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Michaela Vystrčilová

4 accepted papers

2026

OmniMouse: Scaling properties of multi-modal, multi-task Brain Models on 150B Neural Tokens

Konstantin Friedrich Willeke, Polina Turishcheva, Alex Gilbert, Goirik Chakrabarty, Hasan Atakan Bedel, Paul G. Fahey +15
ICLR 2026poster

Scaling data and artificial neural networks has transformed AI, driving breakthroughs in language and vision. Whether similar principles apply to modeling brain activity remains unclear. Here we leveraged a dataset of 3.3 million neurons from the visual cortex of 78 mice across 323 sessions, totali…

Cited by 0Source ↗code ↗Scholar ↗
2025

Learning to cluster neuronal function

Nina S. Nellen, Polina Turishcheva, Michaela Vystrčilová, Shashwat Sridhar, Tim Gollisch, Andreas S. Tolias +1
NeurIPS 2025poster

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 embeddings derived from these models could potentially be used to map the functional landscape or identify cell types. Howeve…

Cited by 0Source ↗code ↗Scholar ↗
2024

Most discriminative stimuli for functional cell type clustering

Max F Burg, Thomas Zenkel, Michaela Vystrčilová, Jonathan Oesterle, Larissa Höfling, Konstantin Friedrich Willeke +12
ICLR 2024poster

Identifying cell types and understanding their functional properties is crucial for unraveling the mechanisms underlying perception and cognition. In the retina, functional types can be identified by carefully selected stimuli, but this requires expert domain knowledge and biases the procedure towar…

Cited by 4Source ↗PDF ↗code ↗Scholar ↗
2024

Retrospective for the Dynamic Sensorium Competition for predicting large-scale mouse primary visual cortex activity from videos

Polina Turishcheva, Paul G. Fahey, Michaela Vystrčilová, Laura Hansel, Rachel E Froebe, Kayla Ponder +19
NeurIPS 2024poster

Understanding how biological visual systems process information is challenging because of the nonlinear relationship between visual input and neuronal responses. Artificial neural networks allow computational neuroscientists to create predictive models that connect biological and machine vision. Ma…

Cited by 3Source ↗PDF ↗Scholar ↗