Acute and Long-Term Circuit-Level Effects in the Auditory Cortex After Sound Trauma - Jeschke, Marcus; Happel, Max F.K.; Tziridis, Konstantin; Krauß, Patrick; Schilling, Achim; Schulze, Holger; Ohl, Frank W. (Frontiers in Neuroscience, 2021)
Harmful environmental sounds are a prevailing source of chronic hearing impairments, including noise induced hearing loss, hyperacusis, or tinnitus. How these symptoms are related to pathophysiological damage to the sensory receptor epithelia and its effects along the auditory pathway, have been documented in numerous studies. An open question concerns the temporal evolution of maladaptive changes after damage and their manifestation in the balance of thalamocortical and corticocortical input to the auditory cortex (ACx). To address these issues, we investigated the loci of plastic reorganizations across the tonotopic axis of the auditory cortex of male Mongolian gerbils (Meriones unguiculatus) acutely after a sound trauma and after several weeks. We used a residual current-source density analysis to dissociate adaptations of intracolumnar input and horizontally relayed corticocortical input to synaptic populations across cortical layers in ACx. A pure tone-based sound trauma caused acute changes of subcortical inputs and corticocortical inputs at all tonotopic regions, particularly showing a broad reduction of tone-evoked inputs at tonotopic regions around the trauma frequency. At other cortical sites, the overall columnar activity acutely decreased, while relative contributions of lateral corticocortical inputs increased. After 4–6 weeks, cortical activity in response to the altered sensory inputs showed a general increase of local thalamocortical input reaching levels higher than before the trauma. Hence, our results suggest a detailed mechanism for overcompensation of altered frequency input in the auditory cortex that relies on a changing balance of thalamocortical and intracortical input and along the frequency gradient of the cortical tonotopic map.
CRIS-Team Scopus Importer:2021-01-29
Keywords: auditory cortex; circuit level analysis; corticocortical; noise-induced hearing loss; thalamocortical

Adaptive ICA for Speech EEG Artifact Removal - Kölbl, Nikola; Schilling, Achim; Krauß, Patrick (2023 5th International Conference on Bio-engineering for Smart Technologies (BioSMART), 2023)
Recently, in cognitive neuroscience, artificial stimuli such as single sentences have been replaced by more naturalistic stimuli such as continuous speech. Since it is already known 'where' in the brain language is processed, the next crucial step is to investigate 'how' these neuronal circuits and processes work. Thus, it is necessary to apply experimental procedures with a high temporal resolution such as electroencephalography (EEG) in order to capture and identify these mechanisms. However, EEG is highly prone to measurement errors as the surface electrodes collect all kinds of electromagnetic noise from physiological and non-physiological sources. Here, we present a procedure to remove those artifacts (with a special focus on eye artifacts) and provide evidence that it is possible to extract event related potentials (ERPs) from EEG data recorded during listening of an audio book. We developed an evaluation pipeline, tested on EEG-data of 36 participants. The pipeline consists of two major steps: spectral filtering (bandpass: 1 Hz-20 Hz) and a custom version of independent component analysis (ICA) filtering. Thus, we defined one channel (Fp1) as our electro-oculogram channel (EOG) and tested which independent components significantly correlate with this channel. All independent components that had a correlation above a fixed threshold were removed. This procedure is highly reproducible and allows to extract clean ERPs from EEG data during continuous speech perception. We show that the ERP responses to adjectives are different from ERPs to verbs in shape as well as latency. We suggest that this advancement in evaluating EEG data may further improve neurolinguistics research and is a further step to develop a unified evaluation pipeline for these kinds of data.
CRIS-Team Scopus Importer:2024-12-16
Keywords: continuous speech perception; EEG; event related potentials (ERP); independent component analysis (ICA); neurolinguistics

Adaptive stochastic resonance for unknown and variable input signals - Krauß, Patrick; Metzner, Claus; Schilling, Achim; Schütz, Christian; Tziridis, Konstantin; Fabry, Ben; Schulze, Holger (Scientific Reports, 2017)

A Founder Mutation in EHD1 Presents with Tubular Proteinuria and Deafness - Issler, Naomi; Afonso, Sara; Weissman, Irith; Jordan, Katrin; Cebrian-Serrano, Alberto; Meindl, Katrin; Dahlke, Eileen; Tziridis, Konstantin; Yan, Guanhua; Robles-López, José M.; Tabernero, Lydia; Patel, Vaksha; et al. (Journal of the American Society of Nephrology, 2022)
BACKGROUND: 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. However, the relevance of EHD1 in human tissues, in particular in the kidney, was unknown. METHODS: Genetic techniques were used in patients with tubular proteinuria and deafness to identify the disease-causing gene. Diagnostic and functional studies were performed in patients and disease models to investigate the pathophysiology. RESULTS: We identified six individuals (5-33 years) with proteinuria and a high-frequency hearing deficit associated with the homozygous missense variant c.1192C>T (p.R398W) in EHD1. Proteinuria (0.7-2.1 g/d) consisted predominantly of low molecular weight proteins, reflecting impaired renal proximal tubular endocytosis of filtered proteins. Ehd1 knockout and Ehd1R398W/R398W knockin mice also showed a high-frequency hearing deficit and impaired receptor-mediated endocytosis in proximal tubules, and a zebrafish model showed impaired ability to reabsorb low molecular weight dextran. Interestingly, ciliogenesis appeared unaffected in patients and mouse models. In silico structural analysis predicted a destabilizing effect of the R398W variant and possible inference with nucleotide binding leading to impaired EHD1 oligomerization and membrane remodeling ability. CONCLUSIONS: A homozygous missense variant of EHD1 causes a previously unrecognized autosomal recessive disorder characterized by sensorineural deafness and tubular proteinuria. Recessive EHD1 variants should be considered in individuals with hearing impairment, especially if tubular proteinuria is noted.
CRIS-Team Scopus Importer:2022-04-22
Keywords: epithelial transport physiology; Eps15 homology domain; genetic renal disease; infertility; megalin; mutation; proximal tubule

Analysis and visualization of sleep stages based on deep neural networks - Krauß, Patrick; Metzner, Claus; Joshi, Nidhi; Schulze, Holger; Traxdorf, Maximilian; Maier, Andreas; Schilling, Achim (Neurobiology of Sleep and Circadian Rhythms, 2021)
Automatic sleep stage scoring based on deep neural networks has come into focus of sleep researchers and physicians, as a reliable method able to objectively classify sleep stages would save human resources and simplify clinical routines. Due to novel open-source software libraries for machine learning, in combination with enormous recent progress in hardware development, a paradigm shift in the field of sleep research towards automatic diagnostics might be imminent. We argue that modern machine learning techniques are not just a tool to perform automatic sleep stage classification, but are also a creative approach to find hidden properties of sleep physiology. We have already developed and established algorithms to visualize and cluster EEG data, facilitating first assessments on sleep health in terms of sleep-apnea and consequently reduced daytime vigilance. In the following study, we further analyze cortical activity during sleep by determining the probabilities of momentary sleep stages, represented as hypnodensity graphs and then computing vectorial cross-correlations of different EEG channels. We can show that this measure serves to estimate the period length of sleep cycles and thus can help to find disturbances due to pathological conditions.
CRIS-Team Scopus Importer:2021-03-19
Keywords: Artificial neural networks; Deep learning; Electroencephalography (EEG); Hypnodensity graphs; Multidimensional scaling (MDS); Polysomnography (PSG); Sleep cycle analysis; Sleep stage scoring

Analysis of argument structure constructions in the large language model BERT - Ramezani, Pegah; Krauß, Patrick; Schilling, Achim (2025)
Understanding how language and linguistic constructions are processed in the brain is a fundamental question in cognitive computational neuroscience. In this study, we investigate the processing and representation of Argument Structure Constructions (ASCs) in the BERT language model, extending previous analyses conducted with Long Short-Term Memory (LSTM) networks. We utilized a custom GPT-4 generated dataset comprising 2000 sentences, evenly distributed among four ASC types: transitive, ditransitive, caused-motion, and resultative constructions. BERT was assessed using the various token embeddings across its 12 layers. Our analyses involved visualizing the embeddings with Multidimensional Scaling (MDS) and t-distributed Stochastic Neighbor Embedding (t-SNE) and calculating the Generalized Discrimination Value (GDV) to quantify the degree of clustering. We also trained feedforward classifiers (probes) to predict construction categories from these embeddings. Results reveal that CLS token embeddings cluster best according to ASC types in layers 2, 3, and 4, with diminished clustering in intermediate layers and a slight increase in the final layers. Token embeddings for DET and SUBJ showed consistent intermediate-level clustering across layers, while VERB embeddings demonstrated a systematic increase in clustering from layers 1 to 12. OBJ embeddings exhibited minimal clustering initially, which increased substantially, peaking in layer 10. Probe accuracies indicated that initial embeddings contained no specific construction information, as seen in low clustering and chance-level accuracies in layer 1. From layer 2 onward, probe accuracies surpassed 90 percent, highlighting latent construction category information not evident from GDV clustering alone. Additionally, Fisher Discriminant Ratio (FDR) analysis of attention weights revealed that OBJ tokens had the highest FDR scores, indicating they play a crucial role in differentiating ASCs, followed by VERB and DET tokens. SUBJ, CLS, and SEP tokens did not show significant FDR scores. Our study underscores the complex, layered processing of linguistic constructions in BERT, revealing both similarities and differences compared to recurrent models like LSTMs. Future research will compare these computational findings with neuroimaging data during continuous speech perception to better understand the neural correlates of ASC processing. This research demonstrates the potential of both recurrent and transformer-based neural language models to mirror linguistic processing in the human brain, offering valuable insights into the computational and neural mechanisms underlying language understandin
Keywords: argument structure constructions; linguistic constructions (CXs); large language models (LLMs); BERT; sentence representation; computational linguistics; natural language processing (NLP); GPT-4

Analysis of Audiometric Differences of Patients with and without Tinnitus in a Large Clinical Database - Gollnast, Dominik; Tziridis, Konstantin; Krauß, Patrick; Schilling, Achim; Hoppe, Ulrich; Schulze, Holger (Frontiers in Neurology, 2017)
Human hearing loss (HL) and comorbidities like tinnitus pose serious problems for people's daily life, which in most severe cases may lead to social isolation, depression, and suicide. Here, we investigate the relationship between hearing deficits and tinnitus. To this end, we conducted a retrospective study on anonymized pure tone and speech audiometric data from patients of the ENT hospital Erlangen in which we compare audiometric data between patients with and without tinnitus. Overall data from 37,661 patients with sensorineural (SHL) or conductive HL (CHL) with (T, 9.5%) or without (NT, 90.5%) a tinnitus percept in different age groups and with different tinnitus pitches were included in this study. The results of the pure tone audiometry comparisons showed significant differences in T patients compared to NT patients. In young patients, we generally found lower hearing thresholds in T compared to NT patients. In adult patients, differences were more heterogeneous: hearing thresholds in T patients were lower in low frequency ranges, while they were higher at high frequencies. Furthermore, lower thresholds were more often found in CHL patients and could rarely be detected in SHL patients. In speech audiometry, only CHL patients with high-pitched tinnitus showed lower thresholds compared to NT patients' thresholds. The results of this study may point to a biologically plausible functional benefit on hearing thresholds in HL tinnitus patients. We hypothesize that the physiological mechanism of stochastic resonance counteracts HL by adding neuronal noise to the system. This neuronal noise may induce changes in the auditory pathway and finally-as a side effect of threshold improvement-lead to the development of a tinnitus percept. We propose a general model of changed hearing thresholds in T patients, being either decreased or increased compared to NT patients.
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Analysis of continuous neuronal activity evoked by natural speech with computational corpus linguistics methods - Schilling, Achim; Tomasello, Rosario; Henningsen-Schomers, Malte R.; Zankl, Alexandra; Surendra, Kishore; Haller, Martin; Karl, Valerie; Uhrig, Peter; Maier, Andreas; Krauß, Patrick (Language, Cognition and Neuroscience, 2020)
In the field of neurobiology of language, neuroimaging studies are generally based on stimulation paradigms consisting of at least two different conditions. Designing those paradigms can be very time-consuming and this traditional approach is necessarily data-limited. In contrast, in computational and corpus linguistics, analyses are often based on large text corpora, which allow a vast variety of hypotheses to be tested by repeatedly re-evaluating the data set. Furthermore, text corpora also allow exploratory data analysis in order to generate new hypotheses. By drawing on the advantages of both fields, neuroimaging and computational corpus linguistics, we here present a unified approach combining continuous natural speech and MEG to generate a corpus of speech-evoked neuronal activity.
CRIS-Team Scopus Importer:2020-08-21
Keywords: computational corpus linguistics; MEG/EEG; natural language processing (NLP); naturalistic continuous speech stimuli; neurobiology of language

Analysis of Multichannel EEG Patterns During Human Sleep: A Novel Approach - Krauß, Patrick; Schilling, Achim; Bauer, Judith; Tziridis, Konstantin; Metzner, Claus; Schulze, Holger; Traxdorf, Maximilian (Frontiers in Human Neuroscience, 2018)
Classic visual sleep stage scoring is based on electroencephalogram (EEG) frequency band analysis of 30 s epochs and is commonly performed by highly trained medical sleep specialists using additional information from submental EMG and eye movements electrooculogram (EOG). In this study, we provide the proof-of-principle in 40 subjects that sleep stages can be consistently differentiated solely on the basis of spatial 3-channel EEG patterns based on root-mean-square (RMS) amplitudes. The polysomnographic 3-channel EEG data are pre-processed by RMS averaging over intervals of 30 s leading to spatial cortical activity patterns represented by 3-dimensional vectors. These patterns are visualized using multidimensional scaling (MDS), allowing a comparison of the spatial cortical activity patterns with the conventional visual sleep scoring system according to the American Academy of Sleep Medicine (AASM). Spatial cortical activity patterns based on RMS amplitudes naturally divide into different clusters that correspond to visually scored sleep stages. Furthermore, these clusters are reproducible between different subjects. Especially the cluster associated with the REM sleep stage seems to be very different from the one associated with the wake state. This study provides a proof-of-principle that it is possible to separate sleep stages solely by analyzing spatially distributed EEG RMS amplitudes reflecting cortical activity and without classical EEG feature extractions like power spectrum analysis.
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Analysis of Structure and Dynamics in Three-Neuron Motifs - Krauß, Patrick; Zankl, Alexandra; Schilling, Achim; Schulze, Holger; Metzner, Claus (Frontiers in Computational Neuroscience, 2019)
Recurrent neural networks can produce ongoing state-to-state transitions without any driving inputs, and the dynamical properties of these transitions are determined by the neuronal connection strengths. Due to non-linearity, it is not clear how strongly the system dynamics is affected by discrete local changes in the connection structure, such as the removal, addition, or sign-switching of individual connections. Moreover, there are no suitable metrics to quantify structural and dynamical differences between two given networks with arbitrarily indexed neurons. In this work, we present such permutation-invariant metrics and apply them to motifs of three binary neurons with discrete ternary connection strengths, an important class of building blocks in biological networks. Using multidimensional scaling, we then study the similarity relations between all 3,411 topologically distinct motifs with regard to structure and dynamics, revealing a strong clustering and various symmetries. As expected, the structural and dynamical distance between pairs of motifs show a significant positive correlation. Strikingly, however, the key parameter controlling motif dynamics turns out to be the ratio of excitatory to inhibitory connections.
CRIS-Team WoS Importer:2019-02-27

A New Statistical Approach for the Evaluation of Gap-prepulse Inhibition of the Acoustic Startle Reflex (GPIAS) for Tinnitus Assessment - Schilling, Achim; Krauß, Patrick; Gerum, Richard; Metzner, Claus; Tziridis, Konstantin; Schulze, Holger (Frontiers in Behavioral Neuroscience, 2017)
Background: An increasingly used behavioral paradigm for the objective assessment of a possible tinnitus percept in animal models has been proposed by Turner and coworkers in 2006. It is based on gap-prepulse inhibition (PPI) of the acoustic startle reflex (ASR) and usually referred to as GPIAS. As it does not require conditioning it became the method of choice to study neuroplastic phenomena associated with the development of tinnitus.Objective:It is still controversial if GPIAS is really appropriate for tinnitus screening, as the hypothesis that a tinnitus percept impairs the gap detection ability ("filling-in interpretation" is still questioned. Furthermore, a wide range of criteria for positive tinnitus detection in GPIAS have been used across different laboratories and there still is no consensus on a best practice for statistical evaluation of GPIAS results. Current approaches are often based on simple averaging of measured PPI values and comparisons on a population level without the possibility to perform valid statistics on the level of the single animal.Methods:A total number of 32 animals were measured using the standard GPIAS paradigm with varying number of measurement repetitions. Based on this data further statistical considerations were performed.Results:We here present a new statistical approach to overcome the methodological limitations of GPIAS. In a first step we show that ASR amplitudes are not normally distributed. Next we estimate the distribution of the measured PPI values by exploiting the full combinatorial power of all measured ASR amplitudes. We demonstrate that the amplitude ratios (1-PPI) are approximately lognormally distributed, allowing for parametrical testing of the logarithmized values and present a new statistical approach allowing for a valid and reliable statistical assessment of PPI changes in GPIAS.Conclusion:Based on our statistical approach we recommend using a constant criterion, which does not systematically depend on the number of measurement repetitions, in order to divide animals into a tinnitus and a non-tinnitus group. In particular, we recommend using a constant threshold based on the effect size as criterion, as the effect size, in contrast to thep-value, does not systematically depend on the number of measurement repetitions.
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A statistical method for analyzing and comparing spatiotemporal cortical activation patterns - Krauß, Patrick; Metzner, Claus; Schilling, Achim; Tziridis, Konstantin; Traxdorf, Maximilian; Wollbrink, Andreas; Rampp, Stefan; Pantev, Christo; Schulze, Holger (Scientific Reports, 2018)
Information in the cortex is encoded in spatiotemporal patterns of neuronal activity, but the exact nature of that code still remains elusive. While onset responses to simple stimuli are associated with specific loci in cortical sensory maps, it is completely unclear how the information about a sustained stimulus is encoded that is perceived for minutes or even longer, when discharge rates have decayed back to spontaneous levels. Using a newly developed statistical approach (multidimensional cluster statistics (MCS)) that allows for a comparison of clusters of data points in n-dimensional space, we here demonstrate that the information about long-lasting stimuli is encoded in the ongoing spatiotemporal activity patterns in sensory cortex. We successfully apply MCS to multichannel local field potential recordings in different rodent models and sensory modalities, as well as to human MEG and EEG data, demonstrating its universal applicability. MCS thus indicates novel ways for the development of powerful read-out algorithms of spatiotemporal brain activity that may be implemented in innovative brain-computer interfaces (BCI).
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Attentional modulation of the cortical contribution to the frequency-following response evoked by continuous speech - Schüller, Alina; Schilling, Achim; Krauß, Patrick; Rampp, Stefan; Reichenbach, Tobias (The Journal of Neuroscience, 2023)
Selective attention to one of several competing speakers is required for comprehending a target speaker amongst 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 high-frequency neural response at the fundamental frequency of speech, the 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 employed magnetoencephalography (MEG) 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.
CRIS-Team Scopus Importer:2023-11-17

Behavioral Assessment of Zwicker Tone Percepts in Gerbils - Schilling, Achim; Tziridis, Konstantin; Schulze, Holger; Krauß, Patrick (Neuroscience, 2023)
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.
CRIS-Team Scopus Importer:2023-05-05
Keywords: acoustic startle reflex; auditory phantom perception; aversive conditioning; shuttle box; tinnitus; Zwicker tone

Cell Adhesion on Surface-Functionalized Magnesium - Wagener, Victoria; Schilling, Achim; Mainka, Astrid; Hennig, Diana; Gerum, Richard; Kelch, Marie-Luise; Keim, Simon; Fabry, Ben; Virtanen, Sannakaisa (ACS Applied Materials and Interfaces, 2016)
The biocompatibility of commercially pure magnesium-based (cp Mg) biodegradable implants is compromised of strong hydrogen evolution and surface alkalization due to high initial corrosion rates of cp Mg in the physiological environment. To mitigate this problem, the addition of corrosion-retarding alloying elements or coating of implant surfaces has been suggested. In the following work, we explored the effect of organic coatings on long-term cell growth. cp Mg was coated with aminopropyltriehtoxysilane + vitamin C (AV), carbonyldiimidazole (CDI), or stearic acid (SA). All three coatings have been previously suggested to reduce initial corrosion and to enhance protein adsorption and hence cell adhesion on magnesium surfaces. Endothelial cells (DH1+/+) and osteosarcoma cells (MG63) were cultured on coated samples for up to 20 days. To quantify Mg corrosion, electrochemical impedance spectroscopy (EIS) was measured after 1, 3, and 5 days of cell culture. We also investigated the speed of initial cell spreading after seeding using fluorescently labeled fibroblasts (NIH/3T3). Hydrogen evolution after contact with cell culture medium was markedly decreased on AV- and SA-coated Mg compared to uncoated Mg. These coatings also showed improved cell adhesion and spreading after 24 h of culture comparable to tissue-treated plastic surfaces. On AV-coated cp Mg, a confluent layer of endothelial cells formed after 5 days and remained intact for up to 20 days. Together, these data demonstrate that surface coating with AV is a viable strategy for improving long-term biocompatibility of cp Mg-based implants. EIS measurements confirmed that the presence of a confluent cell layer increased the corrosion resistance.
Keywords: cell adhesion; corrosion protection; impedance spectroscopy; magnesium; organic coating

Classification at the accuracy limit: facing the problem of data ambiguity - Metzner, Claus; Schilling, Achim; Traxdorf, Maximilian; Tziridis, Konstantin; Maier, Andreas; Schulze, Holger; Krauß, Patrick (Scientific Reports, 2022)
Data 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. We derive the theoretical limit for classification accuracy that arises from this overlap of data categories. By using a surrogate data generation model with adjustable statistical properties, we show that sufficiently powerful classifiers based on completely different principles, such as perceptrons and Bayesian models, all perform at this universal accuracy limit under ideal training conditions. Remarkably, the accuracy limit is not affected by certain non-linear transformations of the data, even if these transformations are non-reversible and drastically reduce the information content of the input data. We further compare the data embeddings that emerge by supervised and unsupervised training, using the MNIST data set and human EEG recordings during sleep. We find for MNIST that categories are significantly separated not only after supervised training with back-propagation, but also after unsupervised dimensionality reduction. A qualitatively similar cluster enhancement by unsupervised compression is observed for the EEG sleep data, but with a very small overall degree of cluster separation. We conclude that the handwritten letters in MNIST can be considered as ’natural kinds’, whereas EEG sleep recordings are a relatively weakly structured data set, so that unsupervised clustering will not necessarily re-cover the human-defined sleep stages.
CRIS-Team Scopus Importer:2023-03-07

Coincidence detection and integration behavior in spiking neural networks - Stoll, Andreas; Maier, Andreas; Krauß, Patrick; Gerum, Richard; Schilling, Achim (Cognitive Neurodynamics, 2023)
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.
CRIS-Team Scopus Importer:2023-12-22
Keywords: Artificial intelligence; Coincidence detection; Computational modeling; Leaky-integrate-and-fire neuron; Neural networks

Conceptual Cognitive Maps Formation with Neural Successor Networks and Word Embeddings - Stöwer, Paul; Schilling, Achim; Maier, Andreas; Krauß, Patrick (IEEE International Conference on Development and Learning 2023 (ICDL), 2023)

Cross-modal stochastic resonance as a universal principle to enhance sensory processing. - Krauß, Patrick; Tziridis, Konstantin; Schilling, Achim; Schulze, Holger (Frontiers in Neuroscience, 2018)

Cross-Modal Stochastic Resonance as a Universal Principle to Enhance Sensory Processing. - Krauß, Patrick; Tziridis, Konstantin; Schilling, Achim; Schulze, Holger (Frontiers in Neuroscience, 2018)
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Deep learning based decoding of single local field potential events - Schilling, Achim; Gerum, Richard; Böhm, Claudia; Rasheed, Jwan; Metzner, Claus; Maier, Andreas; Reindl, Caroline; Hamer, Hajo; Krauß, Patrick (NeuroImage, 2024)
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.
CRIS-Team Scopus Importer:2024-07-19
Keywords: Auditory cortex; Auditory neuroscience; Auto-encoder; Deep learning; Embeddings; Intracranial EEG (iEEG); Local field potentials; Speech perception; Stereotactic EEG (sEEG)

Determining the mechanical properties of plectin in mouse myoblasts and keratinocytes - Bonakdar, Navid; Schilling, Achim; Spörrer, Marina; Lennert, Pablo; Mainka, Astrid; Winter, Lilli; Walko, Gernot; Wiche, Gerhard; Fabry, Ben; Goldmann, Wolfgang (Experimental Cell Research, 2015)
Plectin is the prototype of an intermediate filament (IF)-based cytolinker protein. It affects cells mechanically by interlinking and anchoring cytoskeletal filaments and acts as scaffolding and docking platform for signaling proteins to control cytoskeleton dynamics. The most common disease caused by mutations in the human plectin gene, epidermolysis bullosa simplex with muscular dystrophy (EBS-MD), is characterized by severe skin blistering and progressive muscular dystrophy. Therefore, we compared the biomechanical properties and the response to mechanical stress of murine plectin-deficient myoblasts and keratinocytes with wild-type cells. Using a cell stretching device, plectin-deficient myoblasts exhibited lower mechanical vulnerability upon external stress compared to wild-type cells, which we attributed to lower cellular pre-stress. Contrary to myoblasts, wild-type and plectin-deficient keratinocytes showed no significant differences. In magnetic tweezer measurements using fibronectin-coated paramagnetic beads, the stiffness of keratinocytes was higher than of myoblasts. Interestingly, cell stiffness, adhesion strength, and cytoskeletal dynamics were strikingly altered in plectin-deficient compared to wild-type myoblasts, whereas smaller differences were observed between plectin-deficient and wild-type keratinocytes, indicating that plectin might be more important for stabilizing cytoskeletal structures in myoblasts than in keratinocytes. Traction forces strongly correlated with the stiffness of plectin-deficient and wild-type myoblasts and keratinocytes. Contrary to that cell motility was comparable in plectin-deficient and wild-type myoblasts, but was significantly increased in plectin-deficient compared to wild-type keratinocytes. Thus, we postulate that the lack of plectin has divergent implications on biomechanical properties depending on the respective cell type.
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Editorial: Auditory perception and phantom perception in brains, minds and machines - Schilling, Achim; Schaette, Roland; Sedley, William; Gerum, Richard Carl; Maier, Andreas; Krauß, Patrick (Frontiers in Neuroscience, 2023)
CRIS-Team Scopus Importer:2023-11-03
Keywords: artificial intelligence; auditory neuroscience; auditory phantom perception; cognitive computational neuroscience; deep neural networks; hearing loss; neuroimaging; tinnitus

Erlanger Modell der Tinnitusentstehung – Perspektivwechsel und neue Behandlungsstrategie - Schulze, Holger; Schilling, Achim; Krauß, Patrick; Tziridis, Konstantin (HNO, 2023)
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 5 kHz.
CRIS-Team Scopus Importer:2023-09-22
Keywords: Autocorrelation; Hearing optimization; Low intensity noise tinnitus suppression (LINTS); Rehabilitation; Stochastic resonance

Extracting continuous sleep depth from EEG data without machine learning - Metzner, Claus; Schilling, Achim; Traxdorf, Maximilian; Schulze, Holger; Tziridis, Konstantin; Krauß, Patrick (Neurobiology of Sleep and Circadian Rhythms, 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. 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.
CRIS-Team Scopus Importer:2023-06-02

Integration of Leaky-Integrate-and-Fire Neurons in Standard Machine Learning Architectures to Generate Hybrid Networks: A Surrogate Gradient Approach - Gerum, Richard C.; Schilling, Achim (Neural Computation, 2021)
Up 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. Thus, we have derived simple update rules for LIF units to numerically integrate the differential equations. We apply a surrogate gradient approach to train the LIF units via backpropagation. We demonstrate that tuning the leak term of the LIF neurons can be used to run the neurons in different operating modes, such as simple signal integrators or coincidence detectors. Furthermore, we show that the constant surrogate gradient, in combination with tuning the leak term of the LIF units, can be used to achieve the learning dynamics of more complex surrogate gradients. To prove the validity of our method, we applied it to established image data sets (the Oxford 102 flower data set, MNIST), implemented various network architectures, used several input data encodings and demonstrated that the method is suitable to achieve state-of-the-art classification performance. We provide our method as well as further surrogate gradient methods to train spiking neural networks via backpropagation as an open-source KERAS package to make it available to the neuroscience and machine learning community. To increase the interpretability of the underlying effects and thus make a small step toward opening the black box of machine learning, we provide interactive illustrations, with the possibility of systematically monitoring the effects of parameter changes on the learning characteristics.
CRIS-Team WoS Importer:2021-09-24

Intrinsic Noise Improves Speech Recognition in a Computational Model of the Auditory Pathway - Schilling, Achim; Gerum, Richard; Metzner, Claus; Maier, Andreas; Krauß, Patrick (Frontiers in Neuroscience, 2022)
Noise 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. Here we present a computational model of the auditory pathway based on a deep neural network, trained on speech recognition. We simulate different levels of hearing loss and investigate the effect of intrinsic noise. Remarkably, speech recognition after hearing loss actually improves with additional intrinsic noise. This surprising result indicates that intrinsic noise might not only play a crucial role in human auditory processing, but might even be beneficial for contemporary machine learning approaches.
CRIS-Team Scopus Importer:2022-07-15
Keywords: auditory perception; deep artificial neural networks; dorsal cochlear nucleus; hearing loss; speech processing; stochastic resonance; tinnitus mechanisms; Zwicker tone

Leaky-Integrate-and-Fire Neuron-Like Long-Short-Term-Memory Units as Model System in Computational Biology - Gerum, Richard; Erpenbeck, André; Krauß, Patrick; Schilling, Achim (2023 International Joint Conference on Neural Networks (IJCNN), 2023)
Biological neural networks encode information very efficiently, and dynamically react to sensory input on very small time scales. In contrast to most contemporary machine learning approaches which rely on rate neurons with continuous output, biological neural networks are based on spiking neurons with quasi-binary discrete output. In Artificial Intelligence (AI) Research, time series data are efficiently encoded in Long-Short- Term-Memory (LSTM) networks. Despite their strength in encoding time series data and making predictions, LSTM units are assumed to be biologically implausible. Nevertheless, recent studies show that LSTM unit networks indeed behave similar to biological neural networks. In this study, we show that a particular choice of parameters for the weights and gates in peephole LSTM units causes these units to show similar dynamic behaviour as biologically plausible leaky integrate-and-fire (LIF) neurons, which represent a simple biologically inspired spiking neuron model. We analyzed the spiking characteristics of the restricted peephole LSTM units and characterize the parameter space, in which these units show certain spiking characteristics. We conclude that tackling complex cognitive tasks with biologically plausible and explainable artificial neural networks is an important step to make progress in both fields, neuroscience and artificial intelligence.
CRIS-Team Scopus Importer:2024-12-16

Methodological Considerations in the Analysis of Acoustically Evoked Neural Signals: A Comparative Study of Active EEG, Passive EEG and MEG - Kölbl, Nikola; Tziridis, Konstantin; Krauß, Patrick; Schilling, Achim (2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2024)
Analyzing and deciphering brain signals on a single trial base is the main goal of brain-computer interface (BCI) research as well as neurolinguistics. In the present study, we have evaluated the efficacy of three neuroimaging techniques - active electroencephalography (EEG), passive EEG, and magnetoencephalography (MEG) - in capturing and evaluating brain activity in response to auditory stimuli. The main goals of our research included two primary components: first, to identify ROIs, and second, to determine the appropriate number of stimulus samples needed to achieve a meaningful level of reliability. To estimate this number of measurement repetitions we performed step-wise sub-sampling combined with permutation testing. This involved a detailed comparison of event-related potentials resp. fields (ERPs, ERFs) elicited by auditory stimuli such as acoustic clicks and continuous speech. Our results show that active EEG outperformed passive EEG and MEG in sensor space. However, MEG demonstrated superior signal localization in source space. These results also highlight the complexity of developing real-time speech BCIs.
CRIS-Team Scopus Importer:2025-02-04
Keywords: AEP; EEG; ERP; language; MEG; speech

Microstructure of cortical activity during sleep reflects respiratory events and state of daytime vigilance - Traxdorf, Maximilian; Krauß, Patrick; Schilling, Achim; Schulze, Holger; Tziridis, Konstantin (Somnologie, 2019)
Background: Besides visual sleep stage scoring, it is possible to separate sleep stages by analyzing spatially distributed three-channel electroencephalogram (EEG) root mean square (RMS) amplitudes. This approach allows qualitative and quantitative evaluation of sleep architecture. The aim of this study was to analyze the effect of apnea–hypopnea index (AHI) and state of vigilance on the microstructure of cortical activity during sleep using EEG cluster analysis. Materials and methods: In 31 obstructive sleep apnea (OSA) patients, cortical EEG patterns were recorded during polysomnography and subjected to EEG cluster analysis. The results were subsequently correlated to AHI as well as to subjective (Epworth Sleepiness Scale) and objective (pupillographic sleepiness test) daytime sleepiness using linear regression. In 18/31 patients, cortical EEG patterns were recorded again the following night during continuous positive airway pressure (CPAP) titration and compared to the results of the first night. Results: Linear regression analyses revealed dependencies of specific cortical EEG patterns on AHI (intra-cluster distance of N2 [p = 0.019], N3 [p = 0.024]; inter-cluster distance of wake-N2 [p = 0.046], wake-N3 [p = 0.047], N2–N3 [p = 0.021]) and on objective daytime sleepiness (wake intra-cluster distance [p = 0.047], inter-cluster distance of wake-N3 [p = 0.042]), but not on subjective daytime sleepiness. Specifically, an increase in EEG pattern variability was found in patients with high AHI values, which could be reduced by CPAP therapy. Conclusion: This new approach enables objective analysis and visualization of sleep macro- and microstructure. The variance of neuronal EEG signal patterns enables conclusions on the presence of pathological respiratory events and objectively measured daytime sleepiness.
CRIS-Team Scopus Importer:2019-06-18
Keywords: Continuous positive airway pressure; Daytime sleepiness; Electroencephalography; Root mean square; Sleep apnea; obstructive

Migration in Confined 3D Environments Is Determined by a Combination of Adhesiveness, Nuclear Volume, Contractility, and Cell Stiffness - Lautscham, Lena; Kaemmerer, Christoph; Lange, Janina; Kolb, Thorsten; Mark, Christoph; Schilling, Achim; Strissel, Pamela; Strick, Reiner; Gluth, Caroline Laura Maria; Rowat, Amy C.; Metzner, Claus; Fabry, Ben (Biophysical Journal, 2015)
In cancer metastasis and other physiological processes, cells migrate through the three-dimensional (3D) extracellular matrix of connective tissue and must overcome the steric hindrance posed by pores that are smaller than the cells. It is currently assumed that low cell stiffness promotes cell migration through confined spaces, but other factors such as adhesion and traction forces may be equally important. To study 3D migration under confinement in a stiff (1.77 MPa) environment, we use soft lithography to fabricate polydimethylsiloxane (PDMS) devices consisting of linear channel segments with 20 ?m length, 3.7 ?m height, and a decreasing width from 11.2 to 1.7 ?m. To study 3D migration in a soft (550 Pa) environment, we use self-assembled collagen networks with an average pore size of 3 ?m. We then measure the ability of four different cancer cell lines to migrate through these 3D matrices, and correlate the results with cell physical properties including contractility, adhesiveness, cell stiffness, and nuclear volume. Furthermore, we alter cell adhesion by coating the channel walls with different amounts of adhesion proteins, and we increase cell stiffness by overexpression of the nuclear envelope protein lamin A. Although all cell lines are able to migrate through the smallest 1.7 ?m channels, we find significant differences in the migration velocity. Cell migration is impeded in cell lines with larger nuclei, lower adhesiveness, and to a lesser degree also in cells with lower contractility and higher stiffness. Our data show that the ability to overcome the steric hindrance of the matrix cannot be attributed to a single cell property but instead arises from a combination of adhesiveness, nuclear volume, contractility, and cell stiffness.
EVALuna2:17547

Models of tinnitus development. From cochlea to cortex - Krauß, Patrick; Schilling, Achim; Tziridis, Konstantin; Schulze, Holger (HNO, 2019)
The development of subjective tinnitus is still not mechanistically understood and existing models are controversially discussed. In this overview, the authors discuss three of the main models, all of which propose damage to the cochlea as the initial step in tinnitus development. Based on these models, apossible manifestation of tinnitus-related neuronal activity at the perceptually relevant level of the auditory pathway, the auditory cortex, is presented. Furthermore, it is demonstrated that one of the models offers anew view on the phenomenon, which could potentially help patients to better cope with their condition.
CRIS-Team WoS Importer:2019-03-26

Neural correlates of linguistic collocations during continuous speech perception - Garibyan, Armine; Schilling, Achim; Boehm, Claudia; Zankl, Alexandra; Krauß, Patrick (Frontiers in Psychology, 2022)
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.
CRIS-Team WoS Importer:2023-01-27

Neural network based formation of cognitive maps of semantic spaces and the putative emergence of abstract concepts - Stöwer, Paul; Schilling, Achim; Maier, Andreas; Krauß, Patrick (Scientific Reports, 2023)

Neural network based successor representations to form cognitive maps of space and language - Stöwer, Paul; Schliecker, Christian; Schilling, Achim; Metzner, Claus; Maier, Andreas; Krauß, Patrick (Scientific Reports, 2022)

Neural Networks with Fixed Binary Random Projections Improve Accuracy in Classifying Noisy Data - Yang, Zijin; Schilling, Achim; Maier, Andreas; Krauß, Patrick (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

Objective estimation of sensory thresholds based on neurophysiological parameters. - Schilling, Achim; Gerum, Richard; Krauß, Patrick; Metzner, Claus; Tziridis, Konstantin; Schulze, Holger (Frontiers in Neuroscience, 2019)

Objective estimation of sensory thresholds based on neurophysiological parameters - Schilling, Achim; Gerum, Richard; Krauß, Patrick; Metzner, Claus; Tziridis, Konstantin; Schulze, Holger (Frontiers in Neuroscience, 2019)
Reliable determination of sensory thresholds is the holy grail of signal detection theory. However, there exists no assumption-independent gold standard for the estimation of thresholds based on neurophysiological parameters, although a reliable estimation method is crucial for both scientific investigations and clinical diagnosis. Whenever it is impossible to communicate with the subjects, as in studies with animals or neonates, thresholds have to be derived from neural recordings or by indirect behavioral tests. Whenever the threshold is estimated based on such measures, the standard approach until now is the subjective setting-either by eye or by statistical means-of the threshold to the value where at least a “clear” signal is detectable. These measures are highly subjective, strongly depend on the noise, and fluctuate due to the low signal-to-noise ratio near the threshold. Here we show a novel method to reliably estimate physiological thresholds based on neurophysiological parameters. Using surrogate data we demonstrate that fitting the responses to different stimulus intensities with a hard sigmoid function, in combination with subsampling, provides a robust threshold value as well as an accurate uncertainty estimate. This method has no systematic dependence on the noise and does not even require samples in the full dynamic range of the sensory system. We prove that this method is universally applicable to all types of sensory systems, ranging from somatosensory stimulus processing in the cortex to auditory processing in the brain stem.
CRIS-Team Scopus Importer:2019-07-16
Keywords: Auditory cortex; Auditory neuroscience; Automated threshold estimation; Electrophysiology; Hearing; Psychometric function; Somatosensation; Somatosensory cortex

Offset responses in primary auditory cortex are enhanced after notched noise stimulation - Schilling, Achim; Choi, Byunghee; Parameshwarappa, Vinay; Norena, Arnaud J. (Journal of Neurophysiology, 2023)
Sensory "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). The ZT has been considered a plausible model of a specific tinnitus subtype since it presents some key characteristics in common with tinnitus. Indeed, both the tinnitus percept and ZT can be triggered by a relative "sensory deprivation," and their pitch corresponds to the frequency region that has been sensory deprived. The effects of a NN presentation on the central auditory system are still barely investigated, and the mechanisms of the ZT are elusive. In this study, we analyzed the laminar structure of the neural activity in the primary cortex of anesthetized and awake guinea pigs during and after white noise (WN) and NN stimulation. We found significantly increased offset responses, in terms of both spiking activity and local field potential amplitude, after NN compared with WN presentation. The offset responses were circumscribed to the granular and upper infragranular layers (input layers) and were maximal when the neuron's best frequency was within or near the missing frequency band. The mechanisms of the offset response and its putative link with the ZT are discussed.NEW & NOTEWORTHY Notched noise (white noise with embedded spectral gap) causes significant excitatory offset responses in the auditory cortex of awake and anesthetized guinea pigs. The largest offset responses were located in the infragranular/granular layers, and current source density analysis revealed that offset responses were associated with an early current sink localized in the upper infragranular layers. We discuss the possibility that the offset responses might be associated with an auditory phantom percept (Zwicker tone).
CRIS-Team Scopus Importer:2023-05-26
Keywords: electrophysiology; notched noise; offset response; primary auditory cortex; Zwicker tone

Open(G)PIAS: An open-source solution for the construction of a high-precision acoustic startle response setup for tinnitus screening and threshold estimation in rodents - Gerum, Richard; Rahlfs, Hinrich; Streb, Matthias; Krauß, Patrick; Grimm, Jannik; Metzner, Claus; Tziridis, Konstantin; Günther, Michael; Schulze, Holger; Kellermann, Walter; Schilling, Achim (Frontiers in Behavioral Neuroscience, 2019)
The modulation of the acoustic startle reflex (ASR) by a pre-stimulus called pre-pulse inhibition (PPI, for gap of silence pre-stimulus: GPIAS) is a versatile tool to, e.g., estimate hearing thresholds or identify subjective tinnitus percepts in rodents. A proper application of these paradigms depends on a reliable measurement of the ASR amplitudes and an exact stimulus presentation in terms of frequency and intensity. Here, we introduce a novel open-source solution for the construction of a low-cost ASR setup. The complete software for data acquisition and stimulus presentation is written in Python 3.6 and is provided as an Anaconda package. Furthermore, we provide a construction plan for the sensor system based on low-cost hardware components. Exemplary GPIAS data from two animal models (Mus musculus, Meriones unguiculatus) show that the ratio histograms (1-GPIAS) of the gap-pre-stimulus and no pre-stimulus ASR amplitudes can be well described by a log-normal distribution being in good accordance to previous studies with already established setups. Furthermore, it can be shown that the PPI as a function of pre-stimulus intensity (threshold paradigm) can be approximated with a hard-sigmoid function enabling a reproducible sensory threshold estimation. Thus, we show that the open-source solution could help to further establish the ASR method in many laboratories and, thus, facilitate and standardize research in animal models of tinnitus and/or hearing loss.
CRIS-Team Scopus Importer:2019-07-16
Keywords: 3D acceleration sensor; Anaconda package; Animal model; Low-cost setup; Startle; Tinnitus

Predictive coding and stochastic resonance as fundamental principles of auditory phantom perception - Schilling, Achim; Sedley, William; Gerum, Richard C.; Metzner, Claus; Tziridis, Konstantin; Maier, Andreas; Schulze, Holger; Zeng, Fan Gang; Friston, Karl J.; Krauß, Patrick (Brain, 2023)
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.
CRIS-Team Scopus Importer:2023-12-15
Keywords: artificial intelligence; Bayesian brain; phantom perception; predictive coding; stochastic resonance; tinnitus

Quantifying and Maximizing the Information Flux in Recurrent Neural Networks - Metzner, Claus; Yamakou, Marius; Voelkl, Dennis; Schilling, Achim; Krauß, Patrick (Neural Computation, 2024)
Free-running recurrent neural networks (RNNs), especially probabilistic models, generate an ongoing information flux that can be quantified with the mutual information (Formula Presented) between subsequent system states (Formula Presented). 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 (Formula Presented) reveals a general design principle for theweight 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.
CRIS-Team Scopus Importer:2024-05-03

Quantifying the separability of data classes in neural networks - Schilling, Achim; Maier, Andreas; Gerum, Richard; Metzner, Claus; Krauß, Patrick (Neural Networks, 2021)
We introduce the Generalized Discrimination Value (GDV) that measures, in a non-invasive manner, how well different data classes separate in each given layer of an artificial neural network. It turns out that, at the end of the training period, the GDV in each given layer L attains a highly reproducible value, irrespective of the initialization of the network's connection weights. In the case of multi-layer perceptrons trained with error backpropagation, we find that classification of highly complex data sets requires a temporal reduction of class separability, marked by a characteristic ‘energy barrier’ in the initial part of the GDV(L) curve. Even more surprisingly, for a given data set, the GDV(L) is running through a fixed ‘master curve’, independently from the total number of network layers. Finally, due to its invariance with respect to dimensionality, the GDV may serve as a useful tool to compare the internal representational dynamics of artificial neural networks with different architectures for neural architecture search or network compression; or even with brain activity in order to decide between different candidate models of brain function.
CRIS-Team Scopus Importer:2021-04-19
Keywords: Data class separability; Deep learning interpretability; Discrimination value; Neural architecture search; Neural network analysis; Representational similarity analysis

"Recurrence Resonance" in Three-Neuron Motifs - Krauß, Patrick; Prebeck, Karin; Schilling, Achim; Metzner, Claus (Frontiers in Computational Neuroscience, 2019)
Stochastic Resonance (SR) and Coherence Resonance (CR) are non-linear phenomena, in which an optimal amount of noise maximizes an objective function, such as the sensitivity for weak signals in SR, or the coherence of stochastic oscillations in CR. Here, we demonstrate a related phenomenon, which we call "Recurrence Resonance" (RR): noise can also improve the information flux in recurrent neural networks. In particular, we show for the case of three-neuron motifs with ternary connection strengths that the mutual information between successive network states can be maximized by adding a suitable amount of noise to the neuron inputs. This striking result suggests that noise in the brain may not be a problem that needs to be suppressed, but indeed a resource that is dynamically regulated in order to optimize information processing.
CRIS-Team WoS Importer:2019-10-01

Sleep as a random walk: a super-statistical analysis of EEG data across sleep stages - Metzner, Claus; Schilling, Achim; Traxdorf, Maximilian; Schulze, Holger; Krauß, Patrick (Communications Biology, 2021)
In 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. We first demonstrate the heterogeneity of the random process by showing that each sleep stage has a characteristic distribution and temporal correlation function of the raw EEG signals. Next, we perform a super-statistical analysis by computing hyper-parameters, such as the standard deviation, kurtosis, and skewness of the raw signal distributions, within subsequent 30-second epochs. It turns out that also the hyper-parameters have characteristic, sleep-stage-dependent distributions, which can be exploited for a simple Bayesian sleep stage detection. Moreover, we find that the hyper-parameters are not piece-wise constant, as the traditional hypnograms would suggest, but show rising or falling trends within and across sleep stages, pointing to an underlying continuous rather than sub-divided process that controls human sleep. Based on the hyper-parameters, we finally perform a pairwise similarity analysis between the different sleep stages, using a quantitative measure for the separability of data clusters in multi-dimensional spaces.
CRIS-Team WoS Importer:2021-12-24

Sparsity through evolutionary pruning prevents neuronal networks from overfitting - Gerum, Richard; Erpenbeck, André; Krauß, Patrick; Schilling, Achim (Neural Networks, 2020)
Modern Machine learning techniques take advantage of the exponentially rising calculation power in new generation processor units. Thus, the number of parameters which are trained to solve complex tasks was highly increased over the last decades. However, still the networks fail – in contrast to our brain – to develop general intelligence in the sense of being able to solve several complex tasks with only one network architecture. This could be the case because the brain is not a randomly initialized neural network, which has to be trained from scratch by simply investing a lot of calculation power, but has from birth some fixed hierarchical structure. To make progress in decoding the structural basis of biological neural networks we here chose a bottom-up approach, where we evolutionarily trained small neural networks in performing a maze task. This simple maze task requires dynamic decision making with delayed rewards. We were able to show that during the evolutionary optimization random severance of connections leads to better generalization performance of the networks compared to fully connected networks. We conclude that sparsity is a central property of neural networks and should be considered for modern Machine learning approaches.
CRIS-Team Scopus Importer:2020-06-02
Keywords: Artificial neural networks; Biological plausibility; Evolution; Evolutionary algorithm; Maze task; Overfitting

Spectrally Matched Near-Threshold Noise for Subjective Tinnitus Loudness Attenuation Based on Stochastic Resonance - Tziridis, Konstantin; Brunner, Sarah; Schilling, Achim; Krauß, Patrick; Schulze, Holger (Frontiers in Neuroscience, 2022)
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.
CRIS-Team Scopus Importer:2022-04-29
Keywords: individualized medicine; low intensity acoustic noise; stochastic resonance; tinnitus questionnaires; tinnitus treatment

Stochastic Resonance Controlled Upregulation of Internal Noise after Hearing Loss as a Putative Cause of Tinnitus-Related Neuronal Hyperactivity - Krauß, Patrick; Tziridis, Konstantin; Metzner, Claus; Schilling, Achim; Hoppe, Ulrich; Schulze, Holger (Frontiers in Neuroscience, 2016)
Subjective tinnitus is generally assumed to be a consequence of hearing loss. In animal studies it has been demonstrated that acoustic trauma induced cochlear damage can lead to behavioral signs of tinnitus. In addition it was shown that noise trauma may lead to deafferentation of cochlear inner hair cells (IHC) even in the absence of elevated hearing thresholds, and it seems conceivable that such hidden hearing loss may be sufficient to cause tinnitus. Numerous studies have indicated that tinnitus is correlated with pathologically increased spontaneous firing rates and hyperactivity of neurons along the auditory pathway. It has been proposed that this hyperactivity is the consequence of a mechanism aiming to compensate for reduced input to the auditory system by increasing central neuronal gain, a mechanism referred to as homeostatic plasticity (HP), thereby maintaining mean firing rates over longer timescales for stabilization of neuronal processing. Here we propose an alternative, new interpretation of tinnitus-related development of neuronal hyperactivity in terms of information theory. In particular, we suggest that stochastic resonance (SR) plays a key role in both short- and long-term plasticity within the auditory system and that SR is the primary cause of neuronal hyperactivity and tinnitus. We argue that following hearing loss, SR serves to lift signals above the increased neuronal thresholds, thereby partly compensating for the hearing loss. In our model, the increased amount of internal noise-which is crucial for SR to work-corresponds to neuronal hyperactivity which subsequently causes neuronal plasticity along the auditory pathway and finally may lead to the development of a phantom percept, i.e., subjective tinnitus. We demonstrate the plausibility of our hypothesis using a computational model and provide exemplary findings in human patients that are consistent with that model. Finally we discuss the observed asymmetry in human tinnitus pitch distribution as a consequence of asymmetry of the distribution of auditory nerve type I fibers along the cochlea in the context of our model.
EVALuna2:33193

The Bayesian brain: world models and conscious dimensions of auditory phantom perception - Schilling, Achim; Krauß, Patrick (Journal of Neurophysiology, 2024)
CRIS-Team Scopus Importer:2024-08-09
Keywords: consciousness; phantom perception; predictive coding; tinnitus; world model

The Early Subcortical Response at the Fundamental Frequency of Speech Is Temporally Separated from Later Cortical Contributions. - Schüller, Alina; Schilling, Achim; Krauß, Patrick; Reichenbach, Tobias (Journal of Cognitive Neuroscience, 2024)
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.
EVALuna2:574185

Therapeutic Value of Ginkgo biloba Extract EGb 761® in an Animal Model (Meriones unguiculatus) for Noise Trauma Induced Hearing Loss and Tinnitus - Krauß, Patrick; Tziridis, Konstantin; Buerbank, Stefanie; Schilling, Achim; Schulze, Holger (PLoS ONE, 2016)
Noise induced hearing loss (NIHL) is a common disease in modern societies and may lead to maladaptations within the auditory system that finally result in subjective tinnitus. Available therapies may only alleviate the symptoms rather than restore normal hearing. In a previous study we demonstrated that the prophylactic application of Ginkgo biloba extract EGb 761® significantly reduces NIHL and tinnitus development in our Mongolian gerbil (Meriones unguiculatus) animal model. Here, we tested whether the application of EGb 761® has beneficial effects after the formation of permanent NIHL and tinnitus. To this end we monitored the therapeutic effects of EGb 761® on noise trauma-induced changes in signal processing within the auditory system of our animal model by behavioral (acoustic startle response, ASR) and electrophysiological approaches (auditory brainstem responses, ABR). We found that-in contrast to vehicle-three weeks of daily oral EGb 761® treatment (100 mg/kg body weight) led to a restoration of hearing thresholds back to pre-trauma conditions. In addition, all 9 animals that displayed behavioral signs of subjective tinnitus showed improvement, with 7 of them showing complete relief of tinnitus symptoms during the time of EGb 761® treatment. After discontinuation of EGb 761® treatment, tinnitus related behavior reappeared in all but one of these animals while auditory thresholds remained restored. A detailed analysis of ABR waves revealed that EGb 761® treatment did not simply change auditory processing back to pre-trauma conditions, but led to subtle changes of ABR wave amplitude and latency at different levels of the auditory pathway, with an overall increase of response to low stimulus intensities and a decrease at high intensities. The functional relevance of these changes may be the observed improvement of hearing thresholds while at the same time suppression of responses to high stimulus intensities may point to a global inhibitory mechanism that counteracts tinnitus.
EVALuna2:20023

The stochastic resonance model of auditory perception: A unified explanation of tinnitus development, Zwicker tone illusion, and residual inhibition - Schilling, Achim; Tziridis, Konstantin; Schulze, Holger; Krauß, Patrick (Progress in Brain Research, 2021)
Stochastic 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. As a side effect, the increased somatosensory input corresponding to the observed tinnitus-associated neuronal hyperactivity is then perceived as tinnitus. In addition, the model can also explain transient phantom tone perceptions occurring after ear plugging, or the Zwicker tone illusion. Vice versa, the model predicts that via stimulation with acoustic noise, SR would not be needed to optimize information transmission, and hence somatosensory noise would be tuned down, resulting in a transient vanishing of tinnitus, an effect referred to as residual inhibition.
CRIS-Team Scopus Importer:2021-03-19
Keywords: Auditory phantom perception; Dorsal cochlear nucleus; Residual inhibition; Somatosensory projections; Speech perception; Stochastic resonance; Tinnitus; Zwicker tone

Tinnitus development is associated with synaptopathy of inner hair cells in Mongolian gerbils - Tziridis, Konstantin; Forster, Jan; Buchheidt-Dörfler, Isabelle; Krauß, Patrick; Schilling, Achim; Wendler, Olaf; Sterna, Elisabeth; Schulze, Holger (European Journal of Neuroscience, 2021)
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.
CRIS-Team WoS Importer:2021-07-09

Tinnitus is associated with improved cognitive performance and speech perception-Can stochastic resonance explain? - Schilling, Achim; Krauß, Patrick (Frontiers in Aging Neuroscience, 2022)
CRIS-Team WoS Importer:2023-01-20

Tinnitus is associated with increased extracellular matrix density in the auditory cortex of Mongolian gerbils - Tziridis, Konstantin; Maul, Antonia; Rasheed, Jwan; Krauß, Patrick; Schilling, Achim; Schulze, Holger (BMC Neuroscience, 2024)
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. We utilized a mild monaural acoustic noise trauma in overall 22 gerbils to induce tinnitus and a sham exposure in 16 control (C) animals. Tinnitus was assessed by a behavioral response paradigm. Animals were separated for a presence (T) or absence (NT) of a tinnitus percept by a behavioral task. The ECM density 7 and 13 days after trauma was quantified using immunofluorescence luminance of Wisteria floribunda lectin-fluoresceine-5-isothiocyanate (WFA-FITC) on histological slices of the primary AC, relative to the non-auditory brainstem as a reference area. At both timepoints, we found that the WFA-FITC luminance of the AC of NT animals was not significantly different from that of C animals. However, we found a significant increase of luminance in T animals’ ACs compared to NT or C animals’ cortices. This effect was found exclusively on the AC side contralateral to the trauma ear. These results point to a hemisphere specific process of stabilization of synaptic connections in primary AC, which may be involved in the chronic manifestation of tinnitus.
CRIS-Team Scopus Importer:2024-10-25
Keywords: Animal model; Auditory cortex; Extracellular matrix; Rodents; Tinnitus

Treatment of keratinocytes with 4-phenylbutyrate in epidermolysis bullosa: Lessons for therapies in keratin disorders - Spörrer, Marina; Prochnicki, Ania; Tölle, Regine C.; Nyström, Alexander; Esser, Philipp R.; Homberg, Melanie; Athanasiou, Ioannis; Zingkou, Eleni; Schilling, Achim; Gerum, Richard; Thievessen, Ingo; Winter, Lilli; et al. (EBioMedicine, 2019)
Background: Missense mutations in keratin 5 and 14 genes cause the severe skin fragility disorder epidermolysis bullosa simplex (EBS)by collapsing of the keratin cytoskeleton into cytoplasmic protein aggregates. Despite intense efforts, no molecular therapies are available, mostly due to the complex phenotype of EBS, comprising cell fragility, diminished adhesion, skin inflammation and itch. Methods: We extensively characterized KRT5 and KRT14 mutant keratinocytes from patients with severe generalized EBS following exposure to the chemical chaperone 4-phenylbutyrate (4-PBA). Findings: 4-PBA diminished keratin aggregates within EBS cells and ameliorated their inflammatory phenotype. Chemoproteomics of 4-PBA-treated and untreated EBS cells revealed reduced IL1β expression- but also showed activation of Wnt/β-catenin and NF-kB pathways. The abundance of extracellular matrix and cytoskeletal proteins was significantly altered, coinciding with diminished keratinocyte adhesion and migration in a 4-PBA dose-dependent manner. Interpretation: Together, our study reveals a complex interplay of benefits and disadvantages that challenge the use of 4-PBA in skin fragility disorders.
CRIS-Team Scopus Importer:2019-05-17
Keywords: 4-PBA; Epidermolysis bullosa; Keratinocyte; Keratins; Skin blistering

Weight statistics controls dynamics in recurrent neural networks - Krauß, Patrick; Schuster, Marc; Dietrich, Verena; Schilling, Achim; Schulze, Holger; Metzner, Claus (PLoS ONE, 2019)
Recurrent neural networks are complex non-linear systems, capable of ongoing activity in the absence of driving inputs. The dynamical properties of these systems, in particular their long-time attractor states, are determined on the microscopic level by the connection strengths w(ij) between the individual neurons. However, little is known to which extent network dynamics is tunable on a more coarse-grained level by the statistical features of the weight matrix. In this work, we investigate the dynamics of recurrent networks of Boltzmann neurons. In particular we study the impact of three statistical parameters: density (the fraction of non-zero connections), balance (the ratio of excitatory to inhibitory connections), and symmetry (the fraction of neuron pairs with w(ij)= w(ij)). By computing a 'phase diagram' of network dynamics, we find that balance is the essential control parameter: Its gradual increase from negative to positive values drives the system from oscillatory behavior into a chaotic regime, and eventually into stationary fixed points. Only directly at the border of the chaotic regime do the neural networks display rich but regular dynamics, thus enabling actual information processing. These results suggest that the brain, too, is fine-tuned to the 'edge of chaos' by assuring a proper balance between excitatory and inhibitory neural connections.
CRIS-Team WoS Importer:2019-04-23

Word class representations spontaneously emerge in a deep neural network trained on next word prediction - Surendra, Kishore; Schilling, Achim; Stöwer, Paul; Maier, Andreas; Krauß, Patrick (Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023, 2023)
How do humans learn language, and can the first language be learned at all? These fundamental questions are still hotly debated. In contemporary linguistics, there are two major schools of thought that give completely opposite answers. According to Chomsky's theory of universal grammar, language cannot be learned because children are not exposed to sufficient data in their linguistic environment. In contrast, usage-based models of language assume a profound relationship between language structure and language use. In particular, contextual mental processing and mental representations are assumed to have the cognitive capacity to capture the complexity of actual language use at all levels. The prime example is syntax, i.e., the rules by which words are assembled into larger units such as sentences. Typically, syntactic rules are expressed as sequences of word classes. However, it remains unclear whether word classes are innate, as implied by universal grammar, or whether they emerge during language acquisition, as suggested by usage-based approaches. Here, we address this issue from a machine learning and natural language processing perspective. In particular, we trained an artificial deep neural network on predicting the next word, provided sequences of consecutive words as input. Subsequently, we analyzed the emerging activation patterns in the hidden layers of the neural network. Strikingly, we find that the internal representations of nine-word input sequences cluster according to the word class of the tenth word to be predicted as output, even though the neural network did not receive any explicit information about syntactic rules or word classes during training. This surprising result suggests, that also in the human brain, abstract representational categories such as word classes may naturally emerge as a consequence of predictive coding and processing during language acquisition.
CRIS-Team Scopus Importer:2024-12-16
Keywords: ChatGPT; cognitive maps; construction grammar; deep neural networks; successor representations; syntax; text prediction; usage-based models of language acquisition; word classes

β-Secretase BACE1 Is Required for Normal Cochlear Function - Dierich, Marlen; Hartmann, Stephanie; Dietrich, Nadine; Möser, Philip; Brede, Franziska; Johnson Chacko, Lejo; Tziridis, Konstantin; Schilling, Achim; Krauß, Patrick; Heßler, Sabine; Karch, Sandra; Schrott-Fischer, Anneliese; et al. (The Journal of Neuroscience, 2019)
Cleavage of amyloid precursor protein (APP) by β-secretase BACE1 initiates the production and accumulation of neurotoxic amyloid-β 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.
CRIS-Team Scopus Importer:2019-11-15
Keywords: Alzheimer's disease; auditory function; BACE1; BACE1 inhibitor NB-360; BACE1 knock-out mice; Neuregulin-1