The stochastic resonance model of auditory perception: A unified explanation of tinnitus development, Zwicker tone illusion, and residual inhibition

Schilling, Achim; Tziridis, Konstantin; Schulze, Holger; Krauß, Patrick

doi:10.1016/bs.pbr.2021.01.025

Cited by 42 publications

(29 citation statements)

References 114 publications

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“…They have been shown to play a crucial role for neural information processing (Moss et al, 2004 ; Krauss et al, 2018 ; Schilling et al, 2020 ). In particular with respect to the auditory system, it has been argued that resonance phenomena like stochastic resonance are actively exploited by the brain to maintain optimal information processing (Krauss et al, 2016 , 2017 , 2018 ; Schilling et al, 2021b ). For instance, in a theoretical study it could be demonstrated that stochastic resonance improves speech recognition in an artificial neural network as a model of the auditory pathway (Schilling et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Dynamics and Information Import in Recurrent Neural Networks

Metzner

Krauß

2022

Front. Comput. Neurosci.

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Recurrent neural networks (RNNs) are complex dynamical systems, capable of ongoing activity without any driving input. The long-term behavior of free-running RNNs, described by periodic, chaotic and fixed point attractors, is controlled by the statistics of the neural connection weights, such as the density d of non-zero connections, or the balance b between excitatory and inhibitory connections. However, for information processing purposes, RNNs need to receive external input signals, and it is not clear which of the dynamical regimes is optimal for this information import. We use both the average correlations C and the mutual information I between the momentary input vector and the next system state vector as quantitative measures of information import and analyze their dependence on the balance and density of the network. Remarkably, both resulting phase diagrams C(b, d) and I(b, d) are highly consistent, pointing to a link between the dynamical systems and the information-processing approach to complex systems. Information import is maximal not at the “edge of chaos,” which is optimally suited for computation, but surprisingly in the low-density chaotic regime and at the border between the chaotic and fixed point regime. Moreover, we find a completely new type of resonance phenomenon, which we call “Import Resonance” (IR), where the information import shows a maximum, i.e., a peak-like dependence on the coupling strength between the RNN and its external input. IR complements previously found Recurrence Resonance (RR), where correlation and mutual information of successive system states peak for a certain amplitude of noise added to the system. Both IR and RR can be exploited to optimize information processing in artificial neural networks and might also play a crucial role in biological neural systems.

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Section: Discussionmentioning

confidence: 99%

Dynamics and Information Import in Recurrent Neural Networks

Metzner

Krauß

2022

Front. Comput. Neurosci.

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“…This reduction is detected by the neuronal system by a reduction of the described autocorrelation, leading to an increase (e.g., by reducing neuronal inhibition) in neuronal noise. As indicated above, such SR would then result in an increased amount of information at the DCN output ( Douglass et al, 1993 ; Faisal et al, 2008 ; Mino, 2014 ; Liberman et al, 2015 ; Krauss et al, 2016 , 2017 , 2019 ; Schilling et al, 2021 ). In the view of our hypothesis, the internal neuronal noise is permanently adjusted at a millisecond timescale to meet the environmental conditions of the auditory scenery, thereby optimizing information transmission constantly.…”

Section: Introductionmentioning

confidence: 99%

“…Based on our physiological model of tinnitus development ( Krauss et al, 2016 ; Schilling et al, 2021 ) – which may be only valid for tinnitus development based on cochlear defects – we are currently developing a new treatment strategy, especially for tinnitus patients without or with only mild hearing loss (HL). This strategy is based, first, on our hypothesis that tinnitus is a byproduct of a neurophysiological mechanism that permanently optimizes information transmission into the auditory system by means of stochastic resonance (SR) – a mechanism well described in other neuronal systems ( Douglass et al, 1993 ; Faisal et al, 2008 ; Mino, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

Spectrally Matched Near-Threshold Noise for Subjective Tinnitus Loudness Attenuation Based on Stochastic Resonance

et al. 2022

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Recently, we proposed a model of tinnitus development based on a physiological mechanism of permanent optimization of information transfer from the auditory periphery to the central nervous system by means of neuronal stochastic resonance utilizing neuronal noise to be added to the cochlear input, thereby improving hearing thresholds. In this view, tinnitus is a byproduct of this added neuronal activity. Interestingly, in healthy subjects auditory thresholds can also be improved by adding external, near-threshold acoustic noise. Based on these two findings and a pilot study we hypostatized that tinnitus loudness (TL) might be reduced, if the internally generated neuronal noise is substituted by externally provided individually adapted acoustic noise. In the present study, we extended the data base of the first pilot and further optimized our approach using a more fine-grained adaptation of the presented noise to the patients’ audiometric data. We presented different spectrally filtered near-threshold noises (−2 dB to +6 dB HL, 2 dB steps) for 40 s each to 24 patients with tonal tinnitus and a hearing deficit not exceeding 40 dB. After each presentation, the effect of the noise on the perceived TL was obtained by patient’s response to a 5-scale question. In 21 out of 24 patients (13 women) TL was successfully subjectively attenuated during acoustic near-threshold stimulation using noise spectrally centered half an octave below the individual’s tinnitus pitch (TP). Six patients reported complete subjective silencing of their tinnitus percept during stimulation. Acoustic noise is able to reduce TL, but the TP has to be taken into account. Based on our findings, we speculate about a possible future treatment of tinnitus by near-threshold bandpass filtered acoustic noise stimulation, which could be implemented in hearing aids with noise generators.

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“…for creating so-called embeddings of the raw data (Krauss et al, 2020). Moreover, as proposed by Kriegeskorte and Douglas (2018), our neural corpus can serve to test (Schilling et al, 2018) 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 (Gerum et al, 2020;Schilling, Gerum, et al, 2020), in order to iteratively increase biological and cognitive fidelity (Kriegeskorte & Douglas, 2018).…”

Section: Discussionmentioning

confidence: 99%

Analysis of continuous neuronal activity evoked by natural speech with computational corpus linguistics methods

Schilling

Tomasello

Henningsen-Schomers

et al. 2020

Language, Cognition and Neuroscience

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In the field of neurobiology of language, neuroimaging studies are generally based on stimulation paradigms consisting of at least two different conditions. Designing those paradigms can be very time-consuming and this traditional approach is necessarily data-limited. In contrast, in computational and corpus linguistics, analyses are often based on large text corpora, which allow a vast variety of hypotheses to be tested by repeatedly re-evaluating the data set.Furthermore, text corpora also allow exploratory data analysis in order to generate new hypotheses. By drawing on the advantages of both fields, neuroimaging and computational corpus linguistics, we here present a unified approach combining continuous natural speech and MEG to generate a corpus of speech-evoked neuronal activity.

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The stochastic resonance model of auditory perception: A unified explanation of tinnitus development, Zwicker tone illusion, and residual inhibition

Cited by 42 publications

References 114 publications

Dynamics and Information Import in Recurrent Neural Networks

Dynamics and Information Import in Recurrent Neural Networks

Spectrally Matched Near-Threshold Noise for Subjective Tinnitus Loudness Attenuation Based on Stochastic Resonance

Analysis of continuous neuronal activity evoked by natural speech with computational corpus linguistics methods

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