Perceptual Modeling of Tinnitus Pitch and Loudness

Gault, Richard; McGinnity, TM; Coleman, Sonya

doi:10.1109/tcds.2020.2964841

Cited by 7 publications

(5 citation statements)

References 48 publications

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“…By extending the current model implementation to a multiscale framework, where changes in membrane potential at a smaller spatial scale could affect model parameters at the WCCM level, such alternative HSP mechanism could be implemented and tested. Furthermore, it may be of interest to extend the model with a biologically inspired ear module such that interindividual differences in peripheral hearing loss may be modeled (Gault et al, 2018; Zilany et al, 2014), or embed computational models that focus on the tinnitus percept (Gault et al, 2020; Hu et al, 2021). Finally, within the dynamic causal modeling (DCM) framework (Friston et al, 2003), nonlinear biophysical generative models have been proposed that capture the translation from neuronal responses to non-invasive neuroimaging data (a hemodynamic model for fMRI: Havlicek et al (2015); Stephan et al (2008), and a lead field model for EEG and MEG: Kiebel et al (2006)).…”

Section: Discussionmentioning

confidence: 99%

Modelling homeostatic plasticity in the auditory cortex results in neural signatures of tinnitus

Schultheiβ

Zulfiqar²,

Moerel

2022

Preprint

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Tinnitus is a clinical condition where a sound is perceived without external sound source. Homeostatic plasticity (HSP), serving to increase neural activity as compensation for the reduced input to the auditory pathway after hearing loss, has been proposed as causal mechanism underlying tinnitus. In support, animal models of tinnitus show evidence of increased neural activity after hearing loss, including increased spontaneous and sound-driven firing rate, as well as increased neural noise throughout the auditory processing pathway. Bridging these findings to human tinnitus, however, has proven to be challenging. Here we implement hearing loss-induced HSP in a Wilson-Cowan Cortical Model of the auditory cortex to predict how homeostatic principles operating at the microscale translate to the meso- to macroscale accessible through human neuroimaging. We observed HSP-induced response changes in the model that were previously proposed as neural signatures of tinnitus. As expected, HSP increased spontaneous and sound-driven responsiveness in hearing-loss affected frequency channels of the model. We furthermore observed evidence of increased neural noise and the appearance of spatiotemporal modulations in neural activity, which we discuss in light of recent human neuroimaging findings. Our computational model makes quantitative predictions that require experimental validation, and may thereby serve as the basis of future human tinnitus studies.

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Section: Discussionmentioning

confidence: 99%

Modelling homeostatic plasticity in the auditory cortex results in neural signatures of tinnitus

Schultheiβ

Zulfiqar²,

Moerel

2022

Preprint

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“…Furthermore, Dotan and Shriki introduced a recurrent neural network model where tinnitus-like "hallucinations" are elicited by sensory deprivation as a result of entropy maximization [50]. Finally, Gault and coworkers fitted audiometric data with assessed tinnitus pitches in order to create a linear regression model of tinnitus pitch and loudness [51]. Besides these computational models that rest upon a mathematical formulation, there exist several phenomenological models, such as the thalamo-cortical dysrhythmia model [52,53], the thalamic low-threshold calcium spike model [54], the frontostriatal gating hypothesis [55,56], and the overlapping sub-network theory [57,58].…”

Section: Introductionmentioning

confidence: 99%

Predictive coding and stochastic resonance as fundamental principles of auditory perception

Schilling¹,

Sedley²,

Gerum³

et al. 2022

Preprint

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Cognitive computational neuroscience (CCN) suggests that to gain a mechanistic understanding of brain function, hypothesis driven experiments should be accompanied by biologically plausible computational models. This novel research paradigm offers a way from alchemy to chemistry, in auditory neuroscience. With a special focus on tinnitus -as the prime example of auditory phantom perception -we review recent work at the intersection of artificial intelligence, psychology, and neuroscience, foregrounding the idea that experiments will yield mechanistic insight only when employed to test formal or computational models. This view challenges the popular notion that tinnitus research is primarily data limited, and that producing large, multi-modal, and complex data-sets, analyzed with advanced data analysis algorithms, will lead to fundamental insights into how tinnitus emerges. We conclude that two fundamental processing principles -being ubiquitous in the brain -best fit to a vast number of experimental results and therefore provide the most explanatory power: predictive coding as a top-down, and stochastic resonance as a complementary bottom-up mechanism. Furthermore, we argue that even though contemporary artificial intelligence and machine learning approaches largely lack biological plausibility, the models to be constructed will have to draw on concepts from these fields; since they provide a formal account of the requisite computations that underlie brain function. Nevertheless, biological fidelity will have to be addressed, allowing for testing possible treatment strategies in silico, before application in animal or patient studies. This iteration of computational and empirical studies may help to open the 'black boxes' of both machine learning and the human brain.

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“…While existing computational models successfully account for some of the characteristics of tinnitus [42], many of them are based on lateral inhibition [43][44][45] or gain adaptation [46], and do not take into account long-term neural plasticity. Plasticity-based models for tinnitus are usually phenomenological models, where plasticity is described as a homeostatic process [47][48][49][50][51][52][53] or an amplification of central noise [54], and not as a process which serves a computational goal. Another computational model for tinnitus is based on stochastic resonance and suggests that tinnitus arises from an adaptive optimal noise level [55,56].…”

Section: Introductionmentioning

confidence: 99%

Tinnitus-like “hallucinations” elicited by sensory deprivation in an entropy maximization recurrent neural network

Dotan

Shriki

2021

PLoS Comput Biol

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Sensory deprivation has long been known to cause hallucinations or “phantom” sensations, the most common of which is tinnitus induced by hearing loss, affecting 10–20% of the population. An observable hearing loss, causing auditory sensory deprivation over a band of frequencies, is present in over 90% of people with tinnitus. Existing plasticity-based computational models for tinnitus are usually driven by homeostatic mechanisms, modeled to fit phenomenological findings. Here, we use an objective-driven learning algorithm to model an early auditory processing neuronal network, e.g., in the dorsal cochlear nucleus. The learning algorithm maximizes the network’s output entropy by learning the feed-forward and recurrent interactions in the model. We show that the connectivity patterns and responses learned by the model display several hallmarks of early auditory neuronal networks. We further demonstrate that attenuation of peripheral inputs drives the recurrent network towards its critical point and transition into a tinnitus-like state. In this state, the network activity resembles responses to genuine inputs even in the absence of external stimulation, namely, it “hallucinates” auditory responses. These findings demonstrate how objective-driven plasticity mechanisms that normally act to optimize the network’s input representation can also elicit pathologies such as tinnitus as a result of sensory deprivation.

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Perceptual Modeling of Tinnitus Pitch and Loudness

Cited by 7 publications

References 48 publications

Modelling homeostatic plasticity in the auditory cortex results in neural signatures of tinnitus

Modelling homeostatic plasticity in the auditory cortex results in neural signatures of tinnitus

Predictive coding and stochastic resonance as fundamental principles of auditory perception

Tinnitus-like “hallucinations” elicited by sensory deprivation in an entropy maximization recurrent neural network

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