7 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…
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…
A separability-based approach to quantifying generalization: which layer is best?
Luciano Dyballa, Evan Gerritz, Steven W. Zucker
Generalization to unseen data remains poorly understood for deep learning classification and foundation models, especially in the open set scenario. How can one assess the ability…
Learning dynamic representations of the functional connectome in neurobiological networks
Luciano Dyballa, Samuel Lang, Alexandra Haslund-Gourley +2
The static synaptic connectivity of neuronal circuits stands in direct contrast to the dynamics of their function. As in changing community interactions, different neurons can part…