61 citations · 137 across the 23 of their papers we have counts for
26 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…
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…
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…
Deep Reinforcement Learning for Optimum Order Execution: Mitigating Risk and Maximizing Returns
Khabbab Zakaria, Jayapaulraj Jerinsh, Andreas Maier +3
Optimal Order Execution is a well-established problem in finance that pertains to the flawless execution of a trade (buy or sell) for a given volume within a specified time frame.…
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…
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…
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…
Analyzing Narrative Processing in Large Language Models (LLMs): Using GPT4 to test BERT
Patrick Krauss, Jannik Hösch, Claus Metzner +3
The ability to transmit and receive complex information via language is unique to humans and is the basis of traditions, culture and versatile social interactions. Through the disr…
Multi-Modal Cognitive Maps based on Neural Networks trained on Successor Representations
Paul Stoewer, Achim Schilling, Andreas Maier +1
Cognitive maps are a proposed concept on how the brain efficiently organizes memories and retrieves context out of them. The entorhinal-hippocampal complex is heavily involved in e…
Conceptual Cognitive Maps Formation with Neural Successor Networks and Word Embeddings
Paul Stoewer, Achim Schilling, Andreas Maier +1
The human brain possesses the extraordinary capability to contextualize the information it receives from our environment. The entorhinal-hippocampal plays a critical role in this f…
Joint MR sequence optimization beats pure neural network approaches for spin-echo MRI super-resolution
Hoai Nam Dang, Vladimir Golkov, Thomas Wimmer +3
Current MRI super-resolution (SR) methods only use existing contrasts acquired from typical clinical sequences as input for the neural network (NN). In turbo spin echo sequences (T…
Federated learning for secure development of AI models for Parkinson's disease detection using speech from different languages
Soroosh Tayebi Arasteh, Cristian David Rios-Urrego, Elmar Noeth +4
Parkinson's disease (PD) is a neurological disorder impacting a person's speech. Among automatic PD assessment methods, deep learning models have gained particular interest. Recent…
Word class representations spontaneously emerge in a deep neural network trained on next word prediction
Kishore Surendra, Achim Schilling, Paul Stoewer +2
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 sc…
Neural Network based Formation of Cognitive Maps of Semantic Spaces and the Emergence of Abstract Concepts
Paul Stoewer, Achim Schilling, Andreas Maier +1
The hippocampal-entorhinal complex plays a major role in the organization of memory and thought. The formation of and navigation in cognitive maps of arbitrary mental spaces via pl…
Classification at the Accuracy Limit -- Facing the Problem of Data Ambiguity
Claus Metzner, Achim Schilling, Maximilian Traxdorf +3
Data classification, the process of analyzing data and organizing it into categories, is a fundamental computing problem of natural and artificial information processing systems. I…
Bayesian Convolutional Neural Networks for Limited Data Hyperspectral Remote Sensing Image Classification
Mohammad Joshaghani, Amirabbas Davari, Faezeh Nejati Hatamian +2
Employing deep neural networks for Hyperspectral remote sensing (HSRS) image classification is a challenging task. HSRS images have high dimensionality and a large number of channe…
Predictive coding and stochastic resonance as fundamental principles of auditory perception
Achim Schilling, William Sedley, Richard Gerum +7
How is information processed in the brain during perception? Mechanistic insight is achieved only when experiments are employed to test formal or computational models. In analogy t…
Neural Network based Successor Representations of Space and Language
Paul Stoewer, Christian Schlieker, Achim Schilling +3
How does the mind organize thoughts? The hippocampal-entorhinal complex is thought to support domain-general representation and processing of structural knowledge of arbitrary stat…