Person
Steffen Schneider
Related publications (7)
Robust machine learning for neuroscientific inference
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 ...
EPFL2024Learnable 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 PORTFOLIO2023Dimensionality reduction of time-series data, and systems and devices that use the resultant embeddings
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 ...
2023Multi-animal pose estimation, identification and tracking with DeepLabCut
Alexander Mathis, Mackenzie Mathis, Steffen Schneider, Shaokai Ye, Jessy Lauer, Tanmay Nath
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 PORTFOLIO2022Motor 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 PRESS2021Pretraining boosts out-of-domain robustness for pose estimation
Alexander Mathis, Mackenzie Mathis, Steffen Schneider, Matthias Bethge, Thomas Ray Biasi, Mert Yüksekgönül
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 SOC2021A Primer on Motion Capture with Deep Learning: Principles, Pitfalls, and Perspectives
Alexander Mathis, Mackenzie Mathis, Steffen Schneider, Jessy Lauer
Extracting behavioral measurements non-invasively from video is stymied by the fact that it is a hard computational problem. Recent advances in deep learning have tremendously advanced our ability to predict posture directly from videos, which has quickly ...
2020