Sound Change Integration Error: An Explanatory Model of Tinnitus

Noda, Kazuhiro; Kitahara, Tadashi; Doi, Kunio

doi:10.3389/fnins.2018.00831

“…An illuminating perspective on the emergence of hallucinations, such as tinnitus, as a consequence of sensory deprivation comes from the framework of Bayesian inference [83–85]. According to this framework, sensory systems generate perception by combining the incoming stimuli with prior expectations in a way that takes into account the relative uncertainty of each.…”

Section: Discussionmentioning

confidence: 99%

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

Dotan

¹

,

Shriki

²

2021

Preprint

1

0

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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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“…The perception-update (PU) model (Figure 2) was developed as a method for applying differential PCM to the structure of the brain (Noda et al, 2018). In this model, when the visual stimulus changes in a stepwise manner, the stepped "change" creates the subsequent perception, as does differential PCM.…”

Section: Predictive Coding and Differential Pulse Code Modulation (Pc...mentioning

confidence: 99%

Predictive Coding Correctly Predicts Perceived Chromatic Negative Afterimages

Noda¹

2022

Preprint

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Although the opponent model has traditionally been considered to explain the mechanisms underlying visual afterimages, recent studies suggest that some predictions of the opponent model are obviously incorrect. In the current study, we modeled afterimages using predictive coding, in which perception is generated via efficient information processing. The only assumption we made to apply information processing to the afterimage process was that " if the input information remains constant beyond the limit of brain memory, perception shifts toward a reasonable value (the average of past values)." In our experiments, because the inducer color did not change, its perception was expected to shift to gray due to adaptation. In contrast, because the reference color changed to the opposite color every second, its perception was expected to be maintained. Systematic comparisons of the model predictions with participants’ perception revealed high accuracy. Importantly, this model is the first method to continuously present accurate afterimage predictions on the same screen as the inducer at the same time. This showed that, contrary to previous reports, adaptation varied with inducer presentation time, and the afterimage changed accordingly.These results suggest that the brain constructs perception via information processing, and that visual afterimages result from its effects.

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“…An illuminating perspective on the emergence of hallucinations, such as tinnitus, as a consequence of sensory deprivation comes from the framework of Bayesian inference [109][110][111]. According to this framework, sensory systems generate perception by combining the incoming stimuli with prior expectations in a way that takes into account the relative uncertainty of each.…”

Section: Plos Computational Biologymentioning

confidence: 99%

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

Dotan

¹

,

Shriki

²

2021

View full text Add to dashboard Cite

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.

show abstract

Sound Change Integration Error: An Explanatory Model of Tinnitus

Cited by 5 publications

References 88 publications

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

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

Predictive Coding Correctly Predicts Perceived Chromatic Negative Afterimages

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

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