9 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…
Modeling Dynamic Neural Activity by combining Naturalistic Video Stimuli and Stimulus-independent Latent Factors
Finn Schmidt, Polina Turishcheva, Suhas Shrinivasan +1
The neural activity in the visual processing is influenced by both external stimuli and internal brain states. Ideally, a neural predictive model should account for both of them. C…
A Circular Argument : Does RoPE need to be Equivariant for Vision?
Chase van de Geijn, Timo Lüddecke, Polina Turishcheva +1
Rotary Positional Encodings (RoPE) have emerged as a highly effective technique for one-dimensional sequences in Natural Language Processing spurring recent progress towards genera…
Learning to cluster neuronal function
Nina S. Nellen, Polina Turishcheva, Michaela VystrÄilová +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…
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…