Filter
Associated Lab
- Ahrens Lab (2) Apply Ahrens Lab filter
- Branson Lab (5) Apply Branson Lab filter
- Card Lab (1) Apply Card Lab filter
- Cardona Lab (1) Apply Cardona Lab filter
- Funke Lab (5) Apply Funke Lab filter
- Keller Lab (1) Apply Keller Lab filter
- Otopalik Lab (1) Apply Otopalik Lab filter
- Pachitariu Lab (1) Apply Pachitariu Lab filter
- Reiser Lab (3) Apply Reiser Lab filter
- Rubin Lab (1) Apply Rubin Lab filter
- Saalfeld Lab (3) Apply Saalfeld Lab filter
- Sternson Lab (1) Apply Sternson Lab filter
- Tebo Lab (2) Apply Tebo Lab filter
- Turaga Lab (51) Apply Turaga Lab filter
- Turner Lab (1) Apply Turner Lab filter
Associated Project Team
Publication Date
- 2025 (5) Apply 2025 filter
- 2024 (3) Apply 2024 filter
- 2023 (1) Apply 2023 filter
- 2022 (2) Apply 2022 filter
- 2021 (12) Apply 2021 filter
- 2020 (1) Apply 2020 filter
- 2019 (2) Apply 2019 filter
- 2018 (7) Apply 2018 filter
- 2017 (3) Apply 2017 filter
- 2016 (2) Apply 2016 filter
- 2015 (2) Apply 2015 filter
- 2014 (1) Apply 2014 filter
- 2013 (2) Apply 2013 filter
- 2011 (1) Apply 2011 filter
- 2010 (2) Apply 2010 filter
- 2009 (1) Apply 2009 filter
- 2007 (1) Apply 2007 filter
- 2006 (1) Apply 2006 filter
- 2004 (1) Apply 2004 filter
- 2003 (1) Apply 2003 filter
Type of Publication
51 Publications
Showing 51-51 of 51 resultsThe body of an animal influences how its nervous system generates behavior1. Accurately modeling the neural control of sensorimotor behavior requires an anatomically detailed biomechanical representation of the body. Here, we introduce a whole-body model of the fruit fly Drosophila melanogaster in a physics simulator. Designed as a general-purpose framework, our model enables the simulation of diverse fly behaviors, including both terrestrial and aerial locomotion. We validate its versatility by replicating realistic walking and flight behaviors. To support these behaviors, we develop new phenomenological models for fluid and adhesion forces. Using data-driven, end-to-end reinforcement learning we train neural network controllers capable of generating naturalistic locomotion along complex trajectories in response to high-level steering commands. Additionally, we show the use of visual sensors and hierarchical motor control, training a high-level controller to reuse a pre-trained low-level flight controller to perform visually guided flight tasks. Our model serves as an open-source platform for studying the neural control of sensorimotor behavior in an embodied context. Preprint: www.biorxiv.org/content/early/2024/03/14/2024.03.11.584515