Neuroengineering Laboratory

Latest research

Welcome to the Ramdya Lab, Firmenich Next Generation Chair of Neuroengineering

We are reverse-engineering the fly, Drosophila melanogaster, to understand how animals leverage social information, learn about the world, and generate flexible motor behaviors. We believe that our efforts will uncover general insights into biological intelligence and can inform the design of better artificial systems and robots.

Flies are ideal for this goal: they generate complex behaviors yet have a small nervous system and are genetically malleable. For our research we develop and use a variety of approaches including microscopy, machine learning, genetics, and computational modeling. At EPFL, we are part of the Brain Mind Institute and Institute of Bioengineering in the School of Life Sciences. Enjoy your visit!

Some of our recent science and new technologies

Uncovering information flow between the brain and motor system

We combine genetics, 2-photon microscopy, and machine learning-based image analysis to establish links between neural activity and behavior  Relevant publications: Chen et al., Nature Neuroscience, 2023Braun et al., bioRxiv 2023

Building a neuromechanical model of Drosophila in a physics environment

We are developing a data-driven simulation of the fly to identify core neuromechanical principles that govern behavior. In the long-term this simulation can be used to synthesize biological data, generate predictions for future experiments, and inspire the development of bioinspired robotic controllers. Relevant publications: Lobato et al., Nature Methods, 2022, Chen-Wang et al., bioRxiv, 2023

Recording neural activity in the motor system during behavior

We have developed dissection, genetic, and microengineering tools to access motor circuit activity in behaving flies. Relevant publications: Hermans, Kaynak et al., Nature Communications 2022, Chen, Hermans et al., Nature Communications 2018

Using deep networks to efficiently and precisely quantify behavior

We have developed deep-learning based pose estimation tools to quantify the links between neural activity and behavior. Relevant publications: Gosztolai, Günel et al. Nature Methods 2021, Günel et al. Elife 2019, Günel et al. IJCV 2023

Robotic experimental automation

We build robotic systems and computational data analysis pipelines to automate high-throughput biological experimentation. Relevant publications: Ramdya et al., Nature 2015, Maesani, Ramdya et al., PLoS Computational Biology 2015


Latest News

23-10-23: The lab was awarded a Kavli Exploration Award
23-10-13: Gizem was awarded a Google PhD Fellowship in Computational Neural and Cognitive Sciences
23-05-05: Our review article on interactions between neuroscience and robotics was accepted to Science Robotics
23-04-04: Jasper was awarded an HFSP Postdoctoral Fellowship
23-02-14: Our study revealing ascending neuron behavioral encoding was accepted to Nature Neuroscience
22-12-12: Jasper was awarded an EMBO Postdoctoral Fellowship
22-11-06: Jasper was awarded a Helen Hay Whitney Foundation Postdoctoral Fellowship
22-10-13: Our work describing descending neuron population activity during behavior was accepted to Elife
22-07-14: Femke was awarded a Boehringer Ingelheim Fonds PhD Fellowship
22-06-24: Our work describing microengineered devices for long-term neural recordings was accepted to Nature Communications
22-03-23: Sibo was awarded a Boehringer Ingelheim Fonds PhD Fellowship
22-03-03: Our review on network theoretical analysis of connectomes and animal collectives was accepted to Current Opinion in Neurobiology
21-12-23: Our work describing a neuromechanical model of Drosophila, NeuroMechFly, was accepted to Nature Methods
21-06-08: Matthias was awarded a Fondation Fyssen Postdoctoral Fellowship
21-05-14: Our work describing LiftPose3D pose estimation software was accepted to Nature Methods