6 papers
Decoding Alignment without Encoding Alignment: A critique of similarity analysis in neuroscience
Johannes Bertram, Luciano Dyballa, T. Anderson Keller +2
Decoding approaches are widely used in neuroscience and machine learning to compare stimulus representations across neural systems, such as different brain regions, organisms, and…
Inferring Active Neural Circuits Using Diffusion Scores
Savik Kinger, Johannes Bertram, Luciano Dyballa +2
In biological systems, neural circuits compute through directed, short-latency interactions whose effects unfold across multiple time scales and behavioral contexts. We address the…
NESSiE: The Necessary Safety Benchmark -- Identifying Errors that should not Exist
Johannes Bertram, Jonas Geiping
We introduce NESSiE, the NEceSsary SafEty benchmark for large language models (LLMs). With minimal test cases of information and access security, NESSiE reveals safety-relevant fai…
How 'Neural' is a Neural Foundation Model?
Johannes Bertram, Luciano Dyballa, Anderson Keller +2
Foundation models have shown remarkable success in fitting biological visual systems; however, their black-box nature inherently limits their utility for understanding brain functi…
Manifolds and Modules: How Function Develops in a Neural Foundation Model
Johannes Bertram, Luciano Dyballa, T. Anderson Keller +2
Foundation models have shown remarkable success in fitting biological visual systems; however, their black-box nature inherently limits their utility for understanding brain functi…
Towards User-Focused Research in Training Data Attribution for Human-Centered Explainable AI
Elisa Nguyen, Johannes Bertram, Evgenii Kortukov +2
Explainable AI (XAI) aims to make AI systems more transparent, yet many practices emphasise mathematical rigour over practical user needs. We propose an alternative to this model-c…