Alexander Mathis
Alexander Mathis
EPFL (Ecole Polytechnique Fédérale de Lausanne / Swiss Federal Institute of Technology)
Verified email at - Homepage
Cited by
Cited by
DeepLabCut: markerless pose estimation of user-defined body parts with deep learning
A Mathis, P Mamidanna, KM Cury, T Abe, VN Murthy, MW Mathis, ...
Nature neuroscience, 1, 2018
Using DeepLabCut for 3D markerless pose estimation across species and behaviors.
T Nath*, A Mathis*, AC Chen, A Patel, M Bethge, MW Mathis
Nature protocols 14, 2152–2176, 2019
Optimal population codes for space: Grid cells outperform place cells
A Mathis, AVM Herz, M Stemmler
Neural Computation 24 (9), 2280-2317, 2012
Connecting multiple spatial scales to decode the population activity of grid cells
M Stemmler*, A Mathis*, AVM Herz
Science Advances 1 (11), e1500816, 2015
Neuronal Representation of Social Information in the Medial Amygdala of Awake Behaving Mice
Y Li, A Mathis, BF Grewe, JA Osterhout, B Ahanonu, MJ Schnitzer, ...
Cell 171 (5), 1176-1190. e17, 2017
Somatosensory Cortex Plays an Essential Role in Forelimb Motor Adaptation in Mice
MW Mathis, A Mathis, N Uchida
Neuron 93 (6), 1493-1503. e6, 2017
Deep learning tools for the measurement of animal behavior in neuroscience
MW Mathis, A Mathis
Current Opinion in Neurobiology 60 (Preprint: arXiv preprint arXiv:1909.1386 …, 2020
Resolution of nested neuronal representations can be exponential in the number of neurons
A Mathis, AVM Herz, MB Stemmler
Physical Review Letters 109 (1), 018103, 2012
Multiscale codes in the nervous system: The problem of noise correlations and the ambiguity of periodic scales
A Mathis, AVM Herz, MB Stemmler
Phys. Rev. E 88 (022713), 2013
Probable nature of higher-dimensional symmetries underlying mammalian grid-cell activity patterns
A Mathis, MB Stemmler, AVM Herz
Elife 4, e05979, 2015
Reading out olfactory receptors: feedforward circuits detect odors in mixtures without demixing
A Mathis*, D Rokni*, V Kapoor, M Bethge, VN Murthy
Neuron 91 (5), 1110-23, 2016
Markerless tracking of user-defined features with deep learning
A Mathis, P Mamidanna, T Abe, KM Cury, VN Murthy, MW Mathis, ...
arXiv preprint arXiv:1804.03142, 2018
Pretraining boosts out-of-domain robustness for pose estimation
A Mathis, T Biasi, S Schneider, M Yüksekgönül, B Rogers, M Bethge, ...
Proceedings of the IEEE/CVF Winter Conference / arXiv preprint arXiv:1909.11229, 2021
On the inference speed and video-compression robustness of DeepLabCut
A Mathis, RA Warren
bioRxiv, doi:, 2018
Periodic population codes: From a single circular variable to higher dimensions, multiple nested scales, and conceptual spaces
AVM Herz, A Mathis, M Stemmler
Current Opinion in Neurobiology 46, 99-108, 2017
A Primer on Motion Capture with Deep Learning: Principles, Pitfalls, and Perspectives
A Mathis, S Schneider, J Lauer, MW Mathis
Neuron 108 (1), 44-65, 2020
Real-time, low-latency closed-loop feedback using markerless posture tracking.
M Kane, G.A., Lopes, G., Sanders, J.L., Mathis, A. and Mathis
Elife 9 (p.e61909.), 2020
Task-driven hierarchical deep neural networkmodels of the proprioceptive pathway
KJ Sandbrink*, P Mamidanna*, C Michaelis, MW Mathis, M Bethge, ...
bioRxiv, 2020
Highlights from the 29th Annual Meeting of the Society for the Neural Control of Movement
A Mathis, AR Pack, RS Maeda, SD McDougle
Journal of neurophysiology 122 (4), 1777-1783, 2019
The representation of space in mammals: resolution of stochastic place and grid codes
A Mathis
Ludwig-Maximilians-University Munich, 2012
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