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Selective attention to one of several competing speakers is required for comprehending a target speaker among other voices and for successful communication with them. It moreover has been found to involve the neural tracking of low-frequency speech rhythms in the auditory cortex. Effects of selective attention have also been found in subcortical neural activities, in particular regarding the frequency-following response related to the fundamental frequency of speech (speech-FFR). Recent investigations have, however, shown that the speech-FFR contains cortical contributions as well. It remains unclear whether these are also modulated by selective attention. Here we used magnetoencephalography to assess the attentional modulation of the cortical contributions to the speech-FFR. We presented both male and female participants with two competing speech signals and analyzed the cortical responses during attentional switching between the two speakers. Our findings revealed robust attentional modulation of the cortical contribution to the speech-FFR: the neural responses were higher when the speaker was attended than when they were ignored. We also found that, regardless of attention, a voice with a lower fundamental frequency elicited a larger cortical contribution to the speech-FFR than a voice with a higher fundamental frequency. Our results show that the attentional modulation of the speech-FFR does not only occur subcortically but extends to the auditory cortex as well.SIGNIFICANCE STATEMENT Understanding speech in noise requires attention to a target speaker. One of the speech features that a listener can use to identify a target voice among others and attend it is the fundamental frequency, together with its higher harmonics. The fundamental frequency arises from the opening and closing of the vocal folds and is tracked by high-frequency neural activity in the auditory brainstem and in the cortex. Previous investigations showed that the subcortical neural tracking is modulated by selective attention. Here we show that attention affects the cortical tracking of the fundamental frequency as well: it is stronger when a particular voice is attended than when it is ignored.
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Corteza Auditiva , Percepción del Habla , Humanos , Masculino , Femenino , Habla , Percepción del Habla/fisiología , Corteza Auditiva/fisiología , Magnetoencefalografía , Potenciales Evocados Auditivos del Tronco Encefálico/fisiología , Estimulación Acústica , Electroencefalografía/métodosRESUMEN
Most parts of speech are voiced, exhibiting a degree of periodicity with a fundamental frequency and many higher harmonics. Some neural populations respond to this temporal fine structure, in particular at the fundamental frequency. This frequency-following response to speech consists of both subcortical and cortical contributions and can be measured through EEG as well as through magnetoencephalography (MEG), although both differ in the aspects of neural activity that they capture: EEG is sensitive to both radial and tangential sources as well as to deep sources, whereas MEG is more restrained to the measurement of tangential and superficial neural activity. EEG responses to continuous speech have shown an early subcortical contribution, at a latency of around 9 msec, in agreement with MEG measurements in response to short speech tokens, whereas MEG responses to continuous speech have not yet revealed such an early component. Here, we analyze MEG responses to long segments of continuous speech. We find an early subcortical response at latencies of 4-11 msec, followed by later right-lateralized cortical activities at delays of 20-58 msec as well as potential subcortical activities. Our results show that the early subcortical component of the FFR to continuous speech can be measured from MEG in populations of participants and that its latency agrees with that measured with EEG. They furthermore show that the early subcortical component is temporally well separated from later cortical contributions, enabling an independent assessment of both components toward further aspects of speech processing.
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Electroencefalografía , Percepción del Habla , Humanos , Electroencefalografía/métodos , Habla , Magnetoencefalografía/métodos , Corteza Cerebral/fisiología , Percepción del Habla/fisiologíaRESUMEN
How is information processed in the cerebral cortex? In most cases, recorded brain activity is averaged over many (stimulus) repetitions, which erases the fine-structure of the neural signal. However, the brain is obviously a single-trial processor. Thus, we here demonstrate that an unsupervised machine learning approach can be used to extract meaningful information from electro-physiological recordings on a single-trial basis. We use an auto-encoder network to reduce the dimensions of single local field potential (LFP) events to create interpretable clusters of different neural activity patterns. Strikingly, certain LFP shapes correspond to latency differences in different recording channels. Hence, LFP shapes can be used to determine the direction of information flux in the cerebral cortex. Furthermore, after clustering, we decoded the cluster centroids to reverse-engineer the underlying prototypical LFP event shapes. To evaluate our approach, we applied it to both extra-cellular neural recordings in rodents, and intra-cranial EEG recordings in humans. Finally, we find that single channel LFP event shapes during spontaneous activity sample from the realm of possible stimulus evoked event shapes. A finding which so far has only been demonstrated for multi-channel population coding.
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Aprendizaje Profundo , Electroencefalografía , Humanos , Animales , Electroencefalografía/métodos , Corteza Cerebral/fisiología , Masculino , Aprendizaje Automático no Supervisado , Ratas , Adulto , FemeninoRESUMEN
Free-running recurrent neural networks (RNNs), especially probabilistic models, generate an ongoing information flux that can be quantified with the mutual information I[xâ(t),xâ(t+1)] between subsequent system states xâ. Although previous studies have shown that I depends on the statistics of the network's connection weights, it is unclear how to maximize I systematically and how to quantify the flux in large systems where computing the mutual information becomes intractable. Here, we address these questions using Boltzmann machines as model systems. We find that in networks with moderately strong connections, the mutual information I is approximately a monotonic transformation of the root-mean-square averaged Pearson correlations between neuron pairs, a quantity that can be efficiently computed even in large systems. Furthermore, evolutionary maximization of I[xâ(t),xâ(t+1)] reveals a general design principle for the weight matrices enabling the systematic construction of systems with a high spontaneous information flux. Finally, we simultaneously maximize information flux and the mean period length of cyclic attractors in the state-space of these dynamical networks. Our results are potentially useful for the construction of RNNs that serve as short-time memories or pattern generators.
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Mechanistic insight is achieved only when experiments are employed to test formal or computational models. Furthermore, in analogy to lesion studies, phantom perception may serve as a vehicle to understand the fundamental processing principles underlying healthy auditory perception. 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. In particular, we discuss why everyone with tinnitus suffers from (at least hidden) hearing loss, but not everyone with hearing loss suffers from tinnitus. We argue that intrinsic neural noise is generated and amplified along the auditory pathway as a compensatory mechanism to restore normal hearing based on adaptive stochastic resonance. The neural noise increase can then be misinterpreted as auditory input and perceived as tinnitus. This mechanism can be formalized in the Bayesian brain framework, where the percept (posterior) assimilates a prior prediction (brain's expectations) and likelihood (bottom-up neural signal). A higher mean and lower variance (i.e. enhanced precision) of the likelihood shifts the posterior, evincing a misinterpretation of sensory evidence, which may be further confounded by plastic changes in the brain that underwrite prior predictions. Hence, two fundamental processing principles provide the most explanatory power for the emergence of auditory phantom perceptions: predictive coding as a top-down and adaptive stochastic resonance as a complementary bottom-up mechanism. We conclude that both principles also play a crucial role in healthy auditory perception. Finally, in the context of neuroscience-inspired artificial intelligence, both processing principles may serve to improve contemporary machine learning techniques.
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Pérdida Auditiva , Acúfeno , Humanos , Acúfeno/psicología , Teorema de Bayes , Inteligencia Artificial , Percepción Auditiva , Vías AuditivasRESUMEN
BACKGROUND: About one sixth of the population of western industrialized nations suffers from chronic, subjective tinnitus, causing socioeconomic treatment and follow-up costs of almost 22 billion euros per year in Germany alone. According to the prevailing view, tinnitus develops as a consequence of a maladaptive neurophysiological process in the brain triggered by hearing loss. OBJECTIVES: The Erlangen model of tinnitus development presented here is intended to propose a comprehensive neurophysiological explanation for the initial occurrence of the phantom sound after hearing loss. Based on the model, a new treatment strategy will be developed. MATERIALS AND METHODS: The model summarized here is based on various animal and human physiological studies conducted in recent years. RESULTS: The Erlangen model considers subjective tinnitus as a side effect of a physiological mechanism that permanently optimizes information transmission into the auditory system by means of stochastic resonance (SR) even in the healthy auditory system. In fact, hearing-impaired patients with tinnitus hear better on average than those without tinnitus. This unfamiliar perspective on the phantom percept may already help affected patients to cope better with their suffering. In addition, based on the model, low intensity noise tinnitus suppression (LINTS) has been developed as a new, individually adapted treatment strategy for tonal tinnitus and has already been successfully tested in patients. CONCLUSIONS: A possible limiting factor for the model and treatment strategy is the pitch of the tinnitus percept, which may require adjustments to the treatment strategy for frequencies above about 5â¯kHz.
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Sordera , Efectos Colaterales y Reacciones Adversas Relacionados con Medicamentos , Acúfeno , Animales , Humanos , Acúfeno/diagnóstico , Acúfeno/terapia , Audición , EncéfaloRESUMEN
Subjective tinnitus is the conscious perception of sound in the absence of any acoustic source. The literature suggests various tinnitus mechanisms, most of which invoke changes in spontaneous firing rates of central auditory neurons resulting from modification of neural gain. Here, we present an alternative model based on evidence that tinnitus is: (1) rare in people who are congenitally deaf, (2) common in people with acquired deafness, and (3) potentially suppressed by active cochlear implants used for hearing restoration. We propose that tinnitus can only develop after fast auditory fiber activity has stimulated the synapse formation between fast-spiking parvalbumin positive (PV+) interneurons and projecting neurons in the ascending auditory path and coactivated frontostriatal networks after hearing onset. Thereafter, fast auditory fiber activity promotes feedforward and feedback inhibition mediated by PV+ interneuron activity in auditory-specific circuits. This inhibitory network enables enhanced stimulus resolution, attention-driven contrast improvement, and augmentation of auditory responses in central auditory pathways (neural gain) after damage of slow auditory fibers. When fast auditory fiber activity is lost, tonic PV+ interneuron activity is diminished, resulting in the prolonged response latencies, sudden hyperexcitability, enhanced cortical synchrony, elevated spontaneous γ oscillations, and impaired attention/stress-control that have been described in previous tinnitus models. Moreover, because fast processing is gained through sensory experience, tinnitus would not exist in congenital deafness. Electrical cochlear stimulation may have the potential to reestablish tonic inhibitory networks and thus suppress tinnitus. The proposed framework unites many ideas of tinnitus pathophysiology and may catalyze cooperative efforts to develop tinnitus therapies.
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Vías Auditivas/fisiología , Implantes Cocleares , Sordera/fisiopatología , Acúfeno/fisiopatología , Animales , Vías Auditivas/crecimiento & desarrollo , Vías Auditivas/fisiopatología , Sordera/terapia , Potenciales Evocados Auditivos , Humanos , NeurogénesisRESUMEN
Human hearing loss (HL) is often accompanied by comorbidities like tinnitus, which is affecting up to 15% of the adult population. Rodent animal studies could show that tinnitus may not only be a result of apparent HL due to cochlear hair cell damage but can also be a consequence of synaptopathy at the inner hair cells (IHCs) already induced by moderate sound traumata. Here, we investigate synaptopathy previously shown in mice in our animal model, the Mongolian gerbil, and relate it to behavioral signs of tinnitus. Tinnitus was induced by a mild monaural acoustic trauma leading to monaural noise induced HL in the animals, quantified by auditory brainstem response (ABR) audiometry. Behavioral signs of tinnitus percepts were detected by measurement of prepulse inhibition of the acoustic startle response in a gap-noise paradigm. Fourteen days after trauma, the cochleae of both ears were isolated, and IHC synapses were counted within several spectral regions of the cochlea. Behavioral signs of tinnitus were only found in animals with IHC synaptopathy, independent of type of HL. On the other hand, animals with apparent HL but without behavioral signs of tinnitus showed a reduction in amplitudes of ABR waves I&II but no significant changes in the number of synapses at the IHC. We conclude-in line with the literature-that HL is caused by damage to the IHC or by other reasons but that the development of tinnitus, at least in our animal model, is closely linked to synaptopathy at the IHC.
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Pérdida Auditiva Provocada por Ruido , Acúfeno , Animales , Umbral Auditivo , Cóclea , Potenciales Evocados Auditivos del Tronco Encefálico , Gerbillinae , Células Ciliadas Auditivas Internas , Humanos , Ratones , Reflejo de Sobresalto , Acúfeno/etiologíaRESUMEN
Cleavage of amyloid precursor protein (APP) by ß-secretase BACE1 initiates the production and accumulation of neurotoxic amyloid-ß peptides, which is widely considered an essential pathogenic mechanism in Alzheimer's disease (AD). Here, we report that BACE1 is essential for normal auditory function. Compared with wild-type littermates, BACE1-/- mice of either sex exhibit significant hearing deficits, as indicated by increased thresholds and reduced amplitudes in auditory brainstem responses (ABRs) and decreased distortion product otoacoustic emissions (DPOAEs). Immunohistochemistry revealed aberrant synaptic organization in the cochlea and hypomyelination of auditory nerve fibers as predominant neuropathological substrates of hearing loss in BACE1-/- mice. In particular, we found that fibers of spiral ganglion neurons (SGN) close to the organ of Corti are disorganized and abnormally swollen. BACE1 deficiency also engenders organization defects in the postsynaptic compartment of SGN fibers with ectopic overexpression of PSD95 far outside the synaptic region. During postnatal development, auditory fiber myelination in BACE1-/- mice lags behind dramatically and remains incomplete into adulthood. We relate the marked hypomyelination to the impaired processing of Neuregulin-1 when BACE1 is absent. To determine whether the cochlea of adult wild-type mice is susceptible to AD treatment-like suppression of BACE1, we administered the established BACE1 inhibitor NB-360 for 6 weeks. The drug suppressed BACE1 activity in the brain, but did not impair hearing performance and, upon neuropathological examination, did not produce the characteristic cochlear abnormalities of BACE1-/- mice. Together, these data strongly suggest that the hearing loss of BACE1 knock-out mice represents a developmental phenotype.SIGNIFICANCE STATEMENT Given its crucial role in the pathogenesis of Alzheimer's disease (AD), BACE1 is a prime pharmacological target for AD prevention and therapy. However, the safe and long-term administration of BACE1-inhibitors as envisioned in AD requires a comprehensive understanding of the various physiological functions of BACE1. Here, we report that BACE1 is essential for the processing of auditory signals in the inner ear, as BACE1-deficient mice exhibit significant hearing loss. We relate this deficit to impaired myelination and aberrant synapse formation in the cochlea, which manifest during postnatal development. By contrast, prolonged pharmacological suppression of BACE1 activity in adult wild-type mice did not reproduce the hearing deficit or the cochlear abnormalities of BACE1 null mice.
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Secretasas de la Proteína Precursora del Amiloide/metabolismo , Ácido Aspártico Endopeptidasas/metabolismo , Cóclea/metabolismo , Potenciales Evocados Auditivos del Tronco Encefálico , Secretasas de la Proteína Precursora del Amiloide/genética , Animales , Ácido Aspártico Endopeptidasas/genética , Cóclea/fisiología , Homólogo 4 de la Proteína Discs Large/genética , Homólogo 4 de la Proteína Discs Large/metabolismo , Femenino , Masculino , Ratones , Ratones Endogámicos C57BL , Vaina de Mielina/metabolismo , Neurregulina-1/genética , Neurregulina-1/metabolismo , Ganglio Espiral de la Cóclea/metabolismo , Ganglio Espiral de la Cóclea/fisiologíaRESUMEN
The growth of cell colonies is determined by the migration and proliferation of the individual cells. This is often modeled with the Fisher-Kolmogorov (FK) equation, which assumes that cells diffuse independently from each other, but stop to proliferate when their density reaches a critial limit. However, when using measured, cell-line specific parameters, we find that the FK equation drastically underestimates the experimentally observed increase of colony radius with time. Moreover, cells in real colonies migrate radially outward with superdiffusive trajectories, in contrast to the assumption of random diffusion. We demonstrate that both dicrepancies can be resolved by assuming that cells in dense colonies are driven apart by repulsive, pressure-like forces. Using this model of proliferating repelling particles (PRP), we find that colony growth exhibits different dynamical regimes, depending on the ratio between a pressure-related equilibrium cell density and the critial density of proliferation arrest.
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Recently, the interest in spiking neural networks (SNNs) remarkably increased, as up to now some key advances of biological neural networks are still out of reach. Thus, the energy efficiency and the ability to dynamically react and adapt to input stimuli as observed in biological neurons is still difficult to achieve. One neuron model commonly used in SNNs is the leaky-integrate-and-fire (LIF) neuron. LIF neurons already show interesting dynamics and can be run in two operation modes: coincidence detectors for low and integrators for high membrane decay times, respectively. However, the emergence of these modes in SNNs and the consequence on network performance and information processing ability is still elusive. In this study, we examine the effect of different decay times in SNNs trained with a surrogate-gradient-based approach. We propose two measures that allow to determine the operation mode of LIF neurons: the number of contributing input spikes and the effective integration interval. We show that coincidence detection is characterized by a low number of input spikes as well as short integration intervals, whereas integration behavior is related to many input spikes over long integration intervals. We find the two measures to linearly correlate via a correlation factor that depends on the decay time. Thus, the correlation factor as function of the decay time shows a powerlaw behavior, which could be an intrinsic property of LIF networks. We argue that our work could be a starting point to further explore the operation modes in SNNs to boost efficiency and biological plausibility. Supplementary Information: The online version of this article (10.1007/s11571-023-10038-0) contains supplementary material, which is available to authorized users.
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The pore size of biopolymer networks governs their mechanical properties and strongly impacts the behavior of embedded cells. Confocal reflection microscopy and second harmonic generation microscopy are widely used to image biopolymer networks; however, both techniques fail to resolve vertically oriented fibers. Here, we describe how such directionally biased data can be used to estimate the network pore size. We first determine the distribution of distances from random points in the fluid phase to the nearest fiber. This distribution follows a Rayleigh distribution, regardless of isotropy and data bias, and is fully described by a single parameter--the characteristic pore size of the network. The bias of the pore size estimate due to the missing fibers can be corrected by multiplication with the square root of the visible network fraction. We experimentally verify the validity of this approach by comparing our estimates with data obtained using confocal fluorescence microscopy, which represents the full structure of the network. As an important application, we investigate the pore size dependence of collagen and fibrin networks on protein concentration. We find that the pore size decreases with the square root of the concentration, consistent with a total fiber length that scales linearly with concentration.
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Biopolímeros/química , Microscopía , Colágeno/química , Fibrina/química , PorosidadRESUMEN
The Zwicker tone illusion - an auditory phantom percept after hearing a notched noise stimulus - can serve as an interesting model for acute tinnitus. Recent mechanistic models suggest that the underlying neural mechanisms of both percepts are similar. To date it is not clear if animals do perceive the Zwicker tone, as up to now no behavioral paradigms are available to objectively assess the presence of this phantom percept. Here we introduce, for the first time, a modified version of the gap pre-pulse inhibition of the acoustic startle reflex (GPIAS) paradigm to test if it is possible to induce a Zwicker tone percept in our rodent model, the Mongolian gerbil. Furthermore, we developed a new aversive conditioning learning paradigm and compare the two approaches. We found a significant increase in the GPIAS effect when presenting a notched noise compared to white noise gap pre-pulse inhibition, which is consistent with the interpretation of a Zwicker tone percept in these animals. In the aversive conditioning learning paradigm, no clear effect could be observed in the discrimination performance of the tested animals. When investigating the first 33% of the correct conditioned responses, an effect of a possible Zwicker tone percept can be seen, i.e. animals show identical behavior as if a pure tone was presented, but the paradigm needs to be further improved. Nevertheless, the results indicate that Mongolian gerbils are able to perceive a Zwicker tone and can serve as a neurophysiological model for human tinnitus generation.
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Ilusiones , Acúfeno , Humanos , Animales , Gerbillinae , Audición , Ruido , Reflejo de Sobresalto/fisiología , Estimulación AcústicaRESUMEN
How do we make sense of the input from our sensory organs, and put the perceived information into context of our past experiences? The hippocampal-entorhinal complex plays a major role in the organization of memory and thought. The formation of and navigation in cognitive maps of arbitrary mental spaces via place and grid cells can serve as a representation of memories and experiences and their relations to each other. The multi-scale successor representation is proposed to be the mathematical principle underlying place and grid cell computations. Here, we present a neural network, which learns a cognitive map of a semantic space based on 32 different animal species encoded as feature vectors. The neural network successfully learns the similarities between different animal species, and constructs a cognitive map of 'animal space' based on the principle of successor representations with an accuracy of around 30% which is near to the theoretical maximum regarding the fact that all animal species have more than one possible successor, i.e. nearest neighbor in feature space. Furthermore, a hierarchical structure, i.e. different scales of cognitive maps, can be modeled based on multi-scale successor representations. We find that, in fine-grained cognitive maps, the animal vectors are evenly distributed in feature space. In contrast, in coarse-grained maps, animal vectors are highly clustered according to their biological class, i.e. amphibians, mammals and insects. This could be a putative mechanism enabling the emergence of new, abstract semantic concepts. Finally, even completely new or incomplete input can be represented by interpolation of the representations from the cognitive map with remarkable high accuracy of up to 95%. We conclude that the successor representation can serve as a weighted pointer to past memories and experiences, and may therefore be a crucial building block to include prior knowledge, and to derive context knowledge from novel input. Thus, our model provides a new tool to complement contemporary deep learning approaches on the road towards artificial general intelligence.
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Redes Neurales de la Computación , Semántica , Animales , Inteligencia Artificial , Cognición , MamíferosRESUMEN
The human sleep-cycle has been divided into discrete sleep stages that can be recognized in electroencephalographic (EEG) and other bio-signals by trained specialists or machine learning systems. It is however unclear whether these human-defined stages can be re-discovered with unsupervised methods of data analysis, using only a minimal amount of generic pre-processing. Based on EEG data, recorded overnight from sleeping human subjects, we investigate the degree of clustering of the sleep stages using the General Discrimination Value as a quantitative measure of class separability. Virtually no clustering is found in the raw data, even after transforming the EEG signals of each 30-s epoch from the time domain into the more informative frequency domain. However, a Principal Component Analysis (PCA) of these epoch-wise frequency spectra reveals that the sleep stages separate significantly better in the low-dimensional sub-space of certain PCA components. In particular the component C1(t) can serve as a robust, continuous 'master variable' that encodes the depth of sleep and therefore correlates strongly with the 'hypnogram', a common plot of the discrete sleep stages over time. Moreover, C1(t) shows persistent trends during extended time periods where the sleep stage is constant, suggesting that sleep may be better understood as a continuum. These intriguing properties of C1(t) are not only relevant for understanding brain dynamics during sleep, but might also be exploited in low-cost single-channel sleep tracking devices for private and clinical use.
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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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The phenomenon of self-induced stochastic resonance (SISR) requires a nontrivial scaling limit between the deterministic and the stochastic timescales of an excitable system, leading to the emergence of coherent oscillations which are absent without noise. In this paper, we numerically investigate SISR and its control in single neurons and three-neuron motifs made up of the Morris-Lecar model. In single neurons, we compare the effects of electrical and chemical autapses on the degree of coherence of the oscillations due to SISR. In the motifs, we compare the effects of altering the synaptic time-delayed couplings and the topologies on the degree of SISR. Finally, we provide two enhancement strategies for a particularly poor degree of SISR in motifs with chemical synapses: (1) we show that a poor SISR can be significantly enhanced by attaching an electrical or an excitatory chemical autapse on one of the neurons, and (2) we show that by multiplexing the motif with a poor SISR to another motif (with a high SISR in isolation), the degree of SISR in the former motif can be significantly enhanced. We show that the efficiency of these enhancement strategies depends on the topology of the motifs and the nature of synaptic time-delayed couplings mediating the multiplexing connections.
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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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Language is fundamentally predictable, both on a higher schematic level as well as low-level lexical items. Regarding predictability on a lexical level, collocations are frequent co-occurrences of words that are often characterized by high strength of association. So far, psycho- and neurolinguistic studies have mostly employed highly artificial experimental paradigms in the investigation of collocations by focusing on the processing of single words or isolated sentences. In contrast, here we analyze EEG brain responses recorded during stimulation with continuous speech, i.e., audio books. We find that the N400 response to collocations is significantly different from that of non-collocations, whereas the effect varies with respect to cortical region (anterior/posterior) and laterality (left/right). Our results are in line with studies using continuous speech, and they mostly contradict those using artificial paradigms and stimuli. To the best of our knowledge, this is the first neurolinguistic study on collocations using continuous speech stimulation.