Most scientists agree that subjective tinnitus is the pathological result of an interaction of damage to the peripheral auditory system and central neuroplastic adaptations. Here we investigate such tinnitus related adaptations in the primary auditory cortex (AC) 7 and 13 days after noise trauma induction of tinnitus by quantifying the density of the extracellular matrix (ECM) in the AC of Mongolian gerbils (Meriones unguiculatus). The ECM density has been shown to be relevant for neuroplastic processes and synaptic stability within the cortex.
View Article and Find Full Text PDFUnlabelled: 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.
View Article and Find Full Text PDFHow 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.
View Article and Find Full Text PDFFree-running recurrent neural networks (RNNs), especially probabilistic models, generate an ongoing information flux that can be quantified with the mutual information I[x→(t),x→(t+1)] between subsequent system states x→. 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.
View Article and Find Full Text PDFMost 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.
View Article and Find Full Text PDFSelective 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).
View Article and Find Full Text PDFBackground: 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.
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.
View Article and Find Full Text PDFNeurobiol Sleep Circadian Rhythms
May 2023
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.
View Article and Find Full Text PDFThe 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.
View Article and Find Full Text PDFSensory "aftereffects" are a subgroup of sensory illusions that can be defined as an illusory phenomenon triggered after prolonged exposure to a given sensory inducer. These phenomena are interesting because they can provide insights into the mechanisms of perception. In auditory modality, there is a special interest in the so-called "Zwicker tone" (ZT), an auditory aftereffect triggered after the presentation of a notched noise (NN, broadband noise with a missing frequency band).
View Article and Find Full Text PDFHow 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.
View Article and Find Full Text PDFLanguage 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.
View Article and Find Full Text PDFData classification, the process of analyzing data and organizing it into categories or clusters, is a fundamental computing task of natural and artificial information processing systems. Both supervised classification and unsupervised clustering work best when the input vectors are distributed over the data space in a highly non-uniform way. These tasks become however challenging in weakly structured data sets, where a significant fraction of data points is located in between the regions of high point density.
View Article and Find Full Text PDFHow does the mind organize thoughts? The hippocampal-entorhinal complex is thought to support domain-general representation and processing of structural knowledge of arbitrary state, feature and concept spaces. In particular, it enables the formation of cognitive maps, and navigation on these maps, thereby broadly contributing to cognition. It has been proposed that the concept of multi-scale successor representations provides an explanation of the underlying computations performed by place and grid cells.
View Article and Find Full Text PDFNoise is generally considered to harm information processing performance. However, in the context of stochastic resonance, noise has been shown to improve signal detection of weak sub- threshold signals, and it has been proposed that the brain might actively exploit this phenomenon. Especially within the auditory system, recent studies suggest that intrinsic noise plays a key role in signal processing and might even correspond to increased spontaneous neuronal firing rates observed in early processing stages of the auditory brain stem and cortex after hearing loss.
View Article and Find Full Text PDFRecently, 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.
View Article and Find Full Text PDFBackground: The endocytic reabsorption of proteins in the proximal tubule requires a complex machinery and defects can lead to tubular proteinuria. The precise mechanisms of endocytosis and processing of receptors and cargo are incompletely understood. EHD1 belongs to a family of proteins presumably involved in the scission of intracellular vesicles and in ciliogenesis.
View Article and Find Full Text PDFIn clinical practice, human sleep is classified into stages, each associated with different levels of muscular activity and marked by characteristic patterns in the EEG signals. It is however unclear whether this subdivision into discrete stages with sharply defined boundaries is truly reflecting the dynamics of human sleep. To address this question, we consider one-channel EEG signals as heterogeneous random walks: stochastic processes controlled by hyper-parameters that are themselves time-dependent.
View Article and Find Full Text PDFUp to now, modern machine learning (ML) has been based on approximating big data sets with high-dimensional functions, taking advantage of huge computational resources. We show that biologically inspired neuron models such as the leaky-integrate-and-fire (LIF) neuron provide novel and efficient ways of information processing. They can be integrated in machine learning models and are a potential target to improve ML performance.
View Article and Find Full Text PDFHuman 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.
View Article and Find Full Text PDFStochastic resonance (SR) has been proposed to play a major role in auditory perception, and to maintain optimal information transmission from the cochlea to the auditory system. By this, the auditory system could adapt to changes of the auditory input at second or even sub-second timescales. In case of reduced auditory input, somatosensory projections to the dorsal cochlear nucleus would be disinhibited in order to improve hearing thresholds by means of SR.
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