBoxes represent the inter-quartile
range, the horizontal line is the median, and the vertical line is the full range of MI
values. (C, E) Stimulus-–response plots showing the distribution of approach-to-
target responses for two stimulus durations, (C) 1000 ms (C) and (E) 40 ms
(E)illiseconds. For each target location, the size of the dots is proportional to the
probability of responses to different target locations. The corresponding percent
correct and MI values for these stimulus durations (1000 and 40 milliseconds) are
shown in panels A and B, respectively. (D, F) Stimulus-–response plots showing
the distribution of final head bearings (binned in steps of 7.5°) for each target
location. The size of the dots is proportional to the response probability. The gray
lines represent the mean final bearing for each target location.
<source>Reproduced with permission from Nodal et al. [74].</source>
Fig. 29.3.Measuring sensitivity to binaural localization cues in ferrets. (A)
Photograph of the head of a ferret wearing lightweight titanium headphone holders
attached to a chronic implant on the skull, which enable earphones to be
positioned close to the entrance of the ear canal. <source>Reproduced from Nodal
et al. [7210].</source> (B) Mean interaural time difference (ITD) thresholds
across different animals are shown for noise bursts as a function of stimulus
duration. Data for individual animals are denoted by symbols of the same color
and are shown for two testing sessions (indicated by the different lines) separated
by 16 weeks to illustrate the stability of these measurements. (C) Corresponding
data for interaural level difference (ILD) thresholds obtained from the same
ferrets. <source>Data from Keating et al. [2411].</source>
Fig. 29.4.Comparison of human and ferret vocalizations. Spectrograms showing
examples of human speech (A) and ferret vocalizations (B) In each case, the sound
is produced by the repeated opening and closing of the vocal folds, resulting in
glottal pulse trains that are made up of a fundamental frequency and a number of
higher harmonics. Transmission of these pulse trains through the resonant cavities
of the vocal tract then enhances some harmonics and suppresses others. The
resulting frequency structure is visible as roughly horizontal bands in the
spectrograms. Note the difference in the scale of the y- axis in the two panels: the
ferret calls contain higher frequency components than human speech, reflecting
differences in the size and structure of the vocal apparatus between these species.
Fig. 29.5.Pitch discrimination by ferrets. The animals performed a two2-
alternative forced choice task, in which they indicated by approaching one of two
reward spouts whether a target sound was higher or lower in pitch than a
preceding reference sound. Each panel shows the psychometric functions for
individual animals. The grayscale of each curve corresponds to the fundamental
frequency of the reference vowel, as indicated to the right. The different reference
values are represented by gray circles at 50% choice probability.
<source>Reprinted, with permission, from Walker et al. [16].</source>
Fig. 29.6.Organization of the auditory pathway in the ferret. (A) Photograph of
the brainstem of a ferret after removing the cerebellum and cerebral hemispheres
to expose most of the subcortical auditory relay stations. (B) Photograph of the
ferret cortex in which the location of different auditory fields on the ectosylvian
gyrus (EG) is indicated. (C) Simplified diagram depicting the main ascending
(black) and descending (red) connections of the auditory pathway. For clarity, all
lines have been drawn with the same thickness independent of the size of the
projection they represent. Each of the connections is shown on one side of the
brain only. Abbreviations: AAF, anterior auditory field; A1, primary auditory field;
ADF, anterior dorsal field; as, ansinate sulculs; AVF, anterior ventral field; CN,
cochlear nuclei; cns, coronal sulcus; crs, cruciate sulcus; IC, inferior colliculus;
SC, superior colliculus; MGB, medial geniculate body; ls, lateral sulcus, PPF,
posterior pseudosylvian field; PSF, posterior suprasylvian field; pss, pseudosylvian
sulcus; SOC, superior olivary complex; sss, suprasylvian sulcus; VP, ventro
posterior field.
Fig. 29.7.Ferret auditory nerve physiology. (A) Frequency response areas from
six auditory nerve fibers in one animal with <termDef>characteristic
frequencies</termDef> (<abbrev>CFs</abbrev>) ranging from below 0.6 kHz to
above 17 kHz. Sound level is expressed in decibelB attenuation, where 0 dB
attenuation is ~100 dB sound pressure level (SPL). (B) Minimum thresholds as a
function of CF. The black continuous line is the ferret audiogram [1]. The gray
area is the outline of minimum thresholds reported in the inferior colliculusIC and
auditory cortex [39,40,97]. The dashed line is the outline when the frequency
tuning curves from all of the fibers are superimposed. AN, auditory nerve; CNS,
central nervous system. (C) <termDef>Equivalent rectangular
bandwidth</termDef> (<abbrev>ERB</abbrev>), a measure of frequency
selectivity, versus CF. The solid line shows the fit ERBkHz = 0.31CFkHz0.533. The
dashed line shows a fit to human psychophysical data [ERBkHz = 0.0247
(0.00437CFkHz + 1)] [98]. (D) Phase locking as a function of stimulus frequency.
Each point represents a significant vector strength value for a single stimulus
frequency in a single fibrefiber. Also shown are the range of values reported by
Palmer and Russell [99] in the guinea -pig (light gray) and by Johnson [100] in the
cat (dark gray). <source>Reproduced, with permission, from Sumner and Palmer
Fig. 29.8.Identifying the factors that shape the maturation of the spatial
sensitivity of neurons in the auditory cortex of the ferret. (A) Representative
examples of spatial receptive fields recorded at a sound level of 10 dB above
neuronal threshold for different neurons in the auditory cortex of infant and adult
ferrets. These plots illustrate the region of space within which a sound can
modulate the firing rate of the neurons. The receptive fields recorded from infant
cortex (P33–P39; left column) are more irregular and larger than those recorded in
adults (right column). The cross indicates the direction of the <termDef>spatial
response field</termDef> (<abbrev>SRF</abbrev>) centroid vector. The color bar
denotes the evoked spike count per stimulus presentation, averaged over five
repetitions. The black contour line runs along the half-maximal response level,
demarcating the 50% response area. (B) Pair-wise comparisons of near-threshold
50% area and number of bits of information transmitted between the stimulus
location and response for spatial receptive fields recorded from infant cortex
through the animals’' own ears (“infant own ears”) and then through virtual adult
ears (“infant adult ears”). Note that the 50% areas become smaller, and the
transmitted information increases when mature localization cue values are
provided. The histograms show the mean 50% area and information values derived
from infant (own-ear and adult-ear) SRFs and adult (own-ear) SRFs recorded at
near threshold (5–15 dB above unit threshold) and at higher sound levels (20–
35 dB above threshold; n = 40) (n.s., not significant; *P < 0.05; **P < 0.01). Error
bars indicate S.E.M. <source>Adapted from Mrsic-Flogel et al. [76].</source>
Fig. 29.9.Effect of abnormal auditory experience on the development of binaural
unmasking in ferrets. (A) Adult ferrets can be trained to detect a 500-Hz tone
(delivered from speaker A) in the presence of a noise (delivered from speakers A
and B) when a light flashed. The noise stimuli were presented continuously. On
50% of trials, a tone was presented when the ferret contacted spout 1. Success in
the task was measured by the ferret correctly identifying the presence or absence
of the tone (by going to spouts 2 or 3, respectively). In the control condition (tones
and noise interaurally in phase), both speakers were positioned on the ferret’'s
right side. In the bilateral condition, the noise was made out of phase with the tone
by moving speaker B (noise alone) to the ferret’'s left side. The difference in tone
threshold between the two conditions measured binaural unmasking. Unilateral or
asymmetric conductive hearing loss, as seen in middle ear disease in children and
otosclerosis in adults, can lead to poor binaural hearing. In the laboratory, these
diseases are modeled using earplugs in experimental animals to control for
variability and to investigate mechanisms. (B) Mean psychometric functions from
three groups of ferrets (blue, normally hearing animals; red, animals reared with a
unilateral conductive hearing loss and tested as adults; green, animals with a
unilateral conductive hearing loss that began in adulthood). Threshold (56.5%
correct) is shown by the horizontal dashed line. Like humans, Ferrets learned to
perform this task at a high level; the dashed line shows performance that was
statistically above chance (binomial distribution). However, normal ferrets with
normal , like normal humanshearing, have thresholds that are about 10 dB better in
the bilateral condition than in the control condition, showing binaural unmasking.
Unilateral conductive hearing loss impaired bilateral thresholds without
substantially affecting control thresholds (C) Mean (and S.D.) level of binaural
unmasking is shown for normally hearing ferrets and in animals that received a
unilateral hearing loss for several months. The reduction in unmasking seen during
earplugging takes several months to recover following its removalUnilateral or
asymmetric conductive hearing loss, as seen in middle ear disease in children and
otosclerosis in adults, can lead to poor binaural hearing. In the laboratory, these
diseases are modelledmodeled using ear plugs in experimental animals to control
for variability and to investigate mechanisms. Ferrets receiving several months of
unilateral ear plugging had reduced binaural unmasking for several more months
after the plug was removed. <source>Adapted from Moore et al. [80].</source>
Fig. 29.10.Auditory experience shapes the maturation of sound localization
behavior and the map of auditory space in the superior colliculusSC. (A)
Stimulus-–response plots showing the combined data of three3 normally -reared
ferrets (“NNormal adult ferrets”), another three3 animals just after inserting an
earplug into the left ear (“Adult left earplug”), and three3 ferrets that had been
raised and tested with the left ear occluded with a plug that produced 30–40 dB
attenuation (“Reared with left earplug”). These plots illustrate the distribution of
approach-to-target responses (ordinate) as a function of stimulus location
(abscissa). The stimuli were bursts of broadband noise. The size of the dots
indicates, for a given speaker angle, the proportion of responses made to different
response locations. Correct responses fall on the diagonal (x = y). Occluding one
ear disrupts sound localization accuracy, but adaptive changes take place during
development that enable the juvenile plugged ferrets to localize sound almost as
accurately as the controls. (B) The map of auditory space in the ferret SC,
illustrated by plotting the best azimuth of neurons versus their location within the
nucleus. Occluding one ear disrupts this spatial tuning, but, as with the behavioral
data, near-normal spatial tuning is present in ferrets that were raised with one ear
occluded. Based on Hartley King et al. (2000).
Fig. 29.11.Representation of sensory space in the ferret superior colliculusSC.
(A) The superficial layers of the SC are exclusively visual, whereas neurons in the
deeper layers respond to auditory (as well as visual and tactile) stimuli. Stimulus
azimuth is mapped onto the rostro-caudal axis (black arrow), and elevation onto
the medio-lateral (gray arrow) axis of the nucleus. The portion of visual and
auditory space represented within the SC is indicated by the arrows around the
ferret’'s head, and corresponds approximately to the extent of the visual field of
the contralateral eye. Based on King and Hutchings [46]. (B) Visual calibration of
the developing auditory space map in the ferret superior colliculusSC. Removal of
the medial rectus muscle just before natural eye opening induces a small, outward
deviation of the eye. The visual field is therefore shifted laterally relative to the
head. This is shown schematically as the receptive field of a superficial- layer
neuron in the contralateral SC shifting from position V to V*. Electrophysiological
recordings in adult ferrets revealed that the auditory receptive fields of deep SC
neurons are also shifted laterally by a corresponding amount (A A*) following
this procedure. As a result of this adaptive change, the correspondence between the
superficial-layer visual map and the deep-layer auditory map is preserved.
<source>Data source from King et al. (1988).</source>
Fig. 29.12.Cortical tuning changes during behavior. (A) Ferrets were trained to
detect a pure tone target (red) after a random number of reference noise sounds
(blue) using two different behavioral methods. During the approach behavior
(timeline, lLower), the animals were positively rewarded with water for licking a
water spout 0.1–1.0 second after target onset (green bar) and punished with a
timeout for licking earlier (red bar). During the avoidance task, the ferrets were
rewarded by licking a continuously flowing stream of water during the reference
sounds and punished with a mild tail shock if they failed to cease licking
0.4 second after target offset. (B) Data from one neuron during the approach task.
The spectrotemporal receptive field (STRF) of the neuron was estimated from
responses to reference sounds during passive listening (lLeft) and indicates which
stimulus frequencies and time lags correlated with excited increased (red) or
suppressed (blue) neural spiking. This neuron was excited broadly by 8000- to
16,000-Hz stimuli, which overlapped the target tone (11,046 Hz). During behavior
(cCenter), a notch appeared at the target frequency in the excitatory region, and
the difference between the active and passive STRFs (rRight) shows a 23%
decrease in gain at the target frequency (“X”). (C) STRF change for a second
neuron during approach, plotted as in B. Here the target frequency (11,300 Hz)
overlapped an inhibitory subregion of the passive STRF. During behavior, the
inhibition grew stronger, producing a net 7% decrease at the target frequency. (D)
STRF change for a neuron during avoidance behavior, with the target positioned
on the shoulder of an excitatory region of the passive STRF (1250 Hz). The STRF
showed a selective 10% increase at the target frequency during behavior. (E) Data
from a second neuron recorded during avoidance, with the target positioned over
an inhibitory region (1350 Hz). The inhibition was mostly abolished during
behavior, resulting in a 30% response increase at the target frequency.
<source>Reprinted, with permission, from David et al. [89].</source>
Fig. 29.13.Experience- dependent plasticity of spatial hearing in adult ferrets. (A)
Stimulus-–response plots showing the distribution of responses (ordinate) made by
a ferret as a function of stimulus location in the horizontal plane (abscissa). Prior
to occlusion of the left ear, the animal achieved 100% correct scores at all stimulus
directions (left panel), but performed poorly, particularly on the side of the
earplug, when the left ear was occluded (middle). Further testing with the earplug
still in place, however, led to a recovery in localization accuracy (right).
<source>Modified from Kacelnik et al. [93].</source> (B) Effect of cortical
inactivation on adaptation. Each panel shows data from a different group of
animals that received a subdural implant of a slow release polymer over some part
of auditory cortex. The top left panel are drug-free controls, while the other three
panels show the effects of inactivating the cortex by placing muscimol-Elvax over
the middle (MEG), anterior (AEG), or posterior (PEG) ectosylvian gyrus. The
percentage of correct trials measured every day from individual animals and
averaged across all speaker locations are shown by the dashed lines, with the mean
performance for each group indicated by the solid lines. Data are shown prior to
insertion of an earplug in the left ear (Pre), on each of the 9 days that the earplug
was in place (dDays 1–9), and following its removal (Post). Deactivating any of
these cortical areas impaired adaptation, whereas the drug-free controls learned
normally. <source>Modified from Nodal et al. [74].</source> (C) Elimination of
descending corticocollicular neurons by chromophore-targeted laser photolysis
also impaired the ability of ferrets to adapt to an earplug. Different symbols
correspond to different animals and the lines show the mean performance of
control (gray) and lesioned (black) groups. The photomicrographs on the right
illustrate the loss of layer V pyramidal cells in the lesioned cortex compared to
with intact cortex. <source>Modified from Bajo et al. [37].</source>
Fig. 29.14.Ferret cochlear implantation. (A) Nucleus ESPrit 3G speech processor
(1) attached to a modified Nucleus Cochlear implant CI24RE emulator (2).
Pockets were incorporated within the neckline of a detachable “backpack” (3) that
was attached to a jacket made from a ferret harness and elasticated tubular
bandage (4). (B) Ferrets carried their cochlear implants within the jacket, which
held the microphone of the left and right speech processor immediately posterior
to the ipsilateral pinna and enabled animals to carry on with their normal activities
during chronic stimulation. (C) Hhigh resolution micro-focus radiograph in
anterior–posterior view of a ferret skull showing bilateral intracochlear arrays
inserted to an even depth into the basal turn of both cochleas relative to the
<termDef>round window niche</termDef> (<abbrev>RWN</abbrev>) marked
with a fine wire. The apical electrode in the electrode array is marked with an
arrowhead. <source>Reproduced, with permission, from Hartley et al.
... The black-footed ferret has extremely specific habitat needs (USFWS 2013), and ferrets have an exceptionally capable hearing on par with humans below 20 kHz, and extending to an upper frequency limit of 40 kHz (Nodal and King 2014). Acute hearing is crucial for hunting in subterranean burrows and for avoiding predation by mesocarnivores above ground. ...
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Synopsis Global expansion of lighting and noise pollution alters how animals receive and interpret environmental cues. However, we lack a cross-taxon understanding of how animal traits influence species vulnerability to this growing phenomenon. This knowledge is needed to improve the design and implementation of policies that mitigate or reduce sensory pollutants. We present results from an expert knowledge survey that quantified the relative influence of 21 ecological, anatomical, and physiological traits on the vulnerability of terrestrial vertebrates to elevated levels of anthropogenic lighting and noise. We aimed not only to quantify the importance of threats and the relative influence of traits as viewed by sensory and wildlife experts, but to examine knowledge gaps based on the variation in responses. Identifying traits that had less consensus can guide future research for strengthening ecologists’ and conservation biologists’ understanding of sensory abilities. Our findings, based on 280 responses of expert opinion, highlight the increasing recognition among experts that sensory pollutants are important to consider in management and conservation decisions. Participant responses show mounting threats to species with narrow niches; especially habitat specialists, nocturnal species, and those with the greatest ability to differentiate environmental visual and auditory cues. Our results call attention to the threat specialist species face and provide a generalizable understanding of which species require additional considerations when developing conservation policies and mitigation strategies in a world altered by expanding sensory pollutant footprints. We provide a step-by-step example for translating these results to on-the-ground conservation planning using two species as case studies.
... This is the first application of 2-photon calcium imaging and Neuropixels recordings to study auditory processing in ferrets. Ferrets are a popular animal model in auditory neuroscience because they have a gyrencephalic cortex, their A1 is easily accessible, they are readily trained on behavioural tasks, and (unlike mice) their hearing range overlaps well with the human audiogram (Nodal and King, 2014). ...
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Primary cortical areas contain maps of sensory features, including sound frequency in primary auditory cortex (A1). Two-photon calcium imaging in mice has confirmed the presence of these global tonotopic maps, while uncovering an unexpected local variability in the stimulus preferences of individual neurons in A1 and other primary regions. Here we show that local heterogeneity of frequency preferences is not unique to rodents. Using two-photon calcium imaging in layers 2/3, we found that local variance in frequency preferences is equivalent in ferrets and mice. Neurons with multipeaked frequency tuning are less spatially organized than those tuned to a single frequency in both species. Furthermore, we show that microelectrode recordings may describe a smoother tonotopic arrangement due to a sampling bias towards neurons with simple frequency tuning. These results help explain previous inconsistencies in cortical topography across species and recording techniques.
... This is the first application of 2-photon calcium imaging and Neuropixels recordings to study auditory processing in ferrets. Ferrets are a popular animal model in auditory neuroscience because they have a gyrencephalic cortex, their A1 is easily accessible, they are readily trained on behavioural tasks, and (unlike mice) their hearing range overlaps well with the human audiogram (Nodal and King, 2014). ...
Full-text available
Primary cortical areas contain maps of sensory features, including sound frequency in primary auditory cortex (A1). Two-photon calcium imaging in mice has confirmed the presence of these global tonotopic maps, while uncovering an unexpected local variability in the stimulus preferences of individual neurons in A1 and other primary regions. Here we show that local heterogeneity of frequency preferences is not unique to rodents. Using two-photon calcium imaging in layers 2/3, we found that local variance in frequency preferences is equivalent in ferrets and mice. Neurons with multipeaked frequency tuning are less spatially organized than those tuned to a single frequency in both species. Furthermore, we show that microelectrode recordings may describe a smoother tonotopic arrangement due to a sampling bias towards neurons with simple frequency tuning. These results help explain previous inconsistencies in cortical topography across species and recording techniques.
... 27 Auditory function in the ferret is similar to in the cat, although the auditory response may be more primitive. 27,35 The hearing range is about 20 Hz to 44 kHz. 50 Ferrets with white markings, such as pandas and blazes, are prone to deafness associated with a congenital Waardenburg-like syndrome. ...
The ferret was domesticated in Europe some 2000 years ago and has since been used to control rodent populations, hunt rabbits, and as a biomedical research model. Currently, ferrets are popular pets in many parts of the world. Ferrets are strict carnivores and their anatomy and physiology are similar to other small carnivores, although there are important differences in their reproductive physiology.
... This is the first application of 2-photon calcium imaging and Neuropixels recordings to study auditory processing in ferrets. Ferrets are a popular animal model in auditory neuroscience because they have a gyrencephalic cortex, their A1 is easily accessible, they are readily trained on behavioural tasks, and (unlike mice) their hearing range overlaps well with the human audiogram (Nodal and King, 2014). ...
Full-text available
Primary cortical areas contain maps of sensory features, including sound frequency in primary auditory cortex (A1). Two-photon calcium imaging in mice has confirmed the presence of these global tonotopic maps, while uncovering an unexpected local variability in the stimulus preferences of individual neurons in A1 and other primary regions. Here we show that local heterogeneity of frequency preferences is not unique to rodents. Using two-photon calcium imaging in layers 2/3, we found that local variance in frequency preferences is equivalent in ferrets and mice. Neurons with multipeaked frequency tuning are less spatially organized than those tuned to a single frequency in both species. Furthermore, we show that microelectrode recordings may describe a smoother tonotopic arrangement due to a sampling bias towards neurons with simple frequency tuning. These results help explain previous inconsistencies in cortical topography across species and recording techniques.
... To compare with the model, we recorded responses of 114 auditory neurons (including 76 single units) in A1 and the anterior auditory field (AAF) of 5 anesthetized ferrets and measured their spectrotemporal RFs (see Materials and methods). Ferrets are commonly used for auditory research, because they are readily trained in a range of sound detection, discrimination or localization tasks (Nodal and King, 2014), the frequency range of their hearing (approximately 40 Hz-40 kHz [Kavanagh and Kelly, 1988]) overlaps well with (and extends beyond) the human range, and most of their auditory cortex is not buried in a sulcus and hence easily accessible for electrophysiological or optical measurements. , Network trained on movie clips of natural scenes, aims to predict immediate future frame of each clip from recent past frames. ...
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Neurons in sensory cortex are tuned to diverse features in natural scenes. But what determines which features neurons become selective to? Here we explore the idea that neuronal selectivity is optimized to represent features in the recent sensory past that best predict immediate future inputs. We tested this hypothesis using simple feedforward neural networks, which were trained to predict the next few moments of video or audio in clips of natural scenes. The networks developed receptive fields that closely matched those of real cortical neurons in different mammalian species, including the oriented spatial tuning of primary visual cortex, the frequency selectivity of primary auditory cortex and, most notably, their temporal tuning properties. Furthermore, the better a network predicted future inputs the more closely its receptive fields resembled those in the brain. This suggests that sensory processing is optimized to extract those features with the most capacity to predict future input.
... All rights reserved. Hammond-Kenny et al., 2017), and has been employed extensively in a range of auditory detection, discrimination and localization tasks (reviewed by Fritz et al., 2007;Nodal & King, 2014). ...
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Ray Guillery was a neuroscientist known primarily for his ground‐breaking studies on the development of the visual pathways and subsequently on the nature of thalamocortical processing loops. The legacy of his work, however, extends well beyond the visual system. Thanks to Ray Guillery's pioneering anatomical studies, the ferret has become a widely used animal model for investigating the development and plasticity of sensory processing. This includes our own work on the auditory system, where experiments in ferrets have revealed the role of sensory experience during development in shaping the neural circuits responsible for sound localization, as well as the capacity of the mature brain to adapt to changes in inputs resulting from hearing loss. Our research has also built on Ray Guillery's ideas about the possible functions of the massive descending projections that link sensory areas of the cerebral cortex to the thalamus and other subcortical targets, by demonstrating a role for corticothalamic feedback in the perception of complex sounds and for corticollicular projection neurons in learning to accommodate altered auditory spatial cues. Finally, his insights into the organization and functions of transthalamic corticocortical connections have inspired a raft of research, including by our own laboratory, which has attempted to identify how information flows through the thalamus. This article is protected by copyright. All rights reserved.
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Abstract Background Noise-induced hearing loss (NIHL) is the most prevalent form of acquired hearing loss and affects about 40 million US adults. Among the suggested therapeutics tested in rodents, suberoylanilide hydroxamic acid (SAHA) has been shown to be otoprotective from NIHL; however, these results were limited to male mice. Methods Here we tested the effect of SAHA on the hearing of 10-week-old B6CBAF1/J mice of both sexes, which were exposed to 2 h of octave-band noise (101 dB SPL centered at 11.3 kHz). Hearing was assessed by measuring auditory brainstem responses (ABR) at 8, 16, 24, and 32 kHz, 1 week before, as well as at 24 h and 15–21 days following exposure (baseline, compound threshold shift (CTS) and permanent threshold shift (PTS), respectively), followed by histologic analyses. Results We found significant differences in the CTS and PTS of the control (vehicle injected) mice to noise, where females had a significantly smaller CTS at 16 and 24 kHz (p
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The precise encoding of temporal features of auditory stimuli by the mammalian auditory system is critical to the perception of biologically important sounds, including vocalizations, speech, and music. In this study, auditory gap-detection behavior was evaluated in adult pigmented ferrets (Mustelid putorius furo) using bandpassed stimuli designed to widely sample the ferret's behavioral and physiological audiogram. Animals were tested under positive operant conditioning, with psychometric functions constructed in response to gap-in-noise lengths ranging from 3 to 270 ms. Using a modified version of this gap-detection task, with the same stimulus frequency parameters, we also tested a cohort of normal-hearing human subjects. Gap-detection thresholds were computed from psychometric curves transformed according to signal detection theory, revealing that for both ferrets and humans, detection sensitivity was worse for silent gaps embedded within low-frequency noise compared with high-frequency or broadband stimuli. Additional psychometric function analysis of ferret behavior indicated effects of stimulus spectral content on aspects of behavioral performance related to decision-making processes, with animals displaying improved sensitivity for broadband gap-in-noise detection. Reaction times derived from unconditioned head-orienting data and the time from stimulus onset to reward spout activation varied with the stimulus frequency content and gap length, as well as the approach-to-target choice and reward location. The present study represents a comprehensive evaluation of gap-detection behavior in ferrets, while similarities in performance with our human subjects confirm the use of the ferret as an appropriate model of temporal processing. (PsycINFO Database Record (c) 2015 APA, all rights reserved).
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Although many studies have examined the precedence effect (PE), few have tested whether it shows a buildup and breakdown in nonhuman animals comparable to that seen in humans. These processes are thought to reflect the ability of the auditory system to adjust to a listener's acoustic environment, and their mechanisms are still poorly understood. In this study, ferrets were trained on a two-alternative forced-choice task to discriminate the azimuthal direction of brief sounds. In one experiment, pairs of noise bursts were presented from two loudspeakers at different interstimulus delays (ISDs). Results showed that localization performance changed as a function of ISD in a manner consistent with the PE being operative. A second experiment investigated buildup and breakdown of the PE by measuring the ability of ferrets to discriminate the direction of a click pair following presentation of a conditioning train. Human listeners were also tested using this paradigm. In both species, performance was better when the test clicks and conditioning train had the same ISD but deteriorated following a switch in the direction of the leading and lagging sounds between the conditioning train and test clicks. These results suggest that ferrets, like humans, experience a buildup and breakdown of the PE.
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Identifying behaviorally relevant sounds in the presence of background noise is one of the most important and poorly understood challenges faced by the auditory system. An elegant solution to this problem would be for the auditory system to represent sounds in a noise-invariant fashion. Since a major effect of background noise is to alter the statistics of the sounds reaching the ear, noise-invariant representations could be promoted by neurons adapting to stimulus statistics. Here we investigated the extent of neuronal adaptation to the mean and contrast of auditory stimulation as one ascends the auditory pathway. We measured these forms of adaptation by presenting complex synthetic and natural sounds, recording neuronal responses in the inferior colliculus and primary fields of the auditory cortex of anaesthetized ferrets, and comparing these responses with a sophisticated model of the auditory nerve. We find that the strength of both forms of adaptation increases as one ascends the auditory pathway. To investigate whether this adaptation to stimulus statistics contributes to the construction of noise-invariant sound representations, we also presented complex, natural sounds embedded in stationary noise, and used a decoding approach to assess the noise tolerance of the neuronal population code. We find that the code for complex sounds in the periphery is affected more by the addition of noise than the cortical code. We also find that noise tolerance is correlated with adaptation to stimulus statistics, so that populations that show the strongest adaptation to stimulus statistics are also the most noise-tolerant. This suggests that the increase in adaptation to sound statistics from auditory nerve to midbrain to cortex is an important stage in the construction of noise-invariant sound representations in the higher auditory brain.
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For over a century, the duplex theory has guided our understanding of human sound localization in the horizontal plane. According to this theory, the auditory system uses interaural time differences (ITDs) and interaural level differences (ILDs) to localize low-frequency and high-frequency sounds, respectively. Whilst this theory successfully accounts for the localization of tones by humans, some species show very different behaviour. Ferrets are widely used for studying both clinical and fundamental aspects of spatial hearing, but it is not known whether the duplex theory applies to this species or, if so, to what extent the frequency range over which each binaural cue is used depends on acoustical or neurophysiological factors. To address these issues, we trained ferrets to lateralize tones presented over earphones and found that the frequency dependence of ITD and ILD sensitivity broadly paralleled that observed in humans. Compared with humans, however, the transition between ITD and ILD sensitivity was shifted toward higher frequencies. We found that the frequency dependence of ITD sensitivity in ferrets can partially be accounted for by acoustical factors, although neurophysiological mechanisms are also likely to be involved. Moreover, we show that binaural cue sensitivity can be shaped by experience, as training ferrets on a 1-kHz ILD task resulted in significant improvements in thresholds that were specific to the trained cue and frequency. Our results provide new insights into the factors limiting the use of different sound localization cues and highlight the importance of sensory experience in shaping the underlying neural mechanisms.
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Neural systems must weight and integrate different sensory cues in order to make decisions. However, environmental conditions often change over time, altering the reliability of different cues and therefore the optimal way for combining them. To explore how cue integration develops in dynamic environments, we examined the effects on auditory spatial processing of rearing ferrets with localization cues that were modified via a unilateral earplug, interspersed with brief periods of normal hearing. In contrast with control animals, which rely primarily on timing and intensity differences between their two ears to localize sound sources, the juvenile-plugged ferrets developed the ability to localize sounds accurately by relying more on the unchanged spectral localization cues provided by the single normal ear. This adaptive process was paralleled by changes in neuronal responses in the primary auditory cortex, which became relatively more sensitive to these monaural spatial cues. Our behavioral and physiological data demonstrated, however, that the reweighting of different spatial cues disappeared as soon as normal hearing was experienced, showing for the first time that this type of plasticity can be context specific. These results show that developmental changes can be selectively expressed in response to specific acoustic conditions. In this way, the auditory system can develop and simultaneously maintain two distinct models of auditory space and switch between these models depending on the prevailing sensory context. This ability is likely to be critical for maintaining accurate perception in dynamic environments and may point toward novel therapeutic strategies for individuals who experience sensory deficits during development.
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Although the ferret has become an important model species for studying both fundamental and clinical aspects of spatial hearing, previous behavioral work has focused on studies of sound localization and spatial release from masking in the free field. This makes it difficult to tease apart the role played by different spatial cues. In humans and other species, interaural time differences (ITDs) and interaural level differences (ILDs) play a critical role in sound localization in the azimuthal plane and also facilitate sound source separation in noisy environments. In this study, we used a range of broadband noise stimuli presented via customized earphones to measure ITD and ILD sensitivity in the ferret. Our behavioral data show that ferrets are extremely sensitive to changes in either binaural cue, with levels of performance approximating that found in humans. The measured thresholds were relatively stable despite extensive and prolonged (>16 weeks) testing on ITD and ILD tasks with broadband stimuli. For both cues, sensitivity was reduced at shorter durations. In addition, subtle effects of changing the stimulus envelope were observed on ITD, but not ILD, thresholds. Sensitivity to these cues also differed in other ways. Whereas ILD sensitivity was unaffected by changes in average binaural level or interaural correlation, the same manipulations produced much larger effects on ITD sensitivity, with thresholds declining when either of these parameters was reduced. The binaural sensitivity measured in this study can largely account for the ability of ferrets to localize broadband stimuli in the azimuthal plane. Our results are also broadly consistent with data from humans and confirm the ferret as an excellent experimental model for studying spatial hearing.
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The nucleus basalis (NB) in the basal forebrain provides most of the cholinergic input to the neocortex and has been implicated in a variety of cognitive functions related to the processing of sensory stimuli. However, the role that cortical acetylcholine release plays in perception remains unclear. Here we show that selective loss of cholinergic NB neurons that project to the cortex reduces the accuracy with which ferrets localize brief sounds and prevents them from adaptively reweighting auditory localization cues in response to chronic occlusion of one ear. Cholinergic input to the cortex was disrupted by making bilateral injections of the immunotoxin ME20.4-SAP into the NB. This produced a substantial loss of both p75 neurotrophin receptor (p75(NTR))-positive and choline acetyltransferase-positive cells in this region and of acetylcholinesterase-positive fibers throughout the auditory cortex. These animals were significantly impaired in their ability to localize short broadband sounds (40-500 ms in duration) in the horizontal plane, with larger cholinergic cell lesions producing greater performance impairments. Although they localized longer sounds with normal accuracy, their response times were significantly longer than controls. Ferrets with cholinergic forebrain lesions were also less able to relearn to localize sound after plugging one ear. In contrast to controls, they exhibited little recovery of localization performance after behavioral training. Together, these results show that cortical cholinergic inputs contribute to the perception of sound source location under normal hearing conditions and play a critical role in allowing the auditory system to adapt to changes in the spatial cues available.
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The neural processing of sensory stimuli involves a transformation of physical stimulus parameters into perceptual features, and elucidating where and how this transformation occurs is one of the ultimate aims of sensory neurophysiology. Recent studies have shown that the firing of neurons in early sensory cortex can be modulated by multisensory interactions [1-5], motor behavior [1, 3, 6, 7], and reward feedback [1, 8, 9], but it remains unclear whether neural activity is more closely tied to perception, as indicated by behavioral choice, or to the physical properties of the stimulus. We investigated which of these properties are predominantly represented in auditory cortex by recording local field potentials (LFPs) and multiunit spiking activity in ferrets while they discriminated the pitch of artificial vowels. We found that auditory cortical activity is informative both about the fundamental frequency (F0) of a target sound and also about the pitch that the animals appear to perceive given their behavioral responses. Surprisingly, although the stimulus F0 was well represented at the onset of the target sound, neural activity throughout auditory cortex frequently predicted the reported pitch better than the target F0.
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In addition to their ascending pathways that originate at the receptor cells, all sensory systems are characterized by extensive descending projections. Although the size of these connections often outweighs those that carry information in the ascending auditory pathway, we still have a relatively poor understanding of the role they play in sensory processing. In the auditory system one of the main corticofugal projections links layer V pyramidal neurons with the inferior colliculus (IC) in the midbrain. All auditory cortical fields contribute to this projection, with the primary areas providing the largest outputs to the IC. In addition to medium and large pyramidal cells in layer V, a variety of cell types in layer VI make a small contribution to the ipsilateral corticocollicular projection. Cortical neurons innervate the three IC subdivisions bilaterally, although the contralateral projection is relatively small. The dorsal and lateral cortices of the IC are the principal targets of corticocollicular axons, but input to the central nucleus has also been described in some studies and is distinctive in its laminar topographic organization. Focal electrical stimulation and inactivation studies have shown that the auditory cortex can modify almost every aspect of the response properties of IC neurons, including their sensitivity to sound frequency, intensity, and location. Along with other descending pathways in the auditory system, the corticocollicular projection appears to continually modulate the processing of acoustical signals at subcortical levels. In particular, there is growing evidence that these circuits play a critical role in the plasticity of neural processing that underlies the effects of learning and experience on auditory perception by enabling changes in cortical response properties to spread to subcortical nuclei.
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