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Paul G. Fahey

5 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 ↗
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 ↗
2020

Rotation-invariant clustering of neuronal responses in primary visual cortex

Ivan Ustyuzhaninov, Santiago A. Cadena, Emmanouil Froudarakis, Paul G. Fahey, Edgar Y. Walker, Erick Cobos +5
ICLR 2020talk

Similar to a convolutional neural network (CNN), the mammalian retina encodes visual information into several dozen nonlinear feature maps, each formed by one ganglion cell type that tiles the visual space in an approximately shift-equivariant manner. Whether such organization into distinct cell typ…

Cited by 15Source ↗Scholar ↗
2019

A rotation-equivariant convolutional neural network model of primary visual cortex

Alexander S. Ecker, Fabian H. Sinz, Emmanouil Froudarakis, Paul G. Fahey, Santiago A. Cadena, Edgar Y. Walker +4
ICLR 2019poster

Classical models describe primary visual cortex (V1) as a filter bank of orientation-selective linear-nonlinear (LN) or energy models, but these models fail to predict neural responses to natural stimuli accurately. Recent work shows that convolutional neural networks (CNNs) can be trained to predic…

Cited by 63Source ↗PDF ↗code ↗Scholar ↗