A Computational Model of Thalamocortical Dysrhythmia in People With Tinnitus

Gault, Richard; McGinnity, T.M.; Coleman, Sonya

doi:10.1109/tnsre.2018.2863740

Cited by 9 publications

(15 citation statements)

References 67 publications

(97 reference statements)

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“…Previous computational models relied on phenomenological homeostasis-driven plasticity to demonstrate tinnitus elicited by sensory deprivation [33][34][35][36][37][38]. Here, we used an objective-driven plasticity, namely, the main mechanism underlying the network's plasticity is optimizing an explicit computational goal.…”

Section: Discussionmentioning

confidence: 99%

“…Plasticity-based models for tinnitus are usually phenomenological models, where plasticity is described as a homeostatic process [33][34][35][36][37][38][39] or an amplification of central noise [40], rather than 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, but it is focused on a single auditory frequency and has yet to be further explored [41].…”

Section: Introductionmentioning

confidence: 99%

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

Dotan

Shriki

2021

Preprint

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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 homeostasis 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.Author summaryTinnitus or “ringing in the ears” is a common pathology. It may result from mechanical damage in the inner ear, as well as from certain drugs such as salicylate (aspirin). A common approach toward a computational model for tinnitus is to use a neural network model with inherent plasticity applied to early auditory processing, where the input layer models the auditory nerve and the output layer models a nucleus in the brain stem. However, most of the existing computational models are phenomenological in nature, driven by a homeostatic principle. Here, we use an objective-driven learning algorithm based on information theory to learn the feed-forward interactions between the layers, as well as the recurrent interactions within the output layer. Through numerical simulations of the learning process, we show that attenuation of peripheral inputs drives the network into a tinnitus-like state, where the network activity resembles responses to genuine inputs even in the absence of external stimulation; namely, it “hallucinates” auditory responses. These findings demonstrate how plasticity mechanisms that normally act to optimize network performance can also lead to undesired outcomes, such as tinnitus, as a result of reduced peripheral hearing.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Dotan

Shriki

2021

Preprint

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“…It is thought that most tinnitus cases stem from peripheral noise-induced hearing loss creating a reduction in auditory input [6] with approximately 90% of people with tinnitus have apparent hearing loss [7]. It is thought that the remaining people with tinnitus have hidden hearing loss [8], [9], [10], [11]. The loss of auditory input in regions of hearing loss causes adaptation within the auditory system leading to increased spontaneous neuronal activity, or hyperactivity, in the auditory pathway.…”

Section: Introductionmentioning

confidence: 99%

“…Computational models to date primarily focus on changes in the ascending, or bottom-up, auditory signals. The descending signals in the auditory pathway, or top-down signals, have also been shown to be crucial for the generation of tinnitusrelated activity [11], [39]. It is challenging however to directly measure the top-down signals in a non-invasive way.…”

Section: Introductionmentioning

confidence: 99%

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Perceptual Modeling of Tinnitus Pitch and Loudness

Gault

McGinnity

Coleman

2020

IEEE Trans. Cogn. Dev. Syst.

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Tinnitus is the phantom perception of sound, experienced by 10-15% of the global population. Computational models have been used to investigate the mechanisms underlying the generation of tinnitus-related activity. However, existing computational models have rarely benchmarked the modelled perception of a phantom sound against recorded data relating to a person's perception of tinnitus characteristics; such as pitch or loudness. This paper details the development of two perceptual models of tinnitus. The models are validated using empirical data from people with tinnitus and the models' performance is compared with existing perceptual models of tinnitus pitch. The first model extends existing perceptual models of tinnitus, while the second model utilises an entirely novel approach to modelling tinnitus perception using a Linear Mixed Effects (LME) model. The LME model is also used to model the perceived loudness of the phantom sound which has not been considered in previous models. The LME model creates an accurate model of tinnitus pitch and loudness and shows that both tinnitus-related activity and individual perception of sound are factors in the perception of the phantom sound that characterizes tinnitus.

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