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Andreas S. Tolias

12 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 ↗
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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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 ↗
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 ↗
2023

Taking the neural sampling code very seriously: A data-driven approach for evaluating generative models of the visual system

Suhas Shrinivasan, Konstantin-Klemens Lurz, Kelli Restivo, George Denfield, Andreas S. Tolias, Edgar Y. Walker +1
NeurIPS 2023poster

Prevailing theories of perception hypothesize that the brain implements perception via Bayesian inference in a generative model of the world. One prominent theory, the Neural Sampling Code (NSC), posits that neuronal responses to a stimulus represent samples from the posterior distribution over late…

Cited by 5Source ↗PDF ↗Scholar ↗
2021

A flow-based latent state generative model of neural population responses to natural images

Mohammad Bashiri, Edgar Y. Walker, Konstantin-Klemens Lurz, Akshay Kumar Jagadish, Taliah Muhammad, Zhiwei Ding +3
NeurIPS 2021spotlight

We present a joint deep neural system identification model for two major sources of neural variability: stimulus-driven and stimulus-conditioned fluctuations. To this end, we combine (1) state-of-the-art deep networks for stimulus-driven activity and (2) a flexible, normalizing flow-based generative…

Cited by 33Source ↗PDF ↗code ↗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 ↗
2021

Towards robust vision by multi-task learning on monkey visual cortex

Shahd Safarani, Arne Nix, Konstantin Friedrich Willeke, Santiago A Cadena, Kelli Restivo, George Denfield +2
NeurIPS 2021poster

Deep neural networks set the state-of-the-art across many tasks in computer vision, but their generalization ability to simple image distortions is surprisingly fragile. In contrast, the mammalian visual system is robust to a wide range of perturbations. Recent work suggests that this generalization…

Cited by 53Source ↗PDF ↗code ↗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 ↗