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

2021 ◽  
Vol 17 (12) ◽  
pp. e1008664
Author(s):  
Aviv Dotan ◽  
Oren Shriki
Keyword(s):  
Hearing Loss ◽  
Auditory Processing ◽  
Computational Models ◽  
Learning Algorithm ◽  
Sensory Deprivation ◽  
Recurrent Network ◽  
Dorsal Cochlear Nucleus ◽  
Network Activity ◽  
External Stimulation ◽  
Entropy Maximization

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.

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

2021 ◽  
Author(s):  
Aviv Dotan ◽  
Oren Shriki
Keyword(s):  
Neural Network ◽  
Hearing Loss ◽  
Auditory Processing ◽  
Computational Models ◽  
Learning Algorithm ◽  
Sensory Deprivation ◽  
Network Activity ◽  
External Stimulation ◽  
Output Layer ◽  
Auditory Responses

AbstractSensory 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.

Auditory processing of complex sounds: an overview

1992 ◽  
Vol 336 (1278) ◽  
pp. 295-306 ◽  
Keyword(s):  
Auditory System ◽  
Auditory Processing ◽  
Dynamic Range ◽  
Auditory Brainstem ◽  
Frequency Selectivity ◽  
Dorsal Cochlear Nucleus ◽  
Cochlear Nerve ◽  
Complex Sounds ◽  
Wide Range ◽  
Time Coding

The past 30 years has seen a remarkable development in our understanding of how the auditory system - particularly the peripheral system - processes complex sounds. Perhaps the most significant has been our understanding of the mechanisms underlying auditory frequency selectivity and their importance for normal and impaired auditory processing. Physiologically vulnerable cochlear filtering can account for many aspects of our normal and impaired psychophysical frequency selectivity with important consequences for the perception of complex sounds. For normal hearing, remarkable mechanisms in the organ of Corti, involving enhancement of mechanical tuning (in mammals probably by feedback of electro-mechanically generated energy from the hair cells), produce exquisite tuning, reflected in the tuning properties of cochlear nerve fibres. Recent comparisons of physiological (cochlear nerve) and psychophysical frequency selectivity in the same species indicate that the ear’s overall frequency selectivity can be accounted for by this cochlear filtering, at least in band width terms. Because this cochlear filtering is physiologically vulnerable, it deteriorates in deleterious conditions of the cochlea - hypoxia, disease, drugs, noise overexposure, mechanical disturbance - and is reflected in impaired psychophysical frequency selectivity. This is a fundamental feature of sensorineural hearing loss of cochlear origin, and is of diagnostic value. This cochlear filtering, particularly as reflected in the temporal patterns of cochlear fibres to complex sounds, is remarkably robust over a wide range of stimulus levels. Furthermore, cochlear filtering properties are a prime determinant of the ‘place’ and ‘time’ coding of frequency at the cochlear nerve level, both of which appear to be involved in pitch perception. The problem of how the place and time coding of complex sounds is effected over the ear’s remarkably wide dynamic range is briefly addressed. In the auditory brainstem, particularly the dorsal cochlear nucleus, are inhibitory mechanisms responsible for enhancing the spectral and temporal contrasts in complex sounds. These mechanisms are now being dissected neuropharmacologically. At the cortical level, mechanisms are evident that are capable of abstracting biologically relevant features of complex sounds. Fundamental studies of how the auditory system encodes and processes complex sounds are vital to promising recent applications in the diagnosis and rehabilitation of the hearing impaired.

scholarly journals Temporal Alterations to Central Auditory Processing without Synaptopathy after Lifetime Exposure to Environmental Noise

2021 ◽  
Author(s):  
Florian Occelli ◽  
Florian Hasselmann ◽  
Jérôme Bourien ◽  
Jean-Luc Puel ◽  
Nathalie Desvignes ◽  
...  
Keyword(s):  
Hearing Loss ◽  
Auditory Cortex ◽  
Auditory Processing ◽  
Noise Exposure ◽  
Sound Intensity ◽  
Environmental Noise ◽  
Central Auditory Processing ◽  
Adult Rats ◽  
Cochlear Hair Cells ◽  
Lifetime Exposure

Abstract People are increasingly exposed to environmental noise through the cumulation of occupational and recreational activities, which is considered harmless to the auditory system, if the sound intensity remains <80 dB. However, recent evidence of noise-induced peripheral synaptic damage and central reorganizations in the auditory cortex, despite normal audiometry results, has cast doubt on the innocuousness of lifetime exposure to environmental noise. We addressed this issue by exposing adult rats to realistic and nontraumatic environmental noise, within the daily permissible noise exposure limit for humans (80 dB sound pressure level, 8 h/day) for between 3 and 18 months. We found that temporary hearing loss could be detected after 6 months of daily exposure, without leading to permanent hearing loss or to missing synaptic ribbons in cochlear hair cells. The degraded temporal representation of sounds in the auditory cortex after 18 months of exposure was very different from the effects observed after only 3 months of exposure, suggesting that modifications to the neural code continue throughout a lifetime of exposure to noise.

scholarly journals Severe Sexual Abuse Reduces Frontoparietal Network Activity during Model-Based Reinforcement Learning Updates

2019 ◽  
Author(s):  
Allison Letkiewicz ◽  
Amy L. Cochran ◽  
Josh M. Cisler
Keyword(s):  
Sexual Abuse ◽  
Reinforcement Learning ◽  
Learning Styles ◽  
Computational Models ◽  
Learning Strategy ◽  
Network Activity ◽  
Model Based ◽  
Model Free ◽  
Frontoparietal Network ◽  
Assaultive Trauma

Trauma and trauma-related disorders are characterized by altered learning styles. Two learning processes that have been delineated using computational modeling are model-free and model-based reinforcement learning (RL), characterized by trial and error and goal-driven, rule-based learning, respectively. Prior research suggests that model-free RL is disrupted among individuals with a history of assaultive trauma and may contribute to altered fear responding. Currently, it is unclear whether model-based RL, which involves building abstract and nuanced representations of stimulus-outcome relationships to prospectively predict action-related outcomes, is also impaired among individuals who have experienced trauma. The present study sought to test the hypothesis of impaired model-based RL among adolescent females exposed to assaultive trauma. Participants (n=60) completed a three-arm bandit RL task during fMRI acquisition. Two computational models compared the degree to which each participant’s task behavior fit the use of a model-free versus model-based RL strategy. Overall, a greater portion of participants’ behavior was better captured by the model-based than model-free RL model. Although assaultive trauma did not predict learning strategy use, greater sexual abuse severity predicted less use of model-based compared to model-free RL. Additionally, severe sexual abuse predicted less left frontoparietal network encoding of model-based RL updates, which was not accounted for by PTSD. Given the significant impact that sexual trauma has on mental health and other aspects of functioning, it is plausible that altered model-based RL is an important route through which clinical impairment emerges.

scholarly journals Neuronal and Astrocytic Regulations in Schizophrenia: A Computational Modelling Study

2021 ◽  
Vol 15 ◽  
Author(s):  
Lea Fritschi ◽  
Johanna Hedlund Lindmar ◽  
Florian Scheidl ◽  
Kerstin Lenk
Keyword(s):  
Computational Models ◽  
Glutamate Release ◽  
Signal Transmission ◽  
Computational Modelling ◽  
Atp Release ◽  
Network Activity ◽  
Inhibitory Neurons ◽  
Neuronal Network Activity ◽  
Cell Densities ◽  
Synapse Model

According to the tripartite synapse model, astrocytes have a modulatory effect on neuronal signal transmission. More recently, astrocyte malfunction has been associated with psychiatric diseases such as schizophrenia. Several hypotheses have been proposed on the pathological mechanisms of astrocytes in schizophrenia. For example, post-mortem examinations have revealed a reduced astrocytic density in patients with schizophrenia. Another hypothesis suggests that disease symptoms are linked to an abnormality of glutamate transmission, which is also regulated by astrocytes (glutamate hypothesis of schizophrenia). Electrophysiological findings indicate a dispute over whether the disorder causes an increase or a decrease in neuronal and astrocytic activity. Moreover, there is no consensus as to which molecular pathways and network mechanisms are altered in schizophrenia. Computational models can aid the process in finding the underlying pathological malfunctions. The effect of astrocytes on the activity of neuron-astrocyte networks has been analysed with computational models. These can reproduce experimentally observed phenomena, such as astrocytic modulation of spike and burst signalling in neuron-astrocyte networks. Using an established computational neuron-astrocyte network model, we simulate experimental data of healthy and pathological networks by using different neuronal and astrocytic parameter configurations. In our simulations, the reduction of neuronal or astrocytic cell densities yields decreased glutamate levels and a statistically significant reduction in the network activity. Amplifications of the astrocytic ATP release toward postsynaptic terminals also reduced the network activity and resulted in temporarily increased glutamate levels. In contrast, reducing either the glutamate release or re-uptake in astrocytes resulted in higher network activities. Similarly, an increase in synaptic weights of excitatory or inhibitory neurons raises the excitability of individual cells and elevates the activation level of the network. To conclude, our simulations suggest that the impairment of both neurons and astrocytes disturbs the neuronal network activity in schizophrenia.

scholarly journals Functional connectivity in task-negative network of the Deaf: effects of sign language experience

2014 ◽  
Author(s):  
Evie Malaia ◽  
Thomas M Talavage ◽  
Ronnie B Wilbur
Keyword(s):  
Functional Connectivity ◽  
Sign Language ◽  
Language Processing ◽  
Auditory Processing ◽  
Right Hemisphere ◽  
Parietal Lobe ◽  
Sensory Deprivation ◽  
Brain Regions ◽  
Visual Signal ◽  
Deaf Signers

Prior studies investigating cortical processing in Deaf signers suggest that life-long experience with sign language and/or auditory deprivation may alter the brain’s anatomical structure and the function of brain regions typically recruited for auditory processing (Emmorey et al., 2010; Pénicaud, et al., 2012 inter alia). We report the first investigation of the task-negative network in Deaf signers and its functional connectivity – the temporal correlations among spatially remote neurophysiological events. We show that Deaf signers manifest increased functional connectivity between posterior cingulate/precuneus and left medial temporal gyrus (MTG), but also inferior parietal lobe and medial temporal gyrus in the right hemisphere- areas that have been found to show functional recruitment specifically during sign language processing. These findings suggest that the organization of the brain at the level of inter-network connectivity is likely affected by experience with processing visual language, although sensory deprivation could be another source of the difference. We hypothesize that connectivity alterations in the task negative network reflect predictive/automatized processing of the visual signal.

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