Publications by Steffen Schneider

SuperAnimal pretrained pose estimation models for behavioral analysis

Tian Qiu, Maxime Michel André Vidal, Mackenzie Mathis, Alexander Mathis, Shaokai Ye, Jessy Lauer, Steffen Schneider

Quantification of behavior is critical in diverse applications from neuroscience, veterinary medicine to animal conservation. A common key step for behavioral analysis is first extracting relevant keypoints on animals, known as pose estimation. However, re ...

Nature Research2024

Robust machine learning for neuroscientific inference

Steffen Schneider

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

EPFL2024

Dimensionality reduction of time-series data, and systems and devices that use the resultant embeddings

Mackenzie Mathis, Steffen Schneider

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for dimensionality reduction of time-series using contrastive learning. A method can include receiving multidimensional input time series data that includes ...

2023

Learnable latent embeddings for joint behavioural and neural analysis

Mackenzie Mathis, Steffen Schneider, Jin Hwa Lee

Mapping behavioural actions to neural activity is a fundamental goal of neuroscience. As our ability to record large neural and behavioural data increases, there is growing interest in modelling neural dynamics during adaptive behaviours to probe neural re ...

NATURE PORTFOLIO2023

Multi-animal pose estimation, identification and tracking with DeepLabCut

Tanmay Nath, Mackenzie Mathis, Alexander Mathis, Shaokai Ye, Jessy Lauer, Steffen Schneider

Estimating the pose of multiple animals is a challenging computer vision problem: frequent interactions cause occlusions and complicate the association of detected keypoints to the correct individuals, as well as having highly similar looking animals that ...

NATURE PORTFOLIO2022

Pretraining boosts out-of-domain robustness for pose estimation

Mackenzie Mathis, Alexander Mathis, Steffen Schneider, Thomas Ray Biasi, Mert Yüksekgönül, Matthias Bethge

Neural networks are highly effective tools for pose estimation. However, as in other computer vision tasks, robustness to out-of-domain data remains a challenge, especially for small training sets that are common for real-world applications. Here, we probe ...

IEEE COMPUTER SOC2021

Contrastive Learning Inverts the Data Generating Process

Steffen Schneider

Contrastive learning has recently seen tremendous success in self-supervised learning. So far, however, it is largely unclear why the learned representations generalize so effectively to a large variety of downstream tasks. We here prove that feed-forward ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2021

Motor control: Neural correlates of optimal feedback control theory

Mackenzie Mathis, Steffen Schneider

Recent work is revealing neural correlates of a leading theory of motor control. By linking an elegant series of behavioral experiments with neural inactivation in macaques with computational models, a new study shows that premotor and parietal areas can b ...

CELL PRESS2021