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Alexander S. Ecker

17 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…

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2026

PriVi: Towards a General-Purpose Video Model for Primate Behavior in the Wild

Felix B. Mueller, Jan F. Meier, Timo Lueddecke, Richard Vogg, Roger L. Freixanet, Valentin Hassler +17
CVPR 2026

Non-human primates are our closest living relatives, and analyzing their behavior is central to research in cognition, evolution, and conservation. Computer vision could greatly aid this research, but existing methods often rely on human-centric pretrained models and focus on single datasets, which

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2025

A Circular Argument: Does RoPE need to be Equivariant for Vision?

Chase van de Geijn, Timo Lüddecke, Polina Turishcheva, Alexander S. Ecker
NeurIPS 2025poster

Rotary Positional Encodings (RoPE) have emerged as a highly effective technique for one-dimensional sequences in Natural Language Processing spurring recent progress towards generalizing RoPE to higher-dimensional data such as images and videos. The success of RoPE has been thought to be due to its…

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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 ↗
2025

What should a neuron aim for? Designing local objective functions based on information theory

Andreas Christian Schneider, Valentin Neuhaus, David Alexander Ehrlich, Abdullah Makkeh, Alexander S Ecker, Viola Priesemann +1
ICLR 2025oral

In modern deep neural networks, the learning dynamics of individual neurons are often obscure, as the networks are trained via global optimization. Conversely, biological systems build on self-organized, local learning, achieving robustness and efficiency with limited global information. Here, we sh…

Cited by 2Source ↗PDF ↗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

Reproducibility of predictive networks for mouse visual cortex

Polina Turishcheva, Max F Burg, Fabian H. Sinz, Alexander S Ecker
NeurIPS 2024spotlight

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 shared set of nonlinear basis functions, which are linearly combined via a learned weight vector to represent a neuron'…

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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 ↗
2021

Generalization in data-driven models of primary visual cortex

Konstantin-Klemens Lurz, Mohammad Bashiri, Konstantin Willeke, Akshay Jagadish, Eric Wang, Edgar Y. Walker +6
ICLR 2021spotlight

Deep neural networks (DNN) have set new standards at predicting responses of neural populations to visual input. Most such DNNs consist of a convolutional network (core) shared across all neurons which learns a representation of neural computation in visual cortex and a neuron-specific readout that…

Cited by 56Source ↗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 ↗
2018

Diverse feature visualizations reveal invariances in early layers of deep neural networks

Santiago A. Cadena, Marissa A. Weis, Leon A. Gatys, Matthias Bethge, Alexander S. Ecker
ECCV 2018poster

Visualizing features in deep neural networks (DNNs) can help understanding their computations. Many previous studies aimed to visualize the selectivity of individual units by finding meaningful images that maximize their activation. However, comparably little attention has been paid to visualizing t…

Cited by 33Source ↗PDF ↗code ↗Scholar ↗
2018

Stimulus domain transfer in recurrent models for large scale cortical population prediction on video

Fabian Sinz, Alexander S Ecker, Paul Fahey, Edgar Walker, Erick Cobos, Emmanouil Froudarakis +4
NeurIPS 2018poster

To better understand the representations in visual cortex, we need to generate better predictions of neural activity in awake animals presented with their ecological input: natural video. Despite recent advances in models for static images, models for predicting responses to natural video are scarce…

Cited by 73Source ↗PDF ↗code ↗Scholar ↗
2017

Controlling Perceptual Factors in Neural Style Transfer

Leon A. Gatys, Alexander S. Ecker, Matthias Bethge, Aaron Hertzmann, Eli Shechtman
CVPR 2017poster

Neural Style Transfer has shown very exciting results enabling new forms of image manipulation. Here we extend the existing method to introduce control over spatial location, colour information and across spatial scale. We demonstrate how this enhances the method by allowing high-resolution controll…

Cited by 594PDF ↗code ↗Scholar ↗
2017

Neural system identification for large populations separating “what” and “where”

David Klindt, Alexander S Ecker, Thomas Euler, Matthias Bethge
NeurIPS 2017poster

Neuroscientists classify neurons into different types that perform similar computations at different locations in the visual field. Traditional methods for neural system identification do not capitalize on this separation of “what” and “where”. Learning deep convolutional feature spaces that are sh…

Cited by 151Source ↗PDF ↗code ↗Scholar ↗
2015

Texture Synthesis Using Convolutional Neural Networks

Leon Gatys, Alexander S Ecker, Matthias Bethge
NeurIPS 2015poster

Here we introduce a new model of natural textures based on the feature spaces of convolutional neural networks optimised for object recognition. Samples from the model are of high perceptual quality demonstrating the generative power of neural networks trained in a purely discriminative fashion. Wit…

Cited by 1882Source ↗PDF ↗code ↗Scholar ↗