Publications

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

A. Marin Vargas; A. Bisi; A. S. Chiappa; C. Versteeg; L. E. Miller et al. 

2024.

Task-driven neural network models predict neural dynamics of proprioception: Synthetic muscle spindle datasets

A. Marin Vargas; A. Bisi; A. S. Chiappa; C. Versteeg; L. E. Miller et al. 

2024.

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

A. Marin Vargas; A. Bisi; A. Chiappa; C. Versteeg; L. E. Miller et al. 

2024.

Data Champions Lunch Talks – AI and research data, a new synergy

A. Mathis; S. L. Dürr; G. Barazzetti; R. Castello 

Data Champions Lunch Talks, EPFL, CM 1 120, Sept. 7, 2023.

Contrasting action and posture coding with hierarchical deep neural network models of proprioception

K. J. Sandbrink; P. Mamidanna; C. Michaelis; M. Bethge; M. W. Mathis et al. 

Elife. 2023-05-31. Vol. 12, p. e81499. DOI : 10.7554/eLife.81499.

NeuroAI: If grid cells are the answer, is path integration the question?

M. Frey; M. W. Mathis; A. Mathis 

Current Biology. 2023-03-13. Vol. 33, num. 5, p. R190-R192. DOI : 10.1016/j.cub.2023.01.031.

Task-driven neural network models predict neural dynamics of proprioception

A. Marin Vargas; A. Bisi; A. Chiappa; C. Versteeg; L. Miller et al. 

2023.  p. 1-38. DOI : 10.1101/2023.06.15.545147.

Rethinking Pose Estimation in Crowds: Overcoming the Detection Information Bottleneck and Ambiguity

M. Zhou; L. Stoffl; M. Mathis; A. Mathis 

2023. IEEE/CVF International Conference on Computer Vision (ICCV), Paris, October 2-6, 2023. p. 14689-14699.

Rethinking pose estimation in crowds: overcoming the detection information bottleneck and ambiguity

M. Zhou; L. Stoffl; M. Mathis; A. Mathis 

2023.

Striatal dopamine explains novelty-induced behavioral dynamics and individual variability in threat prediction

K. Akiti; I. Tsutsui-Kimura; Y. Xie; A. Mathis; J. E. Markowitz et al. 

Neuron. 2022-11-16. Vol. 110, num. 22, p. 3789-+. DOI : 10.1016/j.neuron.2022.08.022.

Multi-animal pose estimation, identification and tracking with DeepLabCut

J. Lauer; M. Zhou; S. Ye; W. Menegas; S. Schneider et al. 

Nature Methods. 2022-04-01. Vol. 19, num. 4, p. 496–504. DOI : 10.1038/s41592-022-01443-0.

Perspectives on Individual Animal Identification from Biology and Computer Vision

M. Vidal; N. Wolf; B. Rosenberg; B. P. Harris; A. Mathis 

Integrative And Comparative Biology. 2021-10-01. Vol. 61, num. 3, p. 900-916. DOI : 10.1093/icb/icab107.

Measuring and modeling the motor system with machine learning

S. B. Hausmann; A. Marin Vargas; A. Mathis; M. Mathis 

Current Opinion in Neurobiology. 2021-06-08. Vol. 70, p. 11-23. DOI : 10.1016/j.conb.2021.04.004.

Deep learning tools for the analysis of movement, identity and behavior

A. Mathis 

2021-03-01. Annual Meeting of the Society-for-Integrative-and-Comparative-Biology (SICB), ELECTR NETWORK, Jan 31-Feb 28, 2021. p. E581-E582.

AcinoSet: A 3D Pose Estimation Dataset and Baseline Models for Cheetahs in the Wild

D. Joska; L. Clark; N. Muramatsu; R. Jericevich; F. Nicolls et al. 

2021-01-01. IEEE International Conference on Robotics and Automation (ICRA), Xian, PEOPLES R CHINA, May 30-Jun 05, 2021. p. 13901-13908. DOI : 10.1109/ICRA48506.2021.9561338.

Pretraining boosts out-of-domain robustness for pose estimation

A. Mathis; T. Biasi; S. Schneider; M. Yuksekgonul; B. Rogers et al. 

2021-01-01. IEEE Winter Conference on Applications of Computer Vision (WACV), ELECTR NETWORK, Jan 05-09, 2021. p. 1858-1867. DOI : 10.1109/WACV48630.2021.00190.

Tumor-specific cytolytic CD4 T cells mediate immunity against human cancer

A. Cachot; M. Bilous; Y-C. Liu; X. Li; M. Saillard et al. 

Science Advances. 2021. Vol. 7, num. 9, p. eabe3348. DOI : 10.1126/sciadv.abe3348.

Real-time, low-latency closed-loop feedback using markerless posture tracking

G. A. Kane; G. Lopes; J. L. Saunders; A. Mathis; M. W. Mathis 

Elife. 2020-12-08. Vol. 9, p. e61909. DOI : 10.7554/eLife.61909.

Tumor-Specific Cytolytic Cd4 T Cells Mediate Protective Immunity Against Human Cancer

A. Cachot; M. Bilous; Y-C. Liu; X. Li; A. Rockinger et al. 

2020-11-01.  p. A331-A331. DOI : 10.1136/jitc-2020-SITC2020.0545.

A Primer on Motion Capture with Deep Learning: Principles, Pitfalls, and Perspectives

A. Mathis; S. Schneider; J. Lauer; M. W. Mathis 

Neuron. 2020-10-01. Vol. 108, num. 1, p. 44-65. DOI : 10.1016/j.neuron.2020.09.017.

Real-time, low-latency closed-loop feedback using markerless posture tracking

G. Kane; G. Lopes; J. L. Saunders; A. Mathis; M. W. Mathis 

2020-08-05

ImageNet performance correlates with pose estimation robustness and generalization on out-of-domain data

A. Mathis; B. Thomas; M. Yuksekgonul; B. Rogers; M. Bethge et al. 

2020-07-17. ICML UDL 2020 Workshop on Uncertainty & Robustness in Deep Learning , Virtual event, July 17, 2020.

Task-driven hierarchical deep neural network models of the proprioceptive pathway

K. J. Sandbrink; P. Mamidanna; C. Michaelis; M. W. Mathis; M. Bethge et al. 

2020-05-08

Deep learning tools for the measurement of animal behavior in neuroscience

M. W. Mathis; A. Mathis 

Current Opinion in Neurobiology. 2020. Vol. 60, p. 1-11. DOI : 10.1016/j.conb.2019.10.008.

Highlights from the 29th Annual Meeting of the Society for the Neural Control of Movement

A. Mathis; A. R. Pack; R. S. Maeda; S. D. McDougle 

Journal of Neurophysiology. 2019-10-01. Vol. 122, num. 4, p. 1777-1783. DOI : 10.1152/jn.00484.2019.

Pretraining boosts out-of-domain robustness for pose estimation

A. Mathis; M. Yüksekgönül; B. Rogers; M. Bethge; M. W. Mathis 

2019-09-24

Using DeepLabCut for 3D markerless pose estimation across species and behaviors

T. Nath; A. Mathis; A. C. Chen; A. Patel; M. Bethge et al. 

Nature Protocols. 2019-07-01. Vol. 14, num. 7, p. 2152-2176. DOI : 10.1038/s41596-019-0176-0.

Using DeepLabCut for 3D markerless pose estimation across species and behaviors

T. Nath; A. Mathis; A. C. Chen; A. Patel; M. Bethge et al. 

2018-11-24

On the inference speed and video-compression robustness of DeepLabCut

A. Mathis; R. Warren 

2018-10-30

DeepLabCut: markerless pose estimation of user-defined body parts with deep learning

A. Mathis; P. Mamidanna; K. M. Cury; T. Abe; V. N. Murthy et al. 

Nature Neuroscience. 2018-09-01. Vol. 21, num. 9, p. 1281-1289. DOI : 10.1038/s41593-018-0209-y.

Towards goal-driven deep neural network models to elucidate human arm proprioception

P. Mamidanna; C. Michaelis; A. Mathis; M. Bethge 

2018-05-01. 28th Annual Meeting of the Society for the Neural Control of Movement (NCM 2018), 2018-05. p. 60-61.

Markerless tracking of user-defined features with deep learning

A. Mathis; P. Mamidanna; T. Abe; K. M. Cury; V. N. Murthy et al. 

2018-04-09

Neuronal Representation of Social Information in the Medial Amygdala of Awake Behaving Mice

Y. Li; A. Mathis; B. F. Grewe; J. A. Osterhout; B. Ahanonu et al. 

Cell. 2017-11-01. Vol. 171, num. 5, p. 1176-1190.e17. DOI : 10.1016/j.cell.2017.10.015.

Periodic population codes: From a single circular variable to higher dimensions, multiple nested scales, and conceptual spaces

A. V. Herz; A. Mathis; M. Stemmler 

Current Opinion in Neurobiology. 2017-10-01. Vol. 46, p. 99-108. DOI : 10.1016/j.conb.2017.07.005.

Somatosensory Cortex Plays an Essential Role in Forelimb Motor Adaptation in Mice

M. W. Mathis; A. Mathis; N. Uchida 

Neuron. 2017-03-01. Vol. 93, num. 6, p. 1493-1503.e6. DOI : 10.1016/j.neuron.2017.02.049.

Boosting olfactory cocktail-party performance by semi-supervised learning in mice

A. Mathis; A. Wei; A. Ding; M. Bethge; V. N. Murthy 

2017-02-01. Computational and Systems Neuroscience Meeting (COSYNE 2017), 2017-02. p. 157-157.

Reading Out Olfactory Receptors: Feedforward Circuits Detect Odors in Mixtures without Demixing

A. Mathis; D. Rokni; V. Kapoor; M. Bethge; V. N. Murthy 

Neuron. 2016-09-01. Vol. 91, num. 5, p. 1110-1123. DOI : 10.1016/j.neuron.2016.08.007.

Connecting multiple spatial scales to decode the population activity of grid cells

M. Stemmler; A. Mathis; A. V. M. Herz 

Science Advances. 2015-12-01. Vol. 1, num. 11, p. e1500816. DOI : 10.1126/science.1500816.

Decoding the Population Activity of Grid Cells for Spatial Localization and Goal-Directed Navigation

M. Stemmler; A. Mathis; A. V. Herz 

2015-06-19

Probable nature of higher-dimensional symmetries underlying mammalian grid-cell activity patterns

A. Mathis; M. B. Stemmler; A. V. Herz 

eLife. 2015-04-24. Vol. 4, p. e05979. DOI : 10.7554/eLife.05979.

Multiscale codes in the nervous system: The problem of noise correlations and the ambiguity of periodic scales

A. Mathis; A. V. M. Herz; M. B. Stemmler 

Physical Review E. 2013-08-20. Vol. 88, num. 2, p. 022713. DOI : 10.1103/PhysRevE.88.022713.

Optimal Population Codes for Space: Grid Cells Outperform Place Cells

A. Mathis; A. V. M. Herz; M. Stemmler 

Neural Computation. 2012-09-01. Vol. 24, num. 9, p. 2280-2317. DOI : 10.1162/NECO_a_00319.

Resolution of Nested Neuronal Representations Can Be Exponential in the Number of Neurons

A. Mathis; A. V. M. Herz; M. B. Stemmler 

Physical Review Letters. 2012-07-06. Vol. 109, num. 1, p. 018103. DOI : 10.1103/PhysRevLett.109.018103.

A physiologically inspired model for global remapping in the hippocampus

A. Kammerer; A. Mathis; M. Stemmler; A. Herz; C. Leibold 

BMC Neuroscience. 2011-07-18. Vol. 12, num. Suppl 1, p. P194-P194. DOI : 10.1186/1471-2202-12-s1-p194.

How good is grid coding versus place coding for navigation using noisy, spiking neurons?

A. Mathis; M. Stemmler; A. Herz 

BMC Neuroscience. 2010-07-01. Vol. 11, num. S1, p. O20. DOI : 10.1186/1471-2202-11-S1-O20.