Pavan P Ramdya
Associate Professor
pavan.ramdya@epfl.ch +41 21 693 69 60 https://ramdya-lab.epfl.ch/
Citizenship: USA
+41 21 693 69 60
EPFL
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SV
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SV-SSV
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SSV-ENS
Web site: Web site: https://sv.epfl.ch/education
EPFL SV BMI UPRAMDYA
AAB 1 05 (Bâtiment AAB)
Station 19
1015 Lausanne
+41 21 693 69 60
+41 21 693 40 74
Office:
AAB 1 05
EPFL
>
SV
>
BMI
>
UPRAMDYA
Web site: Web site: https://ramdya-lab.epfl.ch
EPFL SV IBI-GE
AAB 1 05 (Bâtiment AAB)
Station 19
1015 Lausanne
Web site: Web site: https://bioengineering.epfl.ch/
Fields of expertise
Neuroscience Behavior Neuroengineering Synthetic neurobiology
Mission
We use genetics, microscopy, modeling, and quantitative behavioral analysis to understand how the brain works.Biography
https://ramdya-lab.epfl.ch/Education
PhD
Neurobiology
Harvard University
2009
Awards
Publications
Infoscience publications
Infoscience
Uncovering interactions between Descending Neurons as a functional principle of behavioural control
Lausanne, EPFL, 2024. DOI : 10.5075/epfl-thesis-10712.The neuromechanics of animal locomotion: From biology to robotics and back
Science Robotics. 2023-05-31. DOI : 10.1126/scirobotics.adg0279.Ascending neurons convey behavioral state to integrative sensory and action selection brain regions
Nature Neuroscience. 2023-03-23. DOI : 10.1038/s41593-023-01281-z.Overcoming the Domain Gap in Neural Action Representations
International Journal Of Computer Vision. 2022-12-19. DOI : 10.1007/s11263-022-01713-6.Descending neuron population dynamics during odor-evoked and spontaneous limb-dependent behaviors
Elife. 2022-10-26. DOI : 10.7554/eLife.81527.Microengineered devices enable long-term imaging of the ventral nerve cord in behaving adult Drosophila
Nature Communications. 2022-08-25. DOI : 10.1038/s41467-022-32571-y.NeuroMechFly, a neuromechanical model of adult Drosophila melanogaster
Nature Methods. 2022-05-11. DOI : 10.1038/s41592-022-01466-7.NeuroMechFly: an integrative simulation testbed for studying Drosophila behavioral control
Nature Methods. 2022-05-01. DOI : 10.1038/s41592-022-01411-8.Connecting the dots in ethology: applying network theory to understand neural and animal collectives
Current Opinion In Neurobiology. 2022-04-01. DOI : 10.1016/j.conb.2022.102532.Uncovering the neural substrates of communication between the brain and ventral nerve cord in Drosophila melanogaster (italic)
Lausanne, EPFL, 2022. DOI : 10.5075/epfl-thesis-9476.Methods for acute and long-term imaging of the ventral nerve cord in behaving adult Drosophila
Lausanne, EPFL, 2022. DOI : 10.5075/epfl-thesis-9466.Uncovering the neural encoding of behavior in the adult Drosophila motor system
Lausanne, EPFL, 2022. DOI : 10.5075/epfl-thesis-8966.Using Animal Motion Capture to Learn Neural Representations
Lausanne, EPFL, 2022. DOI : 10.5075/epfl-thesis-9124.LiftPose3D, a deep learning-based approach for transforming two-dimensional to three-dimensional poses in laboratory animals
Nature Methods. 2021-08-01. DOI : 10.1038/s41592-021-01226-z.Extensive and diverse patterns of cell death sculpt neural networks in insects
Elife. 2020-09-07. DOI : 10.7554/eLife.59566.Deformation-aware Unpaired Image Translation for Pose Estimation on Laboratory Animals
2020-09-01. Conference on Computer Vision and Pattern Recognition (CVPR). p. 13155-13165. DOI : 10.1109/CVPR42600.2020.01317.Serotonergic Modulation of Walking in Drosophila
Current Biology. 2019-12-16. DOI : 10.1016/j.cub.2019.10.042.DeepFly3D, a deep learning-based approach for 3D limb and appendage tracking in tethered, adult Drosophila
eLife. 2019-10-04. DOI : 10.7554/eLife.48571.Imaging neural activity in the ventral nerve cord of behaving adult Drosophila
Nature Communications. 2018-10-22. DOI : 10.1038/s41467-018-06857-z.FlyLimbTracker: An Active Contour Based Approach for Leg Segment Tracking in Unmarked, Freely Behaving Drosophila
PLoS One. 2017. DOI : 10.1371/journal.pone.0173433.Climbing favours the tripod gait over alternative faster insect gaits
Nature Communications. 2017. DOI : 10.1038/ncomms14494.Fluctuation-driven neural dynamics reproduce Drosophila locomotor patterns
Plos Computational Biology. 2015. DOI : 10.1371/journal.pcbi.1004577.Mechanosensory interactions drive collective behaviour in Drosophila
Nature. 2015. DOI : 10.1038/nature14024.Fluorescence Behavioral Imaging (FBI) tracks identity in heterogeneous groups of Drosophila
PLOS One. 2012. DOI : 10.1371/journal.pone.0048381.Complementary Function and Integrated Wiring of the Evolutionarily Distinct Drosophila Olfactory Subsystems
Journal Of Neuroscience. 2011. DOI : 10.1523/JNEUROSCI.2360-11.2011.Evolving olfactory systems on the fly
Trends In Genetics. 2010. DOI : 10.1016/j.tig.2010.04.004.Uncovering interactions between Descending Neurons as a functional principle of behavioural control
Lausanne, EPFL, 2024. DOI : 10.5075/epfl-thesis-10712.The neuromechanics of animal locomotion: From biology to robotics and back
Science Robotics. 2023-05-31. DOI : 10.1126/scirobotics.adg0279.Ascending neurons convey behavioral state to integrative sensory and action selection brain regions
Nature Neuroscience. 2023-03-23. DOI : 10.1038/s41593-023-01281-z.Overcoming the Domain Gap in Neural Action Representations
International Journal Of Computer Vision. 2022-12-19. DOI : 10.1007/s11263-022-01713-6.Descending neuron population dynamics during odor-evoked and spontaneous limb-dependent behaviors
Elife. 2022-10-26. DOI : 10.7554/eLife.81527.Microengineered devices enable long-term imaging of the ventral nerve cord in behaving adult Drosophila
Nature Communications. 2022-08-25. DOI : 10.1038/s41467-022-32571-y.NeuroMechFly, a neuromechanical model of adult Drosophila melanogaster
Nature Methods. 2022-05-11. DOI : 10.1038/s41592-022-01466-7.NeuroMechFly: an integrative simulation testbed for studying Drosophila behavioral control
Nature Methods. 2022-05-01. DOI : 10.1038/s41592-022-01411-8.Connecting the dots in ethology: applying network theory to understand neural and animal collectives
Current Opinion In Neurobiology. 2022-04-01. DOI : 10.1016/j.conb.2022.102532.Uncovering the neural substrates of communication between the brain and ventral nerve cord in Drosophila melanogaster (italic)
Lausanne, EPFL, 2022. DOI : 10.5075/epfl-thesis-9476.Methods for acute and long-term imaging of the ventral nerve cord in behaving adult Drosophila
Lausanne, EPFL, 2022. DOI : 10.5075/epfl-thesis-9466.Uncovering the neural encoding of behavior in the adult Drosophila motor system
Lausanne, EPFL, 2022. DOI : 10.5075/epfl-thesis-8966.Using Animal Motion Capture to Learn Neural Representations
Lausanne, EPFL, 2022. DOI : 10.5075/epfl-thesis-9124.LiftPose3D, a deep learning-based approach for transforming two-dimensional to three-dimensional poses in laboratory animals
Nature Methods. 2021-08-01. DOI : 10.1038/s41592-021-01226-z.Extensive and diverse patterns of cell death sculpt neural networks in insects
Elife. 2020-09-07. DOI : 10.7554/eLife.59566.Deformation-aware Unpaired Image Translation for Pose Estimation on Laboratory Animals
2020-09-01. Conference on Computer Vision and Pattern Recognition (CVPR). p. 13155-13165. DOI : 10.1109/CVPR42600.2020.01317.Serotonergic Modulation of Walking in Drosophila
Current Biology. 2019-12-16. DOI : 10.1016/j.cub.2019.10.042.DeepFly3D, a deep learning-based approach for 3D limb and appendage tracking in tethered, adult Drosophila
eLife. 2019-10-04. DOI : 10.7554/eLife.48571.Imaging neural activity in the ventral nerve cord of behaving adult Drosophila
Nature Communications. 2018-10-22. DOI : 10.1038/s41467-018-06857-z.FlyLimbTracker: An Active Contour Based Approach for Leg Segment Tracking in Unmarked, Freely Behaving Drosophila
PLoS One. 2017. DOI : 10.1371/journal.pone.0173433.Climbing favours the tripod gait over alternative faster insect gaits
Nature Communications. 2017. DOI : 10.1038/ncomms14494.Fluctuation-driven neural dynamics reproduce Drosophila locomotor patterns
Plos Computational Biology. 2015. DOI : 10.1371/journal.pcbi.1004577.Mechanosensory interactions drive collective behaviour in Drosophila
Nature. 2015. DOI : 10.1038/nature14024.Fluorescence Behavioral Imaging (FBI) tracks identity in heterogeneous groups of Drosophila
PLOS One. 2012. DOI : 10.1371/journal.pone.0048381.Complementary Function and Integrated Wiring of the Evolutionarily Distinct Drosophila Olfactory Subsystems
Journal Of Neuroscience. 2011. DOI : 10.1523/JNEUROSCI.2360-11.2011.Evolving olfactory systems on the fly
Trends In Genetics. 2010. DOI : 10.1016/j.tig.2010.04.004.Selected publications
| Chen C-L*, Hermans L*, Viswanathan MC, Aymanns F, Unser M, Cammarato A, Dickinson MH, Ramdya P Nature Communications. 2018 Oct 22; 9(1):4390. |
Imaging neural activity in the ventral nerve cord of behaving adult Drosophila. |
| Ramdya P, Thandiackal R, Cherney R, Asselborn T, Benton R, Ijspeert AJ, Floreano D. Nature Communications. 2017 Feb 17;8:14494. |
Climbing favours the tripod gait over alternative faster insect gaits. |
| Ramdya P, Lichocki P, Cruchet S, Frisch L, Tse W, Floreano D, Benton R. Nature. 2015 Mar 12;519(7542):233-6. |
Mechanosensory interactions drive collective behaviour in Drosophila. |
| Pavan Ramdya & Florian Engert Nature Neuroscience, 2008 Sep;11(9):1083-90. |
Emergence of binocular functional properties in a monocular neural circuit. |
Teaching & PhD
Teaching
Life Sciences Engineering
PhD Students
Hurtak Femke Imreh, Lam Thomas Ka Chung, Lobato Rios Victor, Ozdil Pembe Gizem, Stimpfling Victor Alfred, Wang Sibo,Past EPFL PhD Students
Aymanns Florian , Braun Jonas Friedrich , Chen Chin-Lin , Günel Semih , Hermans Laura Joan ,Courses
Planning your scientific journey
Planning Your Scientific Journey: Being successful as a scientist requires more than acquiring knowledge and developing experimental skills. It also requires: (1) asking a good scientific question, (2) establishing a clear plan of action, and (3) seeking advice along the way.
Controlling behavior in animals and robots
Students will acquire an integrative view on biological and artificial algorithms for controlling autonomous behaviors. Students will synthesize and apply this knowledge in oral presentations and computational exercises.
Python Bootcamp
An intensive, hands-on, pragmatic introduction to computer programming. Students learn basic concepts like data types, control structures, string processing, functions, input/output. They perform simulations, write scripts, and analyze and plot biological data. Python is the language of instruction.