10 citations · 11 across the 9 of their papers we have counts for
10 papers
Bracketing Uncertainty in Clustering Under the Manifold Hypothesis
Savik Kinger, Luciano Dyballa, Steven W. Zucker
The manifold hypothesis suggests a natural criterion for clustering: partition data according to the manifold component from which each point is drawn. Whether two components are s…
Decoding Alignment without Encoding Alignment: A critique of similarity analysis in neuroscience
Johannes Bertram, Luciano Dyballa, T. Anderson Keller +2
RSA and CKA are standard metrics for comparing neural representations across brain regions, organisms, and deep learning models. We demonstrate a fundamental weakness: these decodi…
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…
Zero-shot generalization across architectures for visual classification
Evan Gerritz, Luciano Dyballa, Steven W. Zucker
Generalization to unseen data is a key desideratum for deep networks, but its relation to classification accuracy is unclear. Using a minimalist vision dataset and a measure of gen…
IAN: Iterated Adaptive Neighborhoods for manifold learning and dimensionality estimation
Luciano Dyballa, Steven W. Zucker
Invoking the manifold assumption in machine learning requires knowledge of the manifold's geometry and dimension, and theory dictates how many samples are required. However, in app…
Further insights into the interareal connectivity of a cortical network
Luciano Dyballa, Valmir C. Barbosa
Over the past years, network science has proven invaluable as a means to better understand many of the processes taking place in the brain. Recently, interareal connectivity data o…