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Abstract

Stochastic Resonance (SR) has been proposed to play a major role in auditory perception, and to maintain optimal information transmission from the cochlea to the auditory system. By this, the auditory system could adapt to changes of the auditory input at second or even sub-second timescales. In case of reduced auditory input, somatosensory projections to the dorsal cochlear nucleus would be disinhibited in order to improve hearing thresholds by means of SR. As a side effect, the increased somatosensory input corresponding to the observed tinnitus-associated neuronal hyperactivity is then perceived as tinnitus. In addition, the model can also explain transient phantom tone perceptions occurring after ear plugging, or the Zwicker tone illusion. Vice versa, the model predicts that via stimulation with acoustic noise, SR would not be needed to optimize information transmission, and hence somatosensory noise would be tuned down, resulting in a transient vanishing of tinnitus, an effect referred to as residual inhibition.
The Stochastic Resonance model of auditory perception:
A unified explanation of tinnitus development, Zwicker
tone illusion, and residual inhibition.
Achim Schilling1,2, Konstantin Tziridis1, Holger Schulze1, Patrick Krauss1,2,3,4
1 Neuroscience Lab, Experimental Otolaryngology, Friedrich-Alexander University
Erlangen-Nürnberg (FAU), Germany
2 Cognitive Computational Neuroscience Group at the Chair of English Philology and
Linguistics, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Germany
3 FAU Linguistics Lab, Friedrich-Alexander University Erlangen-Nürnberg (FAU),
Germany
4 Department of Otorhinolaryngology/Head and Neck Surgery, University of
Groningen, University Medical Center Groningen, The Netherlands
Keywords: Auditory Phantom Perception, Somatosensory Projections, Dorsal
Cochlear Nucleus, Speech Perception
Abstract
Stochastic Resonance (SR) has been proposed to play a major role in auditory perception,
and to maintain optimal information transmission from the cochlea to the auditory system. By
this, the auditory system could adapt to changes of the auditory input at second or even sub-
second timescales. In case of reduced auditory input, somatosensory projections to the dorsal
cochlear nucleus would be disinhibited in order to improve hearing thresholds by means of
SR. As a side effect, the increased somatosensory input corresponding to the observed
tinnitus-associated neuronal hyperactivity is then perceived as tinnitus. In addition, the model
can also explain transient phantom tone perceptions occurring after ear plugging, or the
Zwicker tone illusion. Vice versa, the model predicts that via stimulation with acoustic noise,
SR would not be needed to optimize information transmission, and hence somatosensory
noise would be tuned down, resulting in a transient vanishing of tinnitus, an effect referred to
as residual inhibition.
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The copyright holder for this preprintthis version posted March 29, 2020. . https://doi.org/10.1101/2020.03.27.011163doi: bioRxiv preprint
Stochastic Resonance
In engineering, the term noise, defined as undesirable disturbances or fluctuations, is
considered to be the “fundamental enemy” (McDonnell & Abbott 2009) for error-free
information transmission, processing, and communication. However, a vast and even
increasing number of studies show the various benefits of noise in the context of signal
detection and processing. Here, the most important phenomena are called stochastic
resonance (McDonnell & Abbott 2009), coherence resonance (Pikovsky & Kurths
1997), and recurrence resonance (Krauss et al., 2019a).
The term stochastic resonance (SR), which has been introduced by Benzi in 1981
(Benzi et al., 1981), refers to the phenomenon that signals otherwise sub-threshold for
a given sensor can be detected by adding a random signal, i.e. noise, of appropriate
intensity to the sensor input (Gammaitoni et al., 1998; Moss et al., 2004). Figure 1
illustrates this principle.
SR has been found ubiquitously in nature in a broad range of systems from physical
to biological contexts (Wiesenfeld & Moss 1995; Hänggi 2002). In particular in
neuroscience, SR has been demonstrated to play an essential role in virtually all kinds
of systems (Faisal et al., 2008): from tactile (Douglass et al., 1993; Collins et al., 1996),
auditory (Mino 2014) and visual (Aihara et al., 2008) perception (Ward et al., 2002),
through memory retrieval (Usher & Feingold 2000) and cognition (Chandrasekharan
et al. 2005), to behavioral control (Ward et al., 2002; Kitajo et al., 2003). SR explains
how the brain processes information in noisy environments at each level of scale from
single synapses (Stacey & Durand 2001), through individual neurons (Nozaki et al.,
1999; Kosko & Mitaim 2003), to complete networks (Gluckman et al., 1996).
In self-adaptive signal detection systems exploiting SR, the optimum intensity of the
noise is continuously adjusted so that information transmission is maximized, even if
the characteristics and statistics of the input signal change (Figure 2). For this
processing principle, the term adaptive SR has been coined (Mitaim & Kosko 1998,
2004; Wenning & Obermayer 2003; Krauss et al., 2017).
Tinnitus Development
In a number of recently published studies, we demonstrated theoretically and
empirically that SR might be a major processing principle of the auditory system that
serves to partially compensate for acute or chronic hearing loss (Krauss et al., 2016,
2017, 2018, 2019b; Gollnast et al., 2017). According to our model, the noise required
for SR is generated within the brain and then perceived as a phantom sound. We have
proposed that it corresponds to increased spontaneous neuronal firing rates in early
processing stages of the auditory brain stem - a phenomenon which is frequently
observed in both humans with subjective tinnitus (Wang et al., 1997; Ahlf et al., 2012;
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Tziridis et al., 2015; Wu et al., 2016) and animal models, where the presence of tinnitus
is tested using behavioral paradigms (Gerum et al., 2019, Schilling et al., 2017, Turner
et al., 2006). Furthermore, tinnitus is assumed to be virtually always caused by some
kind of either apparent (Heller 2003; König et al., 2006; Nelson & Chen 2004; Shore
et al., 2016) or hidden hearing loss (Schaette & McAlpine 2011; Liberman & Liberman
2015). From this point of view, auditory phantom perceptions like tinnitus (or even the
Zwicker tone, cf. below) seem to be a side effect of an adaptive mechanism within the
auditory system whose primary purpose is to compensate for reduced input through
continuous optimization of information transmission (Krauss et al., 2016, 2017, 2018,
2019b). This new interpretation may also explain why auditory sensitivity is increased
in tinnitus ears (Hebert et al., 2013; Gollnast et al., 2017): the increased amount of
neural noise during tinnitus improves auditory sensitivity by means of SR.
The dorsal cochlear nucleus (DCN) has been shown to be the earliest processing
stage where acoustic trauma, including complete cochlea ablation (Zacharek et al.,
2002), causes increased spontaneous firing rates (Kaltenbach et al., 1998; Kaltenbach
& Afman 2000; Zacharek et al., 2002; Wu et al., 2016). Interestingly, this increase in
spontaneous activity, i.e. neural hyperactivity, is correlated with the strength of the
behavioral signs of tinnitus in animal models (Kaltenbach et al., 2004). Furthermore,
the hyperactivity is localized in those regions of the DCN that are innervated by the
damaged parts of the cochlea (Kaltenbach et al., 2002). Gao and colleagues (Gao et
al., 2016) recently described changes in DCN fusiform cell spontaneous activity after
noise exposure that perfectly supports the proposed SR mechanism. In particular, the
time course of spontaneous rate changes shows an almost complete loss of
spontaneous activity immediately after loud sound exposure (as no SR is needed due
to stimulation that is well above threshold), followed by an overcompensation of
spontaneous rates to levels well above pre-exposition rates since SR is now used to
compensate for acute hearing loss (Gao et al., 2016).
It is well known that the DCN receives not only auditory input from the cochlea, but
also input from the somatosensory system (Ryugo et al., 2003; Shore & Zhou 2006;
Wu et al., 2015), and that noise trauma alters long-term somatosensory-auditory
processing in the DCN (Dehmel et al., 2008, 2012; Shore 2011, Wu et al., 2016), i.e.
somatosensory projections are up-regulated after hearing loss (Zeng et al., 2012). In
addition, DCN responses to somatosensory stimulation are enhanced after noise-
induced hearing loss (Shore et al., 2008; Shore 2011; Wu et al., 2016). Therefore, we
previously proposed the possibility that the neural noise which is necessary for SR is
injected into the auditory system via somatosensory projections to the DCN (Krauss
et al., 2016, 2018, 2019b), and that these non-auditory projections into the DCN are
the cause of the altered “spontaneous activity” within the DCN after hearing loss
described previously (Gao et al., 2016). From an information processing point of view,
somatosensory inputs are completely uncorrelated, i.e. have no mutual auditory
information. Hence, these somatosensory inputs are perfectly suited to serve as a
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random signal, i.e. noise, in the context of SR, and this seems to be the reason why
the auditory system does not generate the noise needed for SR itself.
Our idea that cross-modal SR, with cochlear inputs being the signal and
somatosensory projections being the noise (Figure 2), is a key processing principle of
the auditory system and actually takes place in the DCN (Krauss et al., 2018) is
supported by a large number of different findings. For instance, it is well known, that
jaw movements lead to a modulation of subjective tinnitus loudness (Pinchoff et al.,
1998). This may easily be explained within our framework, as jaw movements alter
somatosensory input to the DCN: Since this somatosensory input corresponds to the
noise for SR, auditory input to the DCN is modulated through this mechanism, and the
altered noise level would then be perceived as modulated tinnitus (Krauss et al., 2016,
2018, 2019b). Along the same line, one may explain why both, the temporo-
mandibular joint syndrome and whiplash, frequently cause so called somatic tinnitus
(Levine 1999; Shore et al., 2007).
Furthermore, the finding of Tang and Trussell that somatosensory input and hence
tinnitus sensation may also be modified by serotonergic regulation of excitability of
principal cells in the DCN (Tang & Trussell 2015, 2017) supports the SR model. It even
provides a mechanistic explanation of salicylate induced tinnitus, since salicylate
affects DCN processing by disinhibition of somatosensory inputs (Koerber et al.,1966,
Stoltzberg et al., 2012). Thus, it increases the noise in the auditory system, which then
may again be perceived as a phantom sound.
Finally, and maybe most remarkable, electro-tactile stimulation of finger tips, i.e.
increased somatosensory input, significantly improves both, melody recognition
(Huang et al., 2020) and speech recognition (Huang et al., 2017) in patients with
cochlear implants. Very recently, we were able to reproduce and mechanistically
explain this finding, using a hybrid-computational model that exploits SR. The model
consists of a cochlea model, a DCN model and an artificial deep neural network trained
on a speech recognition task representing all further processing stages of the auditory
pathway beyond the DCN. Simulated hearing loss, i.e. weakening the input from the
cochlea model to the DCN model, reduced accuracy for speech recognition in the deep
neural network, as expected. However, subsequent noise, i.e. somatosensory, input
to the DCN model results in an improved accuracy for speech recognition (Schilling et
al., 2020).
Zwicker Tone Illusion
The Zwicker tone effect was discovered by Eberhard Zwicker in 1964 and is a temporal
auditory phantom percept which was originally induced by the presentation of a 60 dB
broadband noise with a spectral gap (notched noise) with a gap-width of half an octave
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(Zwicker 1964). The Zwicker tone was described as “Negative Auditory After Image”,
although the underlying mechanisms generating an “After Image” are supposed to be
different in the visual system. The Zwicker tone perception is not exclusively induced
by a notched noise stimulus, but can also be caused by low-pass noise or white noise
with a loud pure tone embedded (Fastl et al., 2001, Franosch et al., 2003).
Several models exist trying to explain the Zwicker tone percept. For example,
Franosch and colleagues viewed the Zwicker tone as an asymmetric lateral inhibition
effect along the auditory pathway (Franosch et al. 2003). In this view, the neurons in
the DCN are disinhibited by surrounding neurons, which receive less stimulus driven
activity due to the notch.
Another model suggested the Zwicker tone to be caused by a prediction error within
the cortex in combination with an increased spontaneous rate of auditory pathway
neurons at frequency ranges deprived by the notch within the presented broadband
noise (Hullfish et al., 2019). However, these models have certain shortcomings such
as they do not account for all properties of Zwicker tone percepts (described in the
following) or do not describe the effect on a neuronal network level.
It has previously been proposed that the Zwicker tone and tinnitus and thus also the
neural mechanisms of these two auditory phantom perceptions are closely connected
(Lummis and Guttmann, 1972, Hoke & Hoke, 1996; Mohan et al., 2020,), and a
number of findings support this assumption: For example, Parra and Pearlmutter were
able to show that people with a tinnitus percept are also more likely to perceive a
Zwicker tone percept (Parra & Pearlmutter, 2007). Additionally, Wiegrebe and
coworkers showed that the presence of a Zwicker tone leads to decreased auditory
thresholds of 13 dB even in normal hearing subjects (Wiegrebe et al., Norena et al.
1999), a finding which may easily be explained within our above described model of
SR, since a similar effect can be observed in tinnitus patients (Gollnast et al., 2017,
Krauss et al. 2016) who have improved hearing thresholds in comparison to patients
without tinnitus, at least within frequency ranges below 3 kHz. In this context,
psychoacoustic experiments revealed that notched noise presentation leads to higher
sensitivity to tones embedded in noise (Zhou et al., 2010).
Next, human studies using MEG showed that Zwicker tone perception correlates with
a reduced alpha activity (Leske et al., 2014) in the auditory cortex. Interestingly, the
effect of reduced alpha activity is also correlated to tinnitus perception (Weisz et al.
2007, Weisz et al. 2011).
Furthermore, in most models tinnitus is supposed to be caused by hearing loss (Moffat
et al., 2009) through e.g. cochlea damage or hidden hearing loss which cannot be
detected by pure-tone audiograms but is characterized by a deafferentation of the
inner hair cells (Liberman & Liberman, 2015; Paul et al., 2017). Analogously, the
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induction of the Zwicker tone through notched noise can be viewed as a deprivation
of certain inner hair cells, that is, a temporary and reversible hearing loss (Hullfish et
al., 2019).
These observations and resemblances support the view that the neural mechanisms
of Zwicker tone and acute tinnitus are similar and that therefore the Zwicker tone may
be a good model for tinnitus (Hullfish et al., 2019, Franosch et. al. 2003, Krauss et al.
2018, Wrzosek et al., 2017, Norena et al., 2000, Norena et al., 2002, Norena et al.
1999). As a result, the investigation of the Zwicker tone has recently attracted further
attention. Norena & Eggermont showed that Zwicker tone related neuronal activity
changes can be observed on time scales in the range of seconds (Norena &
Eggermont, 2003). In particular, cats were implanted with multi-electrode arrays and
notched noise stimuli of 1 s duration were presented. It could be shown that neurons
in the auditory cortex representing frequencies within the range of the notch show
increased firing rates after notched noise presentation (Norena et al., 2003). This
result indicates that the Zwicker tone is correlated with a hyperactivity of neurons along
the complete auditory pathway that represent the frequency notch, although to our
knowledge systematic studies of activity along the auditory pathway in animals during
Zwicker tone induction are missing.
Despite all these similarities between the Zwicker tone and acute tinnitus, there are
only few mechanistic explanation approaches on a neural network level (Okamoto et
al., 2005). Our stochastic resonance model (Krauss et al., 2016, see above) provides
such a mechanistic explanation of Zwicker tone percepts. As stated above the
presentation of a notched noise stimulus can be viewed as temporary hearing loss or
deprivation of inner hair cells located within the frequency notch within the tonotopic
gradient (Hullfish et al., 2019; Krauss et al., 2018). According to our model, this
reduced input would cause SR within the auditory system to restore hearing by
optimizing information transmission at the level of the DCN via increased neuronal
noise (as described above). This increase of the neural noise would take place within
the frequency channels of the spectral notch, leading to a hyperactivity of the
respective neurons in the DCN (Krauss et al., 2016). This hyperactivity is transmitted
along the auditory pathway and causes a Zwicker tone percept at the cortical level.
Our explanation is supported by the observation that notched noise stimulation leads
to hyperactivity of auditory cortex neurons representing the notch frequency (cf.
Norena et al., 2003) via disinhibition (cf. Weisz et al., 2007, Weisz et al. 2011).
Furthermore, only the SR mechanism may explain improved hearing thresholds for
frequencies near the Zwicker tone frequency during Zwicker tone perception (cf.
Wiegrebe et al., 1996, Norena et al. 1999): internal noise from the somatosensory
system is increased in the deprived frequency ranges (notch frequency range) in order
to compensate for reduced auditory input by means of SR. This, in turn, leads as a
side effect to improved hearing thresholds for neighboring frequencies above and
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below the notch. Additionally, the SR feedback control circuit (Figure 2) operates on
time scales in the range of or below a second and thus fits to the observation of Zwicker
tone related hyperactivity after 1 s of notched noise presentation (Norena et al., 2003).
According to our model, the increased neural noise to the DCN which is necessary for
SR is supposed to originate from the somatosensory system (Krauss et al., 2016,
2018, 2019b). In analogy to the afore mentioned phenomenon of tinnitus modulation
by voluntary jaw movements, our model also predicts a modulation of the Zwicker tone
perception by somatosensory stimulation. It has indeed been reported that
transcutaneous electrical stimulation has an effect on Zwicker tone perception
(Ueberfuhr et al., 2017).
Residual Inhibition
In 1971 Feldmann found that the presentation of acoustic noise leads to a suppression
of the tinnitus precept after noise offset (Feldmann, 1971), for approximately one
minute (Roberts et al., 2006, Roberts, 2007). This effect was named Residual
Inhibition (RI; Vernon, 1977, Henry & Meikle, 2000).
RI should not be mixed up with tinnitus masking, where tinnitus is perceived less
intense as it is masked by a noise of similar frequency range (Hazell & Wood, 1981,
Terry et al., 1983). In contrast, the presentation of masking noise causes RI after the
end of noise presentation. As RI is a technique to temporarily modulate the tinnitus
percept, it is a potential target for experimental studies on tinnitus mechanisms
(Deklerck et al, 2019).
Interestingly, it was reported that RI works best when the masking noise covers the
range of the hearing loss of the subjects and is related to the tinnitus pitch (Roberts et
al., 2006, 2008). The cause of the suppression of the tinnitus percept during RI has
been discussed to be a decreased spontaneous neural activity after masking noise
offset (Galazyuk et al., 2017). This is in line with the explanation that there is a neural
adaptation along the auditory pathway induced by the noise presentation (Fournier et
al., 2018).
These findings emphasize the idea that spontaneous activity of spiking neurons or in
other words internally generated neural noise are crucial for processing of acoustic
stimuli along the auditory pathway (Galazyuk et al., 2019). This internal noise is
suppressed after the presentation of external acoustic noise. To understand the basic
neural mechanisms of RI as well as auditory phantom perception, it is crucial to gain
a better understanding of how the neural noise contributes to auditory processing.
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The idea that the neural system exploits the effect of SR to improve hearing (Krauss
et al., 2016, 2018, 2019b) provides a putative explanation for the effect of RI. As
described above, tinnitus is potentially induced by the deprivation of neurons along the
auditory pathway in tonotopic regions where a cochlea damage occurred. Thus, the
auditory system tries to compensate for this deprivation, i.e. hearing loss, by adding
internally generated neural noise. This internally generated noise potentially produced
by the somatosensory system and fed to the DCN is propagated along the auditory
pathway to the cortex, where it is perceived as auditory phantom percept. RI is
potentially the consequence of replacing internally generated neural noise by external
acoustic noise. In this view, the external noise would replace the internal noise, thereby
causing its downregulation and thus suppression of the tinnitus percept as already
described in previous publications (Krauss et al., 2016, 2019b).
According to our model, the optimal noise is tuned and controlled on time scales of
seconds via a control circuit (Krauss et al., 2016; Figure 2). From this point of view,
Zwicker tone and tinnitus are basically the same phenomenon, but on different time
scales. Furthermore, the proposed control circuit would work inversely for Zwicker tone
and RI. Whereas, the Zwicker tone corresponds to an upregulation of internal neural
noise caused by a reduced auditory input (i.e. the notch), RI in contrast corresponds
to a downregulation of internal noise, due to increased auditory input (i.e. external
acoustic noise). Thus, both phenomena can be considered to be opposite effects that
may be explained by exactly the same neural control circuitry proposed by our SR
model. To put it in a slogan, the SR model of auditory processing suggests that “RI
can be interpreted as an inverse Zwicker tone illusion”.
Summary and Discussion
In summary, our SR model provides a unified explanation for the induction of acute
subjective tinnitus, Zwicker tone, and RI. Especially a look at the time scales, in which
Zwicker tone (cf. Norena et al., 2003) can be induced or tinnitus can be reduced by
hearing aids or cochlear implants (McNeill et al., 2012, Ito & Sakakihara, 1994,
Baguley & Atlas, 2007), indicates that these phenomena cannot be exclusively
explained by brain plasticity. The SR model, describing tinnitus as a side effect of the
neural system trying to optimize information transmission after hearing loss by
exploiting the SR effect, would offer an explanation of how these phantom perceptions
can be induced or suppressed so quickly. Thus, the neural system does not need any
plasticity as the SR mechanism is optimized by a simple control circuit (Krauss et al,
2016; Figure 2).
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We speculate that in subjects, where the Zwicker tone can be induced by short noise
presentation the RI effect should vanish more quickly, because the tuning of the
optimal noise level works faster in certain subjects and thus the downregulated neural
noise during RI is quickly re-increased. On the other hand, the Zwicker tone is induced
faster as the neural noise is quickly upregulated when notched noise is presented.
Thus, the duration of notched noise needed to induce the Zwicker tone could
potentially correlate with the duration of the RI effect. This would be only the case, if
both effects were produced by the same SR control circuit in the DCN (Figure 2), which
could be a characteristic feature of different individuals. The characteristic parameter
of this control circuit is the time needed for controlling the noise amplitude.
This is a testable hypothesis derived from the SR model, which has to be verified or
falsified in future studies.
However, it is obvious that the SR model has some limitations, such as that -in contrast
to homeostatic plasticity models- it does not predict massive structural and functional
changes (cf. Norena, 2011) along the auditory pathway, which is indeed found in
several studies (Yang et al., 2011, Li et al., 2015, Singer et al., 2013). These findings
are supported by computational models demonstrating the influence of this plasticity
(Schaette & Kempter, 2006, Nagashino et al., 2012).
Additionally, our model does not address the question why not all people with hearing
loss perceive or even suffer from tinnitus. The influence of stress (Mazurek et al., 2012,
2015) and psychological burden (Landgrebe & Langguth 2011; Langguth et al., 2007,
2011) on tinnitus percepts was shown in several studies. Furthermore, the model does
not differentiate between chronic and acute tinnitus.
Despite these limitations, we are convinced that we now have the knowledge to draw
a complete picture in the light of preceding studies. Figures 3 and 4 provide an
overview of the main models and their explanatory power for tinnitus development and
Zwicker tone perception. The different models work on different time scales, as well
as in different brain areas, as illustrated in Figure 5.
Our SR model provides an explanation for the induction of tinnitus after e.g. a loud
acoustic noise presentation, the induction of Zwicker tone by bandpass noise, or the
suppression of the tinnitus percept by acoustic noise presentation (RI). These
mechanisms are very fast and occur within seconds, and thus cannot be explained by
any of the models based on brain plasticity. However, as described above, some
plasticity can be found along the auditory pathway (Yang et al., 2011, Li et al., 2015,
Singer et al., 2013). This plasticity could potentially be the first step in chronic
manifestation of the tinnitus percept. However, it is still unclear why the gating function
of the thalamus does not prevent the neural hyperactivity from being directly
transmitted to the cortex as it does for other unwanted permanent stimuli (McCormick
& Bal, 1994). This effect could be explained by the model of Rauschecker and
coworkers (Rauschecker et al., 2010). There, the auditory input can be cancelled out
by the medial geniculate nucleus within the thalamus. This noise cancellation function
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can be modulated by the limbic system especially the nucleus accumbens, which is
indirectly connected to the medial geniculate nucleus. A breakdown of this system
impairs the gating function of the medial geniculate nucleus (Rauschecker et al., 2010)
and thus brings the neural hyperactivity to consciousness.
De Ridder and coworkers go even one step further and assume a conscious tinnitus
percept to be a consequence of different overlapping brain networks including pre-
frontal areas as well as brain structures responsible for emotional labeling of certain
memories such as the amygdala. Thus, learning effects are involved, which generate
a connection of the phantom percept and distress (De Ridder et al., 2011).
Unfortunately, this model does not provide mechanistic explanations at a neural
network level, but it explains the involvement of different brain structures.
Nevertheless, the model could provide an explanation why not every hearing loss
causes tinnitus, and why not everyone perceiving tinnitus also suffers from it.
Individual memories and neuronal pathways could lead to different effects in different
subjects.
The described models draw a complete and consistent image of tinnitus development,
chronic manifestation, and heterogeneity, and do not “contradict” each other as
described by Sedley and coworkers (Sedley et al., 2016). Furthermore, mechanistic
explanations for RI, Zwicker tone, and better hearing thresholds of tinnitus patients
compared to patients without tinnitus (Krauss et al., 2016, Gollnast et al., 2017)
support the model.
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Figures
Figure 1: Principle of Stochastic Resonance
The auditory input without any added noise is too weak to pass the threshold (A). Also if the
intensity of added noise is too weak, the sum of auditory input and noise cannot pass the
threshold (B). Both cases result in zero output. In contrast, if the optimal amount of noise is
added to the signal before thresholding, the resulting output's envelope resembles the auditory
input signal (C). However, if the noise intensity is further increased, the signal vanishes again
in the noisy output (D).
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Figure 2: Adaptive Stochastic Resonance control circuit in the DCN
In self-adaptive signal detection systems based on SR, the optimum noise level is continuously
adjusted via a feedback loop, so that the system's response in terms of information throughput
remains optimal, even if the properties of the input signal change. In the SR model of tinnitus
development, this process takes place in the DCN. The input signal comes from the cochlea,
the noise from the somatosensory system.
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Figure 3: Explanatory power of different models of tinnitus development
The figure summarizes different models of tinnitus development (rows) and how these models
fit to certain observations (columns). For each model and effect, one exemplary paper is cited.
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Figure 4: Explanatory power of different models of the Zwicker tone illusion
The figure summarizes different models of the Zwicker tone illusion (rows) and how these
models fit to certain observations (columns). For each model and effect, one exemplary paper
is cited.
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Figure. 5: The space of tinnitus models Models of tinnitus development can be defined at
different levels of description and can vary in time scale of the explained observations
(horizontal axis) and in proposed anatomical substrate, i.e. processing stage (vertical axis).
The SR model fills the missing gapin time scales of minutes and seconds.
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(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprintthis version posted March 29, 2020. . https://doi.org/10.1101/2020.03.27.011163doi: bioRxiv preprint
... In addition, state-of-the-art deep learning approaches may be used as a tool for analysing brain data, e.g. for creating so-called embeddings of the raw data . Moreover, as proposed by Kriegeskorte and Douglas (2018), our neural corpus can serve to test computational models of brain function (Krauss et al., 2017(Krauss et al., , 2016Schilling, Tziridis, et al., 2020), in particular models based on neural networks (Krauss, Prebeck, et al., 2019;Krauss, Schuster, et al., 2019;Krauss, Zankl, et al., 2019) and machine learning architectures Schilling, Gerum, et al., 2020), in order to iteratively increase biological and cognitive fidelity (Kriegeskorte & Douglas, 2018). ...
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