10 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…
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…