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Katrin Franke

6 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 ↗
2026

Only Brains Align with Brains: Cross-Region Patterns Expose Limits of Normative Models

Larissa Höfling, Matthias Tangemann, Lotta Piefke, Susanne Keller, Matthias Bethge, Katrin Franke
ICLR 2026poster

Neuroscientists and computer vision scientists alike have relied on model-brain alignment benchmarks to find parallels between artificial and biological vision systems. These benchmarks rank models according to alignment measures (AM) such as representational similarity analysis (RSA) and linear pre…

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2025

TRACE: Contrastive learning for multi-trial time series data in neuroscience

Lisa Schmors, Dominic Gonschorek, Jan Niklas Böhm, Yongrong Qiu, Na Zhou, Dmitry Kobak +6
NeurIPS 2025poster

Modern neural recording techniques such as two-photon imaging or Neuropixel probes allow to acquire vast time-series datasets with responses of hundreds or thousands of neurons. Contrastive learning is a powerful self-supervised framework for learning representations of complex datasets. Existing ap…

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2023

Energy Guided Diffusion for Generating Neurally Exciting Images

Paweł A. Pierzchlewicz, Konstantin Friedrich Willeke, Arne Nix, Pavithra Elumalai, Kelli Restivo, Tori Shinn +6
NeurIPS 2023poster

In recent years, most exciting inputs (MEIs) synthesized from encoding models of neuronal activity have become an established method for studying tuning properties of biological and artificial visual systems. However, as we move up the visual hierarchy, the complexity of neuronal computations in…

Cited by 17Source ↗PDF ↗code ↗Scholar ↗
2021

Removing Inter-Experimental Variability from Functional Data in Systems Neuroscience

Dominic Gonschorek, Larissa Höfling, Klaudia P. Szatko, Katrin Franke, Timm Schubert, Benjamin Adric Dunn +3
NeurIPS 2021spotlight

Integrating data from multiple experiments is common practice in systems neuroscience but it requires inter-experimental variability to be negligible compared to the biological signal of interest. This requirement is rarely fulfilled; systematic changes between experiments can drastically affect the…

Cited by 12Source ↗PDF ↗code ↗Scholar ↗
2020

System Identification with Biophysical Constraints: A Circuit Model of the Inner Retina

Cornelius Schröder, David Klindt, Sarah Strauss, Katrin Franke, Matthias Bethge, Thomas Euler +1
NeurIPS 2020spotlight

Visual processing in the retina has been studied in great detail at all levels such that a comprehensive picture of the retina's cell types and the many neural circuits they form is emerging. However, the currently best performing models of retinal function are black-box CNN models which are agnosti…

Cited by 14Source ↗PDF ↗code ↗Scholar ↗