11 citations · 18 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos
Polina Turishcheva, Paul G. Fahey, Laura Hansel +10
Understanding how biological visual systems process information is challenging due to the complex nonlinear relationship between neuronal responses and high-dimensional visual inpu…