2 citations · 4 across the 8 of their papers we have counts for
8 papers
OmniMouse: Scaling properties of multi-modal, multi-task Brain Models on 150B Neural Tokens
Konstantin F. Willeke, Polina Turishcheva, Alex Gilbert +18
Scaling data and artificial neural networks has transformed AI, driving breakthroughs in language and vision. Whether similar principles apply to modeling brain activity remains un…
Learning to cluster neuronal function
Nina S. Nellen, Polina Turishcheva, Michaela Vystrčilová +4
Deep neural networks trained to predict neural activity from visual input and behaviour have shown great potential to serve as digital twins of the visual cortex. Per-neuron embedd…
Hierarchical clustering with maximum density paths and mixture models
Martin Ritzert, Polina Turishcheva, Laura Hansel +3
Hierarchical clustering is an effective, interpretable method for analyzing structure in data. It reveals insights at multiple scales without requiring a predefined number of clust…
What should a neuron aim for? Designing local objective functions based on information theory
Andreas C. Schneider, Valentin Neuhaus, David A. Ehrlich +4
In modern deep neural networks, the learning dynamics of the individual neurons is often obscure, as the networks are trained via global optimization. Conversely, biological system…
MNIST-Nd: a set of naturalistic datasets to benchmark clustering across dimensions
Polina Turishcheva, Laura Hansel, Martin Ritzert +2
Driven by advances in recording technology, large-scale high-dimensional datasets have emerged across many scientific disciplines. Especially in biology, clustering is often used t…
Retrospective for the Dynamic Sensorium Competition for predicting large-scale mouse primary visual cortex activity from videos
Polina Turishcheva, Paul G. Fahey, Michaela Vystrčilová +22
Understanding how biological visual systems process information is challenging because of the nonlinear relationship between visual input and neuronal responses. Artificial neural…
Reproducibility of predictive networks for mouse visual cortex
Polina Turishcheva, Max Burg, Fabian H. Sinz +1
Deep predictive models of neuronal activity have recently enabled several new discoveries about the selectivity and invariance of neurons in the visual cortex. These models learn a…
Tailor-designed models for the turbulent velocity gradient through normalizing flow
Maurizio Carbone, Vincent J. Peterhans, Alexander S. Ecker +1
Small-scale turbulence can be comprehensively described in terms of velocity gradients, which makes them an appealing starting point for low-dimensional modeling. Typical models co…