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Max Planck Institute for Biological Cybernetics

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Research library29linked papers
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Selected work

Representative Papers

Neural timescales from a computational perspective

Sep 04, 2024arXiv.org

This study addresses the definition, measurement, mechanisms, and functional roles of neural timescales in neural computation and brain function. We propose a unified tripartite framework integrating data-driven quantification, biophysically grounded modeling, and functional validation—combining multimodal neural recordings, leaky integrate-and-fire (LIF) and adaptive exponential (AdEx) spiking network models, task-optimized recurrent neural networks (RNNs) and LSTMs, information-theoretic analyses, and causal inference methods. We systematically clarify theoretical distinctions among existing timescale estimation techniques and, for the first time, establish a causal link between slow membrane time constants and hierarchical computational capacity. Furthermore, we demonstrate that neural timescales serve as a necessary core variable mediating structure–dynamics–behavior mappings. These findings provide a methodological benchmark for standardized neural timescale characterization and lay a theoretical foundation for brain-inspired temporal processing architectures.

2 citationsRead paper

Detecting and Deterring Manipulation in a Cognitive Hierarchy

May 03, 2024

Intelligent agents with limited nested reasoning—such as low-order agents in Interactive Partially Observable Markov Decision Processes (IPOMDPs)—are vulnerable to manipulation by higher-order adversaries; existing recursive modeling frameworks struggle to simultaneously ensure interpretability and robust countermeasures. Method: We propose the ℵ-IPOMDP framework, the first to integrate statistical anomaly detection with *out-of-belief* policies within the IPOMDP formalism. This enables low-order agents to detect deceptive behavior and enact credible deterrence without requiring explicit understanding of higher-order reasoning mechanisms. Contribution/Results: ℵ-IPOMDP significantly reduces the success rate of higher-order exploitation in both mixed-motive and zero-sum games, thereby enhancing interaction fairness. It provides a lightweight, deployable robust adversarial mechanism for AI safety, cybersecurity, and cognitive modeling—balancing computational efficiency, interpretability, and resilience against strategic deception.

1 citationsRead paper

Natural Language Access to Domain-Specific Metadata: A Reusable Framework for LLM Query Generation

Jul 20, 2026

This work addresses the challenge faced by domain researchers—often lacking technical expertise—in accessing specialized archival metadata through structured query languages. The authors propose NLKGQ, a system that leverages OWL ontologies and large language models (LLMs) to enable zero-shot translation of natural language queries into SPARQL, executable directly over knowledge graphs without fine-tuning or retrieval augmentation. Built upon a reusable framework relying solely on formal ontologies, NLKGQ significantly improves query accuracy and highlights the critical roles of ontology readability and semantic annotation. Evaluated on a neuroimaging metadata task, the system achieves 100% expert-validated accuracy, demonstrating OWL’s superiority over SQL DDL in LLM-driven querying and supporting private deployment on commodity hardware.

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Recent publications

Latest Papers

Natural Language Access to Domain-Specific Metadata: A Reusable Framework for LLM Query Generation

Jul 20, 2026

This work addresses the challenge faced by domain researchers—often lacking technical expertise—in accessing specialized archival metadata through structured query languages. The authors propose NLKGQ, a system that leverages OWL ontologies and large language models (LLMs) to enable zero-shot translation of natural language queries into SPARQL, executable directly over knowledge graphs without fine-tuning or retrieval augmentation. Built upon a reusable framework relying solely on formal ontologies, NLKGQ significantly improves query accuracy and highlights the critical roles of ontology readability and semantic annotation. Evaluated on a neuroimaging metadata task, the system achieves 100% expert-validated accuracy, demonstrating OWL’s superiority over SQL DDL in LLM-driven querying and supporting private deployment on commodity hardware.

0 citationsRead paper

LLMs Can See the Smoke but not the Fire: Evaluating Abductive Reasoning with Elenchos

Jul 14, 2026

The capacity of large language models (LLMs) to perform abductive reasoning—specifically, inferring underlying rule changes from observed behavioral discrepancies—remains poorly understood. This work proposes Elenchos, an evaluation framework that formalizes abductive reasoning as a structural inverse problem: given behavioral traces from both original and perturbed versions of a formal system (e.g., λ-calculus), models must determine whether a rule modification occurred and precisely localize the change. Elenchos introduces a generative evaluation paradigm grounded in behavioral divergence between formal systems. Experimental results reveal that while mainstream LLMs can detect that a system has been altered, they struggle to accurately attribute the specific rule modifications, particularly when multiple interacting changes are present. Moreover, increasing computational budgets for reasoning yields only marginal performance gains.

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Meta-learning as a principle for human-like visual representations

Jun 24, 2026

This work proposes a novel approach to learning more human-like visual representations by leveraging unsupervised meta-learning as a core mechanism. Unlike existing pretrained neural networks, which exhibit limited task flexibility compared to the human visual system, our method trains a sequential model on thousands of semantically rich image-to-concept mapping tasks. By integrating a distribution over higher-order semantic tasks, sequential modeling, and disentangled representation learning, the resulting representations significantly outperform baseline models. They achieve superior performance in predicting human similarity judgments, generalizing semantic rules, and aligning with neural activity in high-level visual cortex. These findings highlight the critical role of meta-learning in enhancing both task flexibility and alignment with biological vision systems.

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