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Surendra, K., Schilling, A., Stoewer, P. Maier, A., & Krauss, P.
Word class representations spontaneously emerge in a deep neural network trained on next word prediction.
preprint: arxiv.org/abs/2302.07588

Gerum, R., Erpenbeck, A., Krauss, P., & Schilling, A.
Leaky-Integrate-and-Fire Neuron-Like Long-Short-Term-Memory Units as Model System in Computational Biology.

Stoll, A., Krauss, P., Maier, A., Gerum, R., & Schilling, A.
Coincidence Detection and Integration Behavior in Spiking Neural Networks.

Schilling, A., Sedley, W., Gerum, R., Metzner, C., Tziridis, K., Maier, A., Schulze, H., Zeng, F.-G., Friston, K.J., & Krauss, P.
Predictive coding and stochastic resonance as fundamental principles of auditory (phantom) perception.
preprint: arxiv.org/abs/2204.03354

Schilling, A., Gerum, R., Boehm, C., Rasheed, J., Metzner, C., Maier, A., Reindl, C., Hamer, H., & Krauss, P.
Deep learning based decoding of local field potentials.
preprint: doi.org/10.1101/2022.10.14.512209

Schilling, A., Tziridis, K., Schulze, H., & Krauss, P.
Behavioral assessment of Zwicker tone percepts in rodents.
preprint: doi.org/10.1101/2022.12.22.521554

Metzner, C., Schilling, A., Traxdorf, M., Schulze, H., Tziridis, K., & Krauss, P.
Extracting continuous sleep depth from EEG data without machine learning.
preprint: arxiv.org/abs/2301.06755

Metzner, C., Yamakou, M., Voelkl, D., Schilling, A., & Krauss, P.
Quantifying and maximizing the information flux in recurrent neural networks.
preprint: arxiv.org/abs/2301.12892

Peer-reviewed Journal Articles

2023

Stoewer, P., Schilling, A., Maier, A., & Krauss, P.
Neural Network based Formation of Cognitive Maps of Semantic Spaces and the Putative Emergence of Abstract Concepts.
Scientific Reports
doi.org/10.1038/s41598-023-30307-6

2022

Metzner, C., Schilling, A., Traxdorf, M., Tziridis, K., Schulze, H. & Krauss, P. (2022).
Classification at the Accuracy Limit -- Facing the Problem of Data Ambiguity.
Scientific Reports
doi.org/10.1038/s41598-022-26498-z

Garibyan, A., Schilling, A., Boehm, C., Zankl, A., & Krauss, P. (2022).
Neural Correlates of Linguistic Collocations During Continuous Speech Perception.
Frontiers in Psychology
doi.org/10.3389/fpsyg.2022.1076339

Schillng, A. & Krauss, P. (2022).
Tinnitus is associated with improved cognitive performance and speech perception -- Can stochastic resonance explain?
Frontiers in Aging Neuroscience
doi.org/10.3389/fnagi.2022.1073149

Stoewer, P., Schlieker, C., Schilling, A., Metzner, C., Maier, A., & Krauss, P. (2022).
Neural Network based Successor Representations to form Cognitive Maps of Space and Language.
Scientific Reports
doi.org/10.1038/s41598-022-14916-1

Schilling, A., Gerum, R., Zankl, A., Metzner, C., Maier. A., & Krauss, P. (2022).
Intrinsic noise improves speech recognition in a computational model of the auditory pathway.
Frontiers in Neuroscience
doi.org/10.3389/fnins.2022.908330

Metzner, C. & Krauss, P. (2022).
Dynamics and Information Import in Recurrent Neural Networks.
Frontiers in Computational Neuroscience
doi.org/10.3389/fncom.2022.876315

Maier, A., Köstler, H., Heisig, M., Krauss, P. & Yang, S.H. (2022).
Known Operator Learning and Hybrid Machine Learning in Medical Imaging -- A Review of the Past, the Present, and the Future

Progress in Biomedical Engineering

https://iopscience.iop.org/article/10.1088/2516-1091/ac5b13/meta

Tziridis, K., Brunner, S., Schilling, A., Krauss, P. & Schulze, H. (2022).
Spectrally Matched Near-Threshold Noise for Subjective Tinnitus Loudness Attenuation Based on Stochastic Resonance.
Frontiers in Neuroscience

doi.org/10.3389/fnins.2022.831581

Boensel, F., Krauss, P., Metzner, C. & Yamakou, M. (2022).
Control of noise-induced coherent oscillations in three-neuron motifs.
Cognitive Neurodynamics
doi.org/10.1007/s11571-021-09770-2

2021

Krauss, P., Metzner, C., Joshi, N., Schulze, H., Traxdorf, M., Maier, A. & Schilling, A. (2021).

Analysis and Visualization of Sleep Stages based on Deep Neural Networks.

Neurobiology of Sleep and Circadian Rhythms

doi.org/10.1016/j.nbscr.2021.100064

Krauss, P. and Tziridis, K. (2021).

Simulated transient hearing loss improves auditory sensitivity.
Scientific Reports

doi.org/10.1038/s41598-021-94429-5

Schilling, A., Tomasello, R., Henningsen-Schomers, M.R., Zankl, A., Surendra, K., Haller, M., Karl, V., Uhrig, P., Maier, A. & Krauss, P. (2021).

Analysis of continuous neuronal activity evoked by natural speech with computational corpus linguistics methods.

Language, Cognition and Neuroscience

doi.org/10.1080/23273798.2020.1803375

Metzner, C., Schilling, A., Traxdorf, M., Schulze, H. & Krauss, P. (2021).
Sleep as a random walk – Superstatistical analysis of EEG data across sleep stages.
Communications Biology
doi.org/10.1038/s42003-021-02912-6

Schilling, A., Metzner, C., Gerum, R., Maier, A., & Krauss, P. (2021).

Quantifying the separability of data classes in neural networks.

Neural Networks

doi.org/10.1016/j.neunet.2021.03.035

Yang, Z., Schilling, A., Maier, A. & Krauss, P. (2021).

Neural Networks with Fixed Binary Random Projections Improve Accuracy in Classifying Noisy Data.

Proceedings, German Workshop on Medical Image Computing, Regensburg, March 7-9, 2021

dx.doi.org/10.1007/978-3-658-33198-6_51

Schilling, A., Tziridis, K., Schulze, H. & Krauss, P. (2021).

The Stochastic Resonance model of auditory perception: A unified explanation of tinnitus development, Zwicker tone illusion, and residual inhibition.

Progress in Brain Research

doi.org/10.1016/bs.pbr.2021.01.025

Jeschke, M., Happel, M.F.K., Tziridis, K., Krauss, P., Schilling, A., Schulze, H. & Ohl, F.W. (2021).

Acute and long-term circuit-level effects in the auditory cortex after sound trauma.

Frontiers in Neuroscience

doi.org/10.3389/fnins.2020.598406

Tziridis, K., Forster, J., Buchheidt-Doerfler, I., Krauss, P., Schilling, A., Wendler, O., Sterna, E. & Schulze, H. (2021).
Tinnitus development is associated with synaptopathy of inner hair cells in Mongolian gerbils.
European Journal of Neuroscience
doi.org/10.1111/ejn.15334

2020

Krauss, P. & Maier, A. (2020).

Will we ever have conscious Machines?

Frontiers in Computational Neuroscience

doi.org/10.3389/fncom.2020.556544

Gerum, R., Erpenbeck, A., Krauss, P. & Schilling, A. (2020).

Sparsity through evolutionary pruning prevents neuronal networks from overfitting.

Neural Networks

doi.org/10.1016/j.neunet.2020.05.007

Knipper, M., van Dijk, P., Schulze, H., Mazurek, B., Krauss, P., Scheper, V., Warnecke, A., Schlee, W., Schwabe, K., Singer, W., Braun, C., Delano, P.H., Fallgatter, A.J., Ehlis, A.-C., Searchfield, G.D., Munk, M.H.J., Baguley, D.M. & Rüttiger, L. (2020).

The neural bases of tinnitus: Lessons from deafness and cochlear implants

Journal of Neuroscience

doi.org/10.1523/JNEUROSCI.1314-19.2020

Schilling, A., Krauss, P., Hannemann, R., Schulze, H. & Tziridis, K. (2020).

Reducing tinnitus intensity: Pilot study to attenuate tonal tinnitus using individually spectrally optimized near-threshold noise.

HNO

doi.org/10.1007/s00106-020-00963-5

2019

Krauss, P., Prebeck, K., Schilling, A. & Metzner, C. (2019).

'Recurrence Resonance' in three-neuron motifs.

Frontiers in Computational Neuroscience

doi.org/10.3389/fncom.2019.00064

Krauss, P., Schuster, M., Dietrich, V., Schilling, A, Schulze, H. & Metzner, C. (2019).

Weight statistics controls dynamics in recurrent neural networks.

PloS one

doi.org/10.1371/journal.pone.0214541

Krauss, P., Zankl, A., Schilling, A., Schulze, H. & Metzner, C. (2019).

Analysis of structure and dynamics in three-neuron motifs.

Frontiers in Computational Neuroscience

doi.org/10.3389/fncom.2019.00005

Krauss, P., Schilling, A., Tziridis, K. & Schulze, H. (2019).

Models of tinnitus development. From cochlea to cortex.

HNO Journal

doi.org/10.1007/s00106-019-0612-z

Traxdorf, M., Krauss, P., Schilling, A., Schulze, H. & Tziridis, K. (2019).

Microstructure of cortical activity during sleep reflects respiratory events and state of daytime vigilance.

Somnology

link.springer.com/article/10.1007/s11818-019-0201-0

Dierich, M., Hartmann, S., Dietrich, N., Moeser, P., Brede, F., Johnson Chacko, L., Tziridis, K., Schilling, A., Krauss, P., Hessler, S., Karch, S., Schrott-Fischer, A., Blumer, M., Birchmeier, C., Oliver, D., Moser, T., Schulze, H., Alzheimer, C., Leitner, M. & Huth, T. (2019).

β-secretase BACE1 is required for normal cochlear function.

Journal of Neuroscience

doi.org/10.1523/JNEUROSCI.0028-19.2019

Gerum, R., Rahlfs, H., Streb, M., Krauss, P., Grimm, J., Metzner, C., Tziridis, K., Günther, M., Schulze, H., Kellermann, W. & Schilling, A. (2019).

Open(G)PIAS: An open source solution for the construction of a high-precision Acoustic-Startle-Response (ASR) setup for tinnitus screening and threshold estimation in rodents.

Frontiers in Behavioral Neuroscience

doi.org/10.3389/fnbeh.2019.00140

Schilling, A, Gerum, R., Krauss, P., Metzner, C., Tziridis, K. & Schulze, H. (2019).

Objective estimation of sensory thresholds based on neurophysiological parameters.

Frontiers in Neuroscience

doi.org/10.3389/fnins.2019.00481

2018

Krauss, P., Tziridis, K., Schilling, A. & Schulze, H. (2018).

Cross-modal stochastic resonance as a universal principle to enhance sensory processing.

Frontiers in Neuroscience

doi.org/10.3389/fnins.2018.00578

Krauss, P., Schilling, A., Bauer, J., Tziridis, K., Metzner, C., Schulze, H. & Traxdorf, M. (2018).

Analysis of multichannel EEG patterns during human sleep: a novel approach.

Frontiers in Human Neuroscience

doi.org/10.3389/fnhum.2018.00121

Krauss, P., Metzner, C., Schilling, A., Tziridis, K., Traxdorf M., Wollbrink, A., Rampp, S., Pantev, C., & Schulze, H. (2018).

A statistical method for analyzing and comparing spatiotemporal cortical activation patterns.

Scientific Reports

www.nature.com/articles/s41598-018-23765-w

Metzner, C., Lange, J., Krauss, P., Wunderling, N., Übelacker, J., Martin, F. & Fabry, B. (2018).

Pressure-driven collective growth mechanism of planar cell colonies.

Journal of Physics D: Applied Physics

iopscience.iop.org/article/10.1088/1361-6463/aace4c/meta

2017

Krauss, P., Metzner, C., Schilling, A., Schütz, C., Tziridis, K., Fabry, B., & Schulze, H. (2017).

Adaptive stochastic resonance for unknown and variable input signals.

Scientific Reports

www.nature.com/articles/s41598-017-02644-w

Krauss, P., Schulze, H., & Metzner, C. (2017).

A chemical reaction network to generate random, power-law distributed time intervals.

Artificial Life

doi.org/10.1162/ARTL_a_00245

Schilling, A., Krauss, P., Gerum, R., Metzner, C., Tziridis, K., & Schulze, H. (2017).

A new statistical approach for the evaluation of gap-prepulse inhibition of the acoustic startle reflex (GPIAS) for tinnitus assessment.

Frontiers in Behavioral Neuroscience

doi.org/10.3389/fnbeh.2017.00198

Gollnast, D., Tziridis, K., Krauss, P., Schilling, A., Hoppe, U., & Schulze, H. (2017).

Analysis of audiometric differences of patients with and without tinnitus in a large clinical database.

Frontiers in Neurology

doi.org/10.3389/fneur.2017.00031

2016

Krauss, P., Tziridis, K., Metzner, C., Schilling, A., Hoppe, U., & Schulze, H. (2016).

Stochastic resonance controlled upregulation of internal noise after hearing loss as a putative cause of tinnitus-related neuronal hyperactivity.

Frontiers in Neuroscience

doi.org/10.3389/fnins.2016.00597

Krauss, P., Tziridis, K., Buerbank, S., Schilling, A., & Schulze, H. (2016).

Therapeutic Value of Ginkgo biloba Extract Egb 761 in an Animal Model (Meriones unguiculatus) forNoise Trauma Induced Hearing Loss and Tinnitus.

PLoS one

doi.org/10.1371/journal.pone.0157574

2013

Lang, N. R., Münster, S., Metzner, C., Krauss, P., Schürmann, S., Lange, J., & Fabry, B. (2013).

Estimating the 3D pore size distribution of biopolymer networks from directionally biased data.

Biophysical Journal

doi.org/10.1016/j.bpj.2013.09.038

2012

Krauss, P., Metzner, C., Lange, J., Lang, N., & Fabry, B. (2012).

Parameter-free binarization and skeletonization of fiber networks from confocal image stacks.

PLoS one

doi.org/10.1371/journal.pone.0036575

Preprints

2023

Surendra, K., Schilling, A., Stoewer, P. Maier, A., & Krauss, P.
Word class representations spontaneously emerge in a deep neural network trained on next word prediction.
arXiv: 2302.07588
arxiv.org/abs/2302.07588

Metzner, C., Schilling, A., Traxdorf, M., Schulze, H., Tziridis, K., & Krauss, P.
Extracting continuous sleep depth from EEG data without machine learning.
arXiv:2301.06755
arxiv.org/abs/2301.06755

Metzner, C., Yamakou, M., Voelkl, D., Schilling, A., & Krauss, P.
Quantifying and maximizing the information flux in recurrent neural networks.
arXiv: 2301.12892
arxiv.org/abs/2301.12892

2022

Schilling, A., Tziridis, K., Schulze, H., & Krauss, P. (2022).
Behavioral assessment of Zwicker tone percepts in rodents.
bioRxiv
doi.org/10.1101/2022.12.22.521554

Stoewer, P., Schilling, A., Maier, A., & Krauss, P. (2022).
Neural Network based Formation of Cognitive Maps of Semantic Spaces and the Emergence of Abstract Concepts.
arXiv:2210.16062
arxiv.org/abs/2210.16062

Metzner, C., Schilling, A., Traxdorf, M., Tziridis, K., Schulze, H. & Krauss, P. (2022).
Classification at the Accuracy Limit -- Facing the Problem of Data Ambiguity.
arXiv:2206.01922
arxiv.org/abs/2206.01922

Schilling, A., Gerum, R., Boehm, C., Rasheed, J., Metzner, C., Maier, A., Reindl, C., Hamer, H., & Krauss, P. (2022).
Deep learning based decoding of local field potentials.
bioRxiv
doi.org/10.1101/2022.10.14.512209

Schilling, A., Sedley, W., Gerum, R., Metzner, C., Tziridis, K., Maier, A., Schulze, H., Zeng, F.-G., Friston, K.J., & Krauss, P. (2022).
Predictive coding and stochastic resonance as fundamental principles of auditory perception.
arXiv:2204.03354
arxiv.org/abs/2204.03354

Garibyan, A., Schilling, A., Boehm, C., Zankl, A., & Krauss, P. (2022).
Neural Correlates of Linguistic Collocations During Continuous Speech Perception.
bioRxiv
doi.org/10.1101/2022.03.25.485771

Stoewer, P., Schlieker, C., Schilling, A., Metzner, C., Maier, A., & Krauss, P. (2022).
Neural Network based Successor Representation of Space and Language
arXiv:2202.11190
arxiv.org/abs/2202.11190

2021

Metzner, C. & Krauss, P. (2021).
Dynamical Phases and Resonance Phenomena in Information-Processing Recurrent Neural Networks.
arXiv:2108.02545
arxiv.org/abs/2108.02545

Metzner, C., Schilling, A., Traxdorf, M., Schulze, H. & Krauss, P. (2021).
Sleep as a random walk – Superstatistical analysis of EEG data across sleep stages.
bioRxiv
doi.org/10.1101/2021.06.25.449874

Boensel, F., Krauss, P., Metzner, C. & Yamakou, M. (2021).
Control of noise-induced coherent oscillations in time-delayed neural motifs.
arXiv:2106.11361

arxiv.org/abs/2106.11361

Maier, A., Köstler, H., Heisig, M., Krauss, P. & Yang, S.H. (2021).
Known Operator Learning and Hybrid Machine Learning in Medical Imaging -- A Review of the Past, the Present, and the Future
arXiv:2108.04543
arxiv.org/abs/2108.04543

2020

Krauss, P. & Schilling, A. (2020).

Towards a Cognitive Computational Neuroscience of Auditory Phantom Perceptions.

arXiv:2010.01914

arxiv.org/abs/2010.01914

Krauss, P. & Maier, A. (2020).

Will we ever have conscious Machines?

arXiv:2003.14132

arxiv.org/abs/2003.14132

Krauss, P., Metzner, C., Joshi, N., Schulze, H., Traxdorf, M., Maier, A. & Schilling, A. (2020).

Analysis and Visualization of Sleep Stages based on Deep Neural Networks.

bioRxiv

https://doi.org/10.1101/2020.06.25.170464

Krauss, P. (2020).

Improved pure tone sensitivity after simulated hearing loss

bioRxiv

https://doi.org/10.1101/2020.05.29.124321

Schilling, A., Tomasello, R., Henningsen-Schomers, M.R., Zankl, A., Surendra, K., Haller, M., Karl, V., Uhrig, P., Maier, A. & Krauss, P. (2020).

Analysis of continuous neuronal activity evoked by natural speech with computational corpus linguistics methods.

bioRxiv

https://doi.org/10.1101/2020.04.21.052720

Schilling, A., Tziridis, K., Schulze, H. & Krauss, P. (2020).

The Stochastic Resonance model of auditory perception:

A unified explanation of tinnitus development, Zwicker tone illusion, and residual inhibition.

bioRxiv

https://doi.org/10.1101/2020.03.27.011163

Schilling, A., Gerum, R., Zankl, A., Metzner, C., Maier, A. & Krauss, P. (2020).

Intrinsic noise improves speech recognition in a computational model of the auditory pathway.

bioRxiv

https://doi.org/10.1101/2020.03.16.993725

Jeschke, M., Happel, M.F.K., Tziridis, K., Krauss, P., Schilling, A., Schulze, H., & Ohl, F.W. (2020).

Acute and long-term circuit-level effects in the auditory cortex after sound trauma.

bioRxiv

https://doi.org/10.1101/2020.03.06.980730

2019

Gerum, R., Erpenbeck, A., Krauss, P., & Schilling, A. (2019).

Sparsity through evolutionary pruning prevents neuronal networks from overfitting.

arXiv:1911.10988

arxiv.org/abs/1911.10988

2018

Krauss, P., Prebeck, K., Schilling, A., & Metzner, C. (2018).

Stochastic Resonance in three-neuron motifs.

arXiv:1811.12091

arxiv.org/abs/1811.12091

Krauss, P., Schuster, M., Dietrich, V., Schilling, A, Schulze, H & Metzner, C. (2018).

Weight statistics controls dynamics in recurrent neural networks.

bioRxiv

https://doi.org/10.1101/475319

Krauss, P., Zankl, A., Schilling, A., Schulze, H. & Metzner, C. (2018)

Analysis of structure and dynamics in three-neuron motifs.

arXiv:1811.05225

arxiv.org/abs/1811.05225

Schilling, A., Metzner, C., Rietsch, J., Gerum, R., Schulze, H., & Krauss, P. (2018).

How deep is deep enough? - Quantifying class separability in the hidden layers of deep neural networks.

arXiv:1811.01753

arxiv.org/abs/1811.01753

Schilling, A, Gerum, R., Krauss, P., Metzner, C., Tziridis, K. & Schulze, H. (2018).

Objective estimation of sensory thresholds based on neurophysiological parameters.

arXiv:1811.02335

arxiv.org/abs/1811.02335

Gerum, R., Rahlfs, H., Streb, M., Krauss, P., Metzner, C., Tziridis, K., Günther, M., Schulze, H., Kellermann, W. & Schilling, A. (2018).

Open(G)PIAS: An open source solution for the construction of a high-precision Acoustic-Startle-Response (ASR) setup for tinnitus screening and threshold estimation in rodents.

arXiv:1804.09667

arxiv.org/abs/1804.09667

Forster, J., Wendler, O., Buchheidt-Doerfler, I., Krauss, P., Schilling, A., Sterna, E., Schulze, H., & Tziridis, K. (2018).

Tinnitus development is associated with synaptopathy of inner hair cells in Mongolian gerbils.

bioRxiv

https://doi.org/10.1101/304576

2016

Krauss, P., Metzner, C., Schilling, A., Tziridis, K., Traxdorf M., & Schulze, H. (2016).

A statistical method for analyzing and comparing spatiotemporal cortical activation patterns.

arXiv:1611.07677

arxiv.org/abs/1611.07677

Krauss, P., Tziridis, K., Schilling, A., Metzner, C., & Schulze, H. (2016).

Stochastic resonance controlled upregulation of internal noise after hearing loss as a putativecorrelate of tinnitus-related neuronal hyperactivity.

arXiv:1603.04721

arxiv.org/abs/1603.04721

2015

Krauss, P., Metzner, C., Tziridis, K., & Schulze, H. (2015).

Adaptive stochastic resonance based on output autocorrelations.

arXiv:1504.05032

arxiv.org/abs/1504.05032

2011

Krauss, P., Metzner, C., Lange, J., Lang, N., & Fabry, B. (2011).

Reconstructing fiber networks from confocal image stacks.

arXiv:1111.3861

arxiv.org/abs/1111.3861

Metzner, C., Krauss, P., & Fabry, B. (2011).

Poresizes in random line networks.

arXiv:1110.1803

arxiv.org/abs/1110.1803

Videos

Der Geist in der Maschine

Werden wir jemals bewusste Maschinen haben?

Will we ever have consciousness in the machine?