2 citations · 12 across the 24 of their papers we have counts for
27 papers
Convergent Evolution in Neural Representation Space: Emergent Order in Deep Belief Networks
Patrick Krauss, Achim Schilling, Andreas Maier +2
Deep Belief Networks (DBNs) learn hierarchical generative models without class supervision. Here, we ask whether this purely unsupervised process nevertheless organizes internal re…
Convergent Evolution in Algorithmic Space
Patrick Krauss, Achim Schilling, Andreas Maier +4
In evolutionary biology, unrelated organisms can independently evolve similar structures when exposed to similar functional demands. Here we ask whether an analogous form of conver…
Surviving by Serving: Functional Relevance Drives Self-Organization in Complex Adaptive Systems
Claus Metzner, Ali Ghebleh, Achim Schilling +3
Complex adaptive systems often develop organized structures without centralized control. Yet the local mechanisms by which functional organization emerges and persists remain incom…
A Differentiable Atari VCS:A Complex, Fully Known Ground Truth for Explainable AI
Andreas Maier, Siming Bayer, Patrick Krauss
Explanation requires ground truth: to verify an account of a system we must know its inner functioning-just what is missing where explainable AI (XAI) is most needed. Systems we ca…
Ten Years of the Stochastic Resonance Model of Tinnitus: From Phantom Perception to Adaptive Sensory Optimization
Patrick Krauss, Achim Schilling
Subjective tinnitus - the perception of sound in the absence of an external acoustic stimulus - remains one of the most debated phenomena in auditory neuroscience. In 2016, the sto…
Word-Class and Construction-Like Structure Emerges in Neural Successor Representations Trained on Natural Language
Mathis Immertreu, Achim Schilling, Thomas Kinfe +1
Neural language models are typically trained on next-token prediction, although linguistic structure spans multiple temporal scales. Successor representations (SRs) make this horiz…
Are cortical microcircuits optimized for information flux? -- A simulation-based reverse engineering study
Claus Metzner, Ali Ghebleh, Karin Prebeck +4
A sufficiently large information flux in recurrent neural networks, quantified by the mutual information between successive network states, is considered a prerequisite for rich in…
Neuroscience-Inspired Analyses of Visual Interestingness in Multimodal Transformers
Mathis Immertreu, Fitim Abdullahu, Thomas Kinfe +3
Human attention is the gateway to conscious perception, memory and decision-making. However, its role in modern transformer models remains largely unexplored. As these systems incr…
Structural and dynamical strategies to prevent runaway excitation in reservoir computing
Claus Metzner, Achim Schilling, Andreas Maier +2
Reservoirs, typically implemented as recurrent neural networks with fixed random connection weights, can be combined with a simple trained readout layer to perform a wide range of…
Convergent Representations of Linguistic Constructions in Human and Artificial Neural Systems
Pegah Ramezani, Thomas Kinfe, Andreas Maier +2
Understanding how the brain processes linguistic constructions is a central challenge in cognitive neuroscience and linguistics. Recent computational studies show that artificial n…
Illuminating the Black Box of Reservoir Computing
Claus Metzner, Achim Schilling, Thomas Kinfe +2
Reservoir computers, based on large recurrent neural networks with fixed random connections, are known to perform a wide range of information processing tasks. However, the nature…
The Predictive Brain: Neural Correlates of Word Expectancy Align with Large Language Model Prediction Probabilities
Nikola Kölbl, Konstantin Tziridis, Andreas Maier +4
Predictive coding theory suggests that the brain continuously anticipates upcoming words to optimize language processing, but the neural mechanisms remain unclear, particularly in…
Organizational Regularities in Recurrent Neural Networks
Claus Metzner, Achim Schilling, Andreas Maier +1
Previous work has shown that the dynamical regime of Recurrent Neural Networks (RNNs) - ranging from oscillatory to chaotic and fixpoint behavior - can be controlled by the global…
Probing Internal Representations of Multi-Word Verbs in Large Language Models
Hassane Kissane, Achim Schilling, Patrick Krauss
This study investigates the internal representations of verb-particle combinations, called multi-word verbs, within transformer-based large language models (LLMs), specifically exa…
Author-Specific Linguistic Patterns Unveiled: A Deep Learning Study on Word Class Distributions
Patrick Krauss, Achim Schilling
Deep learning methods have been increasingly applied to computational linguistics to uncover patterns in text data. This study investigates author-specific word class distributions…
Refusal Behavior in Large Language Models: A Nonlinear Perspective
Fabian Hildebrandt, Andreas Maier, Patrick Krauss +1
Refusal behavior in large language models (LLMs) enables them to decline responding to harmful, unethical, or inappropriate prompts, ensuring alignment with ethical standards. This…
Exploring Narrative Clustering in Large Language Models: A Layerwise Analysis of BERT
Awritrojit Banerjee, Achim Schilling, Patrick Krauss
This study investigates the internal mechanisms of BERT, a transformer-based large language model, with a focus on its ability to cluster narrative content and authorial style acro…
Analysis and Visualization of Linguistic Structures in Large Language Models: Neural Representations of Verb-Particle Constructions in BERT
Hassane Kissane, Achim Schilling, Patrick Krauss
This study investigates the internal representations of verb-particle combinations within transformer-based large language models (LLMs), specifically examining how these models ca…
Probing for Consciousness in Machines
Mathis Immertreu, Achim Schilling, Andreas Maier +1
This study explores the potential for artificial agents to develop core consciousness, as proposed by Antonio Damasio's theory of consciousness. According to Damasio, the emergence…
Nonlinear Neural Dynamics and Classification Accuracy in Reservoir Computing
Claus Metzner, Achim Schilling, Andreas Maier +1
Reservoir computing - information processing based on untrained recurrent neural networks with random connections - is expected to depend on the nonlinear properties of the neurons…
Recurrence Resonance -- Noise-Enhanced Dynamics in Recurrent Neural Networks
Claus Metzner, Achim Schilling, Andreas Maier +1
In specific motifs of three recurrently connected neurons with probabilistic response, the spontaneous information flux, defined as the mutual information between subsequent states…
Analysis of Argument Structure Constructions in the Large Language Model BERT
Pegah Ramezani, Achim Schilling, Patrick Krauss
This study investigates how BERT processes and represents Argument Structure Constructions (ASCs), extending previous LSTM analyses. Using a dataset of 2000 sentences across four A…
Analysis of Argument Structure Constructions in a Deep Recurrent Language Model
Pegah Ramezani, Achim Schilling, Patrick Krauss
Understanding how language and linguistic constructions are processed in the brain is a fundamental question in cognitive computational neuroscience. In this study, we explore the…
Data-driven Modeling in Metrology -- A Short Introduction, Current Developments and Future Perspectives
Linda-Sophie Schneider, Patrick Krauss, Nadine Schiering +3
Mathematical models are vital to the field of metrology, playing a key role in the derivation of measurement results and the calculation of uncertainties from measurement data, inf…